Modelling Congestion in Passenger Transit Networks Ektoras Chandakas
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Modelling congestion in passenger transit networks Ektoras Chandakas To cite this version: Ektoras Chandakas. Modelling congestion in passenger transit networks. Architecture, space man- agement. Université Paris-Est, 2014. English. NNT : 2014PEST1011. tel-01148406 HAL Id: tel-01148406 https://tel.archives-ouvertes.fr/tel-01148406 Submitted on 4 May 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ECOLE DOCTORALE VILLE, TRANSPORTS ET TERRITOIRES LABORATOIRE VILLE, MOBILITE, TRANSPORTS THESE DE DOCTORAT SPECIALITE TRANSPORT MODELLING CONGESTION IN PASSENGER TRANSIT NETWORKS Présentée et soutenue publiquement par Ektoras CHANDAKAS Le 01/04/2014 Composition du Jury: M. Eric KROESS 3URIHVVHXUDVVRFLpjO¶8QLYHUVLWp98 président M. Achille FONZONE Chercheur jO¶8QLYHUVLWp1DSLHUG¶ Edinbourgh, rapporteur M. Guido GENTILE Professeur à l ¶8QLYHUVLWpGH5RPH La Sapienza, rapporteur M. Michael FLORIAN Professeur émérite jO¶8QLYHUVLWpGH0RQWUpDO examinateur M. Daniel GRAHAM 3URIHVVHXUjO¶,PSHULDO&ROOHJH Londres, examinateur M. Jean-Patrick LEBACQUE &KHUFKHXUjO¶,)677$5 examinateur M. Fabien LEURENT Profe VVHXUjO¶(FROHGHV3RQWV3DULV7HFK directeur de thèse 5 ȈIJȘȞʌȡȠıȦʌȚțȒȝĮȢǿșȐțȘ 7 Short Abstract A structural model is provided to capture capacity phenomena in passenger traffic assignment to a transit network. That has been founded on a bi-layer representation of the transit network: on the lower layer the model addresses each network sub-system (line, station and access- egress) separately, on the basis of specific capacity effects; on the upper layer a leg-based representation is used with respect to the sub- V\VWHPV¶FRVWVDQGRSHUDWLQJFKDUDFWHULVWLFVWR DGGUHVVWKHWULSPDNHU¶VSDWKFKRLFHV The network model, named CapTA (for Capacitated Transit Assignment), involves a line sub- model which amounts to a sophisticated cost-flow relationship. The capacity constraints pertain to the interaction of passenger and vehicle traffic: vehicle seat capacity drives the in- vehicle comfort; vehicle total capacity determines the in-vehicle comfort and also the platform waiting; passenger flows at vehicle alighting and boarding have an impact on the dwell time, which in turn drives track occupancy and service frequency of any service using the station track infrastructure; the service frequency influences the service capacity and platform waiting. These phenomena are dealt with by line of operations on the basis of a set of local models yielding specific flows and costs. Equivalently, a station sub-model addresses specific capacity constraints and yields the local walking conditions, sensible to the interaction of the passengers in the interior of a station: the instant bottleneck created at the entry of the circulation elements delays the evacuation of the station platforms; the passenger density and presence of heterogeneous passenger flows slows down the passengers who circulate in the station; and the presence of real-time information influences the decision making process of the transit users exposed to. These effects do not only impact locally the in-station path choice, but most notably they modify the choices of transit routes and itineraries on a network level. Short Abstract The Paris Metropolitan Region provides an ideal application field of the capacity constrained transit assignment model. It is mainly used as a showcase of the simulation capabilities and of the finesse of the modelling approach. The transit network involves 1 500 bus routes together with 260 trains routes that include 14 metro lines and 4 light rail lines. Traffic assignment at the morning peak hour is characterized by heavy passenger loads along the central parts of the railway lines. Increased train dwelling, due to boarding and alighting flows, and reduction in the service frequency impact the route and the line capacity. The generalized time of a transit trip is impacted mainly though its in-vehicle comfort component. Detailed results have been provided for the RER A, the busiest commuter rail line in the transit network. 