Encyclopedia of Sustainability Science and Technology

2012 Edition
| Editors: Robert A. Meyers

Advanced Public Transport Systems, Simulation-Based Evaluation

  • Haris N. KoutsopoulosEmail author
  • Moshe Ben-Akiva
Reference work entry
DOI: https://doi.org/10.1007/978-1-4419-0851-3_297

Definition of the Subject and Its Importance

According to the latest UN report on urbanization , more that 50% of the world’s population lives in cities [2]. In Europe and the USA, it approaches 70%. In part due to developments like these, congestion in urban areas continues to grow and create a number of negative impacts, including worsening air quality and noise pollution, consumption of scarce resources, lost productivity.

Public transportation is an important component of the transportation system and potentially a critical element of any strategy toward sustainable mobility in urban areas. The importance of improved public transportation services toward sustainable and efficient transportation systems is well recognized. With the emergence of Intelligent Transportation Systems (ITS) , advanced sensor, communications, and computing technologies have been introduced to facilitate better planning, management, and control of public transportation systems [3]. Known as Advanced Public...

This is a preview of subscription content, log in to check access.


Primary Literature

  1. 1.
    Shannon RE (1975) Systems simulation: the Art and science. Prentice-Hall, Englewood CliffsGoogle Scholar
  2. 2.
    United Nations Population Fund (2010) State of world population 2007. http://www.unfpa.org/swp/2007/english/introduction.html
  3. 3.
    Miles J, Chen K (2004) ITS handbook, 2nd edn. PIARC, ParisGoogle Scholar
  4. 4.
    Casey RF, Labell LN, Moniz L et al (2000) Advanced public transportation systems – the state of the art: update 2000. U.S. Department of Transportation, Federal Transit Administration (FTA), Washington, DCGoogle Scholar
  5. 5.
    Hwang M, Kemp J, Lerner-Lam E, Neuerburg N, Okunieff P (2006) Advanced public transportation systems: state of the art update 2006, FTA-NJ-26-7062-06.1. Federal Transit Administration (FTA), Washington, DCGoogle Scholar
  6. 6.
    Furth P, Hemily B, Muller T, Strahman J (2006) Using archived AVL-APC data to improve transit performance and management, TCRP report 113. Transportation Research Board, Washington, DCGoogle Scholar
  7. 7.
    Furth P (2000) Data analysis for bus planning and monitoring, TCRP synthesis 34. Transportation Research Board, Washington, DCGoogle Scholar
  8. 8.
    Smith H, Hemily B, Ivanovic M (2006) Transit signal priority (TSP): a planning and implementation handbook. ITS America and US Department of Transport, Washington, DCGoogle Scholar
  9. 9.
    Hickman MD, Wilson NHM (1995) Passenger travel time and path choice implications of real-time transit information. Transp Res C 3(4):211–226CrossRefGoogle Scholar
  10. 10.
    Nuzzolo A, Russo F, Crisalli U (2001) A doubly dynamic schedule-based assignment model for transit networks. Transp Sci 35(3):268–285CrossRefGoogle Scholar
  11. 11.
    Wilson NHM, Zhao J, Rahbee A (2004) The potential impact of automated data collection systems on urban public transport planning. In: Wilson NHM, Nuzzolo A (eds) Schedule-based dynamic transit modeling: theory and applications. Academic, Dordrecht, pp 74–99CrossRefGoogle Scholar
  12. 12.
    Vandebona U, Richardson AJ (1985) The effects of fare-collection strategies on transit level of service. Transp Res Rec 1036:79–87Google Scholar
  13. 13.
