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Modelling the public transport capacity constraints’ impact on passenger path choices in transit assignment models

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The objective of this paper is to discuss the replication of passenger congestion (overcrowding) effects on output path choices in public transport assignment models. Based on a comprehensive literature review, the impact of passenger overcrowding effects was summarised in 3 main categories: the inclusion of physical capacity constraints (limits); the feedback effect between transport demand and supply performance; and the feedback effect on travel cost (discomfort penalty). Further on, sample case studies are presented, which prove that the inclusion of capacity constraints might significantly influence the assignment output and overall results in public transport projects’ assessment – yet most state-of-the-practice assignment models would either miss or neglect these overcrowding-induced phenomena. In a classical 4-step demand model, their impact on passengers’ travelling strategies is often limited to path (route) choice stage, while in reality they also have far-reaching implications for modal choices, temporal choices and long-term demand adaptation processes. This notion has been investigated in numerous research works, leading to different assignment approaches to account for impact of public transport capacity constraints – a simplified, implicit approach (implemented in macroscopic-based models, e.g. PTV VISUM), and a more complex, explicit approach (incorporated in mesoscopic-based models, e.g. BusMezzo). In the simulation part of this paper, sample tests performed on a small-scale network aim to provide a general comparison between these two approaches and arising differences in the assignment output. The implicit approach reveals some differences in assignment output once network capacity constraints are accounted for – though in a simplified manner, and producing somewhat ambiguous output (e.g. in higher congestion scenarios). The explicit approach provides a more accurate representation of overcrowding-induced phenomena - especially the evolving demand-supply interactions in the event of arising congestion in the public transport network. Further studies should involve tests on a city-scale, multimodal transport model, as well as empirical model validation, in order to fully assess the effectiveness of these distinct assignment approaches.
Rocznik
Strony
7--28
Opis fizyczny
Bibliogr. 35 poz., rys., tab., wykr.
Twórcy
autor
  • Cracow University of Technology, Faculty of Civil Engineering, Department of Transportation Systems, Cracow, Poland
autor
  • Cracow University of Technology, Faculty of Civil Engineering, Department of Transportation Systems, Cracow, Poland
autor
  • Cracow University of Technology, Faculty of Civil Engineering, Department of Transportation Systems, Cracow, Poland
Bibliografia
  • [1] ATTANUCCI, J. , 2010. Public Transportation Systems, Lecture 14. Lecture presented at the Massachusetts Institute of Technology, Cambridge, MA. Available from: https://ocw.mit.edu/courses/civil-and-environmental-engineering/1-258j-public-transportation-systems-spring-2010/lecture-notes/MIT1_258JS10_lec14.pdf [Accessed 21st May 2017]
  • [2] BARRY, J., 2015. London’s Bus Service – monitoring satisfaction. In Smart Public Transport Conference, Warsaw, Poland.
  • [3] BATARCE, M., MUÑOZ, J. C., ORTUZAR, J. D., RAVEAU, S., MOJICA, C., & RÍOS, R. A., 2015. Valuing crowding in public transport systems using mixed stated/revealed preferences data: the case of Santiago. In TRB 94th Annual Meeting Compendium of Papers, Washington DC.
  • [4] BĄK, R., 2010. Simulation model of the bus stop. Archives of Transport, 22(1), 5-25.
  • [5] BRANSTON, D., 1976. Link capacity functions: A review. Transportation Research, 10(4), 223-236.
  • [6] CASCETTA, E., 2013. Transportation systems engineering: theory and methods (Vol. 49). Springer Science & Business Media
  • [7] CATS, O., 2011. Dynamic Modelling of Transit Operations and Passenger Decisions. Doctoral thesis in Transport Science with specialisation in Transport Systems. KTH – Royal Institute of Technology, Stockholm, Sweden.
  • [8] CATS, O., WEST, J., & ELIASSON, J., 2014. Appraisal of increased public transport capacity. In hEART Conference 2014 in Leeds, UK.
  • [9] DRABICKI, A., 2015. Incorporation of public transport overcrowding effects on passenger path choices in transit assignment. Master Thesis. Cracow University of Technology, Krakow, Poland.
  • [10] DRABICKI, A., KUCHARSKI, R., & SZARATA, A., 2016. Zastosowanie ograniczeń przepustowości sieci transportu publicznego w makroskopowym rozkładzie ruchu. (ENG.: Incorporation of public transport network capacity constraints in macroscopic trip assignment model) Transport Miejski i Regionalny, 08/2016., Krakow, Poland.
  • [11] FONZONE, A., & SCHMÖCKER, J. D., 2014. Effects of transit real-time information usage strategies. Transportation Research Record: Journal of the Transportation Research Board, (2417), 121-129
  • [12] FONZONE, A., SCHMÖCKER, J. D., & LIU, R., 2015. A model of bus bunching under reliability-based passenger arrival patterns. Transportation Research Part C: Emerging Technologies, 59, 164-182.
