Presentation is loading. Please wait.

Presentation is loading. Please wait.

Parsons Brinckerhoff Chicago, Illinois GIS Estimation of Transit Access Parameters for Mode Choice Models GIS in Transit Conference October 16-17, 2013.

Similar presentations

Presentation on theme: "Parsons Brinckerhoff Chicago, Illinois GIS Estimation of Transit Access Parameters for Mode Choice Models GIS in Transit Conference October 16-17, 2013."— Presentation transcript:

1 Parsons Brinckerhoff Chicago, Illinois GIS Estimation of Transit Access Parameters for Mode Choice Models GIS in Transit Conference October 16-17, 2013 Washington, DC

2 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

3 CMAP Trip Based Model PRE-DISTRIBUTION Travel Times and Distances by Mode TRIP DISTRIBUTION Person Trip Tables MODE CHOICE Person Trip Tables by Mode Person Trip Tables by Mode NETWORK ASSIGNMENT 3

4 Mode Choice Estimation 4 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

5 Model Logic Flow 5 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 O-D Trips Repeat for all Origin Zones Set Program Options Destination Zone Zone and Transit/Hwy System Parameters Origin Zone Select Trip Evaluate Logit Mode Choice Equation Simulate Choice Add Trip to Trip Table All Trips to Destination Zone Simulated? Repeat for all Destination Zones No Compute Non-CBD Parking Walk Time and Cost Simulate CBD Parking Walk Time and Cost Compute Auto Operating Costs Simulate Transit Access/Egress Attributes In-Vehicle Time Out-of-Vehicle Time Fares Yes Access/Egress Sub-models

6 Sub-Models 6 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

7 Transit Access Sub-Model 7 Inputs First, last, and priority (modes ordered in the sequence commuter 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

8 Model Estimation of Distance to Transit 8 Many of the transit access mode costs depend on distance to stops and rail stations Often use zone average distance to nearest stop station 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 Zone Centroid

9 Access Sub-Model Calculations 9 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

10 Access Distance Approach 10 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

11 Metra Station Point Layer 11

12 Metra Station Areas of Influence 12

13 Influence Areas 13 Thiessen or Voronoi polygons Each point within polygon is closer to station than any other station

14 Subzones Within CMAP Study Area 14

15 Subzone-Metra Station Match 15

16 Regional Model Zones 16

17 Model Zone Parameters 17

18 Rail Transit and Bus Areas of Influence 18 CTA Rail TransitCTA and PACE Bus

19 19 Rail Transit Extension Example: Initial Areas of Influence

20 Rail Transit Extension Example: Added Stations 20

21 21 Rail Transit Extension Example: Revised Areas of Influence

22 22 Rail Transit Extension Example: Impact on Adjacent Line

23 Final Thoughts 23 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

24 Questions? 24 Ron Eash Parsons Brinckerhoff Chicago, IL

Download ppt "Parsons Brinckerhoff Chicago, Illinois GIS Estimation of Transit Access Parameters for Mode Choice Models GIS in Transit Conference October 16-17, 2013."

Similar presentations

Ads by Google