  Connected vehicles (CVs) can capture and transmit detailed data through
vehicle-to-vehicle and vehicle-to-infrastructure communications, which bring
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. We design a compressive sensing
(CS) approach which allows CVs to capture and compress data in real-time and
later recover the original data accurately and efficiently. We have evaluated
our approach using two case studies. In the first case study, the CS approach
is applied to re-capture 10 million CV Basic Safety Message (BSM) speed samples
from the Safety Pilot Model Deployment program. The recovery performances of
our approach regarding several BSM variables are explored in detail. In the
second case 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. The results show the potential of saving large amounts of OBU
hardware cost. Furthermore, our approach can greatly improve the accuracy of
travel time estimation when CVs are in traffic congestion.
