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2022 | OriginalPaper | Chapter

6. Dynamic Traffic Management: A Bird’s Eye View

Authors : Jaume Barceló, Margarita Martínez-Díaz

Published in: The Evolution of Travel Time Information Systems

Publisher: Springer International Publishing

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Abstract

Traffic systems evolved rapidly, becoming soon a specific case of a complex dynamic system, what raised the need for controlling them in order to achieve an efficient performance. One of the main factors of complexity of traffic systems is a consequence of the variable human traveling behavior in time and space. Therefore, traffic control, in the way it had been conceived and implemented, appeared as a restrictive approach just considering one of the control aspects: the time the vehicles are flowing through the network. This raised the need to move a step forward. Thus, traffic management could be seen as an extension of traffic control that simultaneously controls time and space, and is aimed at adjusting the demand and the capacity to avoid mismatching. This chapter summarily reviews the main concepts and approaches in the development of traffic management systems (TMSs) both in terms of managing the supply as well as managing (or influencing) the demand. In this context, travel times become one of the key factors to induce changes in drivers’ behavior in terms of making decisions on departure times and route choices. To better achieve such objectives, it would be desirable that TMS have predictive capabilities. The main approaches addressed here support the predictive capabilities of dynamic traffic models, one of whose main components is an estimation of the dynamic mobility patterns in terms of origin to destination (OD) matrices. This chapter summarizes the architecture of such approaches.

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previous chapter Travel Time Information Systems in the Era of Cooperative Automated Vehicles

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Aboudolas K, Geroliminis N (2013) Perimeter and boundary flow control in multi-reservoir heterogeneous networks. Trans Res Part B Methodol 55:265–281. https://doi.org/10.1016/j.trb.2013.07.003CrossRef

Aimsun SLU (2020) Aimsun live overview

Alexiadis V (2008a) Integrated corridor management analysis, modeling, and simulation experimental plan for the test corridor. USDOT Integrated Corridor Management (ICM) Initiative, FHWA-JPO-0-035, EDL 14415

Alexiadis V (2008b) Integrated corridor management analysis, modeling, and simulation results for the test corridor. Technical Report, Federal Highway Administration

Allström A, Barceló J, Ekström J, Grumert E, Gundlegård D, Rydergren C (2017) Traffic management for smart cities. In: Angelakis V, Tragos E, Pöhls HC, Kapovits A, Bassi A (eds) Designing, developing and facilitating smart cities. Springer, Switzerland. ISBN 978-3-319-44922-7

Ampountolas K, Kouvelas A (2015). Real-time estimation of critical values of the macroscopic fundamental diagram for maximum network throughput. In: Transportation research board 94th annual meeting, January 11–15, 2015, Washington, D.C.

AMS Framework for DMA and ATDM Programs. Draft report Version 1.4, May 2013, USDOT

Antoniou C, Dimitriou L, Pereira F (2019) Mobility patterns, big data and transportation analytics. Elsevier, Amsterdam, The Netherlands

Barceló J, García D, Kirschfink H (2002) Scenario analysis a simulation based tool for regional strategic traffic management. In: 9th world conference on intelligent transport systems, 2002, Chicago. Paper 2140

Barceló J, Codina E, Casas J, Ferrer JL, García D (2004) Microscopic traffic simulation: a tool for the design, analysis and evaluation of intelligent transport systems. J Intell Rob Syst 41:173–203CrossRef

Barceló J, Kirschfink H, Torday A (2005) An integrated software platform to assist advanced traffic management decisions. In: Modelling and simulation workshop, Sedona

Barceló J, Delgado M, Funes G, García D, Torday A (2007a) An on-line approach based on microscopic traffic simulation to assist real time traffic management. In: 14th world congress on intelligent transport systems, Beijing

Barceló J, Casas J, García D, Perarnau J (2007b) A methodological approach combining macro, meso and micro simulation models for transportation analysis. In: 11th world conference on transportation research, Berkeley

Barceló J, Delgado M, Funes G, García D, Torday A (2007c) On-line microscopic traffic simulation to support real time traffic management strategies. In: 6th European congress on intelligent transport systems and services, Aalborg

Barceló J (ed) (2010) Fundamentals of traffic simulation. Springer, Switzerland

Barceló J, Gilliéron F, Linares MP, Serch O, Montero L (2012) Exploring link covering and node covering formulations of detection layout problem. Transp Res Rec J Transp Res Board 2308:17–26CrossRef

Barceló J (2019) Future trends in sustainable transportation. In: Faulin J, Grasman S, Juan A, Hirsch P (eds) Sustainable transportation and smart logistics. Decision making models and solutions. Elsevier, Amsterdam, The Netherlands. ISBN 978-0-12-814242-4

