Skip to main content
SpringerLink
Log in

Advanced Public Transport Systems, Simulation-Based Evaluation

  • Reference work entry
Encyclopedia of Sustainability Science and Technology

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...

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 6,999.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 549.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Abbreviations

APTS:

Advanced public transportation systems is the transit component of Intelligent Transportation Systems (ITS). It refers to technologies for the collection, communication, and processing of data useful for the management of transit systems.

AVL:

Automated vehicle location systems combine vehicle location and communications technologies to automatically track the locations of a fleet of vehicles. AVL is an integral component of automated vehicle monitoring and control, fleet management, traffic signal priority, and other transit applications. AVL can be used to monitor schedule adherence, estimate arrival times, and communicate location data to a transit operations control center (TOC) or to field-installed devices that require real-time vehicle location data.

APC:

Automatic passenger counters are systems that count passengers as they board and alight the vehicle at a stop. APCs may be used with AVL systems in order to record the spatial distribution of passenger demand along a vehicle’s route. APC technologies include treadle mats, infrared beams, and emerging computer imaging (still at early stage of development). APC records the stop location, the time and date of arrival at the stop, the time the doors open and close, the number of passengers boarding and the number of passengers alighting.

BRT:

Bus rapid transit is an evolving transit concept aiming at combining the quality and capacity of rail transit and the flexibility of bus transit.

Electronic fare payment:

Electronic fare payment includes a range of technologies, such as smart cards, designed to reduce costs associated with fare collection and to improve customer convenience (also known as Automated Fare Collection (AFC) systems).

Fleet management:

Fleet management applications refer to “vehicle-based” technologies that may be used to improve vehicle planning, scheduling, and operations.

ITS:

Intelligent transportation systems is an umbrella term referring to sensor, communication, and computing technologies for improved management of transportation systems. ITS relates to all transportation modes.

Simulation:

“… the process of designing a model of a real system and conducting experiments with this model for the purpose of understanding the behavior of the system or evaluating various strategies for the operation of the system” [1].

Signal priority:

Transit signal priority involves the modification of a traffic signal’s regular timing plan to give preference to transit vehicles. Signal priority is designed to reduce transit vehicle delays at signalized intersections.

Traveler information:

Traveler information refers to technologies designed to provide pre-trip and en route information to travelers to allow them to make informed trip-making decisions.

Transportation demand management:

Transportation demand management refers to systems aimed at improving the utilization of existing transportation network infrastructure, through influencing the demand characteristics.

Bibliography

Primary Literature

  1. Shannon RE (1975) Systems simulation: the Art and science. Prentice-Hall, Englewood Cliffs

    Google Scholar 

  2. United Nations Population Fund (2010) State of world population 2007. http://www.unfpa.org/swp/2007/english/introduction.html

  3. Miles J, Chen K (2004) ITS handbook, 2nd edn. PIARC, Paris

    Google Scholar 

  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, DC

    Google Scholar 

  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, DC

    Google Scholar 

  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, DC

    Google Scholar 

  7. Furth P (2000) Data analysis for bus planning and monitoring, TCRP synthesis 34. Transportation Research Board, Washington, DC

    Google Scholar 

  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, DC

    Google Scholar 

  9. Hickman MD, Wilson NHM (1995) Passenger travel time and path choice implications of real-time transit information. Transp Res C 3(4):211–226

    Article  Google Scholar 

  10. Nuzzolo A, Russo F, Crisalli U (2001) A doubly dynamic schedule-based assignment model for transit networks. Transp Sci 35(3):268–285

    Article  Google Scholar 

  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 74–99

    Chapter  Google Scholar 

  12. Vandebona U, Richardson AJ (1985) The effects of fare-collection strategies on transit level of service. Transp Res Rec 1036:79–87

    Google Scholar 

  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. Barceló J (ed) (2010) Fundamentals of traffic simulation, vol 145, International series in operations research & management science. Springer, New York

    Google Scholar 

  15. Daganzo C (1994) The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transp Res B 28(4):269–287

    Article  Google Scholar 

  16. Messmer A, Papageorgiou M (1990) METANET: a macroscopic simulation program for motorway networks. Traffic Eng Control 31(9):466–470

    Google Scholar 

  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):293–318

    Article  Google Scholar 

  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 York

    Google Scholar 

  19. Mahmassani HS (2001) Dynamic network traffic assignment and simulation methodology for advanced systems management applications. Netw Spat Econ 1(3/4):267–292

    Article  Google Scholar 

  20. Yang Q, Koutsopoulos HN (1996) A microscopic traffic simulator for evaluation of dynamic traffic management systems. Transp Res 4C:113–129

