Transportation Planning and Traffic Estimation CE 453 Lecture 5.

transcript

Transportation Planningand Traffic Estimation

CE 453 Lecture 5

Objectives

1. Identify highway system components 2. Define transportation planning3. Recall the transportation planning process

and its design purposes4. Identify the four steps of transportation

demand modeling and describe modeling basics.

5. Explain how transportation planning and modeling process results are used in highway design.

Highway System Components

1. Vehicle 2. Driver (and peds./bikes)3. Roadway4. Consider characteristics, capabilities,

and interrelationships in design

Start with demand needs (number of lanes?)

Transportation Planning (one definition) Activities that:1. Collect information on performance2. Identify existing and forecast future

system performance levels3. Identify solutions Focus: meet existing and forecast travel

demand

Where does planning fit in?

Transportation Planning in Highway Design

1. identify deficiencies in system2. identify and evaluate alternative

alignment impacts on system3. predict volumes for alternatives

• in urban areas … model? … smaller cities may not need (few options)

• in rural areas … use statewide model if available … else: see lab 3-type approach (note Iowa is developing a statewide model)

Truck Traffic

Planning at 3 levels State … STIP Statewide

Transportation Improvement Program (list of projects)

Regional … MPO Metropolitan Planning Organization (>50,000 pop.), 25 year long range plan and TIP (states now also do LRP)

Local …project identification and prioritization

Four Steps of Conventional Transportation Modeling

1. Trip Generation 2. Trip Distribution 3. Mode Split4. Trip Assignment

Study Area

Clearly define the area under consideration• Where does one entity end?

• May be defined by county boundaries, jurisdiction, town centers

Study Area

May be regional Metropolitan area – Des Moines including

suburbs, Ankeny, etc.• Overall impact to major street/highway network

Local – e.g., impact of trips to new Ames mall•Impact on local street/highway system

•Impact on intersections•Need for turning lane or new signal – can a

model do this level of detail?

Study Area Links and nodes Simple representation of the geometry of

the transportation systems (usually major roads or transportation routes)

Links: sections of roadway (or railway) Nodes: intersection of 2+ links Centroids: center of TAZs Centroid connectors: centroid to roadway

network where trips load onto the network

Travel Analysis Zones (TAZs)

Homogenous urban activities (generate same types of trips)

•Residential

•Commercial

•Industrial May be as small as one city block or as large

as 10 sq. miles Natural boundaries --- major roads, rivers,

airport boundaries Sized so only 10-15% of trips are intrazonal

www.sanbag.ca.gov/ planning/subr_ctp_taz.html

Four Steps of Conventional Transportation Modeling

Divide study area into study zones 4 steps

• Trip Generation • -- decision to travel for a specific purpose (eat lunch)

• Trip Distribution• -- choice of destination (a particular restaurant? The

nearest restaurant?)

• Mode Choice• -- choice of travel mode (by bike)

• Network Assignment• -- choice of route or path (Elwood to Lincoln to US 69)

Trip Generation

Model Step #1…

Trip Generation Calculate number of trips generated

in each zone•500 Households each making 2 morning

trips to work (avg. trip ends ~ 10/day!)

•Worker leaving job for lunch Calculate number of trips attracted

to each zone•Industrial center attracting 500 workers

•McDonalds attracting 200 lunch trips

Trip Generation Number of trips that begin from or

end in each TAZ Trips for a “typical” day Trips are produced or attracted # of trips is a function of:

• TAZs land use activities

•Socioeconomic characteristics of TAZ population

Trip Generation

ModelManager 2000™ Caliper Corp.

Trip Generation 3 variables related to the factors that influence trip

production and attraction (measurable variables) • Density of land use affects production & attraction

•Number of dwellings, employees, etc. per unit of land

•Higher density usually = more trips

• Social and socioeconomic characters of users influence production•Average family income

•Education

•Car ownership

• Location•Traffic congestion

•Environmental conditions

Trip Generation

Trip purpose•Zonal trip making estimated separately by

trip purpose•School trips

•Work trips

•Shopping trips

•Recreational trips

•Travel behavior depends on trip purpose •School & work trips are regular (time of day)

•Recreational trips highly irregular

Trip Generation Forecast # of trips that produced or attracted by

each TAZ for a “typical” day Usually focuses on Monday - Friday # of trips is forecast as a function of other variables Attraction

• Number and types of retail facilities

• Number of employees

• Land use Production

• Car ownership

• Income

• Population (employment characteristics)

