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

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. This paper presents a computationally efficient framework for near-real-time high-dimensional dynamic demand estimation and microscopic traffic simulation in city-scale congested networks. The actual traffic count data is fed to the proposed method as an hourly aggregated stream collected by IoT sensors, and the system continues to function as long as it receives the data without bounding the performance time. The case study of the city of Tartu demonstrates the technique's application to a large-scale network of 2780 nodes and 6700 links, generating 24 hourly microscopic simulations of 197,000 vehicles with high accuracy and under a tight computational budget.image