Use of Particle Filtering to Establish a Time-Varying Car-Following Model
- 34 Downloads
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.
KeywordsTraffic-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).
- 1.Koshi, M.: Capacity of motorway bottlenecks. J. Jpn. Soc. Civ. Eng. 371/IV-5, 1–7. (1986) (in Japanese)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. 45–55Google Scholar
- 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
- 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)Google Scholar
- 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)Google Scholar
- 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