Bikeshare Hawaii Impact...

Bikeshare Hawaii Impact Evaluation

Prepared by: University of Hawaii at Manoa

Department of Urban and Regional Planning Planning Practicum (PLAN 751)

Fall 2015

Robyn R. Kavarsky Yamato Milner Tyler Tsubota Mina Viritua Jr.

February 13, 2016

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“There are two types of mayors in the world: those who have bike-sharing and

those who want bikesharing.”

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Table of Contents 1. Introduction and Background ...................................................................................................... 1

2. Theoretical Framework and Methodology................................................................................... 3

2.1. Logic Model ............................................................................................................................ 3

2.2. Before-After Comparison Method ............................................................................................ 4

2.3. SMART Method ...................................................................................................................... 4

2.4. Method of Calculating Costs for Measuring Indicators ............................................................. 4

3. Outcome ....................................................................................................................................... 5

3.1. Service Area Population ......................................................................................................... 5

3.2. BSH Usage Projections .......................................................................................................... 5

3.2.1. Maximum Usage .............................................................................................. 6

3.2.2. Expected Usage ............................................................................................... 7

3.2.3. Mode switch ..................................................................................................... 7

3.3. Indicators & Metrics ................................................................................................................ 7

3.3.1. Changes in average number of trips per bike..................................................... 8

3.3.2. Changes in total distance traveled .................................................................... 8

3.3.3. Changes in number of bikeshare users ............................................................. 9

4. Impacts ....................................................................................................................................... 10

Four Focus Areas ............................................................................................................................ 10

4.1. Transportation Impact: Changes to private bicycle usage ...................................................... 11

4.1.1. Current private bicycle usage.......................................................................... 11

4.1.2. Maximum impact on private bicycle usage....................................................... 11

4.1.3. Expected impact on private bicycle usage ....................................................... 11

4.1.4. Indicators & Metrics ........................................................................................ 12

4.2. Transportation Impact: Changes to bicycle safety ................................................................. 13

4.2.1. Collisions rates............................................................................................... 13

4.2.2. Helmet use .................................................................................................... 13

4.2.3. Driver behavior............................................................................................... 14

4.2.4. Current injuries and deaths of bicyclists on O’ahu ............................................ 14

4.2.5. Maximum impact on bicycle safety .................................................................. 14

4.2.6. Expected impact on bicycle safety .................................................................. 15


4.3. Transportation Impact: Changes to traffic congestion ............................................................ 16

4.3.1. Maximum and expected impact on traffic congestion ....................................... 16

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4.4. Health Impact: Changes to physical activity........................................................................... 17

4.4.1. Maximum impact on physical activity ............................................................... 17

4.4.2. Expected impacts on physical activity.............................................................. 17


4.5. Environment Impact: Changes in VMT .................................................................................. 19

4.5.1. Maximum impact on VMT ............................................................................... 19

4.5.2. Expected impacts on VMT .............................................................................. 19


4.6. Economic Impact: Changes in Spending Near Bikeshare ...................................................... 21

Stations............................................................................................................................................ 21

4.6.1. Indicators and Metrics for Measuring BSH’s Impacts on Spending ................... 22

4.7 Economic Impact: Changes to Transportation Costs ............................................................ 23

4.7.1 BSH’s Maximum and Expected Impacts on Transportation Cost:...................... 23

4.7.2 Indicators and Metrics for Measuring BSH’s Impacts on ................................... 24

Transportation Costs.................................................................................................... 24

4.8 Social Well-being Impact: Changes to accessibility & induced trips ....................................... 25

4.8.1 Multi-Modal Transportation ............................................................................. 25

4.8.2 Time Saving ................................................................................................... 26

4.8.3 Current walking rates on Oahu........................................................................ 26

4.8.4 Maximum and expected impact on accessibility ............................................... 26

4.8.5 Maximum impact on time-savings ................................................................... 26

4.8.6 Expected impact on time-savings .................................................................... 26

4.9 Social Well-being Impact: Changes to social connections ..................................................... 28

4.9.1 Maximum and expected impact on social connections ..................................... 28

4.9.2 Indicators & Metrics ........................................................................................ 28

5 Data Source ................................................................................................................................ 30

5.1 Timeline................................................................................................................................ 30

5.2 Existing Metrics .......................................................................................................................... 31

5.3 Non-Existing Metrics ............................................................................................................. 35

5.3.1 Survey ........................................................................................................... 35

5.3.2 Monitor .......................................................................................................... 36

5.3.3 Internal .......................................................................................................... 37

6 Conclusion ................................................................................................................................. 39

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7 Bibliography ............................................................................................................................... 40

8 Appendix ...................................................................................................................................... 0

Appendix A: Service Area Population Calculations ............................................................................. 0

Appendix B: Maximum Usage Calculations ........................................................................................ 4

Appendix C: Expected Usage Calculations ......................................................................................... 7

Appendix D: Private bicycle impact calculations................................................................................ 10

Appendix E: Bicycle safety impact calculations ................................................................................. 12

Appendix F: Time-savings impact for service area resident commuter .............................................. 13

Appendix G: Mode switch ................................................................................................................. 16

Appendix H: Analysis of Washington D.C.’s Capital Bikeshare users ................................................ 17

Appendix J: Scope of User and Business Survey Questions ............................................................. 20

1. Introduction and Background Bikeshare is a network of self-service, on demand, short-term bike rental stations

and bikes distributed across a service area. It provides a flexible public transportation option for commuters and recreational users. The adoption of a bikeshare program can have a range of potential impacts – on transportation modes, air pollution, economic activities and personal health.1 These impacts can assist the City and State in achieving many of their stated goals.

There have been numerous efforts by the City, State, and Federal government to support active transportation such as bicycling. In 2006, an amendment to the City Charter passed by 76% of the island’s voters made becoming a pedestrian and bicycle- friendly city one of the Department of Transportation Services’ priorities.2 In 2011, the Hawaii Clean Energy Initiative roadmap established a goal to decrease vehicle miles traveled (VMT) by 8% by 2030 for the purpose of reducing petroleum use in ground transportation.3 In 2012, the City adopted a Complete Street Ordinance that mandated the balancing of the needs and comfort of all modes and users with support of 1) multi-modal transportation options and 2) enhanced street design.4 As a guiding document, the 2012 Oʻahu Bike Plan identifies goals for bicycle mobility and accessibility around the island. The plan aims to support a transportation system that enables people and goods to move safely, efficiently, and at a reasonable cost.5 Part of the Hawaii Physical Activity and Nutrition Plan for 2020 sets goals to encourage physical activity as a means of reducing obesity.6 Alternative transportation efforts are supported at a national level, through the Transportation Alternatives Program, which provides funding for projects and programs pertaining to alternative transportation.7 One such program is Safe Routes to School which encourages children in grades K through 8 to walk and bicycle to and from school.

In 2015, Honolulu was deemed a bicycle-friendly community by the League of American Cyclists at the bronze level.8 However, with most main thoroughfares lacking bicycle facilities and only 1.8% of commuters within the primary urban core currently bicycling to work,9 the journey to becoming a notable bicycle-friendly community has only just begun.

In an effort to move towards these stated goals, the City, in partnership with the State have supported the creation of Bikeshare Hawaii (BSH). It is a non-profit organization that plans to establish approximately 150 stations, and 1,500 bikes in urban Honolulu by 2016. The initial service area will extend from Diamond Head to Chinatown, including Makiki and the University of Hawaii at Manoa.

In phases leading to the creation of BSH, the City’s Department of Planning and

Permitting released a bikeshare organizational study in June 2014. The study was completed by a transportation planning firm, Nelson\Nygaard Consulting Associates. In this study, Nelson\Nygaard identified some of the potential benefits to implementing a bikeshare system in Honolulu. For example, annual estimations predict: the burning of 141-173 million calories and 45,000 pounds of fat; the reduction of 4.3 million in VMT and 3.9-4.3 million pounds of carbon; an increase in retail spending near stations by $255,000; and the creation of 33 to 36 new jobs.10

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In this report, we provide recommendations for evaluating the impacts of BSH over time. This includes identifying impacts and providing metrics needed to measure the indicators that evaluate the impacts. Based on the experience of bikeshare systems elsewhere, the impacts of BSH have been categorized into four focus areas: transportation, health and the environment, the local economy, and social well-being.

This report is organized as follows: Section 2 describes the framework and methodology used for evaluation; Section 3 presents the outcomes of BSH, which is the maximum and expected usage of BSH; Section 4 illustrates the impacts and provides projections in the four focus areas, based on existing bikeshare systems. Indicators for tracking BSH’s impacts and the metrics of measurement are also presented in the section. Section 5 provides details on the data sources and section 6 provides conclusions.

Four focus areas: transportation, health and the environment, the local economy, and social well-being

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2. Theoretical Framework and Methodology An impact evaluation is a tool used to assess the changes that a particular project,

program, or policy brings to the affected parties.11 This assessment includes monitoring the intervention, and collection of data and analysis. This can be used to determine the degree of impact of the intervention and inform implementation, management, and decision making. There are two categories of impact evaluations: prospective and retrospective. Prospective impact evaluations are often developed simultaneously with program design phases and built into the implementation process. It requires baseline data to be identified and collected during program implementation to improve credible evaluation results.11 Retrospective impact evaluation is conducted post implementation. Since BSH has the opportunity to craft a proactive plan for evaluation prior to launch of their system, a prospective approach is recommended and used in this report.

2.1. Logic Model A logic model is a tool used to evaluate how an organization or program works,

specifically looking at the theory and assumptions that underlie its impact.12 Logic models generally utilize 5 major components: inputs, activities, outputs, outcomes, and impacts.13,11,14

● Inputs - resources, including human and financial, invested in the project. ● Activities - actions taken with the inputs to create outputs. ● Outputs - tangible goods and services directly related to project activities. ● Outcomes - direct changes as a result of the outputs. ● Impacts - indirect changes as result of the outcomes.

Applying a program to the logic model allows for a visualization of a program’s

components.15 See table 1 below for a chart of the logic model applied to BSH.

Table 1: Logic model applied to BSH

Inputs Activities Outputs Outcomes Impacts

Administrative staff

Hardware

Funding

Install docking stations

Stock docks with

bikes (rebalancing)

Bike maintenance

Bikeshare bike

availability

Bikeshare usage

(see section 3)

Nine impacts categorized into four focus areas (see section 4)

Using the logic model, the team is able to follow the implementation and operation

of BSH to uncover the impacts of the program. The results of BSH are comprised of outputs, outcomes, and impacts.11 In this report, we focus on the outcomes which are discussed in detail in section 3 and impacts which are discussed in detail in section 4.

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2.2. Before-After Comparison Method Ideally, the impacts of an intervention are measured using a counterfactual. A

counterfactual compares the differences between a subject group and a similar group that has not received the intervention to determine the impacts. To truly measure the impact of an intervention, the counterfactual must hold all other variables constant.11 This is known as a with-without comparison.16 For example, the outcome and impacts of BSH must be compared to a place similar to urban Honolulu except for the absence of a bikeshare program. Given how establishing a credible counterfactual is an enormous challenge,17 a before-after comparison provides an alternative methodology with less onerous requirements.

In the before-after method, impacts are measured by comparing participants to themselves before and after the program is implemented.11 This method allows the data collected before implementation to be the baseline by which data collected after is then compared. When using the before-after method, it is imperative to collect pertinent data prior to launching the program to have a well-developed baseline for comparison. Although this method falls short of proving true causality since other interventions may also affect the group over time, it is an appropriate alternative for this report considering the extensive complexity of determining a counterfactual.

2.3. SMART Method The SMART Method provides a framework for goal-setting and adoption of criteria

for metrics, measurement, and tracking.11 SMART is an acronym that stands for:

● Specific - measure the information required as accurately as possible. ● Measurable - information can be readily obtained. ● Attributable/Attainable - each measure is linked to the project’s efforts or

achievable by the project. ● Realistic - data can be obtained with reasonable frequency, cost, and certainty. ● Targeted/Time-Bound - specific to the objective population or within time.11,18

The five characteristics form a set of criteria for identifying impacts and metrics

that can be quantified, measured, and attributed to BSH. In terms of planning for ongoing evaluation, it is necessary to ensure that the burden of data collection and analysis is realistic for BSH to accomplish. The metrics for measuring the impacts of BSH are listed within each focus area in section 4 and the method of obtaining these metrics are detailed in section 5.

2.4. Method of Calculating Costs for Measuring Indicators The costs of measuring each indicator is comprised of the labor for data collection

and analysis, as well as any fees for data purchase or survey creation. The cost of labor can be calculated by multiplying the quantity of hours estimated by the percentage of “full time employment” (FTE), recognizing the need for ongoing data collection and management. The average annual FTE salary of a data analyst in Honolulu is $60,300.19

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3. Outcome The Logic Model defines outcome as direct changes resulting from the outputs.