Bellei G, Gentile G, Papola N (2005) A within-day dynamic traffic assignment model for urban road networks. Transp Res Part B Methodol 39:1–29CrossRef

Ben-Akiva M, Bierlaire M, Bottom J, Koutsopoulos HN, Mishalani RG (1997) Development of a route guidance generation system for real-time application. In: Proceedings of the 8th IFAC symposium on transportation systems, 16–18 June 1997, Chania, Greece

Ben-Akiva M, Bierlaire M, Koutsopoulos HN, Mishalani R (2002) Real-time simulation of traffic demand-supply interactions within DynaMIT. In: Gendreau M, Marcotte P (eds) Transportation and network analysis: current trends. Miscellenea in honor of Michael Florian. Kluwer Academic Publishers, Boston/Dordrecht/London, pp 19–36

Ben-Akiva M, Koutsopoulos HN, Antoniou C, Balakrishna R (2010) Traffic simulation with DynaMIT. In: Barceló J (ed) Fundamentals of traffic simulation. Springer, Switzerland. ISBN 978-1-4419-6142-6

Boero M, Kirschfink H, Barceló J, Parodi A (1997) Real time traffic management supporting intermobility and strategic control. In: 5th European congress on fuzzy and intelligent technologies, EUFIT’97, 1997, Aachen, Germany

Boero M (1999) Case studies of systems: the KITS model. In: ERUDIT tutorial on intelligent traffic management models, Helsinki

Boero M, Kirschfink H (1999) Case studies of systems: the ENTERPRICE model. In: ERUDIT tutorial on intelligent traffic management models, Helsinki

Carlson RC, Papamichail I, Papageorgiou M, Messmer A (2010) Optimal motorway traffic flow control involving variable speed limits and ramp metering. Transp Sci 44(2):238–253CrossRef

Castillo E, Conejo AJ, Menéndez JM, Jiménez P (2008) The observability problem in traffic network models. Comput Aided Civil Infrastruct Eng 23:208–222CrossRef

Castillo E, Grande Z, Calviño A, Szeto WY, Lo HK (2015) A state-of-the-art review of the sensor location, flow observability, estimation, and prediction problems in traffic networks. J Sens ID: 903563. https://doi.org/10.1155/2015/903563

Cronin B, Mortensen S, Sheehan R, Thompson D (2010) Integrated corridor management. Public Roads 74(3):6–11

Daganzo CF (2007) Urban gridlock: macroscopic modeling and mitigation approaches. Transp Res Part B Methodol 41(1):49–62. https://doi.org/10.1016/j.trb.2006.03.001CrossRef

Daganzo CF, Gayah VV, Gonzales EJ (2012) The potential of parsimonious models for understanding large scale transportation systems and answering big picture questions. J Transp Logist 1(1–2):49–65

Fang S, Liao H, Fei Y, Chen K, Huang J, Lu Y, Tsao Y (2016) Transportation modes classification using sensors on smartphones. Sensors, 16:1324. https://doi.org/10.3390/s16081324

FDOT (2020) Data and modeling support of off-line and real-time decisions associated with integrated corridor management FDOT project BDV29-977-38. Lehman Center for Transportation Research

FHWA (2005) Integrated corridor management system (ICMS) Work Plan

Gartner NH (1985) Demand-responsive traffic signal control research. Transp Res Part a: Policy Pract 19A(516):369–373CrossRef

Gentile G, Meschini L, Papola N (2007) Spillback congestion in dynamic traffic assignment: a macroscopic flow model with time-varying bottlenecks. Transp Res Part b: Methodol 41:1114–1138CrossRef

Gentile G (2010) The general link transmission model for dynamic network loading and a comparison with the DUE algorithm. In: Immers LGH, Tampere CMJ, Viti F (eds) New developments in transport planning: advances in dynamic traffic assignment. Transport economics, management and policy series. Edward Elgar Publishing, MA, USA

Gentile G, Lunardon D, Arenella A, Doninelli T (2010) A new methodology for automatic building of dynamic models for simulation of road transport networks. In: Proceedings of the SIDT scientific seminar 2010 External costs of transport systems: theory and applications, Rome, Italy

Geroliminis N, Daganzo CF (2007) Macroscopic modeling of traffic in cities. In: 86th annual meeting transportation research board, Washington D.C.

