Citation

Umeda S, Kawasaki Y, Kuwahara M, Iihoshi A. Transp. Res. C Emerg. Technol. 2021; 125: e103005.

Copyright

(Copyright © 2021, Elsevier Publishing)

DOI

10.1016/j.trc.2021.103005

PMID

unavailable

Abstract

A method that evaluates the risk of traffic standstills on winter roads in real time using a state space model is proposed herein. In Japan, large-scale anomaly events such as traffic standstills that cause serious road disturbances occur frequently every year because of heavy snowfall. However, if the risk of anomaly events is known in advance, appropriate preparation and management can be undertaken to prevent such events and/or alleviate their impacts on road traffic. Therefore, this study attempts to evaluate the risk of standstills based on the degraded road performance estimated from probe vehicle speeds using sequential Bayesian filtering in a state space model (SSM). The SSM comprises a system model constructed by learning historical data and a measurement model using several exogenous variables such as snowfall amounts and temperature. The risk of anomaly events is then determined as the deviation of the filtered vehicle speed by the SSM from the statistically feasible speed distribution. The validation is performed by applying the proposed model to 58 traffic-standstill cases in northern Japan, and we confirm that the model successfully evaluates risks at a reasonable level that permits the practical use.

Language: en

Keywords

Anomaly event; Kalman filter; Probe data; Risk evaluation; Road performance; State space model