Paper Info
Title
Research on campus traffic congestion detection using BP neural network and Markov model.
Abstract
The automatic congestion detection of campus traffic presents a significant challenge to the traffic congestion research community. Typically, campus road users can be classified into four types including pedestrian, bike, vehicle and motorbike, which enhances the complexity of traffic condition. Thus, existing descriptors of traffic congestion for highway traffic are not valid when describing the traffic congestion in campus. In this paper, we propose a novel descriptor, road occupancy rate, for measuring campus traffic congestion level, which is statistically proved to be the most effective descriptor among other descriptors (including speed of pedestrian, vehicle, motorbike and bike). Two existing models — Markov model and back propagation neural network (BPNN) — are introduced in this paper to detect the campus traffic congestion combined with the proposed descriptors. And three phases are defined based on three-phase traffic theory to describe the campus traffic congestion levels. Experimental results indicate that the proposed detecting methods are both capable of detecting campus traffic congestion, while the BPNN-based method achieves higher accuracy and more stable performance.
Year
DOI
Venue
2016
10.1016/j.jisa.2016.08.003
Journal of Information Security and Applications
Keywords
Field
DocType
Road occupancy rate,Campus traffic congestion detection,Three-phase traffic theory,Markov model,Back propagation neural network
Traffic optimization,Traffic generation model,Three-phase traffic theory,Traffic flow,Computer science,Floating car data,Computer network,Traffic congestion reconstruction with Kerner's three-phase theory,Network traffic control,Traffic congestion
Journal
Volume
Issue
ISSN
31
C
2214-2126
Citations 
PageRank 
References 
0
0.34
0
Authors
5
Name
Order
Citations
PageRank
Xiaohan Yu127.79
Shengwu Xiong218953.59
Ying He300.68
W. Eric Wong435124.66
Yang Zhao500.34