Trip Purpose Trips are estimated by purpose (categories)

• Work

• School

• Shopping

• Social or recreational

• Others (medical) Travel behavior of trip-makers depends somewhat on trip purpose

• Work trips

• regular

• Often during peak periods

• Usually same origin/destination

• School trips

• Regular

• Same origin/destination

• Shopping recreational

• Highly variable by origin and destination, number, and time of day

Household Based Trips based on “households” rather than individual Individual too complex Theory assumes households with similar characteristics

have similar trip making characteristics However

• Concept of what constitutes a “household” (i.e. 2-parent family, kids, hamster) has changed dramatically

•Domestic partnerships

•Extended family arrangements

•Single parents

•Singles

•roommates

Trip Generation Analysis 3 techniques

• Cross-classification

• Covered in 355

• Multiple regression analysis

• Mathematical equation that describes trips as a function of another variable

• Similar in theory to trip rate

• Won’t go into

• Trip-rate analysis models

• Average trip-production or trip-attraction rates for specific types of producers and attractors

• More suited to trip attractions

Trip attractions

Example: Trip-rate analysis models

For 100 employees in a retail shopping center, calculate the total number of tripsHome-based work (HBW) =

100 employees x 1.7 trips/employee = 170 Home-based Other (HBO) =

100 employees x 10 trips/employee = 1,000Non-home-based (NHB) =

100 employees x 5 trips/employee = 500

Total = 170 + 1000 + 500 = 1,670 daily trips

Trip Distribution

Model Step #2…

Trip Distribution Predicts where trips go from each TAZ Determines trips between pairs of zones

•Tij: trips from TAZ i going to TAZ j Function of attractiveness of TAZ j

•Size of TAZ j

•Distance to TAZ j

•If 2 malls are similar (in the same trip purpose), travelers will tend to go to closest

Different methods but gravity model is most popular

Trip Distribution

Determines trips between pairs of zones

•Tij: trips from TAZ i going to TAZ j Function of attractiveness of TAZ j

•Size of TAZ j

•Distance to TAZ j

•If 2 malls are similar, travelers will tend to go to closest

Different methods but gravity model is most popular

Trip Distribution

Maricopa CountyCaliper Corp.

Gravity ModelTij = Pi AjFijKij Σ AjFijKij

Qij = total trips from i to j

Pi = total number of trips produced in zone i, from trip generation

Aj = number of trips attracted to zone j, from trip generation

Fij = impedance (usually inverse of travel time), calculated

Kij = socioeconomic adjustment factor for pair ij

Mode Choice

Model Step #3…

Mode Choice In most situations, a traveler has a

choice of modes•Transit, walk, bike, carpool, motorcycle,

drive alone Mode choice/mode split determines

# of trips between zones made by auto or other mode, usually transit

Characteristics Influencing Mode Choice Availability of parking Income Availability of transit Auto ownership Type of trip

• Work trip more likely transit

• Special trip – trip to airport or baseball stadium served by transit

• Shopping, recreational trips by auto Stage in life

• Old and young are more likely to be transit dependent

Characteristics Influencing Mode Choice Cost

• Parking costs, gas prices, maintenance?

• Transit fare Safety Time

• Transit usually more time consuming (not in NYC or DC …)

Image• In some areas perception is that only poor ride

transit

• In others (NY) everyone rides transit

Mode Choice Modeling A numerical method to describe

how people choose among competing alternatives (don’t confuse model and modal)

Highly dependent on characteristics of region

Model may be separated by trip purposes

Utility and Disutility Functions Utility function: measures satisfaction derived

from choices Disutility function: represents generalized costs

of each choice Usually expressed as the linear weighted sum of

the independent variables of their transformationU = a0 + a1X1 + a2X2 + ….. + arXr

U: utility derived from choiceXr: attributes

ar: model parameters

Logit Models Calculates the probability of

selecting a particular mode

p(K) = ____eUk__ eUk

p: probability of selecting mode k

Logit Model Example 1Utility functions for auto and transit

U = ak– 0.35t1 – 0.08t2 – 0.005c

ak = mode specific variable

t1 = total travel time (minutes)

t2 = waiting time (minutes)

c = cost (cents)

Do you agree with the relative

magnitude of the time parameters? Is

there double counting/colinearity

Logit Model Example 1 (cont)

Travel characteristics between two zones

Uauto = -0.46 – 0.35(20) – 0.08(8) – 0.005(320) = -9.70

Utransit = -0.07 – 0.35(30) – 0.08(6) – 0.005(100) = -11.55

Variable Auto Transitak -0.46 -0.07

t 1 20 30

t 2 8 6

c 320 100

Do you agree with the relative

magnitude of the mode specific

parameters? How much effect does

cost have?

