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New Tools for Estimating Walking and Bicycling Demand 9 September 2014
Practical Motivations for Estimating Demand 
•How many people are on our trails? 
Ray Irvin, Indy Parks Greenways, 1996 
•Quality of data about “number of bicyclists and pedestrian by facility … is “poor” and the “priority for better data is “high” 
Bureau of Transportation Statistics, 2000 
•What traffic controls are needed at this intersection to protect cyclists and walkers? 
Minneapolis Department of Public Works, 2010
What is Demand? 
•“Pedestrian and bicycle activity” (NCHRP 770, p. 7) 
•Willingness of people to walk or bike 
–Expression of choice – presumed to maximize well-being 
–Contingent on multiple factors (accessibility) 
–Difficult to measure 
•Measures – estimates – of pedestrian or bicycle volumes important for planning, investment, and other decisions
NCHRP 770 Tools for Estimating Demand 
•Tour-Generation and Mode-Split Models 
•GIS-Based Walk-Accessibility Models 
•Enhancements to Trip Based Models 
•Walk-Trip Generation and Flow Models 
•Portland Pedestrian Model 
•Facility Demand Models 
–Route choice models 
–Direct demand models*
Modeling Demand from Counts 
•Bikes on streets; pedestrians on sidewalks (Hankey et al. 2012) 
•Mixed-mode traffic on multiuse trails (Wang et al. 2013) 
•Traffic volume is function of: 
–neighborhood socio-demographics 
–built environment (e.g., land use, jobs) 
–transportation infrastructure 
–weather
Bike & Ped Counts in Minneapolis 
TLC and City of Minneapolis Count 
Locations, 2007-2009 
0 0.5 1 2 3 4 
Miles 
5 
On-Street Bicycle Facility 
Bike Lane, One-Way 
Bike Lanes 
Shared Lane 
Off-Street Trail 
Off-Street Bicycle Facility 
None 
Count Locations 
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
Correlates of Bike & Pedestrian Traffic Hankey et al. 2012 
Bicycle models* 
Pedestrian models* 
• % non-white (+) 
• % college (+) 
• HH income (-) 
• LU mix (+) 
• Distance to CBD (-) 
• Precipitation (-) 
• Arterial (+) 
• On/Off-street (+) 
• Year (+) 
•% non-white (+) 
• % college (+) 
• Distance to water (-) 
• Distance to CBD (-) 
• Precipitation (-) 
• Arterial (+) 
• Collector (+) 
*negative binomial regression; bold is significant at p=0.05
Estimated 12-hour pedestrian traffic
Model Validation
Modeling Mixed-Mode Trail Traffic Wang et al. 2013 
Example Monitoring Site: Midtown Greenway
Correlates of Mixed Mode Trail Traffic 
Variables 
Expected Sign 
Neighborhood Socio-demographic Characteristics 
African American residents (%) 
- 
Residents with college degrees (%) 
+ 
Population over 64 or below 6 (%) 
- 
Median household income. (1,000 dollars) 
+ 
Neighborhood Built Environment 
Population density (per square kilometer). 
+ 
Weather Conditions 
Recorded high temperature.(in Celsius) 
+ 
Deviation from the 30-year normal temperature 
+/- 
Precipitation.(centimeters) 
- 
Average wind speed. (kph) 
- 
Temporal Dummies 
Saturday or Sunday (equals 1, otherwise 0) 
+
Validation of Trail Traffic Models (Wang et al. 2013)
Using Factoring to Estimate Daily Traffic from Short Duration Counts
Estimating Performance Measures: AADT and Trail Miles Traveled in Minneapolis 
Segment AADT 
Mean 
954 
Median 
750 
Max 
3,728 
Min 
39 
•6 reference sites 
•7 day short duration counts on each segment 
> 28 million miles traveled on 80 mile trail network in 2013:
16 
0.0% 
2.0% 
4.0% 
6.0% 
8.0% 
10.0% 
12.0% 
0:00 
6:00 
12:00 
18:00 
0:00 
% of daily traffic 
Weekdays 
Weekends 
0.0% 
2.0% 
4.0% 
6.0% 
8.0% 
10.0% 
12.0% 
0:00 
6:00 
12:00 
18:00 
0:00 
% of daily traffic 
Weekdays 
Weekends 
0.0% 
2.0% 
4.0% 
6.0% 
8.0% 
10.0% 
12.0% 
0:00 
6:00 
12:00 
18:00 
0:00 
% of daily traffic 
Weekdays 
Weekends 
Short-duration monitoring identified three different traffic patterns (factor groups). Need new reference monitoring sites. 
Utilitarian (weekday) 
Mixed Recreational – Utilitarian 
(all current reference locations) 
Recreational
Facility Demand Models 
•Require counts or other measures as inputs 
•Useful for planning, understanding system 
•Do not explain causation 
•Have limitations (NCHRP 770): 
–Need to include variables of interest 
–Need to be calibrated 
–Need to be validated 
–Should not be not transferred 
•Can be strengthened (NCHRP 770): 
–Potential to cross-validate with choice models
Questions? 
