Smart city real-time data-driven transportation simulation

This study assesses feasibility aspects of using a real-time data-driven transportation simulation model to evaluate and visualize network performance indices to provide dynamic operational feedback in a real world environment, in a big data context. A hybrid traffic simulation model, consisting of a mix of preset and real-time data-driven intersections, is developed. The hybrid model represents a traffic corridor partially equipped with smart devices generating high velocity, high volume datasets with limited shelf-life. The model used in this study emulates seventeen consecutive intersections on a corridor. Signal controls and vehicle volumes at two of the intersections are driven by real-time data while the remaining intersections are driven by preset data. An optimized architecture is developed to enable control of the signals and the vehicle volumes using real-time data from in-field detectors, and real-time processing of the vehicle trajectories from the simulation output to generate travel-time, energy, and emissions performance indices.