1. Mathematical Modelling of Pedestrian Route Choices in Urban Areas Using Revealed Preference GPS Data
Eka Hintaran ATKINS
European Transport Conference – 6 October

2. Methodology and Modelling approach Case study: Zürich
Mathematical Modelling of Pedestrian Route Choices in Urban Areas Using Revealed Preference GPS Data
Observed trips
Non-chosen trips
Route attributes
Model
Methodology and Modelling approach
Case study: Zürich
Estimation results
Conclusion
Work in practice

3. Introduction to Route Choice Modelling
“Representing route choice behaviour consists in modelling the choice of a certain route within a set of alternative routes. A route choice model predicts the probability that any given path between Origin and Destination is selected to perform a trip, given a transportation network and an OD-pair “
(Bierlaire & Frejinger, 2008)
Observed trips
Non-chosen trips
Route attributes
Model

4. Aim of this route choice model:
Development of a pedestrian route choice model estimated from revealed preference GPS data
Observed trips
Non-chosen trips
Route attributes
Model
Aim of this route choice model:
To understand how pedestrians choose their routes within an urban area and which quantitative environmental street factors influence their route choice behaviour
To use this knowledge to predict future route choices of pedestrians in urban areas
To find out if pedestrian behaviour could be captured in a route choice model

5. Observed trips
Non-chosen trips
Route attributes
Model

6. Methodology and Modelling approach (1)
Modelling approach within the Random Utility Maximization (RUM) framework, described in Discrete Choice Models (DCM)
DCMs predict choices between a set of finite distinct alternatives,
based on utility maximization, assuming that pedestrians make a subjective rational choice between a finite number of choice options.
Observed trips
Non-chosen trips
Route attributes
Model
Discrete Choice Models:
Estimates the probability P for each alternative i of being chosen by individual n from a choice set when the individual maximizes his utility

7. Methodology and Modelling approach (2)
Model Structure:
Path-size Logit Model (Ben-Akiva & Bierlaire, 1999)
Case study: Zürich
Revealed preference experiment
Collecting data
Processing of GPS data
Model development
Calculation of non-chosen alternatives
Calculation of route attributes
Calculation of overlap between routes
Model estimation
Final results
Observed trips
Non-chosen trips
Route attributes
Model

9. Observed trips
Non-chosen trips
Route attributes
Description of overlap

10. Observed routes
Non-chosen routes
Route attributes
Calculation of overlap
Observed trips
Non-chosen trips
Route attributes
Description of overlap

11. Observed routes
Trips: GPS data collected by trackers Only trips in Zürich area 51 participants 3053 stages
Network: Data extracted from OpenStreetMap Elevation model
Observed routes
Non-chosen routes
Route attributes
Calculation of overlap

13. Observed routes
Non-chosen routes
Route attributes
Description of overlap
Observed trips
Non-chose trips
Route attributes
Model

14. Observed routes
Non-chosen routes
Route attributes
Description of overlap
Observed trips
Non-chose trips
Route attributes
Model

15. GPS data processing Filtering (errors)
Smoothing (position errors caused by satellite issues)
Calculating speed and acceleration
Detection of stop-points and stages
Map-matching (OpenStreetMap)
Only walking trips: filtering by mode (speed)
51 participants and 580 stages
Observed routes
Non-chosen routes
Route attributes
Calculation of overlap
Observed trips
Non-chose trips
Route attributes
Model

16. Observed routes
Non-chosen routes
Route attributes
Description of overlap
Observed trips
Non-chose trips
Route attributes
Model

17. Calculation of non-chosen routes
Input:
Observed routes (Origin – Destination)
Network
Choice set generation algorithm developed based on:
Breadth-First-Search on Link Elimination method
Observed routes
Non-chosen routes
Route attributes
Calculation of overlap
Observed routes
Non-chosen routes
Route attributes
Description of overlap
Observed trips
Non-chose trips
Route attributes
Model

18. Choice sets of 6 alternatives (1 is the chosen)
Observed routes
Non-chosen routes
Route attributes
Calculation of overlap
Observed trips
Non-chose trips
Route attributes
Model
Observed route
Output:
Choice sets of 6 alternatives (1 is the chosen)
Choice sets of 7 alternatives (chosen is not generated)
Alternative routes

19. Calculation of Route Attributes
Input:
Choice sets (Observed + Non chosen Routes)
Network (including characteristics)
Elevation model
Output:
Choice set (routes) with calculated route attributes
Distance [km]
Rise and fall characteristics [m/100m]
Road type fraction [0-1]
Observed routes
Non-chosen routes
Route attributes
Calculation of overlap
Observed trips
Non-chose trips
Route attributes
Model

20. Calculation of Overlap
Input:
Choice sets (Observed + Non chosen Routes)
Network
Output:
Path-Size attribute [0-1] for every route
Observed routes
Non-chosen routes
Route attributes
Calculation of overlap
Observed trips
Non-chose trips
Route attributes
Model
1 : 2 : 3  1/3 : 1/3 : 1/3
(1 + 2) : 3  (1/2) : 1/2

21. Based on Utility Maximization theory
Model Development
Based on Utility Maximization theory
Path-Size Logit model
Accounts for overlap
Already applied to active modes (walk, cycle)
Route attributes:
Trip length (Distance)
Maximum Rise (RiseMax)
Road Type (WalkOnly, WalkSafe, WalkAll)
Path-Size attributes (overlap: PS1, PS2 and PSC)
Observed routes
Non-chosen routes
Route attributes
Description of overlap
Observed trips
Non-chose trips
Route attributes
Model

22. Model estimation/calculation
Observed trips
Non-chose trips
Route attributes
Model
Model: Path-Size Logit
Rho-square
0.297
Adjusted rho-square
0.285
Estimated in BIOGEME (Bierlaire, 2003)
Main findings:
Maximum rise largest influence
Overlap (PS) second
Road type Walk & Bike third
Trip length is not consistent
Parameters
Value
Rob.
St err
t-test
BETA_ACLASS
-0.749
0.314
-2.38
BETA_BCLASS
1.26
0.339
3.73
BETA_CCLASS
0.909
0.367
2.48
BETA_DCLASS
-1.42
0.327
-4.35
BETA_RISEMAX
-30.6
7.81
-3.91
BETA_WALKONLY -
0.844
-0.53
BETA_WALKSAFE
1.82
0.811
2.24
BETA_WALKALL
BETA_Log(PS2DIST)
-2.37
0.829
-2.86

23. Conclusion
Aim:
To understand how pedestrians choose their routes within an urban area and which quantitative environmental street factors influence their route choice behaviour
To use this knowledge to predict future route choices of pedestrians in urban areas
To find out if pedestrian behaviour could be captured in a route choice model
In this case study:
Maximum Rise dominant, Overlap second, Road type (Walk & Bike) third
Trip length has influence, but result is not consistent
General Conclusion
Pedestrian behaviour proves to be rational and can be described using Discrete Choice Models within Random Utility Maximization framework
Therefore this methodology can be used in other case studies as well
Observed trips
Non-chose trips
Route attributes
Model

24. Added value of theories and results in practice
As support in policy-making and design process
Plan and design new infrastructures and pedestrian facilities
Inform designers and area operators
Determine optimal pedestrian environments  new walkability guidelines
Plan, organise and manage large events
Observed trips
Non-chose trips
Route attributes
Model

25. Observed trips
Non-chose trips
Route attributes
Model

26. Ideas for Oxford Circus and other Public Realm projects
Methodology to predict future route choices of pedestrians in the area
Observed trips
Non-chose trips
Route attributes
Model
Data processing (Wifi data)
Extract routes from Wifi data and calculate alternative routes based on these routes
Use these data to predict the probability that a given path is chosen between Origin and Destination
Apply split to Oxford Circus Station totals, Crossrail totals, Forecasted totals
Demand as input for static assessment and dynamic modelling (Legion)
Knowledge about pedestrian’s route choices
Determine most influencing factors to inform designers (wayfinding, public realm, street) and operators (TfL)

27. Questions
Observed trips
Non-chose trips
Route attributes
Model