Title
Dynamic Prediction of Vehicle Cluster Distribution in Mixed Traffic: A Statistical Mechanics-Inspired Method
Abstract
The advent of intelligent vehicle technologies holds significant potential to alter the dynamics of traffic flow. Prior work on the effects of such technologies on the formation of self-organized traffic jams has led to analytical solutions and numerical simulations at the mesoscopic scale, which may not yield significant information about the distribution of vehicle cluster size. Since the absence of large clusters could be offset by the presence of several smaller clusters, the distribution of cluster sizes can be as important as the presence or absence of clusters. To obtain a prediction of vehicle cluster distribution, the included work presents a statistical mechanics-inspired method of simulating traffic flow at a microscopic scale via the generalized Ising model. The results of the microscopic simulations indicate that traffic systems dominated by adaptive cruise control (acc)-enabled vehicles exhibit a higher probability of formation of moderately sized clusters, as compared with the traffic systems dominated by human-driven vehicles; however, the trend is reversed for the formation of large-sized clusters. These qualitative results hold significance for algorithm design and traffic control because it is easier to predict and take countermeasures for fewer large localized clusters as opposed to several smaller clusters spread across different locations on a highway.
Year
DOI
Venue
2015
10.1109/TITS.2015.2409798
Intelligent Transportation Systems, IEEE Transactions  
Keywords
Field
DocType
intelligent vehicles,potts model,driver behavior,road transportation,self-organization,traffic control,vehicle dynamics,clustering algorithms,algorithm design and analysis,computational modeling
Intelligent vehicle technologies,Cluster (physics),Traffic flow,Simulation,Traffic simulation,Vehicle dynamics,Intelligent transportation system,Engineering,Cluster analysis,Microscopic scale
Journal
Volume
Issue
ISSN
PP
99
1524-9050
Citations 
PageRank 
References 
1
0.38
3
Authors
4
Name
Order
Citations
PageRank
Kshitij Jerath122.51
Ray, A.2832184.32
Sean N. Brennan3468.92
Vikash V. Gayah4257.28