Efficient Data Collection and Accurate Travel Time Estimation in a Connected Vehicle Environment Via Real-Time Compressive Sensing

Abstract

Abstract 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 provides new opportunities to improve safety and mobility of transportation systems, which can overburden 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 case studies. In the first study, we use our approach to recapture 10 million CV basic safety message (BSM) speed samples as well as other BSM variables. The results show that we can recover the original speed data with root-mean-squared error as low as 0.05 MPH. In the second study, a freeway traffic simulation model is built 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 for our experiments. As a result, our approach provides more accurate estimation than conventional data collection methods by achieving up to 65% relative reduction in travel time estimation error. With a low compression ratio, our approach can still provide accurate estimation, therefore reducing OBU hardware costs. Lastly, our approach can improve travel time estimation accuracy when CVs are in traffic congestion as it provides a broader spatial–temporal coverage of traffic conditions and can accurately and efficiently recover the original CV data.