Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories

Smart cities have given a significant impetus to manage traffic and use transport networks in an intelligent way. For the above reason, intelligent transportation systems （ITSs） and location-based services （LBSs） have become an interesting research area over the last years. Due to the rapid increase of data volume within the transportation domain, cloud environment is of paramount importance for storing, accessing, handling, and processing such huge amounts of data. A large part of data within the transportation domain is produced in the form of Global Positioning System （GPS） data. Such a kind of data is usually infrequent and noisy and achieving the quality of real-time transport applications based on GPS is a difficult task. The map-matching process, which is responsible for the accurate alignment of observed GPS positions onto a road network, plays a pivotal role in many ITS applications. Regarding accuracy, the performance of a map-matching strategy is based on the shortest path between two consecutive observed GPS positions. On the other extreme, processing shortest path queries （SPQs） incurs high computational cost. Current map-matching techniques are approached with a fixed number of parameters, i.e., the number of candidate points （NCP） and error circle radius （ECR）, which may lead to uncertainty when identifying road segments and either low-accurate results or a large number of SPQs. Moreover, due to the sampling error, GPS data with a high-sampling period （i.e., less than 10 s） typically contains extraneous datum, which also incurs an extra number of SPQs. Due to the high computation cost incurred by SPQs, current map-matching strategies are not suitable for real-time processing. In this paper, we propose real-time map-matching （called RT-MM）, which is a fully adaptive map-matching strategy based on cloud to address the key challenge of SPQs in a map-matching process for real-time GPS trajectories. The evaluation of our approach against state-of-the-art approaches is performed through simulations based on both synthetic and real-word datasets.

智慧城市为智能交通管理和交通网络智能应用的发展提供了巨大推动力。近来,智能交通系统(Intelligent transportation systems,ITSs)和移动位置服务(Location-based services,LBSs)也成为了研究领域的热点。交通领域数据量在快速不断增长,云计算在巨量数据的存储、接入、管理和处理方面有着巨大作用。交通领域相当比例的数据为GPS数据,此类数据具有非频繁、含噪声等特性,这使得维护基于GPS的实时交通软件的服务质量较为困难。在诸多智能交通系统应用中,地图匹配处理起着将GPS观测点准确排列于路网中的关键作用。考虑到准确性时,地图匹配策略的性能由两个连续的GPS观测点间的最短路径决定;另一方面,处理最短路径查询(Processing shortest path queries,SPQs)耗费着较高计算量。现有的地图匹配技术采用固定参数(固定的候选点数量,固定的误差圆半径)的办法,这可能导致确认线路分段时产生不确定性,也可导致低精度结果(或需进行大量SPQ处理以保证精度)。此外,由于采样错误的存在,较高采样时间(大于10 s)内的GPS数据常含有冗余数据,这也导致需要额外的SPQ处理。由于SPQ处理导致的高运算量问题,现有的地图匹配策略并不能实现实时应用。在本文中,我们提出一种实时地图匹配方法(Real-time map-matching,RT-MM)。该方法以云计算为基础,是一种全自适应地图匹配策略,能够应对实时GPS轨迹地图匹配中SPQ处理的关键问题。本研究还通过基于虚拟数据和实际数据的仿真,对所述方法与现有方法的性能进行了比较。