Capture and Recovery of Connected Vehicle Data: A Compressive Sensing Approach

TL;DR

This paper presents a compressive sensing method enabling connected vehicles to efficiently capture, compress, and accurately recover large-scale data, reducing storage costs and improving travel time estimates in traffic systems.

Contribution

It introduces a real-time compressive sensing approach for CV data, validated through case studies demonstrating cost savings and enhanced travel time accuracy.

Findings

Effective recovery of 10 million CV speed samples.

Significant reduction in OBU hardware costs.

Improved travel time estimation accuracy in congestion.

Abstract

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.