IDEAS home Printed from https://ideas.repec.org/a/inm/ortrsc/v44y2010i2p151-168.html
   My bibliography  Save this article

Calibrating Steady-State Traffic Stream and Car-Following Models Using Loop Detector Data

Author

Listed:
  • Hesham Rakha

    (Charles E. Via Jr. Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, Virginia 24061)

  • Mazen Arafeh

    (Department of Industrial Engineering, Faculty of Engineering & Technology, The University of Jordan, Amman 11942, Jordan)

Abstract

The research reported in this paper develops a heuristic automated tool (SPD_CAL) for calibrating steady-state traffic stream and car-following models using loop detector data. The performance of the automated procedure is then compared to off-the-shelf optimization software parameter estimates, including the MINOS and Branch and Reduce Optimization Navigator (BARON) solvers. The model structure and optimization procedure is shown to fit data from different roadway types and traffic regimes (uncongested and congested conditions) with a high quality of fit (within 1% of the optimum objective function). Furthermore, the selected functional form is consistent with multiregime models, without the need to deal with the complexities associated with the selection of regime breakpoints. The heuristic SPD_CAL solver, which is available for free, is demonstrated to perform better than the MINOS and BARON solvers in terms of execution time (at least 10 times faster), computational efficiency (better match to field data), and algorithm robustness (always produces a valid and reasonable solution).

Suggested Citation

  • Hesham Rakha & Mazen Arafeh, 2010. "Calibrating Steady-State Traffic Stream and Car-Following Models Using Loop Detector Data," Transportation Science, INFORMS, vol. 44(2), pages 151-168, May.
  • Handle: RePEc:inm:ortrsc:v:44:y:2010:i:2:p:151-168
    DOI: 10.1287/trsc.1090.0297
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/trsc.1090.0297
    Download Restriction: no