Acknowledgements: 
MnDOT: Lisa Austin, Jasna Hadzic 
Minneapolis DPW: Simon Blenski 
Minneapolis Park Board: Ginger Cannon, Jennifer Ringold 
Virginia Tech: Steve Hankey 
USC: Xize Wang 
For more information contact: 
Greg Lindsey (linds301@umn.edu)

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New Tools for Estimating Walking and Bicycling Demand

  • 1. New Tools for Estimating Walking and Bicycling Demand 9 September 2014
  • 2. Practical Motivations for Estimating Demand •How many people are on our trails? Ray Irvin, Indy Parks Greenways, 1996 •Quality of data about “number of bicyclists and pedestrian by facility … is “poor” and the “priority for better data is “high” Bureau of Transportation Statistics, 2000 •What traffic controls are needed at this intersection to protect cyclists and walkers? Minneapolis Department of Public Works, 2010
  • 3. What is Demand? •“Pedestrian and bicycle activity” (NCHRP 770, p. 7) •Willingness of people to walk or bike –Expression of choice – presumed to maximize well-being –Contingent on multiple factors (accessibility) –Difficult to measure •Measures – estimates – of pedestrian or bicycle volumes important for planning, investment, and other decisions
  • 4. NCHRP 770 Tools for Estimating Demand •Tour-Generation and Mode-Split Models •GIS-Based Walk-Accessibility Models •Enhancements to Trip Based Models •Walk-Trip Generation and Flow Models •Portland Pedestrian Model •Facility Demand Models –Route choice models –Direct demand models*
  • 5. Modeling Demand from Counts •Bikes on streets; pedestrians on sidewalks (Hankey et al. 2012) •Mixed-mode traffic on multiuse trails (Wang et al. 2013) •Traffic volume is function of: –neighborhood socio-demographics –built environment (e.g., land use, jobs) –transportation infrastructure –weather
  • 6. Bike & Ped Counts in Minneapolis TLC and City of Minneapolis Count Locations, 2007-2009 0 0.5 1 2 3 4 Miles 5 On-Street Bicycle Facility Bike Lane, One-Way Bike Lanes Shared Lane Off-Street Trail Off-Street Bicycle Facility None Count Locations 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
  • 7. Correlates of Bike & Pedestrian Traffic Hankey et al. 2012 Bicycle models* Pedestrian models* • % non-white (+) • % college (+) • HH income (-) • LU mix (+) • Distance to CBD (-) • Precipitation (-) • Arterial (+) • On/Off-street (+) • Year (+) •% non-white (+) • % college (+) • Distance to water (-) • Distance to CBD (-) • Precipitation (-) • Arterial (+) • Collector (+) *negative binomial regression; bold is significant at p=0.05
  • 10. Modeling Mixed-Mode Trail Traffic Wang et al. 2013 Example Monitoring Site: Midtown Greenway
  • 11. Correlates of Mixed Mode Trail Traffic Variables Expected Sign Neighborhood Socio-demographic Characteristics African American residents (%) - Residents with college degrees (%) + Population over 64 or below 6 (%) - Median household income. (1,000 dollars) + Neighborhood Built Environment Population density (per square kilometer). + Weather Conditions Recorded high temperature.(in Celsius) + Deviation from the 30-year normal temperature +/- Precipitation.(centimeters) - Average wind speed. (kph) - Temporal Dummies Saturday or Sunday (equals 1, otherwise 0) +
  • 12. Validation of Trail Traffic Models (Wang et al. 2013)
  • 13. Using Factoring to Estimate Daily Traffic from Short Duration Counts
  • 14. Estimating Performance Measures: AADT and Trail Miles Traveled in Minneapolis Segment AADT Mean 954 Median 750 Max 3,728 Min 39 •6 reference sites •7 day short duration counts on each segment > 28 million miles traveled on 80 mile trail network in 2013:
  • 15. 16 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 0:00 6:00 12:00 18:00 0:00 % of daily traffic Weekdays Weekends 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 0:00 6:00 12:00 18:00 0:00 % of daily traffic Weekdays Weekends 0.0% 2.0% 4.0% 6.0% 8.0% 10.0% 12.0% 0:00 6:00 12:00 18:00 0:00 % of daily traffic Weekdays Weekends Short-duration monitoring identified three different traffic patterns (factor groups). Need new reference monitoring sites. Utilitarian (weekday) Mixed Recreational – Utilitarian (all current reference locations) Recreational
  • 16. Facility Demand Models •Require counts or other measures as inputs •Useful for planning, understanding system •Do not explain causation •Have limitations (NCHRP 770): –Need to include variables of interest –Need to be calibrated –Need to be validated –Should not be not transferred •Can be strengthened (NCHRP 770): –Potential to cross-validate with choice models
  • 17. Questions? Acknowledgements: MnDOT: Lisa Austin, Jasna Hadzic Minneapolis DPW: Simon Blenski Minneapolis Park Board: Ginger Cannon, Jennifer Ringold Virginia Tech: Steve Hankey USC: Xize Wang For more information contact: Greg Lindsey (linds301@umn.edu)