Hybrid Stochastic Cellular Automata-Driver-Vehicle-Object Simulation Model for Heterogeneous Traffic at Urban Signalized Intersections

Abstract

The traffic flow at intersections is generally chaotic, and signalization is a control measure to reduce this chaos. Heterogeneous traffic at signalized intersections behave much differently from homogeneous traffic. Also, in many countries, nonlane-based traffic prevails; hence, designing control systems for such situations is a challenging task. Traffic simulation helps the analyst to model the behavior of such complex systems. Cellular automata (CA), a recent entrant in traffic flow modeling, represents the traffic flow by means of simple rules, and thus has proved to be a versatile tool in traffic simulation. The present study aims to develop a computationally efficient traffic flow simulation model integrating the concepts of cellular automata and driver-vehicle-objects, thus making a behavioral model of traffic. The model emphasizes the diversity in human behavior, and represents the traffic using the minimal modeling concept of CA. To represent multiple vehicle types, a multicell representation was adopted. Further, to address the issue of nonlane-based movement, new lateral movement rules were proposed. The model incorporated behavior at amber and lateral movements, thus attempting to achieve a near to reality representation of nonlane-based heterogeneous traffic. The model was calibrated and validated using delay data from selected intersections in India. This model was then used to predict saturation flows at signalized intersections. The model performed reasonably well in predicting the delays, but the saturation flow values showed up to 30% variability.