Geroliminis N, Daganzo CF (2008) Existence of urban-scale macroscopic fundamental diagrams: some experimental findings. Transp Res Part B: Methodol 42(9):759–770. https://doi.org/10.1016/j.trb.2008.02.002

Geroliminis N, Sun J (2011) Properties of a well-defined macroscopic fundamental diagram for urban traffic. Transp Res Part B: Methodol 45:605–617

Geroliminis N, Haddad J, Ramezani M (2013) Optimal perimeter control for two urban regions with macroscopic fundamental diagrams: a model predictive approach. IEEE Trans Intell Transp Syst 14(1):348–359. https://doi.org/10.1109/TITS.2012.2216877CrossRef

Hegyi A, Bellemans T, De Schutter B (2009) Freeway traffic management and control. In: Meyers RA (ed) Encyclopedia of complexity and systems science. Springer, New York. ISBN 978-0-38730440-3

Hernández J, Cuena J, Molina M (1999) Real-time traffic management through knowledge-based models: the TRYS approach. In: ERUDIT tutorial on intelligent traffic management models (www.erudit.de), Helsinki, Finland

Hernández J (1999) An intelligent model for real-time private traffic management in urban networks: the FLUIDS/CRITIC approach. In: ERUDIT tutorial on intelligent traffic management models (www.erudit.de), Helsinki, Finland

Ibarra-Espinosa S, Ynoue R, Giannotti M, Ropkins K, Dias de Freitas E (2019) Generating traffic flow and speed regional model data using internet GPS vehicle records. MethodsX 6:2065–2075. https://doi.org/10.1016/j.mex.2019.08.018CrossRef

Ji Y, Geroliminis N (2011) Exploring spatial characteristics of urban transportation networks. In: 14th international IEEE conference on intelligent transportation systems. https://doi.org/10.1109/ITSC.2011.6083062

Ji Y, Geroliminis N (2012) On the spatial partitioning of urban transportation networks. Transp Res Part B: Methodol 46(10):1639–1656. https://doi.org/10.1016/j.trb.2012.08.005CrossRef

Keyvan-Ekbatani M, Papageorgiou M, Knoop VL (2015) Comparison of on-line time-delayed and non-time-delayed urban traffic control via remote gating. In: 94th transportation research board annual meeting, Paper 15-4289, Washington DC

Keyvan-Ekbatani M, Carlson RC, Knoop VL, Hoogendoorn SP, Papageorgiou M (2016) Queuing under perimeter control: analysis and control strategy. In: 2016 IEEE 19th international conference on intelligent transportation systems (ITSC), pp 1502–1507. https://doi.org/10.1109/ITSC.2016.7795756

Keyvan-Ekbatani M, Carlson RC, Knoop VL, Papageorgiou M (2017) Balancing delays and relative queues at the urban network periphery under perimeter control. In: Transportation research board 96th annual meeting, Paper 17-05029, Washington DC

Kirschfink H, Boero M, Barceló J (1997) Real-time traffic management supporting intermobility and strategic control. In: Proceedings of the 4th ITS world conference, Berlin, Germany

Kirschfink H, Hernández J, Boero M (2000) Intelligent traffic management models. In: Proceedings of European symposium on intelligent techniques, ESIT'2000, Aachen, Germany

Kirschfink H, Riegelhuth G, Barceló J (2003) Scenario analysis to support strategic traffic management in the region Frankfurt Rhein-Main. In: 10th world conference on intelligent transport systems, 2003, Madrid

Kotsialos A, Papageorgiou M, Diakaki C, Pavlis Y, Middelham F (2002) Traffic flow modeling of large-scale motorway networks using the macroscopic modeling tool METANET. IEEE Trans Intell Transp Syst 3:282–292CrossRef

Krishnan R, Hodge V, Austin J, Polak J, Jackson T, Smith M, Lee T-C (2010) Intelligent decision support for traffic management. In: Proceedings of 17th ITS world congress, 25–29 October 2011, Busan, South Korea

Leclercq L, Chiabaut N, Trinquier B (2014) Macroscopic fundamental diagrams: a cross-comparison of estimation methods. Transp Res Part b: Methodol 62:1–12. https://doi.org/10.1016/j.trb.2014.01.007CrossRef

Mahmassani HS, Fei X, Eisenman S, Zhou X, Qin X (2005) DYNASMART-X evaluation for real-time TMC application: chart test bed. Final Report. Maryland Transportation Initiative, University of Maryland College Park, Maryland, US

Mahmassani HS, Saberi M, Ali Zockaie K (2013) Urban network gridlock: theory, characteristics, and dynamics. In: 20th international symposium on transportation and traffic theory. Procedia Soc Behav Sci 80:79–98

Mahmassani HS, Hong Z, Xu X, Mittal A, Yelchuru B, Kamalanathsharma R (2017) Analysis, modeling, and simulation (AMS) testbed development and evaluation to support dynamic mobility applications (DMA) and active transportation and demand management (ATDM) programs evaluation report for the Chicago testbed. FHWA Final ReportApril 2017, FHWA-JPO-16-387

Martínez-Díaz M (2018) Highway travel time information systems: from traditional to cooperative driving environments. PhD Dissertation, Universidade da Coruña, Spain