    Transportation Demand Management User Service (2005) National ITS architecture. US Department of Transportation, Washington, DC. www.iteris.com/itsarch/html/user/usr18.htm
  14. 14.
    Barceló J (ed) (2010) Fundamentals of traffic simulation, vol 145, International series in operations research & management science. Springer, New YorkGoogle Scholar
  15. 15.
    Daganzo C (1994) The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transp Res B 28(4):269–287CrossRefGoogle Scholar
  16. 16.
    Messmer A, Papageorgiou M (1990) METANET: a macroscopic simulation program for motorway networks. Traffic Eng Control 31(9):466–470Google Scholar
  17. 17.
    Ben-Akiva M, Bierlaire M, Burton D, Koutsopoulos HN, Mishalani R (2001) Network state estimation and prediction for real-time transportation management applications. Netw Spat Econ 1(3/4):293–318CrossRefGoogle Scholar
  18. 18.
    Ben-Akiva M, Koutsopoulos HN, Antoniou C, Balakrishna R (2010) Traffic simulation with DynaMIT (chap 10). In: Barcelo J (ed) Fundamentals of traffic simulation. Springer, New YorkGoogle Scholar
  19. 19.
    Mahmassani HS (2001) Dynamic network traffic assignment and simulation methodology for advanced systems management applications. Netw Spat Econ 1(3/4):267–292CrossRefGoogle Scholar
  20. 20.
    Yang Q, Koutsopoulos HN (1996) A microscopic traffic simulator for evaluation of dynamic traffic management systems. Transp Res 4C:113–129Google Scholar
  21. 21.
    Ben-Akiva M, Koutsopoulos HN, Toledo T, Yang Q, Choundhury C, Antoniou C, Balakrishna R (2010) Traffic simulation with MITSIMLab (chap 6). In: Barcelo J (ed) Fundamentals of traffic simulation. Springer, New YorkGoogle Scholar
  22. 22.
    Burghout W, Koutsopoulos HN, Andreasson I (2005) Hybrid mesoscopic-microscopic traffic simulation. Transp Res Rec 1934:218–225CrossRefGoogle Scholar
  23. 23.
    Algers S, Bernauer E, Boero M, Breheret L, Dougherty M, Fox K, Gabard JF (1997) Review of microsimulation models. In: SMARTEST project deliverable D3, EU, Institute for Transport Studies, University of Leeds, Leeds. Available at: http://www.its.leeds.ac.uk/projects/smartest/deliv3f.html
  24. 24.
    Silva PCM (2001) Modelling interactions between bus operations and traffic flow. Doctoral dissertation, University College LondonGoogle Scholar
  25. 25.
    Khasnabis S, Karanti RR, Rudraraju RK (1996) NETSIM-based approach to evaluation of bus preemption strategies. Transp Res Rec 1554:80–89CrossRefGoogle Scholar
  26. 26.
    Liu R, Clark S, Montgomery F, Watling D (1999) Microscopic modelling of traffic management measures for guided bus operation. In: Proceedings of eighth world conference on transport research, AntwerpGoogle Scholar
  27. 27.
    Chang J, Collura J, Dion F, Rakha H (2003) Evaluation of service reliability impacts of traffic signal priority strategies for bus transit. Transp Res Rec 1841:23–31CrossRefGoogle Scholar
  28. 28.
    Lee J, Shalaby A, Greenough J, Bowie M, Hung S (2005) Advanced transit signal priority control using on-line microsimulation-based transit prediction model. In: Preprints of the transportation research board 84th annual meeting, Washington, DCGoogle Scholar
  29. 29.
    Ding Y, Chien S, Zayas A (2001) Simulating bus operations with enhanced corridor simulator. Transp Res Rec 1731:104–111CrossRefGoogle Scholar
  30. 30.
    Morgan DJ (2002) A microscopic simulation laboratory for advanced public transport system evaluation. Master thesis, Massachusetts Institute of TechnologyGoogle Scholar
  31. 31.
    Liu CL, Pai TW (2006) Methods for path and service planning under route constraints. Int J Comput Appl Technol 25 (1):40–49CrossRefGoogle Scholar
  32. 32.
