An Online Learning Collaborative Method for Traffic Forecasting and Routing Optimization

Recent advances in technologies such as the Internet of Things (IoT) and Cyber-Physical Systems (CPS) have provided promising opportunities to solve problems urban traffic. With help IoT technologies, online data from road segments are captured by monitoring devices, while real-time vehicles collected through preinstalled sensors. Based on these data, a CPS model is constructed depict status dynamic behavior vehicles. An learning data-driven developed extract prior knowledge enhance collaboration between combining short-term traffic forecasting routing optimization. A case study based Xi’an city presented demonstrate feasibility efficiency proposed method, showing reduction travel time with reasonable computation time, without much compromising distance fuel consumption. This work potentially strengthens transparency intelligence systems.