8 Ektoras Chandakas 9 Résumé Court Un modèle structurel HVW IRXUQL DILQ G¶DSSUpKHQGHU OHV SKpQRPqQHV GH FDSDFLWp GDQV XQ PRGqOH G¶DIIHF tation de flux de voyageurs sur un réseau de transport collectifs. Cela a été fondé sur une représentation du réseau de transports collectifs en deux couches : sur la couche inférieure, le modèle traite séparément chaque sous système du réseau (ligne, station et rabattement) en fonction des effets de capacité spécifiques ; sur la couche supérieure, le choix G¶LWLQpUDLUHG¶XQYR\DJHXULQGLYLGXHOHVWDGUHVVpHSDU une représentation du réseau en leg (ou segment de ligne) en utilisant le coût et les caractéristiques opérationnelles des sous-systèmes respectifs. Le modèle de réseau, nommé CapTA (pour Capacitated Transit Assignment ± Affectation en Capacité des Transports Collectifs), inclut un sous-modèle de ligne qui revient à une fonction de coût sophistiquée. /HV FRQWUDLQWHV GH FDSDFLWp FRUUHVSRQGHQW j O¶LQWHUDFWLRQ HQWUH voyageurs et trafic de véhicule OD FDSDFLWp HQ SODFHV DVVLVHV G¶XQ YpKLFXOH LQIOXHQFH OH confort à bord ; the flux de passagers en montée et descente ont une incidence au temps de stationnemHQWTXLGHVRQF{WpDJLWVXUO¶RFFXSDWLRQGHODYRLHHWODIUpTXHQFHGHVHUYLFHGH WRXWHVOHVPLVVLRQVTXLRFFXSHVO¶LQIUDVWUXFWXUHjTXDL ; la fréquence de service influence la FDSDFLWp GH VHUYLFHV HW O¶DWWHQWH HQ SODWHIRUPH &HV SKpQRPqQHV VRQW WUDLW és par ligne G¶H[SORLWDWLRQVXUODEDVHG¶XQHQVHPEOHGHVPRGqOHVORFDX[TXLUHQGHQWGHIOX[HWGHFRW spécifiques. De manière équivalente, le modèle de station traite de contraintes de capacité spécifiques et évalue les conditions locales de marche, qui est sensible aux interactions des voyageurs à O¶LQWpULHXU GH OD VWDWLRQ OH JRXORW LQVWDQWDQp j O¶HQWUpH G¶XQ pOpPHQW GH FLUFXODWLRQ UHWDUG O¶pYDFXDWLRQGHODSODWHIRUPH ODGHQVLWpGHYR\DJHXUVHWO¶KpWpURJpQpLWpGHVOHXUIOX[UDOHQWL les voyageurs qui circulent dans une station OD SUpVHQFH GH O¶LQIRUPDWLRQ HQ WHPSV UpHO Résumé Court influence le processus de décision des voyageurs. Ces effets Q¶RQWSDVVHXOHPHQWXQLPSDFW sur OHFKRL[G¶LWLQpUDLUHjO¶LQWpULHXUHGHODVWDWLRQPDLVQRWDPPHQWLOVPRGLILHQWOHVFKRL[G e service sur le niveau du réseau. La Région Ile-de- )UDQFHIRXUQLWXQFKDPSG¶DSSOLFDWLRQLGpDOSRXUXQPRGqOHG¶DIIHFWDWLRQGH flux de voyageurs en transport collectifs sous contraintes de congestion. Plus précisément, il est utilisé dans le cadre du modèle CapTA pour illustrer les capacités de simulation et la ILQHVVH GH O¶DSSURFKH GH PRGpOLVDWLRQ DGRSWpH Le réseau de transports collectifs contient 1 500 missions de cars et autocars, tout comme 260 missions ferroviaires et inclut 14 lignes de métro et 4 OLJQHV GH WUDPZD\ /¶DIIHFWDWLRQ GH WUDILF j O¶KHXUH GH SRLQWH GX PDWLQ HVW FDUDFWpULVpH G¶XQH FKDUJH LPSRUWDQWH HQ YR\DJHXUV VXU OHV VHFWLRQV FHQWUDOHV GH OLJQHV ferroviaires qui traversent la ville. Un temps de stationnement élevé, en raison de flux de montée et descente, et la réduction de la fréquence de service impactent la capacité des PLVVLRQV HW GHV OLJQHV /H WHPSV JpQpUDOLVp G¶XQ WUDMHW HVW LPSDFWp QRWDPPHQW GH VD composante de confort à bord. De résultats détaillés sont présenté sur le RER A, la ligne la plus chargé du réseau ferroviaire régional. 10 Ektoras Chandakas 11 Acknowledgements Proposing a PhD research to a young engineer is a great honour. I would like to express my profound gratitude to Fabien Leurent, who directed this thesis, for the confidence he showed on me, his constant availability, his exemplary guidance and his encouragement throughout the course of this thesis. I would like to thank Achille Fonzone and Guido Gentile for the attention they brought to my work and for their valuable and constructive comments. My grateful thanks are also extended to Michael Florian, Daniel Graham, Jean-Patrick Lebacque, who kindly accepted to participate to my examination board, and to Eric Kroess who accepted to preside over it. It is impossible to imagine how I can thank my colleagues at the LVMT without at the same time repeating what so others before me have already said. This particular mixture of geographers, sociologists, and engineers, to name some, had been a constant source of inspiration. I owe a deeper debt to Alexis Poulhès. His determination and efficiency over the software implementation highly accelerated this research and had been a key for its success. I would like to express my gratitude for the fellow researchers of the COST Action TU1004. Our discussions during our meetings were valuable. They helped me formulate and hone many of my views present in this research. I owe a more intimate debt to my friends who showed an exquisite combination of patience and encouragement throughout these years. I would like to thank David, Valia, Theo, Dimitris, Nastassia, Angel, Javier and so many others. Finally, the gratitude for the support, patience and trust I owe to my family during this work, and not only, are beyond my limited capacity of expression. I trust they understand how much I owe them. I would like to tell them: « ȈĮȢ İȣȤĮȡȚıIJȫ ĮʌȠ țĮȡįȚȐȢ ». Acknowlogements 12 Ektoras Chandakas