In the case of BSH, the output is bikeshare availability and the outcome is bikeshare usage. This section provides a projection of estimated BSH usage by applying data from other bikeshare systems to BSH’s expected service area and fleet of 1,500 bikes. For the purpose of this section, the service area is considered to be all census tracts which are slated to have a bikeshare docking station. A list of these census tracts can be found in appendix A. Of course, actual BSH usage will differ from other systems given Hawaii’s specific conditions as well as BSH’s consideration of an alternative pricing scheme. However, usage projections are necessary to create realistic expectations of impacts described in section 4.

Findings from existing bikeshare systems show that people who seldom or never ride a bicycle are willing to ride bikeshare, and those who ride bikeshare are willing to ride more often.20 A survey of casual Capital Bikeshare users found that 25% rarely rode a bike and 71% used Capital Bikeshare for the first time.21 Among its members, 86% of respondents increased where they rode and 50% rode a bike much more often. Of employed respondents, those who primarily biked to work increased from 9% to 29%.22 Based on these results, BSH will likely be used by people who do not currently ride a bike. To project the estimated usage, the population within BSH’s service area was first

determined. 3.1. Service Area Population The commuter adjusted average daily population within the service area is

estimated to be approximately 231,000 people. This is an average of weekday estimates (259,000 people) and weekend estimates (162,000 people). The service area population accounts for residents (ages 15 to 69) living within the service area, the number of visitors who lodge in Honolulu, and people who commute to the service area on weekdays. See appendix A for detailed service area population calculations.

Residents currently living within the service area

+ Visitors lodging within Honolulu

+ Weekday commuters to the service area

3.2. BSH Usage Projections The service area population is used to calculate possible maximum and expected

BSH usage. The maximum usage provides a “ceiling” estimate by erring towards an

extreme high and is likely unattainable whereas the expected ridership provides a more probable scenario.

Usage can be documented in numerous ways. The most common metrics are based on the number trips incurred, miles ridden or users. Table 2 below summarizes the maximum and expected usage using these metrics.

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Table 2: Summary of maximum and expected BSH usage

Metric Maximum Expected

Trips per bike 19.5 3.36

Trips per day 29,300 5,040

Trips per year 10,700,000 1,840,000

Miles per hour 12 7

Miles per day 171,000 8,060

Miles per year 62,500,000 2,940,000

Users per day 29,300 660

Users per year 10,700,000 241,000

% of service area population

13 0.3

% of commuters bicycling to work

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3.2.1. Maximum Usage The maximum output projected for BSH is 29,300 trips per day, or 10,700,000

trips per year. This equates to approximately 19.5 trips per bike per day. Assuming each trip is done by a different person, 13% of the average service area population will use bikeshare. These trips will cover a total of 171,000 miles a day, or 62,500,000 miles per year.

There were a few assumptions made in determining these usage rates. Although BSH may keep the system running 24 hours, only the daylight hours of 6:00 a.m. to 7:00 p.m. were considered as other systems have shown that usage significantly decreasing beyond these hours.23,24 Within this 13 hour period, it was assumed that a bike would be utilized for 1.5 trips each hour, with each trip being 30 minutes long. This equates to the bikes being used for 45 minutes per hour, in other words 75% of the time or for 9 hours and 45 minutes of the 13 hour period. It was further assumed that each trip would be made by a different person and that each person would be riding at top speeds which is shown to be 12 miles per hour on a bikeshare bike.25 Based on Washington DC’s Capital Bikeshare usage statistics, it can be expected that 21,900 users are members and 7,310 are casual users. Assuming members are residents within the service area using bikeshare to commute to work, the percentage of population biking to work within the service area will increase by 26.2%, increasing the percentage of bicyclists within the

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service area from 1.8%26 to 28%. See appendix B for details on maximum usage calculations and assumptions.

3.2.2. Expected Usage

Depending on the bikeshare system, usage rates typically vary from 3 to 8 trips per bike per day, but they have been as low as 0.3 to 0.4 trips in Melbourne and Brisbane.1,27 Using the approximate yearly ridership of 1,640,000 provided by Nelson\Nygaard, the estimated total trips per bicycle per day is 3.36. This usage rate is similar to that of DecoBike in Miami, which experiences 3.18 to 3.5 trips per bicycle per day.10 The expectation that Honolulu’s bikeshare ridership will be comparable to the

experience of Miami’s is reasonable since Miami is similar to Honolulu in its weather, tourism base, and bikeshare system size (consisting of 1,000 bicycles and 115 docks).

At 3.36 trips per bicycle per day, the proposed BSH fleet of 1,500 bikes is likely to generate 5,040 trips a day or 1,840,000 trips a year. About 75% of trips will be made by members (people who have purchased an annual pass) while 25% of trips will be made by casual users (people who have purchased a short-term pass).

Based on Washington DC’s Capital Bikeshare usage rates, it can be expected that these trips will be generated by 660 users a day or 241,000 users a year, which is 0.3% of the service area population. It is expected that the 660 daily users will be comprised of 270 first time casual users, 110 returning casual users, and 280 members. Assuming members are residents within the service area using bikeshare to commute to work, the percentage of population biking to work within the service area will increase by 0.3%, from 1.8% to 2.1%.

The average trip distance is 1.6 miles. 1,28 With a fleet of 1,500 bikes, BSH is expected to generate 5,040 trips a day. This would cover a total of 8,064 miles per day, or 2,943,360 miles per year.

3.2.3. Mode switch

Currently, 88% of residents (47,800 people) within BSH’s service area commute for 34 minutes or less to work and about 26% (13,900 people) commute for 19 minutes or less.29 Ideally, these individuals who commute short distances by car would become avid users of BSH. However, it is expected that at most only 10% of bikeshare users will switch from using a car to using bikeshare each day.1 See appendix C for details on expected usage calculations and assumptions.

Assuming 10% of the service area population drive to work shift from driving to bikesharing, approximately 8,359 residence in the service area can expect bikeshare to replace a motor vehicle trip. A maximum of 73,561 residents within BSH’s service area

commuting 34 minutes or less to work can expect to replace car tips with bikeshare accounting for a max of 88% of current commute of 34 minutes or less to work (See appendix G for mode switch calculations).

3.3. Indicators & Metrics Three indicators and six metrics have been identified for measuring BSH usage.

These metrics can be collected internally using BSH’s operating system software. See

the table 3 below for a summary of these indicators, metrics and data source. Following the table is a detailed explanation of each metric, how the metric can be used to measure

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its corresponding indicator and the mathematical calculation to determine the degree of impact.

Table 3: Indicators, metrics and data source for measuring BSH usage

Indicator Metric Source

Changes in average number of trips per bike

Number of trips

Internal

Number of bikes available

Changes in total distance traveled

GPS route monitoring

Check-out / Check-in station

Changes in number of bikeshare users

Number of credit card holders using BSH

Average number of bicycles checked-out at once per credit card holder

3.3.1. Changes in average number of trips per bike

The number of trips per bike focuses on the number of trips generated and determines the frequency at which the bikeshare system is used. It is common to see usage expressed by the number of trips per bike per day. Since this data can be collected automatically by BSH’s operation software, further analysis could be made on an hourly

basis for frequently used stations to determine turnover rates at a station. The average trips per bike can be used to easily compare across time and with other bikeshare systems. Trips per bike can be measured using two metrics: Metrics

● Number of trips is the total number of trips made during the designated time period.

● Number of bikes available is the total number of bicycles available within the service area or at a station. This excludes bicycles in repair.

Calculation

Number of trips ÷ Number of bikes available

3.3.2. Changes in total distance traveled

The total distance traveled by BSH users is helpful in understanding the usefulness of BSH as a transportation mode. Since this data can be collected automatically by BSH’s operation software, it is recommended to be collected for all trips

during the first year of operation and reduced to a percentage of all trips in subsequent years. The accuracy of the data will depend on the number of BSH bikes that are

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equipped with a GPS device. Although accuracy is compromised, in lieu of a GPS device, the bicycle check-out and check-in station can be used to measure distance. Metrics

● GPS route monitoring allows for data to be collected during a user’s ride and

provides accurate information on the user’s route which is used to determine

distance traveled. Although GPS tracking systems are preferred for data collection, the device increases the cost per bicycle and may not be financially appropriate.

● Check-out / Check-in station information can be used in lieu of a GPS tracking system. BSH’s back-end software will need to monitor at which station a bicycle is checked-out and returned. The distance between the two stations will determine the trip distance. This method of data collection is the least accurate when a trip is nonlinear such as a trip which begins and ends at the same station. Nonlinear trips may often be seen among recreational users.

Calculation

Each metric is independent of the other and calculation is not required.

3.3.3. Changes in number of bikeshare users

The number of bikeshare users can reveal the number of new people using bikeshare and the number of people who discontinued using bikeshare. Since this data can be collected automatically by BSH’s back-end software, it is recommended to be analyzed on a daily basis for at least the first few years after launch to discover trends in the number of users based on the day of the week and day of the year. For further analysis, the data can be periodically collected on an hourly basis to discover trends in user behavior within the day. Regardless of the data collection timeframe, the data should be routinely reported on a monthly and annual basis. The number of bikeshare users can be measured using two metrics. Metrics

● Number of unique credit card holders using BSH is the number of people who have used one or more credit cards to check-out a bicycle and represents the number of users financially responsible for using BSH.

● Average number of bicycles checked-out at once per credit card holder is necessary since a credit card holder may check-out more than one bicycle at a time. Since the financial burden is upon the credit card holder, multiple bike check-outs are often only seen when people are traveling as a family.

● Calculation

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Number of unique credit card holders using BSH

× Average number of bicycles checked-out at once

per credit card holder

4. Impacts The Logic Model defines impacts as indirect changes that result from the

outcomes. Nine impacts have been identified for BSH and organized into four focus areas. The impacts and the metrics necessary to measure the indicators of the impact are detailed within each section of the four focus areas.

Four Focus Areas

Transportation: Impacts which affect people’s travel behavior

Bikeshare programs increase bicycle use by integrating cycling into the transportation system and making it more convenient and attractive to users.30 The presence of a bikeshare system can assist a community to further cycling as a viable transportation mode.30 The increase in bikeshare usage has been shown in other places to impact private bicycle usage, bicycle safety and traffic congestion.

Health & Environment: Impacts which affect people’s health

Bikeshare is a form of active transportation that impacts both health and the environment by increasing physical activity and reducing VMT.31 Bikeshare can impact a user’s health by helping them meet the recommended level of daily physical activity.32 Bikeshare systems have also been shown to encourage people to switch from using a vehicle to riding a bike which reduces VMT. VMT reductions impact the environment by significantly reducing greenhouse gas (GHG) emissions and other harmful pollutants expelled by vehicles, thus improving our health.

Local Economy: Impacts which affect people’s monetary spending and savings

It is expected that bikeshare will create local economic impacts on spending near bikeshare stations and changes to transportation costs for users.

Social Well-being: Impacts which affects people’s accessibility and social life

The impacts presented on transportation, health/environment, and economics, are all important aspects of a person’s well-being.33,34 However, this section will focus on the social aspects - accessibility and social connections.

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4.1. Transportation Impact: Changes to private bicycle usage

There is evidence from other cities that bikeshare increases the number of people

who bike in general. Bikeshare members are also more likely to use private bikes than non-members.1 A telephone survey conducted two years after Montreal, Quebec launched its BIXI bikeshare program revealed that people who were exposed to the bikeshare program were more likely to ride a bicycle than people who had no exposure to the bikeshare program. In 2014, 52% of Capital Bikeshare’s member survey

respondents also owned a bicycle. Within the first year of implementing bikeshare, the city of Lyon experienced a 44% increase in bicycle usage with 96% of bikeshare users being new users who had never cycled in the city before.35 After bikeshare systems were installed, total bike usage increased in both Barcelona and Paris, by 230% and 250%, respectively.30,36 In cities with pre-existing low cycling use, bike mode share increased from 1.0% to 1.5% after bikeshare was launched.36,22 However, increased bicycle usage was likely also attributable to bicycle infrastructure improvements made simultaneously in many of these cities.36

4.1.1. Current private bicycle usage

From 2008-012, 1.8% of the urban Honolulu population commuted to work by bike.37 Taking the resident service area population of 83,600, an estimated 1,500 people are currently cycling to work using private bikes.

4.1.2. Maximum impact on private bicycle usage

Assuming private bicycle usage increases by 250% which is similar to Paris,30,36 this would increase the service area bicycle commuter population by 4.5% or 3,760 people, bringing the service area commuter population from 1.8% to 6.3% or 5,270 people. Combining this with the maximum projected 29,250 users per day, 21,938 are projected to be members (see appendix B for calculation). Combining the bikeshare commuters with the private bicycle commuters, the number of total bicycle commuters would be 27,200, or 36% of service area residents (see appendix D for private bicycle impact calculations).

4.1.3. Expected impact on private bicycle usage

Assuming the bicycle mode share among service area commuters increases by 1%,36 which is the typical increase seen among cities with low cycling rates with a launched bikeshare, this would equate to 840 additional people who would begin to commute by bicycle. Among these individuals, 280 commuters would be bikeshare members. This leaves 560 additional people commuting within the service area by private bike, increasing the private bike commuter population within the service area by 37%. Combining the bikeshare commuters with the private bicycle commuters, the

Bikeshare could encourage 560 people to use private bicycles to commute to work

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number of total bicycle commuters would be 2,340 people or 156% of current private bike commuter the service area population (see appendix D for private bicycle impact calculations).