    Google Scholar 

  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 York

    Google Scholar 

  22. Burghout W, Koutsopoulos HN, Andreasson I (2005) Hybrid mesoscopic-microscopic traffic simulation. Transp Res Rec 1934:218–225

    Article  Google Scholar 

  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. Silva PCM (2001) Modelling interactions between bus operations and traffic flow. Doctoral dissertation, University College London

    Google Scholar 

  25. Khasnabis S, Karanti RR, Rudraraju RK (1996) NETSIM-based approach to evaluation of bus preemption strategies. Transp Res Rec 1554:80–89

    Article  Google Scholar 

  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, Antwerp

    Google Scholar 

  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 1841:23–31

    Article  Google Scholar 

  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, DC

    Google Scholar 

  29. Ding Y, Chien S, Zayas A (2001) Simulating bus operations with enhanced corridor simulator. Transp Res Rec 1731:104–111

    Article  Google Scholar 

  30. Morgan DJ (2002) A microscopic simulation laboratory for advanced public transport system evaluation. Master thesis, Massachusetts Institute of Technology

    Google Scholar 

  31. Liu CL, Pai TW (2006) Methods for path and service planning under route constraints. Int J Comput Appl Technol 25 (1):40–49

    Article  Google Scholar 

  32. Liu R, Van Vliet D, Watling D (2006) Microsimulation models incorporating both demand and supply dynamics. Transp Res A 40:125–150, Washington, DC

    Google Scholar 

  33. http://www.caliper.com/

  34. http://www.ptvag.com/index.php?id=345

  35. BRT. http://www.cfte.org/trends/brt.asp

  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, Berkeley

    Google Scholar 

  37. http://www.paramics.com/

  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, DC

    Google Scholar 

  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):157–176

    Article  Google Scholar 

  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 276–295

    Google Scholar 

  41. Wahba M, Shalaby A (2006) MILATRAS: a microsimulation platform for testing transit-ITS policies and technologies. In: Proceedings of the IEEE ITSC, Canada

    Google Scholar 

  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, DC

    Google Scholar 

  43. Boxill SA, Yu L (2000) An evaluation of traffic simulation models for supporting ITS development. Center for Transportation Training and Research, Texas Southern University

    Google Scholar 

  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):1–22

    Google Scholar 

  45. Meignan D, Simonin O, Koukam A (2007) Simulation and evaluation of urban bus-networks using a multiagent approach. Simul Model Pract Theory 15:659–671

    Article  Google Scholar 

  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, DC

    Google Scholar 

  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.2010.02.008

    Article  Google Scholar 

  48. Burghout W (2004) Hybrid microscopic-mesoscopic traffic simulation. Doctoral dissertation, Royal Institute of Technology, Stockholm

    Google Scholar 

  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 Technology

    Google Scholar 

  50. Yang Q, Koutsopoulos HN, Ben-Akiva M (2000) A simulation laboratory for evaluating dynamic traffic management systems. Transp Res Rec 1710:122–130

    Article  Google Scholar 

  51. Levinson HS (1983) Analyzing transit travel time performance. Transp Res Rec 915:1–6

    Google Scholar 

  52. Lin T, Wilson NHM (1992) Dwell time relationships for light rail systems. Transp Res Rec 1361:287–295

    Google Scholar 

  53. TCRP Report 100 (2003) Transit capacity and quality of service manual (TCQSM), 2nd edn. Transportation Research Board, Washington, DC

    Google Scholar 

  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):248–282

    Article  Google Scholar 

  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 95–102

    Google Scholar 

  56. Furth PG, Muller THJ (2000) Conditional bus priority at signalized intersections: better service quality with less traffic disruption. Transp Res Rec 1731:23–30

    Article  Google Scholar 

  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 1841:23–31

    Article  Google Scholar 

  58. Kim W, Rilett LR (2005) An improved transit signal priority system for networks with nearside bus stops. Transp Res Rec 1925:205–214

    Article  Google Scholar 

  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 1925:185–194

    Article  Google Scholar 

  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. 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, Cambridge

    Google Scholar 

  62. Abkowitz M, Tozzi J (1987) Research contributions to managing transit service reliability. Adv Transp J 21:47–65

    Article  Google Scholar 

  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, DC

    Article  Google Scholar 

  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, DC

    Google Scholar 

  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 29:321–345

    Article  Google Scholar 

  66. Eberlein XJ (1995) Real time strategies in transit operations: models and analysis. Ph.D. dissertation, Department of Civil Engineering, MIT

