Open AccessDetecting and positioning of traffic incidents via video‐based analysis of traffic states in a road segment
Author(s) -
Ren Jianqiang,
Chen Yangzhou,
Xin Le,
Shi Jianjun,
Li Baotong,
Liu Yinan
Publication year - 2016
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2015.0022
Subject(s) - computer science , traffic congestion reconstruction with kerner's three phase theory , traffic congestion , support vector machine , feature vector , real time computing , traffic conflict , warning system , transport engineering , traffic flow (computer networking) , floating car data , simulation , artificial intelligence , engineering , computer network , telecommunications
Non‐recurrent traffic incidents (accidents, stalled vehicles and spilled loads) often bring about traffic congestion and even secondary accidents. Detecting and positioning them quickly and accurately has important significance for early warning, timely incident‐disposal and speedy congestion‐evacuation. This study proposes a video‐based detecting and positioning method by analysing distribution characteristics of traffic states in a road segment. Each lane in the monitored segment is divided into a cluster of cells. Traffic parameters in each cell, including flow rate, average travel speed and average space occupancy, are obtained by detecting and tracking traffic objects (vehicles and spilled loads). On the basis of the parameters, traffic states in the cells are judged via a fuzzy‐identification method. For each congested cell, a feature vector is constructed by taking its state together with states of its upstream and downstream neighbouring cells in the same lane. Then, a support vector machine classifier is trained to detect incident point. If a cell is judged to be corresponding to an incident point at least for two successive time periods, an incident is detected and its position is calculated based on the identity number of the cell. Experiments prove the efficiency and practicability of the proposed method.