Martínez-Díaz M, Soriguera F, Pérez I (2019) Autonomous driving: a bird’s eye view. IET Intel Transport Syst 13(4):563–579. https://doi.org/10.1049/iet-its.2018.5061CrossRef

Meschini L, Gentile G (2010) Real-time traffic monitoring and forecast through OPTIMA–optimal path travel information for mobility actions. In: Proceedings of models and technologies for intelligent transportation systems, 2010, Rome, Italy, pp 113–121

Meschini L (2017) Modern traffic control centres and traffic management systems. In: Fusco G (ed) Intelligent transport systems (ITS): past, present and future directions. NOVA Science Publishers, New York, US. ISBN 978-1-53611-815-5

Miller M, Novick L, Li Y, Skabardonis A (2008) San Diego I-15 integrated corridor management (ICM) system: phase I. California PATH Research Report, UCB-ITS-PRR-2008-33

Montero L, Pacheco M, Barceló J, Homoceanu S, Casanovas J (2016) A case study on cooperative car data for traffic state estimation in an urban network. TRR Transp Res Rec J Transp Res Board 2594:127–137CrossRef

Mounce R, Hollier G, Smith M, Hodge VJ, Jackson T, Austin J (2012) A metric for pattern-matching applications to traffic management. Transp Res Part C. https://doi.org/10.1016/j.trc.2012.04.019CrossRef

Papageorgiou M (1983) Application of automatic control concepts to traffic flow modeling and control. Springer-Verlag, New York, USACrossRef

Papageorgiou M (1995) An integrated control approach for traffic corridors. Transp Res Part c: Emerg Technol 3(1):19–30CrossRef

Papageorgiou M, Papamichail I, Messmer A, Wang Y (2010) Traffic simulation with METANET. In: Barceló J (ed) Fundamentals of traffic simulation. Springer, New York. ISBN 978-1-4419-6142-6

Rass S, Kyamakaya K (2007) Artificial intelligence techniques in traffic control. Ögai J 25: 5–11

Reiss R, Gordon R, Neudorff L, Harding J (2006) Integrated corridor management phase I concept development and foundational research: task 3.1 develop alternative definitions. Report No. FHWA-JOP-06-034. United States Department of Transportation ITS Joint Program Office, Federal Highway Administration, Federal Transit Administration

SANDAG (2009) https://www.sandag.org/uploads/publicationid/publicationid_4473_23298.pdf. Accessed 10 May 2021

Soriguera F, Martínez-Díaz M (2020) Freeway travel time information from input-output vehicle counts: a drift correction method based on AVI data. IEEE Trans Intell Transp Syst (In Press). https://doi.org/10.1109/TITS.2020.2992300

Sprague RH, Watson HJ (1986) Decision support systems: putting theory into practice. Prentice- Hall, New Jersey, US

Talebpour A, Mahmassani HS, Bustamante FE (2016) Modeling driver behavior in a connected environment: integrated microscopic simulation of traffic and mobile wireless telecommunication systems. Transp Res Rec J Transp Res Board 2560:75–86CrossRef

The AURA Web Pages, AURA, Advanced Computer Architectures Group. http://www.cs.york.ac.uk/arch/neural-networks/technologies/aura. Accessed 10 May 2021

TRS 1210 (2012) Transportation research synthesis. Minnesota Department of Transportation Office of Policy Analysis, Research & lnnovation Research Services Section

Wang Y, Papageorgiou M (2005) Real-time freeway traffic state estimation based on extended Kalman filter: a general approach. Transp Res Part b: Methodol 39:141–167CrossRef

Wang Y, Papageorgiou M, Messmer A (2006) RENAISSANCE—a unified macroscopic model-based approach to real-time freeway network traffic surveillance. Transp Res Part c: Methodol 14:190–212CrossRef

Wang Y, Papageorgiou M, Messmer A (2008) Real-time freeway traffic state estimation based on extended Kalman filter: adaptive capabilities and real data testing. Transportation Research Part a: Policy and Practice 42(10):1340–1358

Xianfeng TY (2018) Vehicle sensor data (VSD)-based traffic control in connected automated vehicle (CAV) environment. Final Report NITC-SS-1175, University of Utah

Zhou X, Mahmassani HS, Zhang K (2008) Dynamic micro-assignment modeling approach for integrated multimodal urban corridor management. Transp Res Part C: Methodol 16(2):167–186

Title: Dynamic Traffic Management: A Bird’s Eye View
Authors: Jaume Barceló
Margarita Martínez-Díaz
Publisher: Springer International Publishing
Book: The Evolution of Travel Time Information Systems
Print ISBN: 978-3-030-89671-3

Electronic ISBN: 978-3-030-89672-0

Copyright Year: 2022
DOI: https://doi.org/10.1007/978-3-030-89672-0_6

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