A Binary Multi-Subsystems Transportation Networks Estimation using Mobiliti Data

Urban traffic congestion presents critical challenges in big cities in the US. In these areas, there is a crucial need for efficient models to support transportation engineers in developing sustainable traffic systems. In the recent years, a number of approaches have been proposed to estimate urban transport networks parameters. In this work, we develop a new mathematical model for traffic estimation that integrates multi subsystems which include vehicles flow, travel time, and traffic condition. The primary goal of this paper is to design a model that achieve an optimal strategy to efficiently estimate traffic subsystem parameters. We use maximum likelihood estimation (MLE) method to perform optimization in Python programming language, using Mobiliti data that describe a baseline network normal condition. We investigate the separability of our model by performing a full set optimization and comparing with a partitioned subsets optimization with the goal to reduce the computational costs.