    File URL: https://libkey.io/10.1287/trsc.1090.0297?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Warren P. Adams & Hanif D. Sherali, 1990. "Linearization Strategies for a Class of Zero-One Mixed Integer Programming Problems," Operations Research, INFORMS, vol. 38(2), pages 217-226, April.
    2. Youngho Lee & Hanif D. Sherali & Ikhyun Kwon & Seongin Kim, 2006. "A new reformulation approach for the generalized partial covering problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 53(2), pages 170-179, March.
    3. Paul I. Richards, 1956. "Shock Waves on the Highway," Operations Research, INFORMS, vol. 4(1), pages 42-51, February.
    4. G. F. Newell, 1961. "Nonlinear Effects in the Dynamics of Car Following," Operations Research, INFORMS, vol. 9(2), pages 209-229, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hyoshin (John) Park & Ali Haghani & Song Gao & Michael A. Knodler & Siby Samuel, 2018. "Anticipatory Dynamic Traffic Sensor Location Problems with Connected Vehicle Technologies," Service Science, INFORMS, vol. 52(6), pages 1299-1326, December.
    2. Daiheng Ni & John D. Leonard & Chaoqun Jia & Jianqiang Wang, 2016. "Vehicle Longitudinal Control and Traffic Stream Modeling," Transportation Science, INFORMS, vol. 50(3), pages 1016-1031, August.
    3. Cheng, Qixiu & Liu, Zhiyuan & Lin, Yuqian & Zhou, Xuesong (Simon), 2021. "An s-shaped three-parameter (S3) traffic stream model with consistent car following relationship," Transportation Research Part B: Methodological, Elsevier, vol. 153(C), pages 246-271.
    4. Melo, Lucas Eduardo Araújo de & Isler, Cassiano Augusto, 2023. "Integrating link count data for enhanced estimation of deterrence functions: A case study of short-term bicycle network interventions," Journal of Transport Geography, Elsevier, vol. 112(C).
    5. Mustafa Attallah & Jalil Kianfar & Yadong Wang, 2022. "Impact of High Resolution Radar-Obtained Weather Data on Spatio-Temporal Prediction of Freeway Speed," Sustainability, MDPI, vol. 14(22), pages 1-17, November.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bai, Lu & Wong, S.C. & Xu, Pengpeng & Chow, Andy H.F. & Lam, William H.K., 2021. "Calibration of stochastic link-based fundamental diagram with explicit consideration of speed heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 524-539.
    2. Saif Eddin Jabari & Nikolaos M. Freris & Deepthi Mary Dilip, 2020. "Sparse Travel Time Estimation from Streaming Data," Transportation Science, INFORMS, vol. 54(1), pages 1-20, January.
    3. Kai Nagel & Peter Wagner & Richard Woesler, 2003. "Still Flowing: Approaches to Traffic Flow and Traffic Jam Modeling," Operations Research, INFORMS, vol. 51(5), pages 681-710, October.
    4. Fan, De-li & Zhang, Yi-cai & Shi, Yin & Xue, Yu & Wei, Fang-ping, 2018. "An extended continuum traffic model with the consideration of the optimal velocity difference," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 402-413.
    5. Jincheng Jiang & Nico Dellaert & Tom Van Woensel & Lixin Wu, 2020. "Modelling traffic flows and estimating road travel times in transportation network under dynamic disturbances," Transportation, Springer, vol. 47(6), pages 2951-2980, December.
    6. Jin, Wen-Long, 2010. "A kinematic wave theory of lane-changing traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 1001-1021, September.
    7. Xin Chang & Xingjian Zhang & Haichao Li & Chang Wang & Zhe Liu, 2022. "A Survey on Mixed Traffic Flow Characteristics in Connected Vehicle Environments," Sustainability, MDPI, vol. 14(13), pages 1-22, June.
    8. Wu, Chengyuan & Yang, Liangze & Du, Jie & Pei, Xin & Wong, S.C., 2024. "Continuum dynamic traffic models with novel local route-choice strategies for urban cities," Transportation Research Part B: Methodological, Elsevier, vol. 181(C).
    9. Zheng, Fangfang & Jabari, Saif Eddin & Liu, Henry X. & Lin, DianChao, 2018. "Traffic state estimation using stochastic Lagrangian dynamics," Transportation Research Part B: Methodological, Elsevier, vol. 115(C), pages 143-165.
    10. Michael Z. F. Li, 2008. "A Generic Characterization of Equilibrium Speed-Flow Curves," Transportation Science, INFORMS, vol. 42(2), pages 220-235, May.
    11. Oluwaseun Farotimi & Kuppalapalle Vajravelu, 2020. "Formulation of a maximum principle satisfying a numerical scheme for traffic flow models," Partial Differential Equations and Applications, Springer, vol. 1(4), pages 1-11, August.
    12. Niek Baer & Richard J. Boucherie & Jan-Kees C. W. van Ommeren, 2019. "Threshold Queueing to Describe the Fundamental Diagram of Uninterrupted Traffic," Transportation Science, INFORMS, vol. 53(2), pages 585-596, March.
    13. Juan Carlos Muñoz & Carlos F. Daganzo, 2003. "Structure of the Transition Zone Behind Freeway Queues," Transportation Science, INFORMS, vol. 37(3), pages 312-329, August.
    14. Li, Xiaopeng & Ouyang, Yanfeng, 2011. "Characterization of traffic oscillation propagation under nonlinear car-following laws," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1346-1361.
    15. Yu Wang & Xiaopeng Li & Junfang Tian & Rui Jiang, 2020. "Stability Analysis of Stochastic Linear Car-Following Models," Transportation Science, INFORMS, vol. 54(1), pages 274-297, January.
    16. Jiang, Rui & Wu, Qing-Song, 2003. "Study on propagation speed of small disturbance from a car-following approach," Transportation Research Part B: Methodological, Elsevier, vol. 37(1), pages 85-99, January.
    17. Newell, G. F., 2002. "A simplified car-following theory: a lower order model," Transportation Research Part B: Methodological, Elsevier, vol. 36(3), pages 195-205, March.
    18. Jabari, Saif Eddin & Zheng, Jianfeng & Liu, Henry X., 2014. "A probabilistic stationary speed–density relation based on Newell’s simplified car-following model," Transportation Research Part B: Methodological, Elsevier, vol. 68(C), pages 205-223.
    19. Jin, Wen-Long, 2016. "On the equivalence between continuum and car-following models of traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 543-559.
    20. Jeong, Jaehee & Premsankar, Gopika & Ghaddar, Bissan & Tarkoma, Sasu, 2024. "A robust optimization approach for placement of applications in edge computing considering latency uncertainty," Omega, Elsevier, vol. 126(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ortrsc:v:44:y:2010:i:2:p:151-168. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.