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Building Evidence for Active Travel: Counting and Modeling Non-motorized Traffic
Scope of Talk
• Motivation for research – Need for evidence (measures of demand)
• Counting non-motorized traffic • Modeling non-motorized traffic • Opportunities for collaborative research
Motivation for Research
Ray Irvin, Indy Parks Greenways (1995):
How many people are on our trails?
Reasons to Count Bikes and Peds
• Improve systems planning • Document use of facilities (Indianapolis) • Allocate resources (Indiana) • Assess efficiency of investments • Assess exposure & increase safety (MSP) • Optimize facility operations & maintenance • Test hypotheses and build theory
Multi-use Trails in Indianapolis
Min Max Mean
Week Days 79 2,017 436
Weekend Days 105 3,670 834
3,500
150
500
1,500
Understand Spatial & Temporal Variation in
Trail Traffic, Indianapolis, IN (9/04)
Annual Trail Traffic
• Maximum: 606,900 • Minimum : 21,700 • Mean: 146,438 • Median: 101,578
Mean Daily Count
Monon Trail
Monon Trail
CBD
Allocate Resources • Indiana 2004-2005 Trail Funding Program
– $10-11 million available – $73 million requested; 66 applications; – Only 20 applicants estimated use in proposal
• Mean predicted annual traffic 22% > trail segment in Indianapolis with highest traffic
Improve Traffic Safety: Minneapolis Midtown Greenway Street-Trail Crossings
Street (mid-block
crossing)
Street Average
Daily Traffic
Bicycle Daily
Traffic
Recommendations For Stop Sign
Trail
Recommendations For Stop Sign
Street
Local 1 420 3,280 Remove Add Local 2 2,026 3,280 Remove Add Local 3 2,400 3,280 Remove Add Local 4 1,680 2,900 Remove Add Yield Sign Minor Arterial
7,267 2,740 Keep Add Yellow Flasher, Reduce lanes 4 to 2
Minneapolis Dept. of Public Works , Feb. 15, 2010
Intergovernmental Involvement
in Monitoring Traffic Motor Vehicle Traffic
• FHWA sets standards in Traffic Monitoring Guide (TMG) & helps fund
• State DOTs implement • Locals collaborate
Bike & Ped Traffic • Little federal support;
FHWA TMG in 2012 • Few state DOTs count:
CO, WA, MN, VT • National Bike & Ped
Documentation Project • National Academies Bike
and Pedestrian Data Subcommittee
• Locals lead!
Traffic Monitoring in Minnesota
Motor Vehicle Traffic
• MnDOT: 32,000 locations – +1,000 reference sites with
automated continuous monitoring
– +31,000 sites with automated short duration (48 hour) monitoring once per five years
• Local governments supplement MnDOT counts
Bike & Ped Traffic • MnDOT: 0 locations
– Launched pilot project; will establish 6-10 sites in 2013
• Local governments and nonprofits: < 500 sites – Minneapolis DPW & Transit
for Livable Communities: 350-400 sites: 2 hour counts
– UMN: 6 sites; continuous automated counts
– Met Council, park districts
Street Bike Blvd
Sidewalk Multi-use Trail
Counting Method With Bike Lane
Without Bike Lane
Street Shoulder
Manual field observations
B MN*
B MN*
B MN
B MN
P, B MN*
B, P MN
Video with manual processing
B B B B P, B MN
B, P MN
Inductive Loop detectors
B
B
B
B B MN*
Active Infrared MM MN*
Passive infrared P, MM MN
MM MN
Pressure Pads P
P, B?
