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Use of Particle Filtering to Establish a Time-Varying Car-Following Model

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Abstract

Methods for predicting the flow of vehicular traffic have been studied in order to anticipate short-term changes in service level. However, if the target route is an expressway with capacity bottlenecks (e.g., sag sections), it can be difficult to predict when a breakdown in the traffic flow will occur. There is a need to model the traffic dynamics from the free-flowing state to a congested state. Although previous studies have treated the parameters of traffic-flow models as being static, it is likely that they are actually time varying. This variation may be either random (i.e., white noise), influenced by longitudinal alignment, or both. To assess the critical traffic-flow state at a sag section, we use traffic-flow data collected from a driving simulator, these being more homogenous than actual flow data. Each participant repeated the course five times and from the second to fifth run followed a lead car corresponding to the same participant’s previous run. We estimate time-varying parameters to assess the influence of longitudinal alignment. To counteract operational randomness, we calculate the average parameters of the five repeated car-following runs for each participant. To minimize the computational cost, we use particle-filtering methods rather than Markov-chain Monte Carlo methods. Finally, we suggest future improvements to flow-breakdown modeling.

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References

  1. Koshi, M.: Capacity of motorway bottlenecks. J. Jpn. Soc. Civ. Eng. 371/IV-5, 1–7. (1986) (in Japanese)

  2. Lighthill, M.J., Whitham, G.B.: On kinematic waves. I. Flood movement in long rivers. II. A theory of traffic flow on long crowded roads. Proc. R. Soc. A. 229, 281–345 (1955)

    MATH  Google Scholar 

  3. Richards, P.I.: Shock waves on the highway. Oper. Res. 4(1), 42–51 (1956)

    Article  MathSciNet  Google Scholar 

  4. Daganzo, C.F.: A variational formulation of kinematic waves: basic theory and complex boundary conditions. Transp. Res. B. 39(2), 187–196 (2005)

    Article  Google Scholar 

  5. Pipes, L.A.: An operational analysis of traffic dynamics. J. Appl. Phys. 24(3), 274–287 (1953)

    Article  MathSciNet  Google Scholar 

  6. Chandler, R.E., Herman, R., Montroll, E.W.: Traffic dynamics: studies in Car following. Oper. Res. 6(2), 165–184 (1958)

    Article  MathSciNet  Google Scholar 

  7. Gazis, D.C., Herman, R., Rothery, R.W.: Non-linear follow-the leader models of traffic flow. Oper. Res. 9(4), 545–567 (1961)

    Article  Google Scholar 

  8. Bando, M., Hasebe, K., Nakayama, A., Shibata, A., Sugiyama, Y.: Dynamic model of congestion and numerical simulation. Phys. Rev. E. 51(2), 1035–1042 (1995)

    Article  Google Scholar 

  9. Treiber, M., Hennecke, A., Helbing, D.: Congested traffic states in empirical observations and microscopic simulations. Phys. Rev. E. 62(2), 1805–1824 (2000)

    Article  Google Scholar 

  10. Bham, G.H., Benekohal, R.F.: A high fidelity traffic simulation model based on cellular automata and car-following concepts. Transp. Res. C. 12(1), 1–32 (2004)

    Article  Google Scholar 

  11. Kasai, M., Uchiyama, H., Nonaka, Y.: A modelling for car-following behavior illustrated as spiral movement. J. Jpn. Soc. Civ. Eng., Part D. 63, 65–75 (2007) (in Japanese)

    Google Scholar 

  12. H. Ozaki. (1995). Reaction and Anticipation in the Car-Following Behavior, Proceedings of the 12th ISTTT, pp. 45–55

  13. Goñi-Ros, B., Knoop, V.L., Shiomi, Y., Takahashi, T., van Arem, B., Hoogendoorn, S.P.: Modeling traffic at sags. Int. J. Intell. Transp. Syst. Res. 14, 64–74 (2014)

    Google Scholar 

  14. Brackstone, M., Sultan, B., McDonald, M.: Motorway driver behavior: studies on car following. Transp. Res. F. 5(1), 31–46 (2002)

    Article  Google Scholar 

  15. Kasai, M., Shibagaki S., Terabe, S.: Application of hierarchical bayesian estimation to calibrating a car-following model with time-varying parameters, Proceedings of the 2013 I.E. Intelligent Vehicles Symposium. 870–875 (2013)

  16. Kasai, M., Shibagaki S., Terabe, S.: Extracting characteristics of traffic flow in bottlenecks with exchange interactions in time headway. Proceedings of the 17th International IEEE Conference on Intelligent Transportation Systems. 3144–3150 (2014)

  17. Kasai, M.: An application of seasonal adjustment methods based on hierarchical bayesian estimation to homogenous traffic flow data for modeling capacity bottleneck phenomena. J. Jpn. Soc. Civ. Eng., Ser. D3 (Infrastructure Planning and Management). 74(5), I_917–I_929 (2015) (in Japanese)

    Google Scholar 

  18. Suzuki, H., Nakatsuji, T., Tanaboriboon, Y., Takahashi, K.: Dynamic estimation of origin-destination travel time and flow on a long freeway corridor: neural Kalman filter. J. Transport. Res. Rec. 1739, 67–75 (2000)

    Article  Google Scholar 

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Acknowledgements

This work was supported in part by a JSPS KAKENHI Grant-in-Aid (No. 25820247 and No. 15 K18138) and a grant from the Practical ITS Research Committee of the Japan Society of Civil Engineers. The authors thank Hiroshi Ono (Honda Motor Co. Ltd.) for technical advice regarding collecting data from the DS, and also thank Shintaro Terabe (Tokyo University of Science), Takaaki Otsuki (Eight-Japan Engineering Consultations Inc.), and Kenta Yamaji (Central Japan Railway Company).

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Authors and Affiliations

  1. National Institute of Technology, Akita College, 1-1, Iijima-Bunkyo-cho, Akita, Akita, Japan

    Makoto Kasai & Jian Xing

  2. Nippon Expressway Research Institute Company Limited, 1-4-1 Tadao, Machida, Tokyo, Japan

    Jian Xing

Authors
  1. Makoto Kasai
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  2. Jian Xing
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Correspondence to Makoto Kasai.

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Kasai, M., Xing, J. Use of Particle Filtering to Establish a Time-Varying Car-Following Model. Int. J. ITS Res. 17, 49–60 (2019). https://doi.org/10.1007/s13177-018-0155-9

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  • DOI: https://doi.org/10.1007/s13177-018-0155-9

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