    Liu R, Van Vliet D, Watling D (2006) Microsimulation models incorporating both demand and supply dynamics. Transp Res A 40:125–150, Washington, DCGoogle Scholar
  33. 33.
  34. 34.
  35. 35.
  36. 36.
    Werf JV (2004) SmartBRT: a tool for simulating, visualizing and evaluating bus rapid transit systems. California PATH Research Report, Institute of Transportation Studies, University of California, BerkeleyGoogle Scholar
  37. 37.
  38. 38.
    Cortes CE, Fernandez R, Burgos V (2007) Modeling passengers, buses and stops in traffic microsimulators. The MISTRANSIT approach on the PARAMICS platform. In: Proceedings of the 86th transportation research board annual meeting, Washington, DCGoogle Scholar
  39. 39.
    Fernandez R, Cortes CE, Burgos V (2010) Microscopic simulation of transit operations: policy studies with the MISTRANSIT application programming interface. Transp Plann Technol 33(2):157–176CrossRefGoogle Scholar
  40. 40.
    Wahba M, Shalaby A (2006) A general multi-agent modelling framework for the transit assignment problem – a learning-based approach. In: Innovative internet community systems. Springer, Berlin, pp 276–295Google Scholar
  41. 41.
    Wahba M, Shalaby A (2006) MILATRAS: a microsimulation platform for testing transit-ITS policies and technologies. In: Proceedings of the IEEE ITSC, CanadaGoogle Scholar
  42. 42.
    Wang J, Wahba M, Miller EJ (2010) A comparison of an agent-based transit assignment procedure (MILATRAS) with conventional approaches. In: Proceedings of the 89th transportation research board annual meeting, Washington, DCGoogle Scholar
  43. 43.
    Boxill SA, Yu L (2000) An evaluation of traffic simulation models for supporting ITS development. Center for Transportation Training and Research, Texas Southern UniversityGoogle Scholar
  44. 44.
    Abdelghany KF, Abdelghany AF, Mahmassani HS, Abdelfatah AS (2006) Modeling bus priority using intermodal dynamic network assignment-simulation methodology. J Public Transp 9(5):1–22Google Scholar
  45. 45.
    Meignan D, Simonin O, Koukam A (2007) Simulation and evaluation of urban bus-networks using a multiagent approach. Simul Model Pract Theory 15:659–671CrossRefGoogle Scholar
  46. 46.
    Cats O, Toledo T, Burghout W, Koutsopoulos HN (2010) Mesoscopic modeling of bus public transportation. In: Proceedings and CD of the 89th transportation research board (TRB) annual meeting, Washington, DCGoogle Scholar
  47. 47.
    Toledo T, Cats O, Burghout W, Koutsopoulos HN (2010) Mesoscopic simulation for transit operations. Transp Res C 18(6):896–908. doi:10.1016/j.trc.2010.02.008CrossRefGoogle Scholar
  48. 48.
    Burghout W (2004) Hybrid microscopic-mesoscopic traffic simulation. Doctoral dissertation, Royal Institute of Technology, StockholmGoogle Scholar
  49. 49.
    Yang Q (1997) A simulation laboratory for evaluation of dynamic traffic management systems. Ph.D. thesis, Department of Civil and Environmental Engineering, Massachusetts Institute of TechnologyGoogle Scholar
  50. 50.
    Yang Q, Koutsopoulos HN, Ben-Akiva M (2000) A simulation laboratory for evaluating dynamic traffic management systems. Transp Res Rec 1710:122–130CrossRefGoogle Scholar
  51. 51.
    Levinson HS (1983) Analyzing transit travel time performance. Transp Res Rec 915:1–6Google Scholar
  52. 52.
    Lin T, Wilson NHM (1992) Dwell time relationships for light rail systems. Transp Res Rec 1361:287–295Google Scholar
  53. 53.
    TCRP Report 100 (2003) Transit capacity and quality of service manual (TCQSM), 2nd edn. Transportation Research Board, Washington, DCGoogle Scholar
  54. 54.
