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A stochastic modeling approach to dynamic prediction of section-wide inter-lane and intra-lane traffic variables using point detector data

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  • Sheu, Jiuh-Biing

Abstract

Real-time section-wide lane traffic variables such as density and lane-changing are vital to traffic control and management in urban areas. They can be used as decision variables to determine traffic control and management strategies in real time as well as characterize road traffic congestion for further use in advanced traveler information systems. Therefore, developing techniques which provide real-time information regarding section-wide inter-lane and intra-lane traffic variables is an increasingly important task in the area of advanced transportation management and information systems. This paper presents a stochastic system modeling approach to extracting real-time information of section-wide inter-lane as well as intra-lane traffic (e.g. lane-changing fractions, lane densities, etc.) utilizing lane traffic counts detected from point detectors. The proposed methodology consists of three principle elements: (1) specification of system states, (2) system modeling, and (3) recursive estimation. Preliminary test results indicated that the proposed methodology is promising for estimating real-time section-wide inter-lane as well as intra-lane traffic variables based merely on point detector data. The inter-lane and intra-lane traffic information generated by the proposed method can be further used in developing related technologies such as road traffic congestion detection, automatic incident detection, prediction of driver route choices, variable message signs and in-car navigation devices. ©

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  • Sheu, Jiuh-Biing, 1999. "A stochastic modeling approach to dynamic prediction of section-wide inter-lane and intra-lane traffic variables using point detector data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 33(2), pages 79-100, February.
    • Handle: RePEc:eee:transa:v:33:y:1999:i:2:p:79-100
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    References listed on IDEAS

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    1. Gipps, P.G., 1981. "A behavioural car-following model for computer simulation," Transportation Research Part B: Methodological, Elsevier, vol. 15(2), pages 105-111, April.
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    1. Sheu, Jiuh-Biing & Yang, Hai, 2008. "An integrated toll and ramp control methodology for dynamic freeway congestion management," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4327-4348.
    2. Coifman, Benjamin, 2006. "Extracting More Information from the Existing Freeway Traffic Monitoring Infrastructure," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt34n479gz, Institute of Transportation Studies, UC Berkeley.
    3. Coifman, Benjamin A. & Mallika, Ramachandran, 2007. "Distributed surveillance on freeways emphasizing incident detection and verification," Transportation Research Part A: Policy and Practice, Elsevier, vol. 41(8), pages 750-767, October.
    4. Sheu, Jiuh-Biing & Chou, Yi-Hwa & Shen, Liang-Jen, 2001. "A stochastic estimation approach to real-time prediction of incident effects on freeway traffic congestion," Transportation Research Part B: Methodological, Elsevier, vol. 35(6), pages 575-592, July.
    5. Bekiaris-Liberis, Nikolaos & Roncoli, Claudio & Papageorgiou, Markos, 2017. "Highway traffic state estimation per lane in the presence of connected vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 1-28.
    6. Sheu, Jiuh-Biing, 2005. "A multi-layer demand-responsive logistics control methodology for alleviating the bullwhip effect of supply chains," European Journal of Operational Research, Elsevier, vol. 161(3), pages 797-811, March.
    7. Coifman, Benjamin & Varaiya, Pravin, 2002. "Deployment and Evaluation of Real-Time Vehicle Reidentification from an Operations Perspective," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt6tp5w2gt, Institute of Transportation Studies, UC Berkeley.
    8. Coifman, Benjamin, 2003. "Estimating density and lane inflow on a freeway segment," Transportation Research Part A: Policy and Practice, Elsevier, vol. 37(8), pages 689-701, October.

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