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BusMezzo

Dynamic Modeling of Bus and Car Traffic

Oded Cats

Centre for Traffic Research (CTR)

Kungliga Tekniska Högskolan

2010-01-14 Transportforum 2010 Linköping

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Outline

• Dynamic transit model

• Mezzo simulation

• Supply side: transit operations

• Case study– Design

– Results

– Control strategies

• Demand side: passenger path choice

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Transit model components

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Motivation

• Modeling sources of uncertainties• Departure time from origin terminal

• Traffic conditions

• Passenger arrival process

• Dwell time

• Planning and operations dynamic tool▫ Evaluation

▫ System scenarios

▫ Policies and strategies

▫ Measures of service

▫ Service regularity

▫ Crowding levels

▫ On-time performance

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Running part Queue part

Mezzo

• Mesoscopic traffic simulation

• Event-based

• Stochastic

• Traffic dynamics

▫ Running part: speed-density relationship

▫ Queuing part: turn specific queue servers

• Open source:

http://mezzo_dev.blogspot.com

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Transit operations

• Transit entities ▫ Bus stop, bus line, bus route, bus trip, bus vehicle and bus type

• Transit mechanisms▫ Boarding and alighting rates

▫ Dwell time

▫ Travel time

▫ Trip chaining

▫ Time point control strategies

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• Line 51, Tel-Aviv metropolitan

• High-demand bus line

• Heavily congested urban corridor

• 14km long route

• Max. frequency: 10 buses/hour

Case study - background

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• Static information at stops

• No control strategies

Case study – background (Cont.)

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Case study results

Trajectory

1000

3000

5000

7000

9000

11000

0 2000 4000 6000 8000 10000 12000 14000

Tim

e (

seco

nd

s)

Distance (meters)

bus 12 simulated bus 13 simulated bus 12 scheduled bus 13 scheduled

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Case study results

Service reliability

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11

0

10

20

30

40

50

60

70

80

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

Pa

sse

ng

er

loa

d

Stop number

Short headway Long headway Planned headway

Case study results

Load profiles

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Case study results

Recovery time scenarios

Recovery time policy(percentile of travel time)

Fleet sizeOn-time

performance (%)

Schedule deviation (sec)

Late departures

(%)

55% 15 78.5 196 13.0

70% 16 84.0 169 7.3

85% 17 90.4 131 0.9

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Case study design

Holding control strategies• Setting a criteria for departing from selected locations

• Decisions– How many?

– Where?

– Which criteria?

• Schedule-based vs. Headway-based– Not before the scheduled time

– Not before a minimum headway from the preceding bus

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Case study design

Time points location

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0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

He

ad

wa

y s

tan

da

rd d

evia

tio

n [

seco

nd

s]

Stop

No control Headway-based control Schedule-based control

TP #1 TP #2

TP #3

Case study results

Effects along the route

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0%

20%

40%

60%

80%

100%

120%

SD(H) On-time

performance

Schedule deviation Bunching

No control Headway-based control Schedule-based control

Case study results

System measures comparison

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• The capabilities of Mezzo as an evaluation tool of transit operations had been demonstrated through real-world case study.

• Examples of potential applications▫ Frequency determination

▫ Restoration from major disruptions

▫ Transit link segregation assessment

• Future developments▫ Realistic network validation

▫ Car-bus interaction

▫ Detailed passenger demand modeling

Applications

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Transit loading framework