new tools for estimating walking and bicycling demand
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Title: New Tools for Estimating Walking and Bicycling Demand Track: Sustain Format: 90 minute panel Abstract: Walking and bicycling demand estimates can make a stronger case for investing in new facilities and are necessary inputs to important planning tasks. This session presents state-of-the-art tools to predict walking and bicycling demand at varying geographic scales. Tools include: 1) a framework to incorporate walking into regional travel demand models; 2) a method to estimate bicycle and pedestrian traffic based on count data; 3) new mode choice models; and 4) a web-based repository of non-motorized demand analysis tools. Presenters: Presenter: Patrick Singleton Portland State University Co-Presenter: J. Richard (Rich) Kuzmyak Renaissance Planning Group Co-Presenter: Greg Lindsey University of Minnesota, Humphrey School Co-Presenter: Jeremy Raw Federal Highway AdministrationTRANSCRIPT
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 1 2 3 40.5Miles
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
% o
f d
ail
y t
raff
ic
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
% o
f d
ail
y t
raff
ic
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
% o
f d
ail
y t
raff
ic
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 ([email protected])