OBD-Data-Assisted Cost-Based Map-Matching Algorithm for Low-Sampled Telematics Data in Urban Environments
OBD-Data-Assisted Cost-Based Map-Matching Algorithm for Low-Sampled Telematics Data in Urban Environments
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DOI：
10.1109/tits.2021.3109851
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发表时间：
2022-08
影响因子：
8.5
通讯作者：
Patrick Alrassy;Jinwoo Jang;A. Smyth
中科院分区：
文献类型：
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作者：
Patrick Alrassy;Jinwoo Jang;A. Smyth
无数互联车辆在整个城市收集大规模远程信息处理数据，从而实现基于数据的基础设施规划。为了真正从这项新兴技术中受益，将无处不在的远程信息处理数据与地图数据集成在一起，为交通流量和安全提供更易于处理和可读的信息是很重要的。地图匹配算法可以将噪声轨迹数据投影到地图数据上，作为集成远程信息处理数据的一种手段。然而，由于较高的定位误差和复杂的道路网络，地图匹配带来了挑战。为了提高算法的效率和精度，提出了一种融合车载数据和轨迹数据的地图匹配算法。该算法结合了基于概率和权重的地图匹配框架。该算法的新颖性包括：(i)基于车载速度信息的自适应路段候选搜索机制，（ii）反映全球定位系统（GPS）噪声水平变化的自适应匹配参数，（iii）使用车载速度数据的新颖转移概率，以及（iv）最短路线的后端数据查询系统。地图匹配结果基于使用作者开发的车载传感设备收集的地面真实数据进行验证，并与常用的现成地图匹配平台进行比较。该算法具有较强的鲁棒性，在地图数据较密集、GPS噪声较大的情况下，精度可达97.45%。
A myriad of connected vehicles collects large-scale telematics data throughout cities, enabling data-based infrastructure planning. To truly benefit from this emerging technology, it is important to integrate pervasive telematics data with map data to produce more tractable and readable information for traffic flows and safety. Map-matching algorithms enable the projection of noisy trajectory data onto map data as a means of integrating telematics data. However, map-matching poses challenges due to higher levels of positioning errors and complex road networks. The authors propose a novel map-matching algorithm that can fuse in-vehicle data with trajectory data to improve the efficiency and accuracy of the algorithm. The proposed algorithm combines the probabilistic and weight-based map-matching frameworks. The novelty of the proposed algorithm includes (i) an adaptive segment candidate search mechanism based on in-vehicle speed information, (ii) adaptive matching parameters to reflect the variations in the Global Positioning System (GPS) noise levels, (iii) a novel transition probability that uses in-vehicle speed data, and (iv) a backend data query system for the shortest routes. Map-matching results were validated based on ground-truth data collected using an in-vehicle sensing device developed by the authors, as well as comparing with a commonly-used off-the-shelf map-matching platform. The proposed algorithm is proven to be robust, with an accuracy of 97.45%, particularly where map data are denser and GPS noise is high.