4.1.4. Indicators & Metrics

Two indicators and three metrics are identified for measuring BSH’s impact on private bicycle usage. These metrics are currently being collected to some degree by external sources. See table 4 below for a summary of these indicators and metrics. Table 4: Indicators, metrics and data source for measuring the impacts on private bicycle usage

Table 4: Changes to private bicycle usage

Indicator Metrics Source

Changes in bicycle ownership

Number of bicycles purchased

Existing Changes in number of personal bicycle users

Number of people riding bicycles

Number of people who commute by bicycle

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4.2. Transportation Impact: Changes to bicycle safety

Bikeshare users impact collision rates, helmet use, and driver behavior which in

turn impacts bicycle safety. Bicycle safety is a major factor that can either encourage or prevent bicycle usage.38 A survey of UK residents shows 74% of respondents fear riding on busy streets.39

4.2.1. Collisions rates

At the most basic level, bikesharing is expected to add to the number of bicycle-related injuries simply by increasing the number of bicyclists. However, as bicycle ridership increases, injury rates fall and bicycling becomes safer.40 Collision rates are shown to decline with an increase in the number of people walking or bicycling.41 As the number of pedestrians and bicyclists increases, the number of collisions with a motorist increases at roughly x 0.4 while the probability of a collision declines at roughly x -0.6 (with x representing the number of people walking and bicycling).

Since bikeshare increases the number of people bicycling, bikeshare users could possibly help to lead the trend toward a lower rate of collisions. Cities saw a 28% decline in the injury rate for cyclists after implementation of bikeshare.42 A study in Sweden shows that the number of conflicts involving bicyclists decreases abruptly with more than 50 bicyclists per hour.43 The number of bicyclist fatalities based on distance bicycled also decreases with increasing distance bicycled per capita. Therefore, in addition to traffic engineering and legal policies that modify motorist behavior, policies that increase the numbers of people bicycling can decrease the rate of collisions.

Among bikeshare systems, there is no systematic method of tallying the severity of a collision or agreed upon method for reporting collision rates.30 This may be partially due to the reluctance of bikeshare operators to share such data. While some operators base collisions on the number of trips taken, other operators base collisions on the number of miles ridden. In 2011, there were 1.36 collisions on average per bikeshare system.1 Systems with more than 1,000 bicycles had an average of 4.3 collisions per year. When collecting such data, BSH should keep in mind that an industry standard for reporting may emerge and to monitor it as such.

4.2.2. Helmet use

Cities with bikeshare systems saw a 30% increase in the proportion of bike injuries involving head injury compared to injuries before implementation.44 A study postulates that the increase in head injuries are attributable to a lack of helmet wearing that has been seen in bikeshare systems since helmet use helps prevent head trauma to the bicyclists when a collision occurs. A study revealed that there was a significant difference in helmet use based on gender and whether a public or private bicycle was used.45 Men were 1.6 times more likely to ride unhelmeted than women. Bikeshare riders typically did not wear helmets and were much less likely to wear a helmet than private bicycle riders. After adjusting for sex, day of week, time, and city, results show bikeshare

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riders were 4.4 times more likely to ride unhelmeted than a private bicycle rider. An observation of 3,000 bicyclists in Boston, MA, and Washington, DC found that bikeshare users rode unhelmeted 81% of the time compared to private bicycle riders who rode unhelmeted 49% of the time.45 However, most bikeshare systems do not mandate the use of helmets since such requirements impede the use of bikeshare for spontaneous trips.10,46

4.2.3. Driver behavior

A driver’s behavior may be affected by their perception of the social norm established by other drivers.47 Existing bikeshare systems have been able to positively impact the perception of bicycling as a viable transportation mode.48 If sponsored by a government agency, bikeshare may also increase the perceived legitimacy of cycling. Research conducted for the UK Department for Transport has found that a driver’s negative view towards cyclists stems from a perception of bikers as an outlier.49 As more drivers become bikeshare users, the change in perspective assists in changing the attitude of the drivers towards cyclists.50 Drivers may also provide greater consideration to bikeshare users compared to private bicycle users. While it is difficult to attribute bikeshare to direct changes in driver behavior, it is nonetheless important to monitor changes to driver behavior.

4.2.4. Current injuries and deaths of bicyclists on O’ahu

Bicyclists and pedestrians are 2.3 times more likely than motor vehicle occupants to suffer a fatal injury during any given trip.51 Bicyclists represent 2% of statewide hospital admissions and 1% of emergency room visits.52 There are more than 1,200 bicyclist injuries and between 2 and 4 bicyclist deaths in Hawai‘i each year.52 For every 100,000 riders, 1.3 people die and 90.7 are injured.52 It is unlikely that bikeshare users will be injured at the same rate as many of the injuries occur to people who are too young to use bikeshare and in locations where it will be unlikely for bikeshare to be used. Nearly one-third (31%) of those injured were 5 to 14 years of age52 and an estimated 85% of injuries occurred in “non-traffic” areas such as on private roads, driveways, or off-road environments.52 Deaths, on the other hand, are occurring to individuals who may have been potential bikeshare users. From 2007 to 2011, there were 12 bicyclists killed on Oàhu and most of these deaths were male riders hit by a car traveling straight on the roadway.52 However, to accurately relate collision rates to ridership, collisions should be based on miles traveled rather than the number of users.

4.2.5. Maximum impact on bicycle safety

With a projected maximum of 10,700,000 annual bikeshare users, 650 bikeshare collisions are likely to occur each year with 0.006% probability of a user being hit by a motorist. Using current bicycle death rates for Oàhu, these collisions may result in 139 deaths a year or 12 deaths a month (see appendix E for bicycle safety impact calculations).

Of the 29,250 bikeshare users per day, we project approximately 23,700 or 81% of users will ride unhelmeted and 5,560 or 19% will ride using a helmet.

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4.2.6. Expected impact on bicycle safety With an expected 240,900 annual bikeshare users, 140 bikeshare collisions are

likely to occur each year with 0.06% probability of a user being hit by a motorist. Using current bicycle death rates for Oàhu, these collisions may result in 3 deaths a year (see appendix E for bicycle safety impact calculations).

Of the 660 bikeshare users per day, we project approximately 540 or 81% of users will ride unhelmeted and 130 or 19% will ride using a helmet.


Three indicators and seven metrics are identified for measuring BSH’s impact on bicycle safety. The metrics can be collected using a combination of BSH’s back-end operating system, monitoring bicycle users, and referencing existing data that is being collected by external sources. See the table 5 below for a summary of these indicators and metrics.

Table 5: Indicators, metrics and data source for measuring the impacts on bicycle safety


Changes in bicycle collisions compared to ridership

Number of collisions Existing

Customer calls regarding collisions Internal

BSH bikes and parts replacement rate

Changes in head injuries compared to ridership

Helmet use counts Monitor

Head injury counts Existing

Changes in driver behavior compared to ridership

Driver speed

Monitor Clearance when drivers overtake bicyclist

Approximately 142 bikeshare collisions, resulting in 3 deaths, are expected to occur annually

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4.3. Transportation Impact: Changes to traffic congestion

Without dedicated bicycle infrastructure, bicyclists must share a travel lane with other vehicles. If the travel lane is narrow and prohibits a motor vehicle and bicycle from riding side-by-side, an increase in bicycle usage can dramatically reduce mobility on road segments, especially on roads which carry heavy vehicles such as transit buses.53 The delay is worsened with uphill grades and increasing traffic volumes while inversely proportional to total pavement width.

4.3.1. Maximum and expected impact on traffic congestion Congestion levels are dependent upon a number of variables including the width of

the road that a bicyclist travels, traffic volumes, and whether the road has a dedicated bicycle facility. Due to these metrics being extremely location sensitive, determining accurate maximum and expected congestion levels is not possible within the scope of this study.


One indicator and four metrics are identified for measuring BSH’s impact on traffic congestion. The metrics can be collected using a combination of BSH’s back-end operating system, monitoring bicycle users, and referencing existing data that is being collected by external sources. See the table 6 below for a summary of these indicators and metrics.

Table 6: Indicators, metrics and data source for measuring the impacts on bicycle safety


Changes in traffic congestion

compared to ridership

Bicyclists using roadway Existing

Bicycle infrastructure availability

Headway between bicyclist and car Monitor

Docking station proximity to bicycle infrastructure Internal

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4.4. Health Impact: Changes to physical activity

While any form of physical activity impacts one’s health, there is evidence that

cycling provides an intense enough workout to have greater impacts on cardiovascular health than walking alone.54 Active transportation, such as walking and bicycling, has been found to be positively associated with fitness and inversely associated with specific risk factors for cardiovascular disease.55 There are also significant associations between cycling, chronic disease prevention, and the prevention of cardiovascular deaths. 32,56 A study of three North American bikeshare systems shows 64% of members agree that they get more exercise since joining bikeshare.57 With regards to BSH’s impact on health, we focus on the changes to physical activity since it is a direct impact of BSH usage. Other health benefits which derive from increased physical activity such as cardiovascular health, weight loss, and calories burned are not discussed in this report.

4.4.1. Maximum impact on physical activity

Assuming that every trip is being made by a new user, BSH may change the level of physical activity of 29,300 individuals each day (see appendix B for maximum usage calculations). This is approximately 13% of the adjusted service area population (see appendix A for service area population calculations). To put this in perspective, about 40% of Hawai‘i adults do not meet the aerobic physical activity guidelines.6 Therefore, assuming that each individual is making one bikeshare trip per day, and that this trip is their only physical activity during the day, BSH could help cut the number of Oahu’s adults not meeting the physical activity guidelines by more than 25%.

4.4.2. Expected impacts on physical activity

It is expected that a majority of bikeshare users will not be increasing their physical activity levels because bikeshare often replaces trips previously made by walking or cycling.1 Of the 660 different individuals expected to use the system each day (see appendix C for expected usage calculations), approximately 29%57 or 190 people are expected to replace an automotive trip with bikeshare.


One indicator and one metric are identified for measuring BSH’s impact on physical activity. The metric can be collected by surveying BSH users. See the table 7 below for a summary of the indicator and the metric.

An estimated 190 people a day will increase their physical

activity levels by switching from driving a car to using bikeshare

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Table 7: Indicators, metrics and data source for measuring the impacts on physical activity


Average additional time spent exercising due to BSH

Increase in time spent using active transportation since bikeshare usage

Survey

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4.5. Environment Impact: Changes in VMT

Bikeshare provides users with an alternative to using a motor vehicle and

promotes using public transit and private bicycles.58 Among users of North American bikeshare system, 29% reported a reduction in car usage.57 Bikeshare also encourages VMT reductions by providing “last mile” connectivity to fill the gaps in trips using public transit.30 Of Capital Bikeshare users, 21% had used bikeshare to get to or from a train station 6 or more times in the past month, and 24% of respondents used Capital Bikeshare to access a bus in the past month.22 Overall, Capital Bikeshare members have reduced an estimated 4.4 million driving miles annually.22

The viability of bikeshare to reduce VMT is threatened by the rebalancing process. During rebalancing, bikeshare operators often use fossil fuel powered vehicles to transport bikes from full stations to empty stations.59 In this report, we focus on the changes to VMT since it is a direct impact of BSH usage. Indirect impacts associated with reductions in VMT such as GHG emissions are not discussed in this report.

4.5.1. Maximum impact on VMT

Assuming every BSH trip is replacing of a car trip, the amount of VMT reduced by BSH could be as significant as 171,000 miles a day, or 62,500,000 miles per year (see appendix B for maximum usage calculations). To put this in perspective, this would mean a 5.4% reduction in VMT on arterial streets in Urban Honolulu when compared to 2011 figures which is 3,148,000 miles per day, or 1,150,000,000 over the year.60

4.5.2. Expected impacts on VMT

Approximately 10% of bikeshare trips replace a motor vehicle trip.1 Based on the average bikeshare trip distance of 1.6 miles,1 an estimated 5,000 BSH trips per day, and a 10% mode substitution from cars, BSH is expected to reduce VMT by 8,000 miles per day, or 292,000 miles per year (see appendix B for max ridership calculations). Overall, BSH is expected to reduce VMT on arterial streets in Urban Honolulu by 0.03%.


One indicator and three metrics are identified for measuring BSH’s impact on VMT. The metrics can be collected using a combination of BSH’s back-end operating system and surveys of BSH users. See the table 8 below for a summary of these indicators and metrics.

VMT is expected to decrease by 0.03%.

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Table 8: Indicators, metrics and data source for measuring the impacts on VMT


Net changes in VMT

Frequency BSH replaces a motor vehicle trip Survey

Distance of motor vehicle trip replaced by BSH

Rebalancing vehicle miles traveled Internal

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4.6. Economic Impact: Changes in Spending Near Bikeshare Stations

There is some evidence that the presence of bikeshare stations can economically

impact nearby businesses.61,62,63 Studies have used survey-based data that suggest some positive evidence between bikeshare stations and nearby economic activity to businesses. No studies found are based on actual consumer sales, however, likely due to the difficulty in obtaining data. Intercept surveys were generally conducted while bikeshare users locked their bikes. For nearby businesses, door-to-door surveys were deployed.61 Studies generally conclude, however, that most businesses anticipated positive impacts (or are uncertain), while few anticipated negative impacts.