Pneumatic tubes B
B B B B
Non-motorized Traffic Counting in Minnesota
Working with Counts • Objective is to understand patterns • Variation by mode
– bike vs. pedestrian (mixed mode) • Temporal variability
– Seasonality (monthly variation); day of week; time of day (peak hour)
• Spatial variability – Facility and location
Examples of Counting & Modeling
• Continuous counts: daily traffic model – Multi-use trail traffic (shared path)
• Indianapolis (mixed mode) • Minneapolis (mixed mode, bicycle)
• Short duration (2 hour manual counts): 12-hour traffic model – Street and sidewalk traffic
• Minneapolis (bicycle, pedestrian)
General Approach
Estimating Annual Traffic 1. Establish continuous traffic
monitoring sites 2. Implement quality assurance/control 3. Impute missing daily counts* 4. Estimate annual average daily
cyclists, pedestrians 5. Estimate adjustment factors (day of
week, monthly) 6. Conduct short duration counts (e.g.,
48 hours) at sample sites 7. Extrapolate short duration counts to
average annual traffic using adjustment factors from reference sites
Modeling Daily Traffic 1. Establish sample locations for
continuous or short duration counts 2. Implement quality assurance/control
(Censor partial or inaccurate counts)
3. Create databases of correlates of traffic (socio-demographic, urban form, weather, other variables)
4. Estimate traffic models (regression) 5. Evaluate quality of models (prediction,
validation)
* Models can be used to estimate daily traffic for days when counts missing.
Minneapolis (n=6; mixed mode, bike, ped) Indianapolis (n=30; mixed mode)
Trail Networks and Monitoring Locations
A Calibration Problem: Loop Detector Counts (bikes) > Infrared Counts (bikes & peds)
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Dai
ly T
raffi
c C
ount
Date
Hennepin Ave. Counter Site (Dec 2009 & Jan 2010)
loop detector
infrared
Quality Assurance / Control
Active Infrared Counters: Mixed Mode
Inductive Loop Counters: Bicycles
Hourly adjustment equation: (same across locations)
Hourly adjustment equation: (varies by location)
y=0.7018x
y=0.0002x2+1.0655x-1.2937
y=1.0328x
Temporal and Spatial Variation in Indianapolis Trail Traffic (mixed mode)
Daily Traffic vs Weekday / Weekend Traffic - 2004, Monon 67th St.
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7000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Counts
Daily Traffic Weekday Traffic Weekend Traffic
Max:6155Min: 52
Average Daily Traffic by Day of Week, Monon 67th St.
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c
2001200220032004
Monthly Total Traffic at 4 Locations on Monon, 2004
0
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20,000
30,000
40,000
50,00060,000
70,000
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90,000
100,000
1 2 3 4 5 6 7 8 9 10 11 12
Month
Mon
thly
Tra
ffic
67th
HC
Kes
38th
Mean Hourly Traffic on Weekday & Weekend, Sep 2004 67th on Monon & Mich on White River
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0 1 2 3 4 5 6 7 8 9 10 1112 13 14 15 1617 18 19 2021 22 23Hour
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c
Temporal and Spatial Variation in Minneapolis Trail Traffic
Mixed mode: monthly mean daily traffic Mixed mode: monthly/annual daily traffic
Mixed mode Bikes
Peds
Bicycle: day of week / annual daily traffic
Lake trails
Greenway trails
Weekday and Weekend Time of Day Variation in Trail Traffic
Midtown-Hennepin Lake Calhoun Trail
Weekdays
Weekends
Mixed Mode Annual Trail Traffic
Indianapolis (2004)
Minneapolis (2011)
Monitoring sites 30* 6** Maximum traffic 606,906 1,308,643
Mean traffic 146,438 626,050 Minimum traffic 21,737 116,765 *Monitoring sites covered most of trail network at time. **Monitoring sites cover only 6 of 50 or more segments.
