GIS Estimation of Transit Access Parameters for Mode Choice Models

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GIS Estimation of Transit Access
Parameters for Mode Choice Models
GIS in Transit Conference
October 16-17, 2013
Washington, DC
Parsons Brinckerhoff
Chicago, Illinois
Presentation Outline
 Overview of the Chicago Metropolitan Agency for Planning
mode choice model
 The transit access sub-model
 Access modes
 Data inputs to estimate access distances
 GIS estimation of input parameters in TransCAD/Maptitude
 Sample plots
 Extensions
2
CMAP Trip Based Model
PRE-DISTRIBUTION
Travel Times and
Distances by Mode
TRIP
DISTRIBUTION
Person Trip Tables
MODE CHOICE
Person Trip Tables
by Mode
3
NETWORK
ASSIGNMENT
Mode Choice Estimation
 The CMAP model is a trip based model
 Home-work: transit, single occupant, ride share and carpool auto
 Home-other: transit and auto
 Non-home: transit and auto
 Simulates individuals choice of mode per trip
 Evaluate logit model for probabilities
 Monte Carlo method
 Pre-distribution model is the front end of the mode choice model
 Simulates 100 trips between zone pairs
 Estimates average travel times and distances by mode
4
Model Logic Flow
Set Program Options
Zone and Transit/Hwy
System Parameters
Repeat for all
Origin Zones
Origin Zone
Read Origin Files
(Origin to All Destinations)
1. Person Trips
2. Highway Times/Distances
3. Line-Haul Transit Service Attributes
First, Priority and Last Mode
In-Vehicle and Out-of-Vehicle Time
First Headway
Fares
Repeat for all
Destination Zones
Destination Zone
Repeat for
all O-D Trips
Select Trip
Compute Auto
Operating Costs
Simulate Transit
Access/Egress Attributes
In-Vehicle Time
Out-of-Vehicle Time
Fares
Compute Non-CBD
Parking Walk Time
and Cost
Evaluate Logit Mode
Choice Equation
Simulate Choice
Add Trip to Trip Table
No
5
All Trips to Destination
Zone Simulated?
Yes
Access/Egress
Sub-models
Simulate CBD
Parking Walk Time
and Cost
Sub-Models
 Auto operating costs
 $=f(speed)*distance
 Relationship between $/mile and speed is input
 Speed determined from skimming network
 CBD parking
 Relationship between walk distance and CBD parking cost is input by zone
 Proportion of free CBD parking and auto occupancy also input by zone
 Free versus pay CBD parking determined by Monte Carlo simulation
 Pay CBD parking costs and walking distance determined by:
 Value of time based on income
 Reduction in parking costs due to parking further away from destination
 Auto occupancy also determined by Monte Carlo simulation
 Non-CBD parking
 Fixed rates depending on location
 Average auto occupancy by trip type
 Transit access costs and times
6
Transit Access Sub-Model
 Inputs
 First, last, and priority (modes ordered in the sequence commuter
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rail, rail transit, express bus, local bus) mode
Average speeds for transit access modes walk, bus and auto
Fares
Auto operating costs
Drivers value of time
Park and ride rates
Walk times from park and ride
Distance distribution parameters
 Costs and times for alternative transit access modes walk, bus,
park and ride, kiss and ride, feeder bus (peak only)
 Least “costly” transit access mode selected for simulated trip
7
Model Estimation of Distance to Transit
 Many of the transit access mode costs depend on distance to stops and
rail stations
 Often use zone average distance to nearest stop station
Zone
Centroid
 Challenge to estimate accurate access distances
 Average zone distances often introduce a bias against transit
 Relatively large transportation planning zones
 Location of zone centroids often reflect where activities are located not
where transit is an alternative
8
Access Sub-Model Calculations
 Mean distance to stop/station and standard deviation of
distance are input for each zone
 Normal distribution randomly sampled for each simulated
trip
 Modes
 Commuter rail station
 Rail transit station
 Bus stop
 Feeder bus stop
 Park and ride station
9
Access Distance Approach
 Caliper Corporation Maptitude/TransCAD
 Methodology
 Point layer of stations or stops
 Create areas of influence
 Overlay areas of influence over sub-zones (quarter-sections)
 Assign station/stop to subzone and calculate access distance
 Estimate zone mean access distances from subzone distances
within zone
 Estimate standard deviation of access distance from subzones
distances plus intra-subzone variance
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Metra Station Point Layer
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Metra Station Areas of Influence
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12
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Influence Areas
 Thiessen or Voronoi polygons
 Each point within polygon is closer to station than any other
station
13
Subzones Within CMAP Study Area
14
Subzone-Metra Station Match
15
Regional Model Zones
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Model Zone Parameters
17
Rail Transit and Bus Areas of Influence
18
CTA Rail Transit
CTA and PACE Bus
Rail Transit Extension Example:
Initial Areas of Influence
19
Rail Transit Extension Example:
Added Stations
20
Rail Transit Extension Example:
Revised Areas of Influence
21
Rail Transit Extension Example:
Impact on Adjacent Line
22
Final Thoughts
 Systematic analytic approach that captures the differences
between regional transit alternatives
 Reproducible
 Not dependent on planning judgment
 Directly linked to model coded transit networks
 General approach could be implemented in a variety of
applications
 Improve access calculations in conventional models
 Component of activity based models – simulation of individual
movements
 Substitute General Transit Feed Specification (GTFS) data for
model networks
23
Questions?
Ron Eash
Parsons Brinckerhoff
Chicago, IL
eashrw@pbworld.com
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