Study of traffic flow characteristics using different vehicle-following models under mixed traffic conditions

To understand the congestion problem and the occurrence of bottlenecks and to formulate solutions for it, a thorough study of vehicle-to-vehicle interactions is necessary. Car-following models replicate the behavior of a driver following another vehicle. These models are widely used in the development of traffic simulation models, and in analysis of safety and capacity. In India, traffic on roads is mixed in nature with wide variations in physical dimensions and other vehicular and traffic characteristics with loose lane discipline. In mixed traffic conditions, leader-follower vehicle types are not only car-car cases but also there are different combinations of vehicles (e.g. car-two wheeler, two wheeler-auto rickshaw, and heavy vehicle-two wheeler). The present study focuses on evaluation of different vehicle-following models under mixed traffic conditions. The car-following models such as Gipps, Intelligent Driver Model (IDM), Krauss Model and Das and Asundi were selected for this study. These models were implemented in a microscopic traffic simulation model for a mid-block section. The performance of different vehicle-following models was evaluated based on different Measure of Effectiveness (MoE) using field data collected from a four-lane divided urban arterial road in Chennai city. Speed-concentration and flow-concentration relationships for different vehicle-following models were developed and analyzed for different compositions. Capacity is higher when the proportion of smaller size vehicles is higher, since these vehicles use longitudinal and lateral gaps effectively. The simulation model was also applied to evaluate a range of traffic control measures based on vehicle type and lane (Ex: exclusion of auto-rickshaws, heavy vehicles, auto-rickshaws + heavy vehicles, etc.). The results showed the promise of some measures based on vehicle class, namely, the exclusion of auto rickshaws or auto rickshaws and heavy vehicles. The findings have interesting implications for capacity and PCU estimation and Level of Service (LoS) Analysis.