Information Dissemination Dynamics Through Vehicle-to-Vehicle Communication Built Upon Traffic Flow Dynamics Over Roadway Networks

Abstract

This research is dedicated to develop a discrete mathematical simulation framework to track information dissemination dynamics via V2V communication factoring traffic flow dynamics over a road traffic network. Specifically, we develop information network flow models (IFNMs) including IFNM-a and IFNM-r respectively for tracking information front spreading dynamics at arterial intersections and at highway-ramp intersections. Next, by integrating IFNMs with IT-CTM model developed by (Du et al., 2016) for capturing the information front propagation dynamics on a road segment, we establish a discrete mathematical simulation framework (IFNM-CTM) to track the information front spreading dynamics over a road network at discrete time stamps. Furthermore, by combining the IFNM-CTM framework and the deep search algorithms, this study tracks the information coverage dynamics and investigates its correlation to traffic congestion evolution over a traffic network at discrete time stamps. Our experiments built upon Sioux Falls city network indicate that the IFNM-CTM is able to track the information front spreading, including location and coverage, accurately with the MAE less than 6% and 5% respectively. More importantly, our studies found a strong correlation existing between information front spreading dynamics and traffic congestion evolution over the network. Specifically, a mild congestion condition (i.e., LOS C and D) provides the best traffic condition to sustain information spreading as compared to sparse (i.e., LOS A or B) and heavily congested (i.e., LOS E or F) traffic conditions since neither of them can sustain stable and constant wireless communication due to the limited transmission range of DSRC and interference issues.

Keywords: V2V, DSRC, Information Propagation, IFNM-CTM, Roadway Networks