Leveraging IoT data stream for near-real-time calibration of city-scale microscopic traffic simulation

Abstract

The emergence of smart cities is set to transform transportation systems by leveraging real-time traffic data streams to monitor urban dynamics. This complements traditional microscopic simulation methods, offering a detailed digital portrayal of real-time traffic conditions. A framework for near-real-time city-scale traffic demand estimation and calibration is proposed. By utilising Internet of Things (IoT) sensors on select roads, the framework generates microscopic simulations in congested networks. The proposed calibration method builds upon the standard bi-level optimization formulation. It presents a significant computational advantage over available methods by (i) formulating the optimization problem as a bounded variable quadratic programming, (ii) acquiring sequential optimization technique by splitting computations into short time frames while considering the dependency of the demand in successive time frames, (iii) performing parallel simulations for dynamic traffic assignment in corresponding time frames using the open source tool Simulation of Urban MObility (SUMO), and (iv) feeding traffic count data of each time frame as a stream to the model. The approach accommodates high-dimensional real-time applications without extensive prior traffic demand knowledge. Validation in synthetic networks and Tartu City case study showcases scalability, accuracy, and computational efficiency.