一种用于互联车辆数据采集和恢复的压缩感知方法及其对行程时间估计的影响
A Compressive Sensing Approach for Connected Vehicle Data Capture and Recovery and its Impact on Travel Time Estimation
摘要：
联网车辆（CV）可以通过车辆到车辆和车辆到基础设施的通信来捕获和传输车辆位置和速度等详细数据。丰富的CV数据为提高运输系统的安全性和机动性提供了新的机会。然而，这可能会使存储和通信系统负担过重。为了缓解这个问题，我们提出了一种压缩感知（CS）方法，允许CV实时捕获和压缩数据，然后准确有效地恢复原始数据。使用两个案例研究的方法进行评估。在第一项研究中，我们使用这种方法重新捕获1000万CV基本安全信息（FSM）速度样本。该方法能以最小的均方根误差均方误差恢复原始转速数据。我们还探讨了其他BSM变量的恢复性能。在第二项研究中，建立了一个高速公路交通仿真模型，以评估这种方法对行程时间估计的影响。模拟了具有各种CV市场渗透率、车载单元（OBU）容量、压缩比、到达率模式和数据捕获率的多个场景。结果表明，该方法提供了更准确的估计比传统的数据收集方法，通过高达65%的行程时间估计误差的相对减少。即使当压缩比较低时，该方法也可以提供准确的估计，从而降低OBU硬件成本。此外，它可以提高行程时间估计的准确性时，CV在交通拥堵，因为它提供了一个更广泛的时空覆盖的交通条件，并可以准确和有效地恢复原始CV数据。
英文摘要：
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. However, it is likely to 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. The approach is evaluated using two case studies. In the first study, we use this approach to recapture 10 million CV Basic Safety Message (BSM) speed samples. It can recover the original speed data with root-mean-squared error as low as 0.05. We also explore recovery performance for other BSM variables. In the second study, a freeway traffic simulation model is built to evaluate the impact of this 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 that the approach provides more accurate estimation than conventional data collection methods, through up to 65% relative reduction in travel time estimation error. Even when the compression ratio is low, the approach can provide accurate estimation, thereby reducing OBU hardware costs. Further, it can improve accuracy of travel time estimation 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
