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Strategies to cope with disruptions in urban public transportation networks

Evelien van der Hurk

Department of Decision and Information Sciences

Complexity in Public Transport: http://www.computr.eu

Afbeelding invoegenAN INTRODUCTION

• From Rotterdam, The Netherlands

Afbeelding invoegenAN INTRODUCTION

• From Rotterdam, The Netherlands• Collaboration with Netherlands Railways

Afbeelding invoegenAN INTRODUCTION

• From Rotterdam, The Netherlands• Collaboration with Netherlands Railways• Thesis focus on

– analysing passenger flows/behavior– Disruption Management– Interaction between passenger and logistic system

Afbeelding invoegenAN INTRODUCTION

• From Rotterdam, The Netherlands• Collaboration with Netherlands Railways• Thesis focus on

– analysing passenger flows/behavior– Disruption Management– Interaction between passenger and logistic system

• 3 months at MIT, Prof Larson, Prof Sussman, Prof Wilson

Afbeelding invoegenRESEARCH 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?

Afbeelding invoegenAN EXAMPLE CLOSE TO HOME – MBTA NETWORK

Afbeelding invoegenLONGFELLOW BRIDGE CLOSURE - MBTA’S PLAN

Afbeelding invoegenPLANNING SHUTTLES

Afbeelding invoegenTHE LINE PLANNING PROBLEM – EXAMPLE NETWORK

Station

Red Line

Broadway Downtown crossingKendall/MIT

Back Bay

Community College

Orange Line

Afbeelding invoegenTHE LINE PLANNING PROBLEM – EXAMPLE NETWORK

Station

Red LineOrange Line

Entrance

ExitEnter, exit and transfer arcs

Choose line with operating frequency and capacity

Broadway Downtown crossing

Kendall/MIT

Back Bay

Community College

Afbeelding invoegenTHE LINE PLANNING PROBLEM – EXAMPLE NETWORK

Station

Red LineOrange Line

Entrance

ExitEnter, exit and transfer arcs

Broadway Downtown crossing

Kendall/MIT

Back Bay

Community College

shuttle 1shuttle 2

Choose lines and shuttles with operating frequency and capacity

Afbeelding invoegenLINE PLANNING MODEL

Afbeelding invoegenSUMMARY

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?

• Planned Disruptions• Network effects• Both Passengers and Logistics

• Practical examples (but theoretical model)– MBTA – longfellow bridge– TfL – to be decided

• Outcome: plan for logistics & plan for detour of passengers

Afbeelding invoegenCASE STUDY OF LONGFELLOWBRIDGE

Afbeelding invoegenDISCUSSION

Afbeelding invoegenMOTIVATION – DEDUCTION OF PASSENGER’S ROUTE CHOICE

Knowledge on passenger route choice provides• Estimate demand for capacity• Test assumptions on passenger behavior and route choice• Hind-sight analysis of passenger service (delays)• Forecasting of future denand and effects in network

So far:• Surveys and panel data to deduce route choice• Models for route choice: maximum utilitym regret minimization,…

Now:• Automated Fare Collection (AFC) Systems generetae detailed data on journeys

Question:Can we deduce route choice from the Automated Fare Collection Systems data?

Afbeelding invoegenPROBLEM OVERVIEW ROUTE DEDUCTION FROM AFC

• Which route (time, space, trains) did a passenger take?

Station A

Station BPlatform i Platfor

m k

ci

•cotimeci

co

trains

Time +Station

Time +Station

Conductor check

Afbeelding invoegenDATA

• 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

Afbeelding invoegenMODEL

• 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?

Afbeelding invoegenMODEL - SCHEMATIC

Afbeelding invoegenEXAMPLE

Journey:From To Depature Arrival Card IDA B 8:00 8:46 xxyy

Departure Arrival Transfers Train Numbers

7:55 8:15 0 1101

8:02 8:43 2 100,200,300

8:05 8:45 0 400

8:05 8:43 1 200,300

8:20 8:46 0 1102

8:32 8:57 1 400,500

EXAMPLE – STEP 1 ROUTE GENERATION (PREPROCESSING)

From To Depature Arrival Card IDA B 8:00 8:46 xxyy

Journey:

Preprocessing – Route generation.Results for A-B:

Departure Arrival Transfers Train Numbers

7:55 8:15 0 1101

8:02 8:43 2 100,200,300

8:05 8:45 0 400

8:05 8:43 1 200,300

8:20 8:46 0 1102

8:32 8:57 1 400,500

EXAMPLE – STEP 2 ROUTE SELECTION

From To Depature Arrival Card IDA B 8:00 8:46 xxyy

Journey:

Select Routes within check-in and check-out

EXAMPLE – STEP 2 ROUTE SELECTION

From To Depature Arrival Card IDRotterdam Amsterdam xxyy

Journey:

Select based on Decision rule.4 scenarios for decision rules:

Afbeelding invoegen

FD: First DepartureLA: Last ArrivalLT: Least TransfersSTA: Selected least Transfers last Arrival

EXAMPLE – STEP 2 ROUTE SELECTION

From To Depature Arrival Card IDRotterdam Amsterdam xxyy

Journey:

Select based on Decision rule (tested 4 decision rules)

Afbeelding invoegen

Departure Arrival Transfers Train Numbers

7:55 8:15 0 1101

8:02 8:43 2 100,200,300

8:05 8:45 0 400

8:05 8:43 1 200,300

8:20 8:46 0 1102

8:32 8:57 1 400,500

FD

LT + LA

STA

EXAMPLE – STEP 3 VALIDATION

From To Depature Arrival Card IDRotterdam Amsterdam xxyy

Journey:

Check selection with MCL data :STA is correct choice, Other decision rules or wrong (in example)

From To Time TrainNumber Card IDC D 8:20 400 xxyy

Afbeelding invoegen

Departure Arrival Transfers Train Numbers

8:02 8:43 2 100,200,300

8:05 8:45 0 400

8:20 8:46 0 1102

FD

LT + LASTA

RESULTS

Results for 5 days with extended list of journeys, realized timetable

Results for 1 day with different settings

Extended List: using conductor checks to find addtional routes

Afbeelding invoegenCONCLUSIONS / FUTURE WORK

Conclusions• Method for linking routes up to an accuracy of over 85%• Passengers do not travel only on shortest paths• Increasing path side based on conductor checks improves linking• Based on linking insight into behavior in disruptions can be obtained,

e.g. change in arrival at platform when timetable changes

Future work• Include learning of routes based on historic conductor data• Research individual choice rules instead of one global behavioral rule• Formulate general rules for route choice of passengers

Afbeelding invoegenQUESTIONS?

Questions?

Suggestions?

Thanks for your attention!

DIFFERENCE IN TRAVEL BEHAVIOR

Compare in-vehicle travel time differences with departure-arrival travel time differences between normal days and disrupted days: