LT5: Modeling reliability, cost, travel times, safety, comfort and other relevant

variables of modal choice

LT5: Modeling reliability, cost, travel times, safety, comfort and other relevant

variables of modal choice

Juan Carlos Muñoz, Juan de Dios Ortúzar and Sebastián

Raveau

Departamento de Ingeniería de Transporte y Logística Pontificia Universidad Católica de Chile

How do transit users choose their routes: a case study of Metro in

Santiago

Sebastián Raveau – Juan Carlos Muñoz – Louis de Grange

Origin

Destination

The traveler starts heading in the

opposite direction…

The map influences the Baquedano trip

Only 11% travel through Santa Ana!!

AttributeBaqueda

noSanta Ana

Travel Time

23:40 23:43

Density 5 pax/m2 3 pax/m2

Occupancy

88% 49%

Transfers 1 1

Contents

Resul

ts

Applications

Extensions

Backg

roun

d

Expl

anat

ory

Variabl

esSt

udy

Case

Background

Traditional route choice models usually consider just tangible variables related to the level of service

travel timefarenumber of transfers

These models are sometimes refined including socio-economic variables of the travelers

Background

However, this approach ignores other relevant elements that influence route choice as:

comfortsafetytransfers qualitynetwork topology

These variables are subjective and hard to quantify

Model’s Variables

Travel timeWaiting timeNumber of transfersWalking time when transferringAscending level transfersTransfers without escalatorsMean occupancy rateNetwork knowledgePossibility of not boardingPossibility of getting a seatHow direct is the route (Angular cost)

traditional variables

physical characteristics

(volume as proxy)(initial occupancy ≥

85%)(initial occupancy ≤ 15%)

Model’s Variables

What should the angular cost satisfy:

minimum for 0º and maximum for180º

small marginal variations at these extremes(non-linear effect)

grow with the distance covered

A specification that satisfies these conditions is:

2d sin

Model’s Variables

Origin

Destination

1d

2d3d

1

1 21 2Angular Cost

2 2d sin d sin

2T1

T2

Model’s Variables

Travel timeWaiting timeNumber of transfersWalking time when transferringAscending level transfersTransfers without escalatorsMean occupancy rateNetwork knowledgePossibility of not boardingPossibility of getting a seatAngular costTurning back to the originTurning away from the destination

network topology

easy to obtain!

easy to obtain!

traditional variables

physical characteristics

Study Case

Route choice in the Santiago Metro network

5 lines and 85 stations

2,300,000 daily trips

Period morning peak 7:00 – 9:00 hrs

evening peak 18:00 – 20:00 hrs

790,000 daily trips in peak hours

Study Case

Survey conducted by Metro on October, 2008

Total respondents 92,800

Users that transfer 42,700 (44 %)

One unique route 26,900

Two or more routes 15,800(37 %)

Study Case

We also want to understand the impact of the Metro network schematic map on the users’ behavior

Results

Multinomial Logit Model for the route choice

For every OD pair, the choice set was given by routes traveled by at least one person

We will compare two models:Base Model Proposed Model

traditional variables

physical characteristics

network topology

ResultsAttribute Base Model Proposed Model

Travel time - 0.144 - 46.61 - 0.117 - 27.29

Waiting time - 0.203 - 3.68 - 0.121 - 3.17

Walking time - - 0.229 - 6.87

Number of transfers - 1.220 - 17.06 - 0.420 - 3.69

Ascending transfers - - 0.432 - 8.02

Transfers without escalator

- - 0.457 - 12.34

Occupancy rate - - 1.250 - 3.05

Network knowledge - 0.030 6.81

Possibility of not boarding

- - 0.431 - 6.06

Possibility of getting a seat

- 0.106 1.81

Angular cost - - 0.038 - 6.75

Turn back to the origin

- - 0.503 - 10.65

Turn away from destination

- - 0.512 - 11.59

Log-Likelihood - 7,416 - 7,136

ResultsAttribute Real Distances Map Distances

Travel time - 0.119 - 27.94 - 0.117 - 27.29

Waiting time - 0.111 - 3.17 - 0.121 - 3.17

Walking time - 0.240 - 7.14 - 0.229 - 6.87

Number of transfers - 0.449 - 3.95 - 0.420 - 3.69

Ascending transfers - 0.426 - 7.89 - 0.432 - 8.02

Transfers without escalator

- 0.438 - 11.95 - 0.457 - 12.34

Occupancy rate - 1.430 - 3.53 - 1.250 - 3.05

Network knowledge 0.031 7.14 0.030 6.81

Possibility of not boarding

- 0.426 - 6.00 - 0.431 - 6.06

Possibility of getting a seat

0.126 2.16 0.106 1.81

Angular cost - 0.024 - 5.78 - 0.038 - 6.75

Turn back to the origin

- 0.516 - 10.96 - 0.503 - 10.65

Turn away from destination

- 0.505 - 11.43 - 0.512 - 11.59

Log-Likelihood - 7,142 - 7,136

Results

Marginal rates of substitution

There is a bias when relevant variables are not included

Variable Base ModelProposed

Model

1 min of wait 1.41 min of travel 1.04 min of travel

1 min of walk n. a. 1.96 min of travel

Results

Marginal rates of substitution

Base Model: 8,1 min of travel

Transfer TypeWith

EscalatorWithout

Escalator

Possibility of Sitting

Descending2.7 min of

travel6.6 min of

travel

Ascending6.4 min of

travel10.3 min of

travel

Intermediate

Descending3.6 min of

travel7.5 min of

travel

Ascending7.3 min of

travel11.2 min of

travel

Possibility of not

Boarding

Descending7.3 min of

travel11.2 min of

travel

Ascending11.0 min of

travel14.9 min of

travel

route 1 – Baquedanoroute 2 – Tobalabaroute 3 – Santa Anaroute 4 – La Cisterna

Applications

~ 7,300 trips (peak)Predict route choice for all trips within a set of origins and a set of destinations

