This document summarizes a study that used dynamic modeling and simulation to analyze bus and car traffic. It discusses: 1) The Mezzo simulation tool which models traffic dynamics and transit operations stochastically. 2) A case study of a busy bus line in Tel Aviv which tested the model, different service scenarios, and holding strategies. 3) The results showed the model could evaluate strategies for improving schedule adherence, headway regularity and passenger loads.

1. BusMezzo
Dynamic Modeling of Bus and Car Traffic
Oded Cats
Centre for Traffic Research (CTR)
Kungliga Tekniska Högskolan
2010-01-14 Transportforum 2010 Linköping
1

2. Outline
• Dynamic transit model
• Mezzo simulation
• Supply side: transit operations
• Case study
– Design
– Results
– Control strategies
• Demand side: passenger path choice
2

3. Transit model components
3

4. 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
4

5. Mezzo
• Mesoscopic traffic simulation
• Event-based
• Stochastic
• Traffic dynamics
▫ Running part: speed-density relationship
▫ Queuing part: turn specific queue servers
Running part Queue part
• Open source:
http://mezzo_dev.blogspot.com
5

6. 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
6

7. Case study - background
• Line 51, Tel-Aviv metropolitan
• High-demand bus line
• Heavily congested urban corridor
• 14km long route
• Max. frequency: 10 buses/hour
7

8. Case study – background (Cont.)
• Static information at stops
• No control strategies
8

9. Case study results
Trajectory
11000
9000
Time (seconds)
7000
5000
3000
1000
0 2000 4000 6000 8000 10000 12000 14000
Distance (meters)
bus 12 simulated bus 13 simulated bus 12 scheduled bus 13 scheduled
9

10. Case study results
Service reliability
10

11. 11
Case study results
Load profiles
80
70
60
Passenger load
50
40
30
20
10
0
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
Stop number
Short headway Long headway Planned headway
11

12. 12
Case study results
Recovery time scenarios
Recovery On-time Schedule Late
time policy Fleet size performance deviation departures
(percentile of
travel time)
(%) (sec) (%)
55% 15 78.5 196 13.0
70% 16 84.0 169 7.3
85% 17 90.4 131 0.9
12

13. 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
13

14. Case study design
Time points location
14

15. Case study results
Effects along the route
100
90
Headway standard deviation [seconds]
80
70
60
50
40
30
TP #3
20
TP #1 TP #2
10
No control Headway-based control Schedule-based control
0
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
Stop
15

16. Case study results
System measures comparison
120%
100%
80%
60%
40%
20%
0%
SD(H) On-time Schedule deviation Bunching
performance
No control Headway-based control Schedule-based control
16

17. Applications
• 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
17

18. Transit loading framework
18