Uauto = -9.70

Utransit = -11.55

Logit Model:

p(auto) = ___eUa __ = _____e-9.70 ____ = 0.86 eUa + eUt e-9.70 + e-11.55

p(transit) = ___eUt __ = _____e-11.55 ____ = 0.14 eUa + eUt e-9.70 + e-11.55

Logit Model Example 2

The city decides to spend money to create and improve bike trails so that biking becomes a viable option, what percent of the trips will be by bike?Assume:• A bike trip is similar to a transit trip• A bike trip takes 5 minutes more than a transit trip but with no waiting time• After the initial purchase of the bike, the trip is “free”

Travel characteristics between two zones

Uauto = -0.46 – 0.35(20) – 0.08(8) – 0.005(320) = -9.70

Utransit = -0.07 – 0.35(30) – 0.08(6) – 0.005(100) = -11.55

Ubike = -0.07 – 0.35(35) – 0.08(0) – 0.005(0) = -12.32

Variable Auto Transit Bikeak -0.46 -0.07 -0.07

t 1 20 30 35

t 2 8 6 0

c 320 100 0

Uauto = -9.70, Utransit = -11.55, Ubike = -12.32

Logit Model:

p(auto) = _____eUa ____ = _______e-9.70 ______ = 0.81 eUa + eUt +eUb e-9.70 + e-11.55 + e-12.32

p(transit) = _____eUt__ __ = ______e-11.55 ______ = 0.13 eUa + eUt +eUb e-9.70 + e-11.55 + e-12.32

p(bike) = _____eUt__ __ = ________e-11.55 ______ = 0.06 eUa + eUt +eUb e-9.70 + e-11.55 + e-12.32

Notice that auto lost share even

though its “utility” stayed the same

Traffic Assignment (Route Choice)

Caliper Corp.

Model Step #4…

Trip Assignment

Trip makers choice of path between origin and destination

Path: streets selected Transit: usually set by route Results in estimate of traffic

volumes on each roadway in the network

Person Trips vs. Vehicle Trips Trip generation step calculated total

person trips Trip assignment deals with volume not

person trips Need to adjust person trips to reflect

vehicle trips Understand units during trip generation

Person Trips vs. Vehicle Trips ExampleUsually adjust by average auto occupancyExample:If: average auto occupancy = 1.2 number of person trips from zone 1 = 550

So:Vehicle trips = 550 person trips/1.2 persons per

vehicle = 458.33 vehicle trips

Time of Day Patterns Trip generation usually based on

24-hour period LOS calculations usually based on

hourly time period Hour, particularly peak, is often of

more interest than daily

Time of Day Patterns Common time periods

• Morning peak

• Afternoon peak

• Off-peak Calculation of trips by time of day

• Use of factors (e.g., morning peak may be 11% of daily traffic)

• Estimate trip generation by hour

Minimum Path Theory: users will select the quickest

route between any origin and destination

Several route choice models (all based on some “minimum” path)•All or nothing

•Multipath

•Capacity restraint

Minimum Tree Starts at zone and selects minimum path

to each successive set of nodes Until it reaches destination node

Path from 1 to 5

Minimum Tree

1. Path from 1 to 5 first passes thru 4

2. First select minimum path from 1 to 4

3. Path 1-2-4 has impedance of 5

4. Path 1-3-4 has impedance of 8

5. Select 1-2-4

See CE451/551 notes for more on

shortest path computations –

several methods are available

All or Nothing Allocates all volume between zones

to minimum path based on free-flow link impedances

Does not update as the network loads

Becomes unreliable as volumes and travel time increases

Multi-Path Assumes that all traffic will not use shortest

path Assumes that traffic will allocate itself to

alternative paths between a pair of nodes based on costs

Uses some method to allocate percentage of trips based on cost• Utility functions (logit)

• Or some other relationship based on cost As cost increases, probability that the route will

be chosen decreases

Capacity Restraint Once vehicles begin selecting the

minimum path between a set of nodes, volume increase and so do travel times

Original minimum paths may no longer be the minimum path

Capacity restraint assigns traffic iteratively, updating impedance at each step

Sizing Facilities

Transportation Planning and Traffic Estimation CE 453 Lecture 5.

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