Location / Mode Estimated Total Annual Traffic
Estimated AADT
Percent of Traffic at
Site
Estimated Miles Traveled
(1) Hennepin Ave. & Midtown Greenway (1.7 m)
a. Bicycle 629,262 1,724 87% 1,069,745 b. Pedestrian 91,451 251 13% 155,467 c. Total – mixed-mode 720,714 1,975 100% 1,225,214
(2) West River Pkwy & MGW (1.5 m) a. Bicycle 320,198 877 96% 480,297 b. Pedestrian 13,196 36 4% 19,794 c. Total – mixed-mode 333,395 913 100% 500,093
(3) Cedar Ave. & MGW (2.3 m) a. Total – mixed-mode 738,336 2,023 100% 1,698,173
(4) Lake Calhoun Parkway* (1.2 m) a. Bicycle (outer) 494,209 1,354 38% 593,051 b. Pedestrian (inner) 814,434 2,231 62% 977,321 c. Total – mixed-mode 1,308,643 3,613 100% 1,570,372
(5) Lake Nokomis Parkway* (1.9 m) a. Bicycle (outer) 193,843 531 36% 368,302 b. Pedestrian (inner) 344,604 944 64% 654,748 c. Total – mixed-mode 538,448 1,475 100% 1,023,051
(6) Wirth Parkway – mixed-mode (1.5 m) 116,765 320 100% 175,148
Average Annual Daily Bicycle & Pedestrian Traffic
Factors that Affect Trail Traffic (similar approaches in Indianapolis, Minneapolis)
• Weather (daily, seasonal) • Day of week • Neighborhood socio-demographics • Built environment & urban form
– Neighborhood characteristics – Trail characteristics
Negative Binomial Models of Mixed Mode Trail Traffic Variables 1-General
Model
n=1898
2-Six-location Model
n=1898
Trail-specific Models 3-8
3-Hennepin n=427
4-WRP n=405
5-Cedar n=272
6-Calhoun n=269
7-Nokomis n=261
8 Wirth n=264
Pseudo-R2 0.1329 0.1329 0.1162 0.1283 0.1111 0.0986 0.1197 0.1596 (Constant) 1.297*** 4.331*** 6.221*** 5.397*** 6.448*** 6.611*** 6.029*** 4.166*** Social Demographic Characteristics blkpct 0.154*** - - - - - - - collegepct 0.032*** - - - - - - - yngoldpct -0.412*** - - - - - - - medincthd 0.144*** - - - - - - - Built Environment popden 0.001*** - - - - - - - Climate Conditions tmax 0.082*** 0.082*** 0.083*** 0.085*** 0.077*** 0.087*** 0.074*** 0.093*** maxdev -0.033*** -0.033*** -0.039*** -0.040* -0.044*** -0.017** -0.008 -0.043*** precip -0.213*** -0.213*** -0190*** -0.221*** -0.218*** -0.235*** -0.216*** -0.224*** windavg -0.017*** -0.017*** -0.152*** -0.017*** -0.011** -0.020*** -0.019*** -0.018*** Temporal Dummy weekend 0.294*** 0.294*** 0.202*** 0.282*** -0.076 0.571*** 0.417*** 0.423*** Location Dummies henn - 1.894*** - - - - - - wrp - 1.091*** - - - - - - cedar - 2.033*** - - - - - - calhoun - 2.377*** - - - - - - nokomis - 1.607*** - - - - - -
Asterisks (*, **, ***) indicate variable is significant at the 0.1, 0.05, and 0.01 levels, respectively. Coefficients in bold are consistent with expected signs.