In a study of 333 CaBi users coupled with door-to-door surveys for 140 local businesses within 0.1 miles from five sample CaBi stations, it was found that 34% and 45% of respondents would spend at businesses within two and four blocks of CaBi stations, respectively. Moreover, 23% of respondents stated they were likely to spend more during a CaBi trip compared to using other travel modes; 67% stated that their spending would remain the same compared to using other travel modes, or were unsure (see Appendix J, Table 16 for Scope of User and Business Survey Questions).61 The bikeshare user surveys were conducted over four weekends in October 2013, and consisted of 23 questions which focused on spending and trip frequency. User surveys were conducted over the weekends because it was expected that there would be more non-commuter users and therefore, less time constraints to conduct surveys; secondly, it was expected that the majority of users would be traveling to spending destinations.61

The business surveys concluded that 61% of the businesses were unsure if CaBi had an impact on customer traffic, 10% experienced an increase, 28% experienced no change, and 1% experienced a decrease.61 Moreover, while 43% of businesses were unsure if CaBi had an impact on overall sales, 20% experienced a positive impact, 36% experienced a neutral impact, and 1% experienced a negative impact (see Appendix J, Table 16 for Scope of User and Business Survey Questions).61 The survey targeted staff of local businesses and consisted of unscheduled interviews during a five week period in October and November of 2013. There were a total of 22 questions, which aimed to gather information regarding the staff’s perceptions of CaBi’s impacts.

Studies that focus more broadly on the difference in spending between people who use motorized and non-motorized transportation suggest that non-driving customers shopped more frequently than automobile customers, and their spending amounts were similar or greater than automobile customers. For example, a study conducted in Portland, OR that used intercept surveys for 78 local businesses in 2011 found that businesses estimated that over 25% of their customers arrived via bicycle. Non-automobile customers spent similar or greater amounts, and shopped more frequently than automobile customers. Moreover, they were considered competitive as they provided unique marketing opportunities.64

There is no known study that assesses the impact of bikeshare to the overall economy.

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A similar study showed results from two intercept surveys conducted in a two week period in July of 2008, for ground floor businesses and pedestrians in Toronto, Canada. One survey question focused on identifying whether businesses expected to improve sales or remain the same, should bike lanes be installed on Bloor Street. Nearly 75% of businesses stated that in this occurrence, their business would improve or remain the same, while about 25% stated that installing bike lanes would result in attracting fewer customers. As this study examined potential business impacts from the installation of bike lanes on Bloor Street, results concluded that as the majority of retail spending on Bloor Street was represented by non-motorized customers, efforts to install bike lanes, which would attract more cyclists, would contribute to positive economic impacts for such businesses.65

Overall, research has illustrated there is generally a positive connection between bikeshare stations and impacts to nearby businesses, though the reliance on survey data makes results quite uncertain. Although some users and businesses illustrated perceptions that bikeshare stations have the ability to contribute to positive economic impacts, most were unsure. However, it is evident that most businesses anticipated that the installation of bike lanes would contribute to positive impacts, while few anticipated negative impacts. The research does not support much evidence for negative impacts or decreases in spending. Moreover, whether any increase in spending leads to overall economic gains is unknown. Whether spending near bikeshare stations is a reallocation in consumer spending or newly induced spending is unknown. There was no study found that assesses the impact of bikeshare to the overall economy. Lastly, surveys illustrated in the research were conducted one time, and can be applied to an ongoing BSH evaluation by conducting multiple surveys over an extended period of time.

Due to the difficulty in capturing a baseline for this outcome, and detailed requirements for data within the BSH service area, as well as the need for technical economic estimation methods, estimating maximum and expected local economic impacts is outside the scope of this study.

4.6.1. Indicators and Metrics for Measuring BSH’s Impacts on Spending


Changes in spending at businesses adjacent to bikeshare stations

Net changes in spending (reported by users) Survey

Changes in retail sales at businesses adjacent to bikeshare stations

Net changes in retail sales (reported by businesses) Survey

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The introduction of bikeshare provides an additional transportation option within urban Honolulu. Depending on a user’s typical mode of transportation, their transportation costs could decrease or increase as a result of adoption of bikeshare. Costs will also vary with the level of use of the system. If, for example, a user uses bikeshare to amend the “last mile” of an existing transit trip that is currently through walking, then transportation costs can actually increase. However, if the user is now able to more quickly ride a bike, overall well-being still increases. On the other hand, a study of multiple bikeshare systems found that 25% of CaBi users were motivated to use the system due to cost savings that CaBi offered. For CaBi, the Earth Policy Institute stated that users saved an average of $800 per year in transportation costs.66 In this case, it is possible that bikeshare replaces a more expensive form of travel, like taking a taxi.

In a study focusing on CitiBike in New York City, NY a person may spend $1,344 annually on a Metro Card as well as buy a bikeshare membership for $95 annually.66 On the other hand, if a person actually substitutes a transit trip with a bikeshare trip in New York City, NY the maximum cost savings could be $1,249 annually. In conclusion, based on surveys, research has shown that 30% of users in Montreal, Canada, 49% in Toronto, Canada, 22% in Minneapolis, MN and 36% in Mexico City, Mexico have stated that the primary reasons why they have reduced their bus usage as a result of bikesharing services, are due to lower costs and faster travel times.66

Most research suggests, however, that bikeshare will be used for short trips. It can be expected that subway, bus and car trips that are short can be replaced by bikeshare trips, while longer trips will not. In its interaction with transit, bikeshare will most often be used to solve the “last mile” problem.66

4.7.1 BSH’s Maximum and Expected Impacts on Transportation Cost:

Maximum Impacts for a Current The Bus User: If a Honolulu bus user pays a bus membership of $60/month, or $720 annually,

then they cancel their bus membership, and switch to bikeshare, their maximum annual net cost savings would equate to $720 - $85 = $635.67

Maximum Impacts for a Current Vehicle Driver: The Total True Cost for a car in Hawaii for 2010 (including taxes and fees,

depreciation, finance & interest, insurance, fuel costs, and maintenance & repairs) was $51,200 over a five year period, or $10,240 annually.68 Based on this data, if a car owner in the urban Honolulu service area spends $10,200 annually on car costs, then they get rid of their car and switch to bikeshare, all of their car expenses will be eliminated. Therefore, their maximum cost savings per person, would equate to $10,200 - $85 = $10,115 annually.

4.7 Economic Impact: Changes to Transportation Costs

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Expected Impacts for a Current The Bus user: Based on a survey in Minneapolis, MN, 3% of bus users stated that as a result of

using bikeshare, they use the bus much less often, while 14% of bus users stated that as a result of using bikeshare, they use the bus more often.69 Based on this survey, it is expected that the frequency of bus usage will vary due to bikeshare. Based on this data, it is expected that a bus user will keep their bus membership in addition to purchasing an annual bikeshare membership. Therefore, it is expected that bus users will spend $85 in addition to their annual bus membership.

Expected Impacts for a Current Vehicle Driver: Based on a survey in Minneapolis, MN, 44% of people who drive a personal

vehicle, stated that as a result of using bikeshare, they use their personal vehicle less often.69 Therefore, it is expected that a car owner will reduce their car usage and therefore reduce their fuel costs as a result of using bikeshare; however, it is expected that they will keep their car and continue to have other car expenses such as car payments and insurance, for example. The average fuel costs for a car in Hawaii for 2010 was $12,250, over a five year period, or $2,450 annually.68It is difficult to capture the exact rate of fuel cost reduction for car owners; however, at an illustrative rate of 10%, it can be expected that fuel costs savings would equate to $2,450 x 10% = $245 annually. After the user purchases an annual bikeshare membership, they will spend an additional $85. Therefore, their expected cost savings would equate to $245 - $85 = $160 annually.

4.7.2 Indicators and Metrics for Measuring BSH’s Impacts on Transportation Costs


Change in transportation

costs

Differences between costs from other transportation modes and bikeshare membership

costs. Users have saved money or spent additional money

Survey

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4.8 Social Well-being Impact: Changes to accessibility & induced trips

Accessibility for transportation describes the ease of an individual to reach a

desired destination by a given mode.70 By providing an additional and flexible transportation option, bikeshare systems help users to access previously out of reach destinations. The experience of Capital Bikeshare shows that users 35 years and younger or from lower income groups were more likely to say they chose bikeshare for a recent trip based on their lack of other transportation options. This could be because a desired destination is too far to walk, unavailable or inconvenient by transit, or they lack a car. Even for users who have multiple transportation options, bikeshare can still foster increased accessibility. For Capital Bikeshare users, members use bikeshare most often for personal/non-work trips. Results show that overall, the top two bikeshare trip-purposes were for social/entertainment and errands/personal appointments.22 More than half (56%) had used bikeshare for a trip to have a meal, 40% made a bikeshare trip to shop, and 36% used bikehsare for exercise or recreation.

It is possible that some trips may not have been made without the introduction of a bikeshare system. This phenomenon describes a concept of induced trips. For Capital Bikeshare, approximately 13% of the total amount of bikesharing trips would not have been taken without the bikesharing service. Common reasons for induced trips were related to characteristics of the destination or time of travel, such as: no transit/inconvenient transit to the destination or at that time of day; limited/expensive parking at the destination; too much traffic at destination; or don’t like to drive at that time of day. Nearly two-thirds (64%) of respondents said they would not have made the trips without Capital Bikeshare because it was too far to walk. This suggests respondents might have substituted some induced trips to distant destinations for trips they might have made to locations closer to their origin location.

4.8.1 Multi-Modal Transportation

Bikeshare can play an important role in bridging the gap in existing transportation networks by providing the “last mile” feeder service to reach transit stops.30 By increasing accessibility to public transit, people can be encouraged to use public transit instead of private vehicles.30 It is important for BSH to integrate with public transit as such integration has been shown to strengthen the benefits of both modes.71 Integration with transit is demanded by bikeshare users.72 In Beijing 58% and in Shanghai 55% of respondents combined the train with bikeshare. Research in Melbourne, Australia found a strong relationship between docking station activity and proximity to train stations, especially during peak hour periods.73 Of Capital Bikeshare survey respondents, over half used bikeshare to access the train system.74 64% of respondents said at least one of the bikeshare trips they made last month either started or ended at a Metrorail station and 21% had used bikeshare six or more times for this purpose. While 24% of respondents used Capital Bikeshare to access a bus in the past month.22 By integrating bikeshare into publically accessible transit location, multi-modal transportation serves as a good indicator for measuring accessibility by revealing the proportion of BSH users who use bikeshare in conjunction with other types of transit modes.

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4.8.2 Time Saving A 2008 Vélib’ survey found that 89% of program users agreed that Vélib’ made it

easier to travel through Paris. According to SmartBike, nearly 79% of respondents reported that bikesharing use in Washington, D.C. was faster or more convenient than other options. 80% of Capital Bikeshare 2014 Member Survey respondents chose bikeshare for a recent trip because it was a faster or easier way to reach their destination. Capital Bikeshare members report shifting some trips to bicycle from other modes of transportation, specifically 51% decreased their use of walking implying a shift to bikesharing.

4.8.3 Current walking rates on Oahu

From 2008-2012, 9% of the urban Honolulu population commuted to work by walking.75 Taking the resident service area population of 83,600 (see appendix A for calculations) an estimated 7,524 people are currently walking to work in the service area. The average walking speed in Hawaii is about 3-4 mph.76

4.8.4 Maximum and expected impact on accessibility

Due to the technical factors of the BSH implementation process (i.e. pricing, station placement, ridership, convenience) forming a baseline for comparison, estimating a maximum and expected accessibility impacts is outside the scope of this study, but recommended the metrics below for accessibility measurements.

4.8.5 Maximum impact on time-savings

The maximum sustained speed of bikeshare bikes is around 12 mph (See Table 2. for max and expected bikeshare speeds). If individuals living in the service area who commute 30 minutes or less by walking were to switch to using bikesharing, we can expect a maximum time savings of about 23 minutes (See Appendix F for maximum time-savings calculation).

4.8.6 Expected impact on time-savings

The expected speed of bikeshare is around 7 mph (See Table 2. for max and expected bikeshare speeds). If individuals living and working in the service area who commute 30 minutes or less by walking to work were to switch to using bikeshare, we can expect 17 minutes saved per trip that is less than 30 minutes (See appendix F for expected time-savings calculations).

4.8.6.1 Indicators and metrics for measuring BSH’s impact on accessibility

Two indicators and four metrics are identified for measuring BSH’s impact on accessibility. The data can be collected by email and intercept surveys of BSH users. See the table below for a summary of the indicators and metrics.

