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

  • Makoto KasaiEmail author
  • Jian Xing


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.


Traffic-flow modeling Car-following model Particle filter Traffic-state estimation 



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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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.National Institute of Technology, Akita CollegeAkitaJapan
  2. 2.Nippon Expressway Research Institute Company LimitedTokyoJapan

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