BusMezzoDynamic Modeling of Bus and Car Traffic                     Oded Cats          Centre for Traffic Research (CTR)  ...
Outline•   Dynamic transit model•   Mezzo simulation•   Supply side: transit operations•   Case study    – Design    – Res...
Transit model components                           3
Motivation• Modeling sources of uncertainties   •   Departure time from origin terminal   •   Traffic conditions   •   Pas...
Mezzo•   Mesoscopic traffic simulation•   Event-based•   Stochastic•   Traffic dynamics    ▫ Running part: speed-density r...
Transit operations• Transit entities  ▫ Bus stop, bus line, bus route, bus trip, bus vehicle and bus type• Transit mechani...
Case study - background•   Line 51, Tel-Aviv metropolitan•   High-demand bus line•   Heavily congested urban corridor•   1...
Case study – background (Cont.)• Static information at stops• No control strategies                                  8
Case study resultsTrajectory                    11000                    9000   Time (seconds)                    7000    ...
Case study resultsService reliability                      10
11Case study resultsLoad profiles                    80                    70                    60   Passenger load      ...
12Case study resultsRecovery time scenarios Recovery                       On-time     Schedule       Latetime policy     ...
Case study designHolding control strategies• Setting a criteria for departing from selected locations• Decisions   – How m...
Case study designTime points location                       14
Case study resultsEffects along the route                                  100                                            ...
Case study resultsSystem measures comparison     120%     100%      80%      60%      40%      20%      0%                ...
Applications• The capabilities of Mezzo as an evaluation tool of  transit operations had been demonstrated through  real-w...
Transit loading framework                            18
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Session 55 Oded Cats

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Session 55 Oded Cats

  1. 1. BusMezzoDynamic Modeling of Bus and Car Traffic Oded Cats Centre for Traffic Research (CTR) Kungliga Tekniska Högskolan 2010-01-14 Transportforum 2010 Linköping 1
  2. 2. Outline• Dynamic transit model• Mezzo simulation• Supply side: transit operations• Case study – Design – Results – Control strategies• Demand side: passenger path choice 2
  3. 3. Transit model components 3
  4. 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. 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. 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. 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. 8. Case study – background (Cont.)• Static information at stops• No control strategies 8
  9. 9. Case study resultsTrajectory 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. 10. Case study resultsService reliability 10
  11. 11. 11Case study resultsLoad 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. 12Case study resultsRecovery time scenarios Recovery On-time Schedule Latetime policy Fleet size performance deviation departures(percentile oftravel 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. 13. Case study designHolding 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. 14. Case study designTime points location 14
  15. 15. Case study resultsEffects 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. 16. Case study resultsSystem 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. 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. 18. Transit loading framework 18

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