Study of Traffic Simulation Model for Heterogeneous Traffic

Abstract

Mixed-mode traffic, consisting of diverse vehicle types with varying characteristics,holds significant importance over homogeneous traffic scenarios. Unlike homogeneoustraffic, which comprises vehicles with similar attributes, mixed-modetraffic reflects the reality of real-world road networks. By encompassing differentvehicle classes such as cars, trucks, rickshaws, motorcycles, etc, mixed-modetraffic captures the complexities and challenges that arise from their diverse operatingcharacteristics. Understanding and modeling heterogeneous traffic is crucialfor designing effective transportation systems that cater to the specific needs andbehaviors of each vehicle type. Overall, recognizing the importance of mixedmodetraffic leads to more comprehensive and realistic transportation planningand management approaches that address the unique challenges posed by diversevehicle types on road networks.In this thesis, we have studied the aggregate behavior of mixed-mode traffic andinvestigated the macroscopic parameters of mixed-mode traffic to understandhow they are correlated with each other. Later on, we analyzed the macroscopicparameters such as traffic flow, traffic speed, and traffic density for mixed-modetraffic using traffic simulator called SUMO (Simulation of Urban Mobility). Ourresults showed that traffic simulator SUMO can be used to effectively model mixedmodetraffic. As, the car-following parameters of mixed-mode traffic have beenmanually tuned in our study for SUMO. So, it opens up possibilities for usingmachine learning models to predict the parameters for further improvements.