A Compressive Sensing Approach for Connected Vehicle Data Capture and Recovery and its Impact on Travel Time Estimation

by   Lei Lin, et al.

Connected vehicles (CVs) can capture and transmit detailed data such as vehicle position and speed through vehicle-to-vehicle and vehicle-to-infrastructure communications. The wealth of CV data brings new opportunities to improve the safety, mobility, and sustainability of transportation systems. However, the potential data explosion is likely to over-burden storage and communication systems. To mitigate this issue, we propose a compressive sensing (CS) approach that allows CVs to capture and compress data in real-time and later recover the original data accurately and efficiently. We evaluate our approach using two comprehensive case studies. In the first study, we apply our approach to re-capture 10 million CV Basic Safety Message (BSM) speed samples from the Safety Pilot Model Deployment program. As a result, our approach can recover the original speed data with the root-mean-squared error as low as 0.05. In the second study, we have built a freeway traffic simulation model to evaluate the impact of our approach on travel time estimation. Multiple scenarios with various CV market penetration rates, On-board Unit (OBU) capacities, compression ratios, arrival rate patterns, and data capture rates are simulated. The simulation results show that our approach provides more accurate estimation than conventional data collection methods up to 65 error. We also observe that when the compression ratio is low, our approach can still provide accurate estimations, hence reducing the OBU hardware cost. Last, our approach can greatly improve the accuracy of the travel time estimations when CVs are in traffic congestion. This is due to that our approach provides a broader spatial-temporal converge of traffic conditions and can accurately and efficiently recover the original CV data.


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