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From Transit Systems to Models: Data Representation and Collection

  • Klaus NoekelEmail author
  • Guido Gentile
  • Efthia Nathanail
  • Achille Fonzone
Chapter
Part of the Springer Tracts on Transportation and Traffic book series (STTT)

Abstract

This chapter deals with the data that form input and output of passenger route choice models. All information about supply and demand that is relevant to passenger route choice must be captured in a formal way in order to be accessible to mathematical choice models. Over time standard conventions for this formalisation have emerged. In order to avoid repetition in Part III, they are presented once in Sect. 5.1.

Keywords

Route Choice Travel Demand Transit Network Intelligent Transport System Transit Service 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Alfred Chu KK, Chapleau R (2008) Enriching archived smart card transaction data for transit demand modeling. Transp Res Board 2063:63–72CrossRefGoogle Scholar
  2. Bagchi M, White PR (2005) The potential of public transport smart card data. Transp Policy 12:464–474CrossRefGoogle Scholar
  3. Barry JJ, Newhouser R, Rahbee A, Sayeda S (2002) Origin and destination estimation in New York City with automated fare system data. Transp Res Board 1817:183–187CrossRefGoogle Scholar
  4. Caceres N, Wideberg JP, Benitez FG (2007) Deriving origin–destination data from a mobile phone network. IET Intel Transport Syst 1:15–26CrossRefGoogle Scholar
  5. Caceres N, Wideberg JP, Benitez FG (2008) Review of traffic data estimations extracted from cellular networks. IET Intel Transport Syst 2:179–192CrossRefGoogle Scholar
  6. Camus R, Longo G, Macorini C (2005) Estimation of transit reliability level-of-service based on automatic vehicle location data. Transp Res Rec 1927:277–286CrossRefGoogle Scholar
  7. Chan J (2007) Rail transit OD matrix estimation and journey time reliability metrics using automated fare data. Thesis, Massachusetts Institute of Technology, USAGoogle Scholar
  8. Chu KKA, Chapleau R, Trépanier M (2009) Driver-assisted bus interview. Transp Res Board 2105:1–10CrossRefGoogle Scholar
  9. Csikos D, Currie G (2008) Investigating consistency in transit passenger arrivals: insights from longitudinal automated fare collection data. Transp Res Board 2042:12–19CrossRefGoogle Scholar
  10. El-Geneidy AM, Strathman JG, Kimpel TJ, Crout D (2006) Effects of bus stop consolidation on passenger activity and transit operations. Transp Res Board 1971:32–41CrossRefGoogle Scholar
  11. Enei R (2012) The potential role of ICT in favouring a seamless co-modal transport system. Deliverable 3.1 of COMPASS. 7th Framework Program, European UnionGoogle Scholar
  12. Feng W, Figliozzi M, Price S, Feng W, Hostetler K (2011) Techniques to visualize and monitor transit fleet operations performance in urban areas. In: Proceedings of the 90th annual meeting of transportation research board. Washington, D.C., USAGoogle Scholar
  13. Frumin M, Zhao J (2012) Analyzing passenger incidence behavior in heterogeneous transit services using Smartcard data and schedule-based assignment. Transp Res Board 2274:52–60CrossRefGoogle Scholar
  14. Furth PG, Hemily B, Muller THJ, Strathman JG (2003) Uses of archived AVL-APC data to improve transit performance and management : review and potential. Transp Res Board 113Google Scholar
  15. Golani H (2007) Use of archived bus location, dispatch, and ridership data for transit analysis. Transp Res 1992:101–112Google Scholar
  16. Golledge RG, Gärling T (2001) Spatial behavior in transportation modeling and planning. In: Goulias KG (ed) Transportation systems planning: methods and applications, CRC Press, New YorkGoogle Scholar
  17. González MC, Hidalgo CA, Barabási A-L (2008) Understanding individual human mobility patterns. Nature 453:779–782CrossRefGoogle Scholar
  18. Gordon JB (2012) Intermodal passenger flows on London’s public transport network. Massachusetts Institute of Technology, USAGoogle Scholar
  19. Hammerle M, Haynes M, Mcneil S (2005) Use of automatic vehicle location and passenger count data to evaluate bus operations: experience of the Chicago Transit Authority, Illinois. Transp Res Rec 1903:27–34CrossRefGoogle Scholar
  20. Kusakabe T, Iryo T, Asakura Y (2010) Estimation method for railway passengers’ train choice behavior with smart card transaction data. Transportation 37:731–749CrossRefGoogle Scholar
  21. Lin J, Ruan M (2009) Probability-based bus headway regularity measure. IET Intel Transport Syst 3:400–408CrossRefGoogle Scholar
  22. Lin J, Wang P, Barnum DT (2008) A quality control framework for bus schedule reliability. Transp Res E 44:1086–1098CrossRefGoogle Scholar
  23. Mokhtarian PL, Salomon I (2001) How derived is the demand for travel? Some conceptual and measurement considerations. Transp Res A 35:695–719CrossRefGoogle Scholar