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Table 9: Indicators, metrics and data source for measuring impacts on accessibility & induced trips


New trips made due

to BSH New trips made due to BSH Survey

Average time saving

per trip

Time saved using BSH as part of multi-modal trip

Survey

Time saved using BSH instead of another mode

Number of users who saved time using BSH

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4.9 Social Well-being Impact: Changes to social connections

Humans are social creatures by nature and the frequency of contacts with others

and the quality of personal relationships are crucial determinants of well-being.77 Social connection can be defined as “a person’s subjective sense of having close and positively experienced relationships with others in the social world.”78 Cycling is an activity that facilitates personal connection because people are in closer proximity, compared to being in a vehicle, and cyclist alike identity with one another79 which can serve as a catalyst for social connections.

Modes of transportation have been known to influence identity amongst other personal characteristics. The Italian Vespa and Lambretta scooter was introduced in the 1960, and soon Mod80 culture associated the scooter as an identity-marker of their continental heritage, sophistication, and European-ness, setting them apart from counterpart “Rock” culture who were associated with motor cycles.81 Bicycle culture consists of both a material and a socially constructed dimension. People bike because it’s familiar, there is a sense of camaraderie amongst bicyclist, it put people outside of vehicles, biking creates spaces for social interaction, it’s a convenient, it's safe, it’s a healthy lifestyle and it's cool.79 At the core, bike culture is the individuals who compose the biking population, their commitment, and support for biking and perpetuation of the activity. Bicycling and bike culture have evolved to include bikeshare systems36 and encompassing topics such as: environmentally friendly, economically feasible, convenient, flexible mode of transportation, and healthy for the end user.10

Bikeshare appeals to many different types of users. Monitoring and tracking demographic information on bikeshare users will help to identify the service population, characteristics, and demographic profile over time to better understand the extent of service.

4.9.1 Maximum and expected impact on social connections

Due to the limited research on the impact of bikeshare systems on social connections, as well as the need for technical social connection estimation methods to establish a baseline for comparison, estimating a maximum and expected social connection impact is outside the scope of this study, but recommended using metrics below for measuring social connections.

4.9.2 Indicators & Metrics

One indicator and one metric are identified for measuring BSH’s impact on social connections. The data can be collected via email and intercept surveys of BSH users. See the table below for a summary of the indicator and metric.

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Table 10: Indicators, metrics and data source for measuring impacts on social connections


Number of users who socialize more

Number of people who report socializing more due to BSH

Survey

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5 Data Source Twenty-seven metrics have been identified for measuring the impacts of BSH.

These metrics are organized in this section by their data classification type and source. Each metric falls into one of two broad classifications: existing and non-existing. Existing metrics are currently being collected by an external agency and seven metrics fall under this classification. Non-existing metrics are metrics that have not yet been collected are and this classification is further specified into three types of data sources: survey, monitor, and internal. Survey metrics are collected by asking BSH users questions, monitor metrics are collected through observation, and internal metrics are collected by BSH’s back-end operating system. Each data source is detailed in this section.

5.1 Timeline

Using the before-after method, it is imperative for data to be collected prior to launch of the bikeshare system to create a basis of comparison for post-launch data. Graphic 1 shown below illustrates when each data classification should be collected. The timeline has been separated into two phases: pre-launch and post-launch. Although existing data is available at any time, we recommend the data to be retrieved in both phases as BSH should monitor such data early to understand the environment surrounding its users.

Graphic 1: Timeline showing when data collection should occur

Post-launch

Existing Data

Monitor Data

Survey Data

Internal Data

Pre-launch

Existing Data

Monitor Data

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5.2 Existing Metrics The following six metrics have been identified under this classification:

Metric Focus Area (reference page)

Number of bicycles purchased Transportation (p. 11)

Number of people riding bicycles Transportation (p. 11)

Number of people who commute by bicycle Transportation (p. 11)

Number of collisions Transportation (p. 12)

Head injury counts Transportation (p. 12)

Bicycle infrastructure availability Transportation (p. 15)

These metrics are collected by external agencies. Costs for collecting each metric is therefore incurred by the external agency. Costs for analysis however will be required and will depend on the depth of analysis to be undertaken. Listed below are the details for each metric.

Metric Number of bicycles purchased

Source City and County of Honolulu, Customer Service Department (CSD)

Contact CSD Administration:(808)768-3391

Accuracy and

Limitations

Bicycle registration is required for bicycles with wheels 20” in diameter or larger (§249-14, HRS). Retail stores register bicycles at the point of sale. Therefore, the data is reliable for monitoring the number of bicycles purchased in retail stores. It is unreliable for monitoring bicycles brought to Oahu or secondhand purchases as it is assumed that many of these bicycles go unregistered. Since the bicycle registration license does not expire, the data is unreliable in determining the number of bicycles currently in use or on the island.

Frequency On-going data collected since 1902 and reported annually

Accessibility Data is not published and not in digital format; data available upon request to CSD; accessing detailed information to identify trends could prove to be difficult if a large data set is required

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Cost to BSH Possible cost to request data from CSD

Amenability Difficult for registration process or fields on form to be amended

Metric Number of people riding bicycles

Source Hawaii Bicycling League (HBL) Oahu Bike Count

Contact (808) 735-5756

Accuracy and

Limitations

Counts are conducted annually at 10 pre-determined locations for three consecutive days (Tuesday, Wednesday Thursday) during peak hours (7:00 A.M. to 9:00 A.M., 4:00 A.M. to 6:00 P.M.). Six of the locations are within BSH’s service area. Counts are conducted by volunteers. The data provides information on the number of people bicycling at these locations and the facility (roadway or sidewalk) used.

Frequency Counts taken and published online annually since 2012

Accessibility Website download https://www.hbl.org/advocacy/bike-count/

Cost to BSH No additional cost to BSH

Amenability Amenable to additional data fields or locations; limited by volunteer availability

Metric Number of people who commute by bicycle

Source U.S. Census Bureau American Community Survey (Means of Transportation to Work)

Contact https://www.census.gov/programs-surveys/acs

Accuracy and

Limitations

Data is limited to commuting to work and does not include leisure or commuting to school. Data can be sorted based on numerous demographics such as age, ethnicity, travel time, and place of work.

https://www.hbl.org/advocacy/bike-count/

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Frequency On-going data collection, reported online annually

Accessibility Website download https://www.census.gov/programs-surveys/acs and Application Programming Interface (API)


Amenability Not amendable

Metric Number of collisions / Head injury counts

Source City and County of Honolulu, Police Department (HPD)

Contact (808)529-3111

Accuracy and

Limitations Data is limited to incidents where a police report is made

Frequency On-going data collection, reported annually

Accessibility Police reports are not available to the public. Collision data is not published by HPD and not in digital format but may be available upon request to HPD or the Department of Transportation Services (DTS).

Cost to BSH Possible cost to request data from HPD or DTS

Amenability Not amendable

Source State of Hawaii, Department of Health (DOH), EMS & Injury Prevention Branch

Contact (808) 733-9320, [email protected]

Accuracy and

Limitations Data is limited to incidents where the victim used an ambulance

Frequency Annual updates to injury map

https://www.census.gov/programs-surveys/acs

34

Accessibility Injury map and publications available online http://health.hawaii.gov/injuryprevention/


Amenability It may be possible to obtain additional data or filter data seen on the injury map if the information is provided by EMS and proven to be useful in injury prevention

Metric Bicycle infrastructure availability

Source

City and County of Honolulu, Department of Transportation Services (DTS), Department of Design and Construction (DDC), Department of Planning and Permitting (DPP), and Department of Facility Maintenance (DFM) State of Hawaii, Department of Transportation (DOT)

Contact

DTS, City Bicycle Coordinator (808) 768-8335 DDC, Civil Division (808) 768-8836 DPP, Traffic Review Branch (808) 768-8078 DFM, Road Maintenance Division (808) 768-3622 DOT, Highways Division (808) 587-2220

Accuracy and

Limitations

DTS maintains an online map of existing and proposed City and State bikeway infrastructure. The creation of bicycle facilities on city roadways fall under three city agencies: DTS, DDC, and DPP. Each agency plays a different role in bikeway infrastructure creation. DTS constructs bikeways on existing roadways, DDC constructs bikeways on new roadways, and DPP requires private developers to install bikeways within the public right-of-way. Maintenance of these bikeways falls under the jurisdiction of DFM. Bikeways on State roadways are under the jurisdiction of DOT. Bikeways on private property are created and maintained by the property owner.

Frequency On-going updates to the Oahu Bikeways Map

Accessibility Website application: http://bike.honolulu.gov


Amenability Amenable to additional data fields on map if information is readily

http://health.hawaii.gov/injuryprevention/

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available

5.3 Non-Existing Metrics

5.3.1 Survey

The following ten metrics can be collected using this method:


Frequency of BSH replacing a motor vehicle trip Environment (p. 17) Local Economy (p.19)

Distance of motor vehicle trip replaced by BSH Environment (p. 17) Local Economy (p. 19)

Increase in time spent using active transportation Health (p. 16)

New trips made due to BSH Social Well-being (p. 21)

Number of users who saved time using BSH Social Well-being (p. 22)

Time saved using BSH instead of another mode Social Well-being (p. 22)

Number of people who report socializing more due to BSH

Social Well-being (p. 23)

Net changes in spending (reported by users) Local Economy (p. 22)

Net changes in retail sales (reported by businesses)

Local Economy (p. 22)

Differences between costs from other transportation modes and bikeshare membership costs. Users have saved money or spent additional money

Local Economy (p. 24)

Email surveys and intercept surveys can be used to collect non-existing data on BSH members and casual users. Omnibus surveys are not recommended since they are most often used for collecting a wide variety of data on varying subjects during a single interview and will not be helpful in collecting specific data on BSH users in Honolulu.

Source

Email surveys: ● Survey Monkey (https://www.surveymonkey.com/) ● AYTM (http://aytm.com/)

Intercept surveys:

https://www.surveymonkey.com/

http://aytm.com/

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● SMS Hawaii ● FAQ Hawaii

Contact Hersh Singer at SMS Hawaii ● Phone: (808) 537-3356 ● Email: [email protected] ● Web page: http://www.smshawaii.com/

John M. Itamura at FAQ Hawaii Inc. ● Phone:(808) 537-3887 ● Email: [email protected]

Accuracy and

Limitations

Survey questions can be tailored to solicit specific information from respondents. The Accuracy of data and limitations of conducting surveys can be costly, depending on sample size, frequency, and degree of data analysis, but can also serve as an explicit cost-benefit framework geared toward helping decision makers maximize data quality within the constraints of a limited budget. Incentives such as free annual bikeshare passes have been used in the Capital Bikeshare system to increase completion rate and are recommended.

Frequency Six month to one-year intervals. or BSH discretion and budget availability

Cost to BSH Email surveys: ● Survey monkey has no cost. ● AYTM has no cost.

Intercept surveys: ● SMS Hawaii costs $38-$45 an hour per person administering

the interviews. Averaging 5-6 interviews a person per hour with additional fees for data compilation, analysis, and reporting. Analysis fees vary depending on sample size and degree of analysis. (Contact Hersh Singer for more details)

● FAQ Hawaii Inc. cost $30 (plus Hawaii GE tax) per survey (See FAQ Hawaii Inc. Proposal for details)

5.3.2 Monitor The following four metrics are to be collected using this method:


Helmet use counts Transportation (p. 12)

Driver speed Transportation (p. 13)

Clearance when driver overtakes a bicyclist Transportation (p. 13)

mailto:[email protected]

http://www.smshawaii.com/

37

Headway between bicyclists and car Transportation (p. 13)

Monitoring entails making observations on the interactions between users and external information, while collecting data at designated times and site locations. The most important aspect of this method is choosing site locations, which need to represent all users in the target population.82 To monitor helmet counts, for example, data would be collected to determine if users were or were not wearing helmets. Due to the uncertainty of knowing the depth of monitoring required, and the job title of this method, it is difficult to determine an estimated cost of monitoring. However, three types of observation methods are generally used: stationary observers would remain stationed at a site location, such as a bikeshare station or traffic intersection; continuously mobile observers would monitor from inside a moving vehicle; and other stationary observers would migrate to different site locations.83 Stationary observers should be placed at multiple site locations where the rider density is high. To combine the features of mobile and stationary methods, migratory observation is seen as the most effective type of monitoring; it allows observers to be at fixed site locations for short periods, then rotate. 83 To achieve the most accurate results from monitoring, it is necessary to monitor at the same site locations, during the same time of day, and under the same weather conditions; varying weather conditions have shown evidence of influencing helmet usage.83

5.3.3 Internal The following eight metrics are to be collected using this method:


Number of credit card holders using BSH Transportation (p. 11)

GPS route monitoring Transportation (p. 11)

Number of bikes available Transportation (p. 11)

Number of trips Transportation (p. 11)

Check-out / Check-in station Transportation (p. 11) Environment (p. 17)

Customer calls regarding collisions Transportation (p. 12)

Bikeshare bike and part replacement rates Transportation (p. 12)

Docking station proximity to bicycle infrastructure Transportation (p. 15)

38

Rebalancing vehicle miles traveled Environment (p. 17)

Average number of bicycles checked-out at once per credit card holder

Transportation (p. 11)

These are metrics that relate most directly to bikeshare usage. This data is

typically collected by the bikeshare vendor’s back-end software. While the primary purpose of back-end software is to assist with real-time operation and monitoring of a bikeshare system, it can also be used to collect internal usage data. Because of this, the cost of collection for internal metrics is considered to be free, as back-end software is integral to bikeshare operations in general. Any requests for specific types of data or reports are to be negotiated with the vendor before system launch. If a more robust analysis of data is found to be required post implementation, one option practiced by other bikeshare systems is the creation of a public API to allow anyone the ability to interface with the data.