    Jolliffe JK, Hutchinson TP (1975) A behavioral explanation of the association between Bus and passenger arrivals at a bus stop. Transp Sci 9(3):248–282CrossRefGoogle Scholar
  55. 55.
    Morgan D, Koutsopoulos HN, Ben-Akiva M (2003) Simulation-based evaluation of advanced public transportation systems. In: Wilson NHM, Nuzzolo A (eds) Schedule-based dynamic traffic modeling: theory and applications. Series in operations research/computer science interfaces, vol 28, chap 6. Academic, Boston, pp 95–102Google Scholar
  56. 56.
    Furth PG, Muller THJ (2000) Conditional bus priority at signalized intersections: better service quality with less traffic disruption. Transp Res Rec 1731:23–30CrossRefGoogle Scholar
  57. 57.
    Chang J, Collura J, Dion F, Rakha H (2003) Evaluation of service reliability impacts of traffic signal priority strategies for bus transit. Transp Res Rec 1841:23–31CrossRefGoogle Scholar
  58. 58.
    Kim W, Rilett LR (2005) An improved transit signal priority system for networks with nearside bus stops. Transp Res Rec 1925:205–214CrossRefGoogle Scholar
  59. 59.
    Lee J, Shalaby A, Greenough J, Bowie M, Hung S (2005) Advanced transit signal priority control using on-line microsimulation based transit prediction model. Transp Res Rec 1925:185–194CrossRefGoogle Scholar
  60. 60.
    Kimpel T, Strathman J (Apr 2002) Automatic passenger counter evaluation: implications for national transit database reporting. http://www.upa.pdx.edu/CUS/PUBS/docs/PR124.pdf
  61. 61.
    Abkowitz M, Slavin H, Waksman R, Englisher L, Wilson N (1978) Transit service reliability. Report UMTA-MA-06-0049-78-1. USDOT Transportation Systems Center, CambridgeGoogle Scholar
  62. 62.
    Abkowitz M, Tozzi J (1987) Research contributions to managing transit service reliability. Adv Transp J 21:47–65CrossRefGoogle Scholar
  63. 63.
    Strathman JG, Dueker KJ, Kimpel T, Gerhart R, Turner K, Taylpr P, Callas S, Griffin D, Hopper J (1999) Automated bus dispatching, operations control and service reliability. Transp Res Rec 1666:28–36, Washington, DCCrossRefGoogle Scholar
  64. 64.
    Strathman JG, Kimpel TJ, Dueker KJ, Gerhart RL, Turner K, Griffin D, Callas S (2001) Bus transit operations control: review and experience involving Tri-Met’s automated bus dispatching system. In: Transportation Research Board, 80th Annual Meeting, Washington, DCGoogle Scholar
  65. 65.
    Strathman JG, Kimpel TJ, Dueker KJ, Gerhart RL, Callas S (2002) Evaluation of transit operations: data applications of Tri-Met’s automated bus dispatching system. Transportation 29:321–345CrossRefGoogle Scholar
  66. 66.
    Eberlein XJ (1995) Real time strategies in transit operations: models and analysis. Ph.D. dissertation, Department of Civil Engineering, MITGoogle Scholar
  67. 67.
    Osuna EE, Newell GF (1972) Control strategies for an idealized public transportation system. Transp Sci 6:52–72CrossRefGoogle Scholar
  68. 68.
    Barnett A (1974) On controlling randomness in transit operations. Transp Sci 8(2):102–116CrossRefGoogle Scholar
  69. 69.
    Barnett A (1978) Control strategies for transport systems with nonlinear waiting costs. Transp Sci 12(2):119–136CrossRefGoogle Scholar
  70. 70.
    O’Dell S, Wilson N (1997) Optimal real-time control strategies for rail transit operations during disruption. In: Wilson N (ed) Computer aided transit scheduling, vol 471, Lecture note in economics and mathematical systems. Springer, Berlin, pp 299–323CrossRefGoogle Scholar
  71. 71.
    Hickman M (2001) An analytic stochastic model for the transit vehicle holding problem. Transp Sci 35(3):215–237CrossRefGoogle Scholar
  72. 72.