  24. Moreira-Matias L, Gama J, Mendes-Moreira J, Sousa JF (2010) Validation of both number and coverage of bus Schedules using AVL data. In: Proceedings of the 13th international IEEE conference on intelligent transportation systems (ITSC). Madeira, PortugalGoogle Scholar
  25. Moreira-Matias L, Ferreira C, Gama J, Sousa JF (2012) Bus bunching detection by mining sequences. In: Advances in data mining. Applications and theoretical aspects, Springer, BerlinGoogle Scholar
  26. Munizaga MA, Palma C (2012) Estimation of a disaggregate multimodal public transport Origin-Destination matrix from passive smartcard data from Santiago, Chile. Transp Res C 24:9–18CrossRefGoogle Scholar
  27. Nielsen OA, Landex A, Frederiksen RD (2009) Passenger delay models for rail networks. In: Wilson NHM, Nuzzolo A (eds) Schedule-based modeling of transportation networks. Springer, New YorkGoogle Scholar
  28. OECD (2003) OECD environmental indicators. Development, measurement and use. OECD Environmental Directorate, ParisGoogle Scholar
  29. Rahbee AB (2008) Farecard passenger flow model at Chicago transit authority, Illinois. Transp Res Board 2072:3–9CrossRefGoogle Scholar
  30. Ratti C, Frenchman D, Pulselli RM, Williams S (2006) Mobile landscapes: using location data from cell phones for urban analysis. Environ Plann B 33:727–748CrossRefGoogle Scholar
  31. Reades J, Calabrese F, Ratti C (2009) Eigenplaces: analysing cities using the space—time structure of the mobile phone network. Environ Plann B 36:824–836CrossRefGoogle Scholar
  32. Reddy A, Lu A, Kumar S, Bashmakov V, Rudenko S (2009) Entry-only automated fare-collection system data used to infer ridership, rider destinations, unlinked trips, and passenger miles. Transp Res Board 2110:128–136CrossRefGoogle Scholar
  33. Salicrú M, Fleurent C, Armengol JM (2011) Timetable-based operation in urban transport: Run-time optimisation and improvements in the operating process. Transp Res A 45:721–740Google Scholar
  34. Schmöcker JD, Shimamoto H, Kurauchi F (2013) Generation and calibration of transit hyperpaths. Transp Res C 36:406–418CrossRefGoogle Scholar
  35. Seaborn C, Attanucci J, Wilson NHM (2009) Analyzing multimodal public transport journeys in London with smart card fare payment data. Transp Res Board 2121:55–62CrossRefGoogle Scholar
  36. Sevtsuk A, Ratti C (2010) Does urban mobility have a daily routine? Learning from the aggregate data of mobile networks. J Urban Technol 17:41–60CrossRefGoogle Scholar
  37. Shibayama T, Lemmerer H (2013). The role of ICT in travel data collection. Deliverable D4.2 of COMPASS. 7th Framework Program, European UnionGoogle Scholar
  38. Sohn K, Kim D (2008) Dynamic origin–destination flow estimation using cellular communication system. IEEE Trans Veh Technol 57:2703–2713CrossRefGoogle Scholar
  39. Spiess H, Florian M (1989) Optimal strategies: a new assignment model for transit networks. Transp Res B 23:83–102CrossRefGoogle Scholar
  40. Strathman JG, Kimpel TJ, Kenneth J, Gerhart RL, Callas S (2002) Evaluation of transit operations: data applications of Tri-Met’s automated bus dispatching system. Transportation 29:321–345CrossRefGoogle Scholar
  41. Strathman JG, Kimpel TJ, Callas S (2003) Headway deviation effects on bus passenger loads : analysis of Tri-Met’s archived AVL-APC data. Report PR126. Portland State University Centre for Urban Studies, OregonGoogle Scholar
  42. Sun Y, Xu R (2012) Rail transit travel time reliability and estimation of passenger route choice behavior. Transp Res Board 2275:58–67CrossRefGoogle Scholar
  43. TOOLQIT (2007) Project website. 6th Framework Program, European UnionGoogle Scholar
  44. TRANSFORUM (2006) Project website. 6th Framework Program, European UnionGoogle Scholar
  45. Utsunomiya M, Attanucci J, Wilson N (2006) Marketing and fare policy potential uses of transit smart card registration and transaction data to improve transit planning. Transp Res Rec 1971:119–126CrossRefGoogle Scholar
  46. VDV (2008) Integration interface for automatic vehicle management systems – VDV 453, Version 2.4, Schrift des Verbands Deutscher Verkehrsunternehmen. https://www.vdv.de/service/downloads.aspx?id=100844&forced=true, Accessed 20 Oct 2015
  47. Wang W, Attanucci JP, Wilson NHM (2011) Bus passenger origin-destination estimation and related analyses using automated data collection systems. J Public Transp 14:131–150CrossRefGoogle Scholar
  48. Wilson NHM, Zhao J, Rahbee A (2009) The potential impact of automated data collection systems on urban public transport planning. In: Schedule-based modeling of transportation networks, (eds) Wilson A. Nuzzolo, Springer, New York, USAGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Klaus Noekel
    • 1
    Email author
  • Guido Gentile
    • 2
  • Efthia Nathanail
    • 3
  • Achille Fonzone
    • 4
  1. 1.PTV AGKarlsruheGermany
  2. 2.DICEA—Dipartimento di Ingegneria Civile Edile e AmbientaleSapienza University of RomeRomeItaly
  3. 3.University of Thessaly, Pedion AreosVolosGreece
  4. 4.Transportation Research Institute, Edinburgh Napier UniversityEdinburghUK

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