39

6 Conclusion This document provides BSH with the knowledge to take informed steps towards ongoing impact assessment. Based on the experiences of bikeshare systems elsewhere, it is expected that BSH will impact Honolulu in four distinct areas: Transportation, Health and Environment, the Local Economy, and Social Well-being. Impacts will likely be seen in bicycle use, bicycle safety, traffic congestion, physical exercise, VMT, consumer and transportation spending, time savings, accessibility, and social connections. To capture the degree of BSH’s impact on these areas, this report recommends metrics for collecting

data and compiling indicators over time. The report also helps to set expectations on bikeshare usage, possible impacts and their magnitude.

40

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http://health.hawaii.gov/injuryprevention/files/2013/09/Hawaii_Injury_Prevention_Plan_2012_to_2017_4mb.pdf



http://iopscience.iop.org/article/10.1088/1748-9326/8/1/015028;jsessionid=71DAD9F7A2FD4A2F0D05FAE7F516BD6B.c2.iopscience.cld.iop.org



http://www.med.upenn.edu/beat/docs/Is_Active_Commuting_the_Answer_to_Population.4.pdf

http://www.med.upenn.edu/beat/docs/Is_Active_Commuting_the_Answer_to_Population.4.pdf

http://archinte.jamanetwork.com/article.aspx?articleid=773531

http://dx.doi.org/10.7916/D8988514

http://transweb.sjsu.edu/PDFs/research/1029-public-bikesharing-understanding-early-operators-users.pdf



http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3673500/

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3673500/

http://ivem.eldoc.ub.rug.nl/FILES/ivempubs/dvrapp/EES-2010/EES-2010-102M/EES-2010-102M_BoukeWiersma.pdf

http://ivem.eldoc.ub.rug.nl/FILES/ivempubs/dvrapp/EES-2010/EES-2010-102M/EES-2010-102M_BoukeWiersma.pdf

43

60 City and County of Honolulu (2013) The Mobility Data for Honolulu HI. http://www.honolulu.gov/rep/site/csd/news2013/honol.pdf 61 Buehler, R.; Hamre, A. (2014) Business and Bikeshare User Perceptions of the Economic Benefits of Capital Bikeshare. School of Public and International Affairs; Virginia Tech. Alexandria Center. 62 Wang, X.; Lindsey, G.; Schoner, J.E.; Harrison, A. (2015). Modeling Bike Share Station Activity: Effects of Nearby Businesses and Jobs on Trips to and from Stations. American Society of Civil Engineers. 63 Bush, S.K. (2012). Bike Shares: Past, Present, Future and Bike Share Feasibility Study for Athens, Georgia. University of Georgia. 64 Clifton, K., et al., Consumer Behavior and Travel Choices: A Focus on Cyclists and Pedestrians, in Transportation Research Board 92nd Annual Meeting. 2012: Washington, DC. 65 Sztabinski, F., Bike Lanes, On-Street Parking and Business: A Study of Bloor Street in Toronto’s

Annex Neighborhood. 2009, The Clean Air Partnership: Toronto, Ontario. 66 Davis, L.S. (2014). Rolling Along the Last Mile. Bike-sharing programs blossom nationwide. 67 The Bus website. (2015). Retrieved at: http://www1.honolulu.gov/mobile/dabus.htm 68 Edmunds. (2010) Hawaii and California Most Expensive States to Own a Car While New Hampshire and South Dakota Least Expensive, According to Edmunds.com. retrieve link at: http://www.edmunds.com/about/press/hawaii-and-california-most-expensive-states-to-own-a-car-while-new-hampshire-and-south-dakota-least-expensive-according-to-edmundscom.html 69 Shaheen, S.; Martin, E.; Chan, N.; Cohen,A.; Pogodzinski, M. (2014). Public Bikesharing in North America During a Period of Rapid Expansion: Understanding Business Models, Industry Trends and User Impacts: Mineta Transportation Institute 70 Bhat, C., Handy, S., Kockelman, K., Mahmassani, H., Chen, Q., & Weston, L. (2000). Accessibility measures: Formulation consideration and current application. Center for Transportation Research. https://www.utexas.edu/research/ctr/pdf_reports/4938_2.pdf 71 Brons, M., Givoni, M., & Rietveld, P. (2009). Access to railway stations and its potential in increasing rail use. Transportation Research Part A, 43(2), 136–149. doi: 10.1016/j.tra.2008.08.002 72 Yang, T., Haixiao, P., & Qing, S. (2010). Bike-sharing Systems in Beijing, Shanghai and Hangzhou and their Impact on Travel Behaviour. Paper presented at the Transportation Research Board Annual Meeting 2011, Washington, DC 73 Lansell, K. (2011). Melbourne bike share and public transport integration (Master of Urban Planning Minor thesis). University of Melbourne, Melbourne. 74 LDA Consulting (2011). 2010 capital bikeshare member survey report. Capital Bikeshare. http://www.capitalbikeshare.com/assets/pdf/Capital%20Bikeshare-SurveyReport-Final.pdf 75 http://www.census.gov/hhes/commuting/files/2014/acs-25.pdf 76 Hawaii Department of Transportation (2013) Pedestrian Tool Box. http://hidot.hawaii.gov/highways/files/2013/07/Pedest-Tbox-Introductory_Sections.pdf 77 Office of Economic Cooperation Development. (2011). Compendium of OECD Well-being Indicators. http://www.oecd.org/std/47917288.pdf 78 Emma Seppala, Timothy Rossomando, James R. Doty. (2013). Social Connection and Compassion: Important Predictors of Health and Well-Being, 412. http://ccare.stanford.edu/wp-content/uploads/2015/06/Seppala-et-al-2013-Social-connection-and-compassion-Important-predictors-of-health-and-well-being.pdf 79 Pelzer, P. (2010). Bicycling as a Way of life: A comparative case study of bicycle xulture in Portland, and Amsterdam, 7. 80 Casburn, M. A Concise history of the British mod movement http://www.gbacg.org/costume-resources/original/articles/mods.pdf 81 Furness, Z. (2010). One Less Car: Bicycling and the politics of automobility. Temple University Press, 153. 82 Britt, H. & Miller, G.C. (1997). Collecting Data on Potentially Harmful Events: A Method for Monitoring Incidents in General Practice. Family Practice: Oxford University Press, Vol. 14, No. 2 83 Sacks, J. & Schieber, R. (2001). Measuring Community Bicycle Helmet Use among Children. Public Health Reports, Vol. 116.

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http://www1.honolulu.gov/mobile/dabus.htm

http://www.edmunds.com/about/press/hawaii-and-california-most-expensive-states-to-own-a-car-while-new-hampshire-and-south-dakota-least-expensive-according-to-edmundscom.html

http://www.edmunds.com/about/press/hawaii-and-california-most-expensive-states-to-own-a-car-while-new-hampshire-and-south-dakota-least-expensive-according-to-edmundscom.html

https://www.utexas.edu/research/ctr/pdf_reports/4938_2.pdf

http://www.capitalbikeshare.com/assets/pdf/Capital%20Bikeshare-SurveyReport-Final.pdf


http://hidot.hawaii.gov/highways/files/2013/07/Pedest-Tbox-Introductory_Sections.pdf



http://ccare.stanford.edu/wp-content/uploads/2015/06/Seppala-et-al-2013-Social-connection-and-compassion-Important-predictors-of-health-and-well-being.pdf

http://ccare.stanford.edu/wp-content/uploads/2015/06/Seppala-et-al-2013-Social-connection-and-compassion-Important-predictors-of-health-and-well-being.pdf

http://www.gbacg.org/costume-resources/original/articles/mods.pdf

http://www.gbacg.org/costume-resources/original/articles/mods.pdf

8 Appendix Appendix A: Service Area Population Calculations The service area population projects the number of people who will typically be able to access bikeshare. Assumptions The following assumptions are made when calculating the service area population: ● There is no commuting on weekends. Therefore, commuting is excluded from the

weekend population. ● Visitors lodging outside of Honolulu will not be able to use bikeshare and are

therefore excluded. ● Residents who live outside of the service area and do not commute to the service

area will not be able to use bikeshare and are therefore excluded. ● People under age 15 will not use bikeshare due to BSH’s age restriction and are

therefore excluded from the service area residential population. ● People over age 69 will not use bikeshare due to limited physical ability and are

therefore excluded from the service area residential population. ● The ratio of visitor housing units available in Urban Honolulu to visitor housing units

available island wide is the same ratio of visitors lodging in Urban Honolulu to visitors on Oahu.

● People working within urban Honolulu are all considered to be commuting into the service area because it is the primary employment center.

Demographic Data The demographic data shown in table 2 is necessary for calculating the total service area population.

Table 11: Demographic information for calculating service area population

Demographic Population

Residents (age 15 to 69) living within service area 83,593

Tourists lodging in urban Honolulu 78,792

Commuters to service area from outside of urban Honolulu 45,795

Commuters to service area from within urban Honolulu 50,969

Total commuters to service area 96,764

Residents (age 15 to 69) living within service area

The total population living in the service area was 83,593.84 Only residents ages 15 to 69 were considered when determining the resident population living within the service area. The reason for this limitation is because people under the age of 16 are not allowed to use bikeshare and it was assumed that individuals over 69 would not use

1

bikeshare due to physical limitations. Although people who are 15 years of age are not allowed to use bikeshare, they are included in the residential population because of the grouping structure used by the U.S. Table 1 below shows the service area population breakdown for the 35 different census tracts that are expected to have a BSH docking station. Table 1: Census tracts within service area and population per census tract.

Census Tract

Population (age 15 - 69)

6* 808

8* 2,557

17 1,887

18.01 1,432

18.03 2,728

18.04 1,432

19.01 612

19.03 2,144

19.04 3,129

20.03 2,127

20.04 1,185

20.05 2,000

20.06 1,872

21 3,057

22.01 2,848

22.02 2,652

23 4,298

24.01 2,398

24.02 2,495

25 2,921

26 3,160

27.01* 4,674

35.01* 1,760

35.02* 2,631

36.01 3,107

36.03* 1,910

36.04* 1,979

37 4,328

38 2,975

39 382

40 1,292

41* 3,411

42 2,669

51 2,250

52 2,483

Total 83,593

* BSH service area does not cover the entire census tract *Data from United Stated census Bureau (2013) ACS 1-year estimates Tourists lodging within Urban Honolulu

1

According to the Hawaii Tourism Authority (HTA) the average daily census for the number of visitors to Oahu on any given day is approximately 98,000.85 In order to narrow down this number to visitors within the area of urban Honolulu, the number was scaled with the portion of housing units located in the area. In 2014, the HTA reported a total of 35,864 housing units in all of Honolulu.86 The total number of visitor housing rentals available for tourists in Waikiki/Honolulu is 28,833.87 The percentage of visitor housing in Honolulu is 80.35%.

28,833 visitor housing rentals available in Waikiki/Honolulu

÷ 35,864 housing units in Honolulu

= 80.4% of visitor housing

in Honolulu

If we assume that this proportion accurately reflects the distribution of visitors on

Oahu, we find that approximately 78,743 of the 98,000 visitors are lodging in Honolulu per day.

98,000 visitors on Oahu per day

× 80.4% of visitor

housing in Honolulu =

78,792 visitors lodging in Honolulu

Commuters to urban Honolulu

Commuter adjusted daytime population for urban Honolulu is 452,331.88 The resident population of urban Honolulu is 340,639. Therefore, the net value of commuters to urban Honolulu is 111,692.

452,331 people in urban Honolulu

(including commuters) -

340,639 residents in urban Honolulu

=

111,692 commuters to urban Honolulu from

outside of urban Honolulu

Urban Honolulu, spanning from the area surrounding the Honolulu Airport to Kalihi, consists of 8 zip codes and is larger than the BSH service area. For this report, the three zip codes (96816, 96818, 96819) which do not cover the service area are called outlying urban Honolulu. The other five zip codes (96813, 96814, 96815, 96822, 96826) are expected to have BSH docking stations within them and will be used to represent the service area. To determine the commuter adjusted population of the service area, it is necessary to calculate the number of commuters to the service area from outside of urban Honolulu and from the outlying urban Honolulu. . Commuters to service area from outside of urban Honolulu

2

To determine the number of people commuting from outside of urban Honolulu specifically to the service area, we calculate the number of people working within urban Honolulu outside of the service area (zip codes 96816, 96818, 96819) which totals 65,897.89 This population is subtracted from the total number of commuters to urban Honolulu to determine 45,795 people are commuting from outside of urban Honolulu to the service area.

