A Systematic Approach to Develop Metaheuristic Traffic Simulation Models from Big Data Analytics on Real-World Data 2021-01-0166

Researchers and engineers are utilizing big data analytics to draw further insights into transportation systems. Large amounts of data at the individual vehicle trip level are being collected and stored. The true potential of such data is still to be determined. In this paper, we are presenting a data-driven, novel, and intuitive approach to model driver behaviors using microscopic traffic simulation. Our approach utilizes metaheuristic methods to create an analytical tool to assess vehicle performance. Secondly, we show how microscopic simulation run outputs can be post-processed to obtain vehicle and trip level performance metrics. The methodology will form the basis for a data-driven approach to unearthing trip experiences as realized by drivers in the real world. The methodology will contribute to, A.) Using vehicle trajectory traces to identify underlying vehicle maneuver distributions as obtained from real-world driver data, B.) Developing a virtual traffic environment to conduct sensitivity analysis on driver maneuver behaviors along a path, and C.) Developing a data-driven approach to quantify driver behavior as experienced under varying real-world boundary conditions.