Predicted and Actual Trail Traffic, Mixed Mode, Minneapolis
Indianapolis Models of Trail Traffic
• Best model: pedestrian access zones – 29 variables, all significant at 1% level – OLS; Adj. R2 = 0.7966
• Pragmatic, more general model: tract level analyses – 25 variables, all significant at 1% level – OLS; Adj. R2 = 0.7557
Predicted & Actual Trail Traffic, Mixed Mode, Indianapolis
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actual countspredicted counts
020406080
100120140160180200
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actual countspredicted counts
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actual countspredicted counts
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Best Models - OLS Census Tract – no urban form
Magnitude of Prediction Error
City (monitoring sites)
Mixed Mode Trail Traffic Models
Range of Prediction Error (point estimates)
Mean Prediction Error (point estimates)
Minneapolis (6) General model; (Neg. Binomial)
10.5% - 23.5% 17.1%
Site Specific Models (Neg. Binomial)
11.4% - 19.4% 15.2%
Indianapolis (30; tested 2)
Best Specified Model (OLS)
18.2% - 25.1% 21.7%
Census tract Model (OLS)
20.7% - 39.2% 30%
Bike & Ped Counts in Minneapolis, MN
Count Description
Method of observation
Manual
Traffic observed Cyclist - separate
Pedestrian - separate
Locations in Minneapolis
On /off-street bike facilities and no bike facilities
(n=259)
Period of observation 2007-2010
Number of observations
436
Length of observations
12-hour (n=43) 2-hour peak period
(n=352) Other
Limitations Human error
Counts by road/facility type
Type of Street / Facility
Daily Auto Traffic Volume
% all count locations
% of count location type with bike facilities
Principal Arterial 15,000 - 100,000 1% 0% Minor Arterial 5,000 - 30,000 42% 25% Collector 1,000 - 15,000 18% 20% Local < 1,000 24% 18% Off-street trail 0 15% 100%
Daily patterns in Non-motorized Traffic (n=43)
0%
5%
10%
15%
12:00 AM 6:00 AM 12:00 PM 6:00 PM 12:00 AM
Perc
ent o
f 12-
hour
cou
nt (6
:30-
18:3
0)
Time of Day
Bicycle: Loop Detector Bicycle: Manual Count Pedestrian: Manual Count
Scaling Factors for “Daily” (12-hour) Bike and Ped Traffic
Bicycle Pedestrian
Time period
Percent of 12-hour count
Scale factor
R2 Percent of
12-hour count
Scale factor
R2
7-8am 7.5% 13.2 0.88 6.9% 14.5 0.91 8-9am 9.3% 10.7 0.90 5.3% 18.7 0.96 9-10am 7.8% 12.9 0.89 6.1% 16.4 0.97 10-11am 6.4% 15.6 0.89 5.9% 16.8 0.96 11-noon 5.9% 16.9 0.87 9.2% 10.9 0.99 noon-1pm 5.2% 19.1 0.77 9.7% 10.3 0.99 1-2pm 7.2% 14.0 0.88 8.7% 11.5 0.99 2-3pm 7.5% 13.3 0.84 8.8% 11.4 0.98 3-4pm 9.3% 10.8 0.90 7.8% 12.8 0.98 4-5pm 12.0% 8.4 0.93 10.4% 9.6 0.97 5-6pm 12.6% 7.9 0.89 12.3% 8.2 0.996
Mean Bike Traffic Volumes by Street & Facility Type
(Minneapolis, 12-hour observations (6:30 a.m. – 6:30 p.m.; n=458)
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Principal Arterial A-Minor B-Minor Collector Local
Mea
n D
aily
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ffic
Volu
me
Street Functional Class
On-Street Bike Lane None
Factors that Affect Bike & Ped Volumes Neighborhood SES • Percent white • Age • HH income • Percent w/ college degree • Crime
Neighborhood built environment • Population density • Land use mix • Distance to water • Distance to CBD • Employment access (by transit)
Weather • Max daily temp • Precipitation
Road type • Arterial • Collector • Local • Off-street
Bike facility • On-street facility
Temporal • Year of count
Estimated 12-hour pedestrian traffic
Actual and Predicted Bike & Ped Traffic
0
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12-h
our b
icyc
le c
ount
Bicycle: Negative Binomial
Actual count
Model prediction
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our p
edes
tria
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unt
Pedestrian: Negative Binomial
Actual count Model prediction
Pedestrian models (sidewalks) perform slightly better than bicycle models (street traffic), but both are less accurate than trail traffic models.