111,692 commuters to urban Honolulu from outside of

urban Honolulu -

65,897 employees working within urban Honolulu

outside of the service area =

45,795 commuters to the service area from outside

of urban Honolulu

Commuters to service area from outlying urban Honolulu To determine the number of people commuting from outlying urban Honolulu to the service area, we examined the number of commuters within outlying urban Honolulu by zip code (see table 3 below). We then looked at the percentage of residents who commute to the service area from these zip codes and applied the percentage to the total number of commuters per outlying urban Honolulu zip code. The total number of commuters to the service area from outlying urban Honolulu is 50,969.

Table 12: Number of commuters from urban Honolulu to the service area by zip code

Commuters who live within urban

Honolulu outside of the service area

% of urban Honolulu residents commuting to service area

by zip code

Total urban Honolulu residents commuting

to service area

ZZip

Code Commuter Population 96813 96814 96815 96822 96826 % Population

96816 51,209 18.4 11 12.1 4.1 0 45.6 23,351

96818 51,522 9.7 5.7 0 1.1 1.7 18.2 9,377

96819 52,416 14.2 8.8 10.2 1.6 0 34.8 18,241

Total 50,969

Source: Kao, H. (2010). Commute Map. http://hairycow.name/commute_map *96816, 96818, and 96819 are zip codes in urban Honolulu that do not cover the service area. * 96813, 96814, 96815, 96822, and 96826 are zip codes in urban Honolulu that cover the service area.

Commuter adjusted service area population The service area population is the sum of residents (age 15 to 69) living within the service area, tourists lodging within urban Honolulu, residents commuting from outside of urban Honolulu to the service area, and residents commuting from within urban Honolulu to the service area.

http://hairycow.name/commute_map

3

83,593 residents (ages 15 to 69) living

within the service area

+ 78,792 visitors lodging within

urban Honolulu +

96,764 commuters to the service area

The total population for the service area is 259,149 on weekdays and 162,385 on weekends. Considering five weekdays and two weekends a week, the average service area population is 231,502. The average population is used throughout the report as the service area population. See table 4 below for a summary of the service area population.

Table 13: Summary of the service area population

Demographic Population

Weekday service area population 259,149

Weekend service area population 162,385

Average daily service area population 231,502

4

Appendix B: Maximum Usage Calculations Limitations to maximum usage projections The maximum ridership calculations are not intended to reflect reality. The assumptions are unrealistic due to the nature of a maximum value. While not useful for predicting ridership, the maximum helps put the impacts into perspective, and helps to ensure realistic expectations of BSH. Assumptions The following assumptions are made when calculating the maximum usage:

● Bikeshare bikes will only be used for 13 hours of the day. ● Each trip is on average 30 minutes long. ● Each bikeshare bike is on average utilized for 1.5 trips an hour. ● Maximum sustained speeds of BSH bikes is 11.7 miles per hour. ● Each trip is made by a different user.

Calculations Trips per day The maximum number of trips per day is 29,250. To calculate this, we first determine the number of trips per day per bike. This is calculated use the number of trips per bike per hour and the number of hours the bicycles are accessible. The projected maximum trips per bike per day is 19.5.

1.5 trips per bike per hour

× 13 hours of

bike accessibility =

19.5 trips per day per bike

Using the number of trips per bike per day, we determine the total number of trips per day for a fleet of 1,500 bikes.

1,500 bike fleet × 19.5 trips per day

per bike =

29,250 trips per day

Assuming each trip is 30 minutes long and each bike is used for 1.5 trips per hour,

the utilization rate is 75%. This allows for a 15 minute grace period for bikes to change hands. Although BSH will likely be accessible 24 hours a day, keeping a constant usage throughout the day is unreasonable. Studies show usage significantly declines outside of daylight hours, 6:00 a.m. to 7:00 p.m. Therefore, we have limited our projection to 13 hours of the day with consistent use of 1.5 trips per hour. Miles per day

A triathlete managed to pedal a Capital Bikeshare bike at an average of 11.7 miles per hour for the bike portion of the Nation’s Triathlon.90 If we assume that 11.7 miles per hour is the maximum speed a bikeshare bike can travel during a 30 minute trip, then each trip will cover approximately 5.85 miles.

5

30 minute trips × 11.7 miles per hour = 5.85 miles

covered per trip

With a total of 29,250 trips a day, BSH can cover a total of 171,112.5 miles per day, or 62,456,062.5 miles per year.

29,250 trips per day

× 5.85 miles

covered per trip =

171,112.5 miles per day

Users per day To obtain a maximum projection of users, we assume each trip is made by a different person. With maximum projections of 29,250 trips per day, we project 29,250 users per day or 10,676,250 users per year. This is 12.6% of the commuter adjusted service area population.

29,250 users per day

÷ 231,502 people in

service area =

12.6% of service area population

By applying Washington D.C.’s Capital Bikeshare user statistics of 75% of trips being made by members and 25% of trips being made by casual users (see appendix E) we predict 21,938 users being members and 7,313 users being casual users. Commuter population

From 2008-2012, 1.8% of the urban Honolulu population commuted to work by bike.91 Taking the resident service area population of 83,593, an estimated 1,505 people are currently cycling to work.

83,593 residents in service area

× 1.8% currently

commute by bike =

1,505 people currently commute by bike

Assuming all members are residents within the service area using bikeshare to commute to work, this would increase the percentage of bicycle commuters within the service area by 26.2% from 1.8% to 28%.


+ 21,938

bikeshare commuters =

23,443 people commute by bike

6


÷ 83,593 residents in service area

= 28% commute

by bike

7

Appendix C: Expected Usage Calculations Limitations to determining accurate expectancy

There are many difficulties to determining a proper estimate of usage. There are substantial differences in how bikeshare is used globally and the reasons for such variance is still not clearly known.92 With over 300 bikeshare programs globally, there is no reporting standard and operators may be reluctant to provide accurate trip data publicly.93 This makes it difficult to get reliable comparable data across systems. Differences in operating hours and credit card reader ease play a part in spontaneous usage and therefore changes behavior.94 Not only are the extent of bikeshare bikes and docks different but there are also differences between cities regarding season, infrastructure, attractions, urban density, and user demographic. Assumptions The following assumptions are made when calculating the expected usage:

● Bikeshare bikes will only be used for 13 hours of the day. ● Each trip is on average 30 minutes long. ● Casual riders use bikeshare on average 3 times a day.

Calculations Trips per day

With a fleet of 1,500 bikes, BSH will have an expected 5,040 trips per day. The number of trips per day is determined using the number of trips per bike per day. This is calculated using the total number of trips per year and the total number of bikes available. The Nelson\Nygaard study predicts 1,644,000 trips will be made annually with a moderate bikeshare program of 1,340 bikes and 141 stations. Using these figures, the total trips per bike per day is 3.36.

1,644,000 total trips per year ÷

365 days per year ÷

1,340 bike fleet =

3.36 trips per bike per day

At 3.36 trips per bike per day, the proposed BSH fleet of 1,500 bikes equates to

a total of 5,040 trips being made each day or 1,839,600 trips a year.

3.36 trips per bike per day

× 1,500 bike fleet = 5,040 trips per day

Assuming each trip is on average 30 minutes long and bikeshare is available for 13 hours a day, a total of 26 trips can be made per day. With expected usage being 3.36 trips a day, the utilization rate is 13%, or for just under 2 hours a day.

Miles per day

8

The average trip distance is 1.6 miles.1,95 With a fleet of 1,500 bikes, BSH is expected to generate 5,040 trips a day. This would cover a total of 8,064 miles per day, or 2,943,360 miles per year.

5,040 trips per day

× 1.6 miles

covered per trip =

8,064 miles per day

Users per day We project 660 daily users or 240,900 users annually to ride bikeshare. This is 0.285% of the commuter adjusted service area population.

660 users per day

÷ 231,453 people in

service area =

0.285% of service area population

To determine the number of expected users per day, we first apply Washington D.C.’s Capital Bikeshare user statistics of 75% of trips being made by members and 25% of trips being made by casual users (see appendix E) to Nelson\Nygaard’s prediction of 137,000 trips a month to obtain a projection that 102,750 trips will be made by members and 34,250 trips will be made by casual users. Capital Bikeshare user statistics show members typically take 12.275 trips a month. This equates to 8,371 members using bikeshare each month or 279 members a day.

102,750 trips made by members per month

× 12.275 trips made by

each member =

8,371 members using bikeshare

per month

Assuming a casual user takes 3 trips a month, we project 11,417 casual users will

use bikeshare each month or 381 casual users a day. Of these casual users, 267 (70%) are expected to be first time users and 114 (30%) are expected to be returning casual users.

34,250 trips made by casual users per month

÷ 3 trips made by

each casual user =

11,417 casual users using bikeshare

per month

The sum of members and casual users equates to 660 users per day.

279 members per day

+ 381 casual

users per day =

660 users per day

Commuter population Between 2008 to 2012, 1.8% of the urban Honolulu population commuted to work

by bike.96 Taking the resident service area population of 83,593, an estimated 15,046 people are currently cycling to work.

9


× 1.8% currently

commute by bike =


Assuming all 279 daily bikeshare members are residents within the service area commuting to work, this would increase the percentage of bicycle commuters within urban Honolulu by 0.33% from 1.8% to 2.13%.


+ 279

bikeshare commuters =




= 2.13% commute

by bike

10

Appendix D: Private bicycle impact calculations

From 2008- 2012, 1.8% of the urban Honolulu population commuted to work by bike. Taking the resident service area population of 83,593, an estimated 15,046 people are currently cycling to work.


× 1.8% currently commute by bike

= 1,505 people currently commute by bike

Maximum impact on private bicycle usage

Assuming private bicycle usage increases by 250% which is similar to Paris30,36, this would increase the service area bicycle commuter population by 4.5% or 3,762 people, bringing the service area bicycle commuter population from 1.8% to 6.3% of the population, or 5,266 people.

1.8% of population

currently bike

× 250% increase = 4.5% increase in commuter population


× 4.5% increase in commuter population

= 3,762 additional people commute

by personal bike


× 6.3% commute by bike

= 5,266 people commute by personal bike

Of the maximum projected 29,250 users per day, 21,938 are projected to be members (see appendix B for calculation). Combining the bikeshare commuters with the private bicycle commuters, the number of total bicycle commuters would be 27,204, or 35.54% of service area residents.

5,266 people commute by personal bike

+ 21,938 people commute using BSH

= 27,204 people commute by bike



= 32.54% commute by bike

Expected impact on private bicycle usage

There are 1,505 people who currently commute within the service area using private bikes. Assuming the bicycle mode share among service area commuters increases by 1%Error! Bookmark not defined.,97 which is the typical increase seen among cities

11

with low cycling rates with the launch of bikeshare, this would result in 836 additional people who start commuting by bicycle.


× 1% additional commuters

by bike

= 836 additional people commute by bike

Among these individuals, 279 commuters would be bikeshare members (see appendix C for calculations). This leaves 557 additional people commuting within the service area by private bike, an increase of private bike commuter the service area population by 37%.

836 people commute by bike

− 279 commute using BSH

= 557 additional commuters by personal bike

557 additional commuters by personal bike

÷ 1,505 people currently

commute by bike

= 37% of commuters who

currently bike

Combining the bikeshare commuters with the private bicycle commuters, the

number of total bicycle commuters would be 2,341 people or 156% of current private bike commuter the service area population.


+ 836 additional people commute by personal

bike after launch of BSH

= 2,341 commute by bike after launch of

BSH

2,341 commute by bike

÷ 1,505 people currently commute by bike

= 156% of commuters who

currently bike

12

Appendix E: Bicycle safety impact calculations

1. Maximum impact on bicycle safety With maximum usage of 10,676,250 annual bikeshare users (see appendix B for calculations), 650 bikeshare collisions are likely to occur each year with 0.006% probability of a user being hit by a motorist.

10,676,250 annual bikeshare users^ 0.4 = 647.69 bikeshare collisions

10,676,250 annual bikeshare users^ -0.6

= 0.00006 probability of being hit Using current bicycle death rates for Oàhu, where 1.3 people die for every 100,000 bicyclists, these collisions may result in 139 deaths a year or 12 deaths a month.

10,676,250 bikeshare

users per year

× 0.000013 deaths per

bicyclist

= 139 bikeshare user deaths per year

2. Expected impact on bicycle safety

With an expected 240,900 annual bikeshare users (see appendix C for calculations), 142 bikeshare collisions are likely to occur each year with 0.06% probability of a user being hit by a motorist.

240,900 annual bikeshare users ̂0.4

= 142.15 bikeshare collisions

240,900 annual bikeshare users ̂-0.6

= 0.0006 probability of being hit Using current bicycle death rates for Oàhu, these collisions may result in 3.13 deaths a year.

