A multilane traffic and collision generator for IoV

Abstract

Cooperative driving is an emerging area in the field of vehicular networks and Intelligent Transportation Systems (ITS). It contributes to enhancing safety by reducing accidents as a consequence of rogue driving and overtaking. Thus, to accomplish the requirements of safety, real-time traffic traces are required for comprehensive analysis. However, the real traces are limited in availability and constraint to the environment they are collected from. On the other hand, a common practice is to use a single-lane traffic generation model to address collision modeling, which is limited by collision modeling of rear-end collisions. Therefore, to address these limitations in the literature, this paper proposes a multilane traffic generation (MLTG) model using microscopic modeling. Furthermore, to generate different multilane synthetic traffic traces, traffic flow behaviors and collision scenarios are replicated as use cases by incorporating the perception & human errors. The efficacy of the MLTG model is tested and analyzed by performing extensive simulations in MATLAB by considering varied scenarios such as straight road, merging, and diversion for single-lane and cross-lane collision modeling, respectively. The results show that the performance of the proposed MLTG model outperforms in terms of stability and scalability, compared to the existing state-of-the-art techniques.