    Google Scholar 

  67. Osuna EE, Newell GF (1972) Control strategies for an idealized public transportation system. Transp Sci 6:52–72

    Article  Google Scholar 

  68. Barnett A (1974) On controlling randomness in transit operations. Transp Sci 8(2):102–116

    Article  Google Scholar 

  69. Barnett A (1978) Control strategies for transport systems with nonlinear waiting costs. Transp Sci 12(2):119–136

    Article  Google Scholar 

  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 299–323

    Chapter  Google Scholar 

  71. Hickman M (2001) An analytic stochastic model for the transit vehicle holding problem. Transp Sci 35(3):215–237

    Article  Google Scholar 

  72. Eberlein XJ, Wilson NHM, Bernstein D (2001) The holding problem with real-time information available. Transp Sci 35(1):1–18

    Article  Google Scholar 

  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 335–364

    Chapter  Google Scholar 

  74. Koffman D (1978) A simulation study of alternative real-time Bus headway control strategies. Transp Res Rec 663:41–46

    Google Scholar 

  75. Dessouky M, Hall R, Zhang L, Singh A (2003) Real-time control of buses for schedule coordination at a terminal. Transp Res A 37:145–164

    Google Scholar 

  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. Ben-Akiva M, Lerman S (1985) Discrete choice analysis. Theory and application to travel demand. MIT Press, Cambridge, MA

    Google Scholar 

  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 Island

    Google Scholar 

  79. Koutsopoulos HN, Wang Z (2007) Simulation of urban rail operations. Transp Res Rec 2006:84–91

    Article  Google Scholar 

  80. Daganzo CF (2009) A headway-based approach to eliminate bus bunching: systematic analysis and comparisons. Transp Res B 43:913–921

    Article  Google Scholar 

  81. Abkowitz M, Lepofsky M (1990) Implementing headway-based reliability control on transit routes. J Transp Eng 116(1):49–63

    Article  Google Scholar 

  82. Liu G, Wirasinghe SC (2001) A simulation model of reliable schedule design for a fixed transit route. J Adv Transp 35(2):145–174

    Article  Google Scholar 

  83. Abkowitz M, Engelstein I (1984) Methods for maintaining transit service regularity. Transp Res Rec 961:1–8

    Google Scholar 

  84. Turnquist MA, Blume SW (1980) Evaluating potential effectiveness of headway control strategies for transit systems. Transp Res Rec 746:25–29, Washington, DC

    Google Scholar 

  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 60:161–191

    Article  Google Scholar 

  86. Ben-Akiva M, Bottom J, Gao S, Koutsopoulos HN, Wen Y (2007) Towards disaaggregate dynamic travel forecasting models. Tsinghua Sci Technol 12(2):115–130

    Article  Google Scholar 

Books and Reviews

  • Ceder A (2007) Public transit planning and operation: theory, modeling and practice. Elsevier, Burlington

    Google Scholar 

  • Chung E, Dumont A (eds) (2009) Transport simulation: beyond traditional approaches. EPFL Press, Lausanne

    Google Scholar 

  • Sussman J (2005) Perspectives on intelligent transportation systems (ITS). Springer, New York

    Google Scholar 

  • Vuchik V (2005) Urban transit: operations, planning, and economics. Wiley, Hoboken

    Google Scholar 

  • Vuchik V (2007) Urban transit systems and technology. Wiley, Hoboken

    Book  Google Scholar 

  • Wilson NHM, Nuzzolo A (eds) (2004) Schedule-based dynamic transit modeling: theory and applications. Academic, Dordrecht

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Transport Sciences, The Royal Institute of Technology, KTH, Stockholm, 10044, Sweden

    Dr. Haris N. Koutsopoulos

  2. Department of Civil and Environmental Engineering, MIT, Cambridge, MA, 02139, USA

    Dr. Moshe Ben-Akiva

Authors
  1. Dr. Haris N. Koutsopoulos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dr. Moshe Ben-Akiva
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haris N. Koutsopoulos .

Editor information

Editors and Affiliations

  1. RAMTECH LIMITED, Larkspur, CA, USA

    Robert A. Meyers

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer Science+Business Media, LLC

About this entry

Cite this entry

Koutsopoulos, H.N., Ben-Akiva, M. (2012). Advanced Public Transport Systems, Simulation-Based Evaluation. In: Meyers, R.A. (eds) Encyclopedia of Sustainability Science and Technology. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-0851-3_297

Download citation

Publish with us

Policies and ethics

Search

Navigation