论文标题
使用连接的车辆数据进行公路交通分析的框架
Framework for Highway Traffic Profiling using Connected Vehicle Data
论文作者
论文摘要
连接的车辆(CV)数据可能有可能彻底改变交通监控格局,因为近年来,从原始设备制造商(OEM)收集了专门从原始设备制造公司(OEM)收集的新的CV数据来源。与机构使用的现有CV数据相比,新一代CV数据具有某些优势,包括几乎无处不在的覆盖范围,高时间分辨率,高空间准确性和富集的车辆远程信息处理数据(例如,硬制动事件)。本文提出了一个交通分析框架,该框架以车辆级的性能为目标,跨越机动性,安全性,骑行舒适性,交通流量稳定性和燃油消耗。使用CV数据的主要州际公路(即I-280 NJ)的概念证明研究说明了超越传统的总交通指标的可行性。最后,讨论了历史分析甚至接近实时监控的潜在应用。提出的框架可以轻松缩放,对于希望系统地监视区域或全州道路的机构,而无需对基于基础设施的感应进行大量投资(以及相关的持续维护成本),这一点尤其有价值。
The connected vehicle (CV) data could potentially revolutionize the traffic monitoring landscape as a new source of CV data that are collected exclusively from original equipment manufactures (OEMs) have emerged in the commercial market in recent years. Compared to existing CV data that are used by agencies, the new-generation of CV data have certain advantages including nearly ubiquitous coverage, high temporal resolution, high spatial accuracy, and enriched vehicle telematics data (e.g., hard braking events). This paper proposed a traffic profiling framework that target vehicle-level performance indexes across mobility, safety, riding comfort, traffic flow stability, and fuel consumption. The proof-of-concept study of a major interstate highway (i.e., I-280 NJ), using the CV data, illustrates the feasibility of going beyond traditional aggregated traffic metrics. Lastly, potential applications for either historical analysis and even near real-time monitoring are discussed. The proposed framework can be easily scaled and is particularly valuable for agencies that wish to systemically monitoring regional or statewide roadways without substantial investment on infrastructure-based sensing (and the associated on-going maintenance costs)