240,900 bikeshare users

per year

× 0.000013 deaths per

bicyclist

= 3.13 bikeshare user deaths per

year

13

Appendix F: Time-savings impact for service area resident commuter Time-savings projects the potential time that BSH users, who reside and work in

the service area, can save when switching mode from walking to using BSH for commuting to work. Assumptions The following assumptions are made when calculating time-savings:

● Residents in the service area commute to work by walking, at the same rate as the rest of urban Honolulu

● Commutes made by walking will see the greatest time savings by switching to bikeshare

● Mode switch from walking to bikeshare ● Commutes 30 minutes or less to work location by walking

Time-savings for residents in service area calculations

Time-savings reflects the amount of time an average walker would take to reach a destination at any given distance compared to that of a bikeshare user. Between 2008 and 2012, 9% of the urban Honolulu population commuted to work by walking.98 Taking the resident service area population of 83,593, (see demographic data in appendix A for calculations) an estimated 7,523 people in the service area are currently walking to work.


× 9% commuters by

walking

= 7,523 people currently commute by walking

Due to the lack of available data, time-savings calculations excluded commuters who currently use non-walking or non-bicycling modes of transportation. Walkers are most likely to incur the greatest timesavings when switching to bikeshare, therefore this calculation focused on timesaving’s for the potential BSH members who currently live in the service area and are commuting to work by walking.

Table 14: Summary of travel-time for walkers and bicyclist

Metric Expected distance to destination

Maximum distance to destination

Time spent commuting

Walk 3 Miles 3 Miles Per hour

1.5 Miles 1.5 Per 30 Minutes

Bikeshare speed 7 Miles 12 Miles Per hour

3.5 Miles 6 Miles Per 30 Minutes

Maximum impact on time-savings calculations

14

Time-savings reflects the amount of time an average walker would take to reach a destination at any given distance compared to that of a bikeshare user. The maximum sustained speed of bikeshare bikes is around 12 mph (See Table 2. for max and expected bikeshare speeds). If individuals living in the service area who commute 30 minutes or less by walking were to switch to using bikesharing, we can expect a maximum time-savings of about 23 minutes.

Maximum bikeshare bike speed 12mph

÷ 60 Minutes = Expected .2 miles per minute

Max .2 miles per minute

x 8 Minutes

= 1.6 Miles

Expected impact on time-savings calculations The expected speed of bikeshare is around 7 mph (See Table 2. for max and

expected bikeshare speeds). If individuals living and working in the service area who commute 30 minutes or less by walking to work were to switch to using bikeshare, we can expect 17 minutes saved per trip that is less than 30 minutes (See appendix F for expected time-savings calculations).

Expected bikeshare bike speed 7mph

÷ 60 Minutes = Expected .12 miles per minute

Expected .12 miles per minute

x 13 Minutes = 1.6 Miles

Max and expected time-savings calculations:

Assuming the average resident in the service area travels 1.5 miles (roughly 30 minutes walking) to work, using Bikeshare at an expected 7 mph can save the average walker about 17 minutes, and a max of 22 minutes traveling at 12 mph for a 30 minute bike share trip. Current walking rates on Oahu

From 2008-2012, 9% of the urban Honolulu population commuted to work by walking.99 Taking the resident service area population of 83,600 (see appendix A for calculations) an estimated 7,524 people are currently walking to work in the service area. The average walking speed in Hawaii is about 3-4 mph.100

Average walking speed at 3 mph

÷ 60 Minutes = .05 Miles per minute

15

Average walking speed at .05 Miles per

minute

x 30 Minutes = 1.5 Miles

16

Appendix G: Mode switch

Assumptions The following assumptions are made when calculating time-savings:

● Maximum of 88% of residence in service area population commute for 34 minutes or less to work vehicle

● Expected 10% of bikeshare users will switch from using a vehicle to using bikeshare each day

● Individuals who commute short distances (34 minutes or less) by car would become avid users of BSH

● Mode switch from driving to bikesharing ● 83,593 people reside in service area,

Bikeshare trips replacing a motor vehicle trip.

Approximately 10% of bikeshare trips replace a motor vehicle trip.1 Currently, 88% of residents within BSH’s service area commute for 34 minutes or less to work and about 26% commute for 19 minutes or less.101 Ideally, these individuals who commute short distances by car would become avid users of BSH. However it is expected that at most only 10% of bikeshare users will switch from using a car to using bikeshare each day.1 Assuming 10% of the service area population drive to work shift from driving to bikesharing, approximately 8,359 residence in the service area can expect bikeshare to replace a motor vehicle trip with a maximum of 73,561 replaced car tips accounting for 88% of current residents within BSH’s service area commuting 34 minutes or less to work.


× 88%of service area population area residents who commute 34< minutes by

car

= A maximum of 73,561 residence in the service area replace a motor vehicle trip with bikeshare


× 10% of service area residents who commute 34 minutes or

less by car

= An expected 8,359 residence in the service area replace a motor vehicle trip with bikeshare

17

Appendix H: Analysis of Washington D.C.’s Capital Bikeshare users Washington D.C.’s Capital Bikeshare user demographics are applied to the estimated total number of trips to estimate the demographics of BSH users. Users per month

Washington D.C.’s Capital Bikeshare had a total of 1,048,575 trips made in the 3rd quarter (July to September) of 2015.102 Of these trips, 75% (786,629) were made by members and 25% (261,946) were made by casual users. This means each month there were approximately 349,525 trips made. Of these trips, 262,210 were made by members and 87,315 were made by casual users.

Figure 1: Number of trips members made in a month

Source: 2014 Capital Bikeshare Member Survey Report103

Figure 1 shows the number of trips members made in a month. By applying data provided by figure 1 to the total number of trips made by members within the month (246,983), we can determine that there were approximately 21,361 members who used bikeshare during the month and each member made approximately 12 trips during the month. Figure 2 details the calculations per each grouping and provides the total number of members per month.

Median number of trips per grouping

× Percent of members =

Number of trips per member

Total number of trips by members

÷ Number of trips per

member = Total number of

members

18

Table 15: Number of members and the number of trips made

Median number of trips per grouping

Percent of members

Number of trips per member

Number of members

Number of trips by members

0 7 0 1,495 0

1.5 13 0.195 2,777 4,165

4 21 0.84 4,486 17,943

8 19 1.52 4,059 32,469

15 16 2.4 3,418 51,267

25 12 2.88 2,563 61,520

35 6 2.04 1,282 43,577

40 6 2.4 1,282 51,267

Total 12.275 21,361 262,210

Assuming all casual users took 3 trips a month, this would equate to 29,105 casual users per month. A study of Capital Bikeshare casual users found that more than 70% of causal riders were first time users.104 This equates to 20,374 first time casual users and 30% or 8,732 returning casual users.

87,315 trips by casual users

÷ 3 trips per

month =

29,105 casual users per month


× 70% of casual users are first time users

=

20,374 first time casual users


− 20,374 first time

casual users =

8,732 returning casual users

The 349,525 trips per month were made by 20,374 first time casual users, 8,732 returning casual users and 21,361 members, bringing the total number of monthly users to 50,466.

19

20,374 first time casual users

÷ 8,732 remaining

casual users ÷

21,361 members

=

50,466 users per month

First time casual users made up 40% of total users, returning casual users made up 17% of total users and members made up 42% of total users. With 50,466 users making 349,525 trips per month, approximately 6.9 trips are made per user.

349,525 trips per month

÷ 50,466 users = 6.9 trips per user

20

Appendix J: Scope of User and Business Survey Questions Table 16: Scope of User and Business Survey Questions

User Survey Business Survey What are your top reasons for using bikeshare?

What is the impact of bikeshare on overall customer traffic?

What is the share of users traveling to spending destinations?

What is the impact of bikeshare on overall customer sales?

What is the share of users making new/induced trips?

What is the impact on the surrounding area?

What is the share of users making a trip regardless of bikeshare?

What are the reactions from businesses to replace sidewalk space with a bikeshare station?

What is the share of users spending more because of bikeshare?

What are the reactions from businesses to replace car parking with a bikeshare station?

Source: Buehler and Hamre, 2014. 105

85 Hawaii Tourism Authority. (2015). 2015 Island Highlights: Oahu. Honolulu, HI. http://hawaiitourismauthority.org/research/reports/?tag=Monthly%20Visitor%20Statistics&newSearch=1&display=search 86 Hawaii Tourism Authority. (2014). 2014 Hawaii Visitor Plant Inventory. http://www.hawaiitourismauthority.org/default/assets/File/reports/accommodations/2014%20Visitor%20Plant%20Inventory%20Report%20(FINAL).pdf 87 Hawaii Tourism Authority. (2014). 2014 Hawaii Visitor Plant Inventory. http://www.hawaiitourismauthority.org/default/assets/File/reports/accommodations/2014%20Visitor%20Plant%20Inventory%20Report%20(FINAL).pdf 88 Hawaii Department of Business, Economic Development, and Tourism. (2014) Statistics Brief: Commuter Adjusted Daytime Population on Oahu. Honolulu, HI. http://files.hawaii.gov/dbedt/economic/data_reports/briefs/Daytime_Population_Dec19_2014.pdf 89 United States Census Bureau (2013). 2013 ACS 1-year estimates. U.S. Census Bureau’s American Community Survey. http://www.census.gov/programs-surveys/acs/data/summary-file.2013.html 90 Austermule, M. (2012, Sep 10). To the Triathlete Who Used a Capital Bikeshare Bike at Nation’s Tri:

We Salute You. DCist. http://dcist.com/2012/09/to_the_triathlete_who_used_a_capita.php 91 http://www.census.gov/hhes/commuting/files/2014/acs-25.pdf 92 Fishman, E., Washington, S., & Haworth, N. (2013). Bike share: A synthesis of the literature. Transport Reviews, 33(2), 148-165. http://eprints.qut.edu.au/58276/1/58276A.pdf 93 Fishman, E., Washington, S., & Haworth, N. (2013). Bike share: A synthesis of the literature. Transport Reviews, 33(2), 148-165. http://eprints.qut.edu.au/58276/1/58276A.pdf 94 Fishman, E., Washington, S., & Haworth, N. (2013). Bike share: A synthesis of the literature. Transport Reviews, 33(2), 148-165. http://eprints.qut.edu.au/58276/1/58276A.pdf 95 Vance, S. (2015). Divvy releases trove of bike-share trip data. Streetsblog Chicago.http://chi.streetsblog.org/2014/02/20/divvy-releases-trove-of-bike-share-trip-data/ 96 United States Census Bureau (2013). 2013 ACS 1-year estimates. U.S. Census Bureau’s American Community Survey. http://www.census.gov/hhes/commuting/files/2014/acs-25.pdf 97 LDA Consulting (2015). 2014 capital bikeshare member survey report. Capital Bikeshare. http://www.capitalbikeshare.com/assets/pdf/cabi-2014surveyreport.pdf 98 United States Census Bureau (2013). 2013 ACS 1-year estimates. U.S. Census Bureau’s American Community Survey. http://www.census.gov/hhes/commuting/files/2014/acs-25.pdf

http://hawaiitourismauthority.org/research/reports/?tag=Monthly%20Visitor%20Statistics&newSearch=1&display=search

http://hawaiitourismauthority.org/research/reports/?tag=Monthly%20Visitor%20Statistics&newSearch=1&display=search

http://www.hawaiitourismauthority.org/default/assets/File/reports/accommodations/2014%20Visitor%20Plant%20Inventory%20Report%20(FINAL).pdf




http://files.hawaii.gov/dbedt/economic/data_reports/briefs/Daytime_Population_Dec19_2014.pdf


http://dcist.com/2012/09/to_the_triathlete_who_used_a_capita.php





http://chi.streetsblog.org/2014/02/20/divvy-releases-trove-of-bike-share-trip-data/




21

99 United States Census Bureau (2013). 2013 ACS 1-year estimates. U.S. Census Bureau’s American Community Survey. http://www.census.gov/hhes/commuting/files/2014/acs-25.pdf 100 Hawaii Department of Transportation (2013) Pedestrian Tool Box. http://hidot.hawaii.gov/highways/files/2013/07/Pedest-Tbox-Introductory_Sections.pdf 101 United States Census Bureau (2013). 2013 ACS 1-year estimates. U.S. Census Bureau’s American Community Survey. http://www.census.gov/programs-surveys/acs/data/summary-file.2013.html 102 https://www.capitalbikeshare.com/trip-history-data 2015 2nd quarter trip history data. 103 LDA Consulting (2015). 2014 capital bikeshare member survey report. Capital Bikeshare. http://www.capitalbikeshare.com/assets/pdf/cabi-2014surveyreport.pdf 104 Virginia Tech (2011). Capital bikeshare study. Virginia Tech. https://ralphbu.files.wordpress.com/2012/01/vt-bike-share-study-final3.pdf 105 Buehler, R.; Hamre, A. (2014) Business and Bikeshare User Perceptions of the Economic Benefits of Capital Bikeshare. School of Public and International Affairs; Virginia Tech. Alexandria Center.


http://hidot.hawaii.gov/highways/files/2013/07/Pedest-Tbox-Introductory_Sections.pdf


https://www.capitalbikeshare.com/trip-history-data


https://ralphbu.files.wordpress.com/2012/01/vt-bike-share-study-final3.pdf

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