  • [13] GENTILE, G., & NOEKEL, K. (eds.), 2016. Modelling public transport passenger flows in the era of intelligent transport systems. Gewerbestrasse: Springer International Publishing.
  • [14] HARTL, M., 2013. Route choice in macroscopic and microscopic assignment models for public transport. Master thesis. Universitaet Stuttgart, Germany.
  • [15] HORBACHOV, P., NAUMOV, V., KOLII, O., 2015. Estimation of the bus delay at the stopping point on the base of traffic parameters. Archives of Transport, 35(3), 15-25.
  • [16] KIM, J. K., LEE, B., & OH, S., 2009. Passenger choice models for analysis of impacts of real-time bus information on crowdedness. Transportation Research Record: Journal of the Transportation Research Board, (2112), 119-126.
  • [17] LEURENT, F., 2009. On seat congestion, passenger comfort and route choice in urban transit: a network equilibrium assignment model with application to Paris. In Annual Meeting of the Transportation Research Board Session Transit Capacity and Quality of Service (pp. TRB-09). TRB.
  • [18] LIEBERHERR, J., & PRITSCHER, E., 2012. Capacity-restraint railway transport assignment at SBB-Passenger. In Proceedings of the 12th Swiss Transport Research Conference.
  • [19] LONDON ASSEMBLY TRANSPORT COMMITTEE REPORT, 2009. The Big Squeeze. Rail overcrowding in London. Report commissioned by the Greater London Authority, UK
  • [20] LONDON ASSEMBLY TRANSPORT COMMITTEE REPORT, 2009. Too close for comfort. Passengers’ experiences of the London Underground. Report commissioned by the Greater London Authority, UK.
  • [21] KROES, E., KOUWENHOVEN, M., DEBRINCAT, L., & PAUGET, N., 2013. On the Value of Crowding in Public Tansport for Ile-de-France.
  • [22] MOREIRA-MATIAS, L., FERREIRA, C., GAMA, J., MENDES-MOREIRA, J., & DE SOUSA, J. F., 2012, July. Bus bunching detection by mining sequences of headway deviations. In Industrial Conference on Data Mining (pp. 77-91). Springer Berlin Heidelberg.
  • [23] NEWELL, G. F., & POTTS, R. B., 1964. Maintaining a bus schedule. In Australian Road Research Board (ARRB) Conference, 2nd, 1964, Melbourne (Vol. 2, No. 1).
  • [24] NUZZOLO, A., CRISALLI, U., & ROSATI, L., 2012. A schedule-based assignment model with explicit capacity constraints for congested transit networks. Transportation Research Part C: Emerging Technologies, 20(1), 16-33.
  • [25] PTV AG, 2015. VISUM 15 User Manual. Karlsruhe, Germany.
  • [26] RUDNICKI, A., 1999. Jakość komunikacji miejskiej. (ENG.: Quality of urban public transport.) In Zeszyty Naukowo-Techniczne Oddziału Stowarzyszenia Inżynierów i Techników Komunikacji w Krakowie, (71). Krakow, Poland.
  • [27] SMALL, K. A., & GOMEZ-IBANEZ, J. A., 1999. Urban transportation. Handbook of regional and urban economics, 3, 1937-1999.
  • [28] SPIESS, H., & FLORIAN, M., 1989. Optimal strategies: a new assignment model for transit networks. Transportation Research Part B: Methodological, 23(2), 83-102.
  • [29] SZARATA, A., 2013. Modelowanie podróży wzbudzonych oraz tłumionych zmianą stanu infrastruktury transportowej. (ENG.: Modelling of induced and suppressed trips resulting from changes in transport infrastructure) Cracow University of Technology, Krakow, Poland.
  • [30] SZARATA, A., 2014. The multimodal approach to the modelling of modal split. Archives of Transport, 29(1), 55-63.
  • [31] TIRACHINI, A., HENSHER, D. A., & ROSE, J. M., 2013. Crowding in public transport systems: effects on users, operation and implications for the estimation of demand. Transportation research part A: policy and practice, 53, 36-52.
  • [32] TRANSPORT FOR LONDON, 2013. Capacity for growth at Camden stations. Presented in the London Borough of Camden, 18th September 2013, Greater London, UK.
  • [33] VAN OORT, N., DROST, M., BRANDS, T., & YAP, M., 2015. Data-driven public transport ridership prediction approach including comfort aspects. In 13th CASPT Conference, Rotterdam, The Netherlands.
  • [34] WHELAN, G. A., & CROCKETT, J., 2009, March.. An investigation of the willingness to pay to reduce rail overcrowding. In International Choice Modelling Conference 2009.
  • [35] ŻOCHOWSKA, R., 2014. Selected issues in modelling of transport flows in congested urban networks. Archives of Transport, 29(1), 77-89.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-eb873de2-ab49-4d42-ad94-6776a22f4c64
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