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Connected vehicles (CVs) can capture and transmit detailed data such asvehicle position and speed through vehicle-to-vehicle andvehicle-to-infrastructure communications. The wealth of CV data provides newopportunities to improve safety and mobility of transportation systems.However, it is likely to overburden storage and communication systems. Tomitigate this issue, we propose a compressive sensing (CS) approach that allowsCVs to capture and compress data in real-time and later recover the originaldata accurately and efficiently. The approach is evaluated using two casestudies. In the first study, we use this approach to recapture 10 million CVBasic Safety Message (BSM) speed samples. It can recover the original speeddata with root-mean-squared error as low as 0.05. We also explore recoveryperformance for other BSM variables. In the second study, a freeway trafficsimulation model is built to evaluate the impact of this approach on traveltime estimation. Multiple scenarios with various CV market penetration rates,On-board Unit (OBU) capacities, compression ratios, arrival rate patterns, anddata capture rates are simulated. The results show that the approach providesmore accurate estimation than conventional data collection methods, through upto 65% relative reduction in travel time estimation error. Even when thecompression ratio is low, the approach can provide accurate estimation, therebyreducing OBU hardware costs. Further, it can improve accuracy of travel timeestimation when CVs are in traffic congestion as it provides a broaderspatial-temporal coverage of traffic conditions and can accurately andefficiently recover the original CV data.

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