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BusMezzo

Dynamic Transit Operations Tool with Passenger Route Choice

Oded Cats

Centre for Traffic Research (CTR)

Kungliga Tekniska Högskolan (KTH)

2011-01-13 Transportforum 2011 Linköping

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Outline

• Dynamic transit model

• Model components:

– Traffic Simulation Model

– Transit operations

– Passenger decisions

Choice-set generation

Dynamic route choice

• Case study: Real-time information

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Motivation and Objectives

• Existing transit models are static

– Simplifying assumptions regarding traveler behavior, traffic

conditions and transit operations

– Suitable for strategic planning of route network, time-

tables, etc.

– Not suitable for operations analysis, Advanced Public Transport

Systems (APTS) evaluation

• Need for dynamic transit modeling tool

– Capturing the dynamics of traffic conditions, traveler behavior

and transit operations at a network-wide level

– Experimental tool for assessing how operations strategies address

policy objectives

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Potential Applications

• Planning

▫ Effects of transit route changes

▫ Time-table assessment

▫ Service coordination

• Operations

▫ Public transport performance and level of service analysis

▫ Impacts of transit priority

▫ Restoration from major disruptions

▫ Fleet assignment efficiency

• Real-time

▫ Evaluation of real-time control strategies

▫ Real-time traveler information evaluation

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Transit model componentsPassengers

• Origin-Destination• Choice-set composition

• Path choice decisions

• Travel preferences• Information

Transit operations• Routes

• Time-tables

• Dwell time• Vehicle scheduling

• Boarding and alighting processes

• Control strategies

Traffic dynamics

• Speed-volume relationship• Turning movements

• Traffic signals

• Segregated lanes• Car-bus interaction

• Route choice

APTS

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Mezzo

• A mesoscopic traffic simulation

▫ Event based

▫ Stochastic processes

▫ Traffic dynamics:

▫ Aggregate behavior on links

▫ Turn-specific queue servers

▫ Enables large scale applications

▫ OOP (C++)

▫ Open-source

Running part Queue part

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Transit operations

• Modeling sources of uncertainty

• Suitable for APTS applications

• Transit processes:

▫ Time-tables

▫ Vehicle scheduling

▫ Travel time

▫ Boarding and alighting processes

▫ Dwell time

▫ Capacity constraints

▫ Holding control strategies

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Control strategies study

BusMezzo – Modeling transit dynamics within a mesoscopic traffic simulation

Validating the model by applying the transit operation model to a bus line in Tel-Aviv

Analyzing holding control strategies based on the Tel-Aviv case study

Multi-perspective evaluation of various holding strategies for trunk line 1 in Stockholm

Conducting a field study

Toledo T., Cats O., Burghout W. and Koutsopoulos H.N. (2010). Mesoscopic simulation for transitoperations. Transportation Research Part C – Emerging Technologies, 18(6), 896-908.

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Passenger demand modeling

• Different levels of demand representation

1. Boarding and alighting rates

2. Demand matrix per line

3. OD matrix in terms of stops

4. OD matrix in terms of zones

Attractive set

Passenger loading

Network configurationEstimated travel timesHeadways/ Time-table

Prior-knowledgeTraveler preferencesDecision Rules Actual experienceReal-time informationCapacity

Choice-set

generation

Route choice

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Choice-set generation

• Limited studies (Fiorenzo-Catalano et al. 2004; Van Nes et al.

2008)

• Recursive search method

• Static choice-set as a preliminary phase

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What is an alternative?

• OD stops / OD TAZ

• Connection distances

• Clustering transfer stops

• Clustering common lines

2s

4s

3s

6s5s

8s

7s

1l

2l

3l

4l

5l

6l

1c2c

3c

4c

5c

6c

1s

0c

0c

9s

0c

0c

0c

0c

0c

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Dynamic route choice

Boarding this

vehicle?No

Yes

Start

BOARDING

decision model

Arriving

transit

vehicle

ALIGHTING

decision model

Approaching

transit stop

Alighting at

the next

stop?

No

CONNETION

decision model

Yes

Arrived at

destination?

End

Yes

No

CONNETION

decision model

Traveler information(prior-

knowledge, type, location, com

prehensiveness)

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• 3 lines, 7 branches, 100 stations with 210 platforms

• 10 min headway, schedule-based holding control

• Choice-set generation process: 14,699 alternative paths

Stockholm Metro case study

Experiment description

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Operation conditions / Level of RTI provision

(R) Regularconditions

(DF) Disruptionat green line

service frequency

(DR) Disruption at blue line riding times

(1) Platform-level RTI

R1 DF1 DR1

(2) RTI for all platforms at the

same stop

R2 DF2 DR2

(3) RTI for thewhole network

R3 DF3 DR3

Stockholm Metro case study

Scenarios design• Evaluating the effect of RTI provision on passenger route

choice

• Service disruptions

• A 50% reduction in frequency

• A 15 minutes delay in riding time

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9 min

3 min

2 min

2 min

Stadshagen

Fridhemsplan

Gamla stan

2 X H

=10 min

2 X

H=

10 m

in

3 X H=10 min

T-Centralen

RTI Metro case study

Route choice alternatives

Ropsten

Hässelby strand

Akalla

Norsborg Farsta strand

Gullmarsplan

SlussenAlvik

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RTI Metro case study

Passenger journey time under different operation conditions and levels of RTI

Scenario Total journey time

[sec]

In-vehicle time

[sec]

Out-of-vehicle time

[sec]

R1 1081 554 527

R2 1046 557 489

R3 1035 538 497

DF1 1418 553 865

DF2 1293 545 748

DF3 1260 523 737

DR1 1771 1116 655

DR2 1733 1115 617

DR3 1603 1054 549

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• RTI provision has the potential to yields substantial

path choice shifts and time savings

• Particularly significant time savings in case of irregular

service conditions

• A simple improvement in transfer coordination can be

very beneficial

• The incorporation of walking times is important in the

context of transit route choice

• Proof of concept

Stockholm Metro case study

Conclusions

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On-going developments

• Transit operation strategies

– Preparing a field study for testing the even-headway control strategy

– Optimizing the number and location of time-point stops

– Analyzing the case of a common corridor

• Choice-set generation model

– Formulating an estimation method

– Implementing the method using the survey data

• Designing a validation study of the transit assignment

model