Open AccessReal‐time detection of traffic congestion based on trajectory data
Author(s) -
Yang Qing,
Yue Zhongwei,
Chen Ru,
Zhang Jingwei,
Hu Xiaoli,
Zhou Ya
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.0872
Subject(s) - computer science , traffic congestion , dbscan , traffic congestion reconstruction with kerner's three phase theory , spark (programming language) , cluster analysis , real time computing , trajectory , data mining , real time data , computer network , artificial intelligence , transport engineering , engineering , fuzzy clustering , physics , astronomy , canopy clustering algorithm , world wide web , programming language
Traffic congestion is common in most cities. It not only affects people's normal travel time but also causes more traffic crashes. To solve the traffic congestion and reduce the corresponding hazards, it is necessary to quickly detect the location of traffic congestion. In view of the fact that the trajectory data record the temporal and spatial information of moving objects, this article presents two methods for the real‐time detection of traffic congestion through real‐time processing of trajectory data. One is to use distributed densit‐based spatial clustering of applications with noise (DBSCAN) clustering to detect the location of traffic congestion. The other is to perform distributed topology analysis of trajectory data to find congested areas. Finally, extensive experiments that involved using three real datasets to simulate both real‐time detection methods on Spark Streaming demonstrated the efficiency of the two methods.