Modeling Modal Choice for Reliability, Cost, Travel Times, Safety, and Comfort in Santiago Metro

1. 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

2. How do transit users choose their routes: a case study of Metro in Santiago Sebastián Raveau – Juan Carlos Muñoz – Louis de Grange

3. Only 11% travel through Santa Ana!! The traveler starts heading in the opposite direction… The map influences the Baquedano trip Destination Origin

4. Contents Results Study Case Background Explanatory Variables Applications Extensions

5. Background Traditional route choice models usually consider just tangible variables related to the level of service travel time fare number of transfers These models are sometimes refined including socio-economic variables of the travelers

6. Background However, this approach ignores other relevant elements that influence route choice as: comfort safety transfers quality network topology These variables are subjective and hard to quantify

7. Model’s Variables Travel time Waiting time Number of transfers Walking time when transferring Ascending level transfers Transfers without escalators Mean occupancy rate Network knowledge Possibility of not boarding Possibility of getting a seat How direct is the route (Angular cost) traditional variables physical characteristics (volume as proxy) (initial occupancy ≥ 85%) (initial occupancy ≤ 15%)

8. 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:

9. Model’s Variables T2 T1 Destination Origin

10. Model’s Variables Travel time Waiting time Number of transfers Walking time when transferring Ascending level transfers Transfers without escalators Mean occupancy rate Network knowledge Possibility of not boarding Possibility of getting a seat Angular cost Turning back to the origin Turning away from the destination traditional variables physical characteristics easy to obtain! network topology easy to obtain!

11. 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

12. 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 %)

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

14. 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  

15. Results

16. Results

17. Results Marginal rates of substitution There is a bias when relevant variables are not included

18. Results Marginal rates of substitution Base Model: 8,1 min of travel

19. Applications Predict route choice for all trips within a set of origins and a set of destinations ~ 7,300 trips (peak) route 1 – Baquedano route 2 – Tobalaba route 3 – Santa Ana route 4 – La Cisterna

20. Applications Assignment results MSE Base Model: 18.9 MSE Proposed Model: 10.2

21. Extensions Compare the results and forecasting with other models used in Transport Systems Planning Application to a more dense network Base Proposed MSE OD pairs with 2 alternatives 7,9 6,7 MSE OD pairs with 3 alternatives 20,0 10,7 MSE OD pairs with 4 alternatives 27,8 15,9

22. 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%

23. Extensions

24. Extensions The absence of flow-related variables bias the results What other factors can affect the choice of transfer stations?

25. 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

26. Extensions

27. How do transit users choose their routes: a case study of Metro in Santiago Sebastián Raveau – Juan Carlos Muñoz – Louis de Grange

28. 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.

29. Conferences and seminars • Raveau, S., J.C. Muñoz y J. de D. Ortúzar (2012) ModellingMode 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.

30. 2012

31. 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.

32. 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.

33. 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

34. From the papers to the streets • Several interviews withthe media during 2011 • Interviews with Metro and governmentauthoritiesduring2011 • Metro de Santiago changeditsmapbasedonourresultsto induce a more sociallyoptimalbehavior

35. Applications Changes in the Santiago Metro Map

36. From the papers to the streets • Several interviews withthe media during 2011 • Interviews with Metro and governmentauthoritiesduring2011 • Metro de Santiago changeditsmapbasedonourresultsto induce a more sociallyoptimalbehavior • We are nowworking un usingourassignmentmodeltodesigninterchangestations and predictflowsforthe Metro network in Santiago thatwillconsidertwo extra lines in 2016.