    Eberlein XJ, Wilson NHM, Bernstein D (2001) The holding problem with real-time information available. Transp Sci 35(1):1–18CrossRefGoogle Scholar
  73. 73.
    Shen S, Wilson NHM (2001) Optimal integrated real-time disruption control model for rail transit systems. In: Voss S, Daduna J (eds) Computer-aided scheduling of public transport, vol 505, Lecture notes in economics and mathematical systems. Springer, Berlin, pp 335–364CrossRefGoogle Scholar
  74. 74.
    Koffman D (1978) A simulation study of alternative real-time Bus headway control strategies. Transp Res Rec 663:41–46Google Scholar
  75. 75.
    Dessouky M, Hall R, Zhang L, Singh A (2003) Real-time control of buses for schedule coordination at a terminal. Transp Res A 37:145–164Google Scholar
  76. 76.
    Cats O, Koutsopoulos HN, Burghout W, Toledo T (2011) Effect of real-time transit information on dynamic passenger path choice. Transportation Research Record. J Transp Res B (in press)Google Scholar
  77. 77.
    Ben-Akiva M, Lerman S (1985) Discrete choice analysis. Theory and application to travel demand. MIT Press, Cambridge, MAGoogle Scholar
  78. 78.
    Cats O, Burghout W, Toledo T, Koutsopoulos HN (2010) Evaluation of real-time holding strategies for improved bus service reliability. Submitted for presentation and publication at the 2010 13th international IEEE conference on intelligent transportation systems (ITSC 10), Madeira IslandGoogle Scholar
  79. 79.
    Koutsopoulos HN, Wang Z (2007) Simulation of urban rail operations. Transp Res Rec 2006:84–91CrossRefGoogle Scholar
  80. 80.
    Daganzo CF (2009) A headway-based approach to eliminate bus bunching: systematic analysis and comparisons. Transp Res B 43:913–921CrossRefGoogle Scholar
  81. 81.
    Abkowitz M, Lepofsky M (1990) Implementing headway-based reliability control on transit routes. J Transp Eng 116(1):49–63CrossRefGoogle Scholar
  82. 82.
    Liu G, Wirasinghe SC (2001) A simulation model of reliable schedule design for a fixed transit route. J Adv Transp 35(2):145–174CrossRefGoogle Scholar
  83. 83.
    Abkowitz M, Engelstein I (1984) Methods for maintaining transit service regularity. Transp Res Rec 961:1–8Google Scholar
  84. 84.
    Turnquist MA, Blume SW (1980) Evaluating potential effectiveness of headway control strategies for transit systems. Transp Res Rec 746:25–29, Washington, DCGoogle Scholar
  85. 85.
    Wirasinghe SC, Liu G (1995) Determination of the number and locations of time points in transit schedule design – case of a single run. Ann Oper Res 60:161–191CrossRefGoogle Scholar
  86. 86.
    Ben-Akiva M, Bottom J, Gao S, Koutsopoulos HN, Wen Y (2007) Towards disaaggregate dynamic travel forecasting models. Tsinghua Sci Technol 12(2):115–130CrossRefGoogle Scholar

Books and Reviews

  1. Ceder A (2007) Public transit planning and operation: theory, modeling and practice. Elsevier, BurlingtonGoogle Scholar
  2. Chung E, Dumont A (eds) (2009) Transport simulation: beyond traditional approaches. EPFL Press, LausanneGoogle Scholar
  3. Sussman J (2005) Perspectives on intelligent transportation systems (ITS). Springer, New YorkGoogle Scholar
  4. Vuchik V (2005) Urban transit: operations, planning, and economics. Wiley, HobokenGoogle Scholar
  5. Vuchik V (2007) Urban transit systems and technology. Wiley, HobokenCrossRefGoogle Scholar
  6. Wilson NHM, Nuzzolo A (eds) (2004) Schedule-based dynamic transit modeling: theory and applications. Academic, DordrechtGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Transport SciencesThe Royal Institute of Technology, KTHStockholmSweden
  2. 2.Department of Civil and Environmental EngineeringMITCambridgeUSA