Analyzing various two-wheelers in mixed traffic flow with cars using a cellular automata model incorporating social force

Abstract

This study aims to analyze the characteristics of various types of two-wheelers in the mixed traffic flow with cars, as well as the effectiveness of corresponding management measures. Given the diversity of two-wheelers, including motorcycles, bicycles, e-bikes and over-standard e-bikes, a hybrid model that integrates Cellular Automata (CA) rules with Social Force (SF) principles is proposed, termed the Cellular Automata-Social Force model (CA-SF). This model is designed to simulate the interactions between cars and two-wheelers under mixed traffic conditions. By incorporating social force calculations to govern lateral movement rules, the model can simulate overtaking and lane transgression behaviors. Model parameters are derived from existing literature and real-world data, and the accuracy and reliability of the model have been validated. Through multiple sets of traffic flow simulation experiments, the study evaluates the impacts of various measures on traffic efficiency and safety in a mixed traffic environment. It is found that the physical separation of motorized and non-motorized lanes generally reduces the traffic efficiency of non-motorized two-wheelers. The impact of physical separation on traffic safety varies depending on the composition of traffic. When the proportion of two-wheelers is high, separation benefits high-speed two-wheelers such as motorcycles and over-standard e-bikes. However, when the proportion of two-wheelers is low, the opposite result occurs. Additionally, widening non-motorized lanes improves the overall traffic efficiency of two-wheelers, though the extent of improvement is less significant than the proportional increase in lane width.