Our Latest Publication on Transportation Research Part C: Emerging Technologies
Our new paper has been published on Transportation Research Part C: Emerging Technologies. We investigated the large scale of ride-sourcing vehilce fleet data for network-wide traffic speed prediction. A cell-based map-matching technique is proposed to link vehicle trajectories with road geometries, and to produce network-wide spatio-temporal speed matrices. A case study using data from Chengdu, China, demonstrates that the algorithm performs well even in situations involving continuous data loss over a few hours, and consequently, addresses large-scale network-wide traffic state estimation problems with missing data, while at the same time outperforming other data recovery techniques that were used as benchmarks. Our approach can be used to generate congestion maps that can help monitor and visualize traffic dynamics across the network, and therefore form the basis for new traffic management, proactive congestion identification, and congestion mitigation strategies.
This work is a result of two-year collaboration between researchers from Zhejiang Unviersity (Miss Jingru Yu, Dr Xiqun Chen and Dr Simon Hu) and Imperial College London (Dr Marc Stettler and Dr Panagiotis Angeloudis).
The full paper can be access online via the link here.