Information dissemination dynamics through Vehicle-to-Vehicle communication built upon traffic flow dynamics over roadway networks

This research is dedicated to developing a discrete mathematical simulation framework to track information dissemination dynamics via Vehicle-to-Vehicle (V2V) communication factoring traffic flow dynamics, traffic intersection operation settings, and traffic intersection geometrical design over a road traffic network. Specifically, we develop information network flow models (INFMs), including IFNM-a and IFNM-r, respectively for tracking information wavefront spreading dynamics at arterial intersections and at highway-ramp intersections. Next, by integrating IFNMs with the information and traffic coupled cell transmission model (IT-CTM) model developed by Du et al. [16] 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 mean absolute error (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.(c) 2023 Elsevier Inc. All rights reserved.