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

Connected vehicles (CVs) can capture and transmit detailed data like vehicle position, speed and so on 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 likely will over-burden storage and communication systems. To solve this issue, the authors 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. Two case studies are conducted to test the CS approach. For 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. With a compression ratio of 0.2, it is found that the CS approach can recover the original speed data with the root mean-squared error as low as 0.05; the recovery performances of the CS approach related to other BSM variables are also explored in detail. For the second case study, a freeway traffic simulation model is built to evaluate the impact of the CS 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 travel times from the CS approach are more accurate for all the scenarios. It is also found that when the compression ratio of the CS approach is low, the CS approach can reach a low travel time estimation accuracy with a small CV OBU capacity. Therefore, large amounts of OBU hardware cost can be saved. Furthermore, the CS approach can greatly improve the accuracy of the travel time estimations when CVs are in traffic congestion. The reason that the CS approach has such advantages is mainly because it allows CV data to cover a longer road segment and a longer period of time, and the recovery of the original CV data is accurate and efficient.

• Supplemental Notes:
  • This paper was sponsored by TRB committee ABJ50 Standing Committee on Information Systems and Technology.
• Corporate Authors:

  Transportation Research Board

  ,    
• Authors:
  • Lin, Lei
  • Li, Weizi
  • Peeta, Srinivas
• Conference:
  • Transportation Research Board 98th Annual Meeting
  • Location: Washington DC, United States
  • Date: 2019-1-13 to 2019-1-17
• Date: 2019

Language

• English

Media Info

• Media Type: Digital/other
• Features: Figures; References;
• Pagination: 8p

Subject/Index Terms

• TRT Terms: Connected vehicles; Data collection; Data compression; Travel time
• Subject Areas: Data and Information Technology; Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01697639
• Record Type: Publication
• Report/Paper Numbers: 19-00169
• Files: TRIS, TRB, ATRI
• Created Date: Mar 1 2019 3:51PM