Reverse deduction of vehicle group situation based on dynamic Bayesian network

Xiaoyuan Wang,Yaqi Liu,Jianqiang Wang,Jingheng Wang,Zhenxue Liu

doi:10.1177/1687814017747708

Abstract

Vehicle group is the basic unit of microscopic traffic flow, and also a concept that often involved in the research of active vehicle security. It is of great significance to identify vehicle group...

Highlights

Lots of previous studies have focused on the assessment and prediction of traffic situation
On the basis of the error analysis of the algorithms based on average speed and average travel time, W Zhang et al.[3] promoted the urban traffic situation calculation methods based on probe vehicle data
Poisson’s distribution was used to identify vehicle group situation which was constituted by target vehicle and its neighboring vehicles when the target vehicle arrived at the end of study area

Summary

Introduction

Lots of previous studies have focused on the assessment and prediction of traffic situation. The traffic situation was defined as a distribution over sequences of states that have some meaningful interpretation. The hidden Markov model was used to study the probability distribution and evolution law of traffic situation. M Fujita et al.[2] studied the assessment of road traffic situation in heavy rain through analyzing the data such as route and travel time of vehicle. Most of previous studies did not fully consider the behavior characteristics of driver under the complicated and changeable driving scene and lacked systematic research on vehicle group situation. On the basis of the error analysis of the algorithms based on average speed and average travel time, W Zhang et al.[3] promoted the urban traffic situation calculation methods based on probe vehicle data.

Methods

Findings

Discussion

Conclusion