Observations about Trail Traffic • Multiple methods available for counting • All counts are “wrong” – all measurement methods
require calibration • Traffic varies temporally & spatially, but consistently • Traffic correlated with weather, day of week, location • Statistical models explain up to 80% of variation in
traffic • Models not fully specified; limited to monitoring sites • Correlation is not causation
Observations about Bicycle and Pedestrian Traffic
1. Bicycle traffic • Higher on streets with bike facilities • Increasing over time in Minneapolis
2. Pedestrian traffic • Varies by street functional class • Not increasing over time in Minneapolis
3. Models are useful planning tools • Street and sidewalk models less accurate
than trail models • Quality limited by sample limitations
Ongoing Research Initiatives
• MnDOT Bike and Ped Counting Initiative – Methodologies for Counting Bicyclists and Pedestrians in
Minnesota
• Minneapolis Parks and Recreation Board – Network sampling (50+ two-week trail segment
counts) • Minneapolis Dept. of Public Works
– Analyses of 800 traffic signal timing counts • Rails to Trails Conservancy
– National urban trail model (annual data, multiple locations in NOAA climatic regions)
Questions?
Thank you.
Using 2011Adjustment Factors to Extrapolate 2012 Short Duration Counts
Step in Process Example Value or Calculation
1. Obtain February 2012 sample short duration count • Fri: 175 • Sat: 250
2. Look up 2011 February day of week factors • Fri: 1.04 * monthly daily traffic • Sat: 1.27* monthly daily traffic
3. Calculate 48-hour adjustment factor Sample 48-hour factor =
(1.04+1.27) / 2 = 1.16
4. Calculate 2012 February monthly average daily traffic from 48-hour adjustment factor
(175 + 250) = 183 1.16
5. Look up 2011 February factor (Feb average daily traffic / annual average daily traffic)
0.18
6. Calculate the 2012 annual average daily traffic (183 / 0.18) = 1,023
7. Use 2012 annual average daily traffic to calculate annual traffic volume.
1,023*365 = 373,422
Correlates of Trail Traffic in Indianapolis
Temporal Hypothetical Effect Measured
Effect
Weekend positive positive
Jan – Nov positive positive
StateFair positive positive
Weather
Temperature Deviation from Normal positive positive
Precipitation Deviation from Normal negative negative
Snow Deviation from Normal negative negative
Sunshine Deviation from Normal positive positive
Correlates of Trail Traffic in Indianapolis Neighborhood
Socio-Demographics Hypothetical
Effect Measured
Effect % Population less than 5 and greater than 64 negative negative
% African American negative depends on
model
% other ethnicity, exclude White and African American negative depends on
model
Mean % Population 25+ with College Degree positive positive
Mean Median Household Income, in dollars positive positive
Neighborhood Urban Form
Population density in 1/2 mile network distance to monitor positive positive
Percentage of commercial land use in trail neighborhood positive positive
Parking lots (Square Feet) in trail neighborhood positive positive
Average length of network street segments within 1/2 mile of counter negative positive
Description of Trail Segment Characteristics
Hypothetical
Effect Measured Effect
Openness: Percent total area visible within ½ mile of trail segment positive positive
Interconnectedness: Average value of visual magnitude for segment positive positive
Land Use Diversity: Shannon’s Diversity Index of land use in viewshed positive positive
Greenness: Difference between mean NDVI in neighborhood and trail viewshed positive positive
Percent Not Paved: Percent trail length with non-paved surface (e.g., gravel) negative negative
Railroad Xing: Number of railroad crossings at grade negative negative
Trail Intersection positive negative
Amenity Density: Number art, bench, signs divided by segment length positive positive
Average slope along trail segments ? depends on model
Sinuosity of trail segment ? (positive for nature trails) depends on model
Road Xing Density: Segment length / number of road crossings at grade
negative (interrupts use)
positive (access)
depends on model
Correlates of Trail Traffic in Indianapolis: Trail Segment Characteristics