Strategies to cope with disruptions in urban public transportation networks

1. Department of Decisionand Information Sciences Strategies to cope with disruptions in urban public transportation networks Evelien van der Hurk Complexity in Public Transport: http://www.computr.eu

2. AN introduction • From Rotterdam, The Netherlands

3. AN introduction • From Rotterdam, The Netherlands • Collaborationwith Netherlands Railways

4. AN introduction • From Rotterdam, The Netherlands • Collaborationwith Netherlands Railways • Thesis focus on • analysing passenger flows/behavior • Disruption Management • Interactionbetween passenger andlogistic system

5. AN introduction • From Rotterdam, The Netherlands • Collaborationwith Netherlands Railways • Thesis focus on • analysing passenger flows/behavior • Disruption Management • Interactionbetween passenger andlogistic system • 3 months at MIT, Prof Larson, Prof Sussman, Prof Wilson

6. Research question Is it possible to use the interaction between passenger route choice, the operations, and operation's control to increase service level by dynamically changing the network structure?

7. An Example close to home – MBTA Network

8. Longfellow Bridge Closure - MBTA’s plan

9. Planning shuttles

10. The Line Planning Problem – examplenetwork Community College Station Red Line Orange Line Kendall/MIT Broadway Downtown crossing Back Bay

11. The Line Planning Problem – examplenetwork Community College Station Red Line Orange Line Entrance Exit Downtown crossing Enter, exit and transfer arcs Broadway Kendall/MIT Choose line with operating frequencyandcapacity Back Bay

12. The Line Planning Problem – examplenetwork Community College Station Red Line Orange Line Entrance Exit Downtown crossing Broadway Enter, exit and transfer arcs Kendall/MIT shuttle 1 shuttle 2 Back Bay Chooselinesand shuttles with operating frequencyandcapacity

13. Line Planning model

14. Summary Is it possible to use the interaction between passenger route choice, the operations, and operation's control to increase service level by dynamically changing the network structure? • PlannedDisruptions • Network effects • Both PassengersandLogistics • Practical examples (but theoretical model) • MBTA – longfellow bridge • TfL – tobedecided • Outcome: plan forlogistics & plan fordetour of passengers

15. Case study of longfellowbridge

16. Discussion

17. Motivation – deduction of passenger’s route choice Knowledge on passenger route choiceprovides • Estimatedemandforcapacity • Test assumptions on passenger behaviorand route choice • Hind-sight analysis of passenger service (delays) • Forecasting of futuredenandandeffects in network So far: • Surveysand panel data todeduce route choice • Modelsfor route choice: maximum utilitymregretminimization,… Now: • AutomatedFare Collection (AFC) Systems generetaedetailed data on journeys Question: Can we deduce route choicefrom the AutomatedFare Collection Systems data?

18. Problem overview route deduction from AFC • Which route (time, space, trains) did a passenger take? Time +Station Time +Station ci co Conductor check Platform i Platform k Station A Station B trains • co ci time

19. Data • Smart card data • Origin station, destination station, start time, end time, card id • Realized timetable • Departure time station, arrival time station, train number • Conductor checks • Card id, time, train number General: • 5 days • Over 500,000 journeys, • about 1/3 with conductor check • full Dutch Railway network of Netherlands Railways trains • Comparison between disrupted and non disrupted days

20. Model • Generate Paths Based on Realized Timetable • Link a route to a path: • Find the set of routes leading from O to D that fit within the time interval of check-in, check out • If multiple routes fit, select one based on: 1) First Departure (FD) 2) Last Arrival (LA) 3) Least Transfers (LT) 4) Selected Least Transfers Last Arrival (STA) • Check accuracy of matching based on conductor checks: • does assigned route have train?

21. Model - schematic

22. Example Journey:

23. Example – Step 1 Route generation (preprocessing) Journey: Preprocessing – Route generation. Resultsfor A-B:

24. Example – Step 2 Route Selection Journey: Select Routes within check-in and check-out

25. Example – Step 2 Route Selection Journey: Select based on Decisionrule. 4 scenariosfordecisionrules: FD: First Departure LA: Last Arrival LT: Least Transfers STA: Selectedleast Transfers last Arrival

26. Example – Step 2 Route Selection Journey: Select based on Decisionrule (tested 4 decisionrules) FD STA LT + LA

27. Example – Step 3 Validation Journey: Check selectionwith MCL data : STA is correct choice, Otherdecisionrules or wrong (in example) FD STA LT + LA

28. Results Resultsfor 1 daywith different settings Resultsfor 5 dayswithextended list of journeys, realized timetable Extended List: using conductor checks tofindaddtional routes

29. CONCLUSIONS / FUTURE WORK Conclusions • Method forlinking routes up toanaccuracy of over 85% • Passengers do nottravelonly on shortestpaths • Increasingpath side based on conductor checks improveslinking • Based on linkinginsightintobehavior in disruptionscanbeobtained, e.g. change in arrival at platform when timetable changes Futurework • Includelearning of routes based on historic conductor data • Research individualchoicerulesinstead of oneglobalbehavioralrule • Formulategeneralrulesfor route choice of passengers

30. Questions? Questions? Suggestions? Thanksforyour attention!

31. Difference in travelbehavior Compare in-vehicle travel time differenceswithdeparture-arrivaltravel time differencesbetweennormaldaysanddisrupteddays: