Integrating Multi-sourced Sensor Data for Enhanced Traffic State Estimation

Accurate traffic state estimation, vital for managing urban congestion, is often achieved through simulation. Real-time data is invaluable for this, yet obtaining multi-sensor data is challenging and costly. To bridge this gap, leveraging crowdsourced data from third-party sources, despite its anonymity, enriches available information for precise estimation. This passive crowdsourced data is often reported as estimated time of arrival (ETA) and color-coded traffic patterns. The paper introduces an effective calibration approach for a mesoscopic traffic simulation of a complex urban arterial network, primarily relying on crowdsourced data and incorporating sensor data, if accessible. The approach employs a nested genetic algorithm with average speed data, calculated using ETA to estimate vehicle counts, eliminating the need for time-consuming field surveys. A custom mutation operator-speed deviation adaptive gene mutator, is introduced for generating vehicle counts to replicate real-world traffic conditions in the intermediate time steps of simulation. Additionally, a route selection algorithm is developed using color-coded tracks from Google Maps for prioritizing routes based on congestion patterns. The study demonstrates a novel data-fusion technique, combining sensors with passive crowdsourced information for accurate traffic speed estimation. The proposed methodology was applied to two case studies of urban arterial networks. The values obtained from simulations during validation showed promising proximity to real values, yielding a mean absolute percentage error (MAPE) of 0.29% for a simpler network and 6.31% for the best 10 routes, and 10.33% for all of the 15 priority routes within a complex network.