Assignment results

MSE Base Model: 18.9

MSE Proposed Model: 10.2

AssignmentBaqueda

noSanta Ana

La Cisterna

Tobalaba

Observed Trips 5,362 99 30 1,854Mínimum Time 6,676 668 0 0

Base Model 3,884 361 33 3,067Proposed Model 4,446 270 25 2,603

Applications

Extensions

Compare the results and forecasting with other models used in Transport Systems Planning

Application to a more dense network

Base ProposedMSE OD pairs with 2 alternatives 7,9

6,7MSE OD pairs with 3 alternatives 20,0

10,7MSE OD pairs with 4 alternatives 27,8

15,9

Extensions

Compare the results and forecasting with other models used in Transport Systems Planning

Application to a more dense network

Application to a more distorted network

correlation of distances Santiago 94%correlation of distances London 22%

Extensions

ExtensionsAttribute Santiago Model London Model

Travel time - 0.106 - 29.82 - 0.158 - 18.16

Waiting time - 0.117 - 3.30 - 0.132 - 11.28

Walking time - 0.210 - 6.79 - 0.155 - 11.45

Number of transfers - 0.663 - 8.17 - 0.463 - 5.59

Ascending transfers - 0.335 - 6.97 0.108 2.40

Transfers at level - 0.232 5.09

Transfers without escalator

- 0.409 - 12.67 0.211 4.79

Angular cost - 0.041 - 7.57 - 0.020 - 0.56

Turn back to the origin

- 0.529 - 11.45 - 0.354 - 6.29

Turn away from destination

- 0.576 - 13.55 - 0.467 - 6.522

Sample Size 16,029 2,721The absence of flow-related variables bias the results

What other factors can affect the choice of transfer stations?

Extensions

Compare the results and forecasting with other models used in Transport Systems Planning

Application to a more dense network

Application to a more distorted map

Map design optimization

Extensions

How do transit users choose their routes: a case study of Metro in

Santiago

Sebastián Raveau – Juan Carlos Muñoz – Louis de Grange

Publications and working papers• Raveau, S., J.C. Muñoz, and L. de Grange (2011) A topological route choice

model for metro. Transportation Research Part A, Vol 45 (2), 138–147• Raveau, S., Z. Guo, J.C. Muñoz and N.H. Wilson. (2012) Route Choice

Modelling on Metro Networks: time, Transfer, crowding, and topology. To be submitted to Transportation Research Part A.

• Navarrete, F. and J. de D. Ortúzar (2012) Subjective valuation of the transit transfer experience: the case of Santiago de Chile. Submitted to Transport Policy.

Conferences and seminars• Raveau, S., J.C. Muñoz y J. de D. Ortúzar (2012) Modelling Mode and Route Choices

on Public Transport Systems. Submitted to the International Symposium of Traffic Theory and Transportation to be held in the Netherlands in 2013.

• Raveau, S., J.C. Muñoz y L. de Grange (2011) Modelación y análisis temporal de elección de ruta en Metro. XV Congreso Chileno de Ingeniería de Transporte, Santiago, Chile.

• Raveau, S., Muñoz, J.C. and de Grange, L. (2011) A topological route choice model for metro. Transport for London, London, UK.

• Raveau, S., Muñoz, J.C. y de Grange, L. (2010) El efecto de la topología de la red y las percepciones en la elección de ruta. XVI Congreso Panamericano de Ingeniería de Tránsito y Transporte. Lisboa, Portugal.

2012

In-progress or future research I• Comparison of route choice models for Metro of London and

Santiago. A paper should be submitted to Trans Res A this month.• Extend the study for a route and mode choice models within a

transit system. We made an 1,800 people survey in Santiago with this purpose. This research is being developed by PhD student Sebastian Raveau.

• Develop a similar survey in Bogota, Colombia to understand the role played by a BRT-based network in passengers´ choices.

• Compare results between Santiago and Bogota to understand how much of a Metro service BRT provides in Bogota.

In-progress or future research II

• Build a tactic tool to predict passenger flows in a multimodal transit system. Such a model would have to deal with endogeneity since passengers flows affect travelers´ choices.

• Build a tool to advise passengers how to travel in a complex multimodal transit system.

• Develop a methodology to feed our route and mode choice models with feedback provided by users of the Passenger Travel Advising Tool.

Grants obtained

• FONDEF (2012-2014). A tactic-strategic tool for urban transit systems planning and management. Total funding of US$800,000. Involvement of Metro and Alsacia (bus operator)

Juan Carlos Muñoz and Juan de Dios Ortúzar

• PUC (2011-2012). Interdisciplinary research project to understand how to improve the users transfer experience on a transit system (US$10,000).

Ricardo Giesen, Juan de Dios Ortúzar, Juan Carlos Muñoz, Patricia Galilea, Juan Carlos Herrera, Margarita Greene, Rossana Forray, José Allard

From the papers to the streets

• Several interviews with the media during 2011

• Interviews with Metro and government authorities during 2011

• Metro de Santiago changed its map based on our results to induce a more socially optimal behavior

Applications• Changes in the Santiago Metro Map

From the papers to the streets

• Several interviews with the media during 2011

• Interviews with Metro and government authorities during 2011

• Metro de Santiago changed its map based on our results to induce a more socially optimal behavior

• We are now working un using our assignment model to design interchange stations and predict flows for the Metro network in Santiago that will consider two extra lines in 2016.