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

	Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories
	Aftab Ahmed Chandio; Nikos Tziritas; Fan Zhang; Ling Yin; Cheng-Zhong Xu
刊名	Frontiers of Information Technology & Electronic Engineering
	2016
英文摘要	Smart cities have given a significant impetus to manage traffic and use of transport networks in an intelligent way. For the above reason intelligent transportation systems (ITS) and location-based services (LBS) have become an interesting research area over the last years. Due to the rapid increase of data within the transportation domain, cloud environment is of paramount importance for storing, accessing, handling, and processing such huge amounts of data. A big part of data within the transportation domain is produced in the form of global positioning system (GPS). Such kind of data are mostly infrequent and noisy, rendering the quality of real-time transport applications based on GPS data is a difficult task. map-matching process plays a pivotal role in many ITS applications, which is responsible for an accurate alignment of observed GPS positions onto a road network. 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. The current map-matching techniques are approached with a fixed number of parameters i.e., candidate points (CPs) and error circle radius (ECR) that may lead to uncertainty when identifying road segments, leading in that way to either low-accurate results or experience a large number of SPQs. Moreover, due to the sampling error, GPS data with high-sampling period (i.e., less than 10 seconds) typically contains extraneous datum, which also incurs an extra number of SPQs. Unfortunately, due to the high computation cost incurred by SPQs, the current map-matching strategies are not suitable for real-time processing. Therefore, 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 the current state-of-the-art approaches found in the literature is performed through simulation results based on both synthetic and real-world datasets.
收录类别	SCI
原文出处	http://www.zju.edu.cn/jzus/iparticle.php?doi=10.1631/FITEE.1600027
语种	英语
内容类型	期刊论文
源URL	[http://ir.siat.ac.cn:8080/handle/172644/10216]
专题	深圳先进技术研究院_数字所
作者单位	Frontiers of Information Technology & Electronic Engineering
推荐引用方式 GB/T 7714	Aftab Ahmed Chandio,Nikos Tziritas,Fan Zhang,et al. Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories[J]. Frontiers of Information Technology & Electronic Engineering,2016.
APA	Aftab Ahmed Chandio,Nikos Tziritas,Fan Zhang,Ling Yin,&Cheng-Zhong Xu.(2016).Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories.Frontiers of Information Technology & Electronic Engineering.
MLA	Aftab Ahmed Chandio,et al."Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories".Frontiers of Information Technology & Electronic Engineering (2016).