基于云计算的自适应可调节GPS轨迹地图匹配策略

Aftab Ahmed CHANDIO; Nikos TZIRITAS; Fan ZHANG; Ling YIN; Cheng-Zhong XU

doi:10.1631/FITEE.1600027

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基于云计算的自适应可调节GPS轨迹地图匹配策略

常规文章 | Updated：2022-05-19

- 基于云计算的自适应可调节GPS轨迹地图匹配策略
- Towards adaptable and tunable cloud-based map-matching strategy for GPS trajectories#
- 信息与电子工程前沿（英文） 2016年17卷第12期页码：1305-1319
- Affiliations：
  
  1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
  2Institute of Mathematics and Computer Science, University of Sindh, Jamshoro 70680, Pakistan
  3Department of Electrical and Computer Engineering, Wayne State University, Detroit 48202, USA
- Author bio：
  
  E-mail:chandio.aftab@usindh.edu.pk
  E-mail:zhangfan@siat.ac.cn
- Funds：
  
  Project supported by the National Basic Research Program (973) of China (No. 2015CB352400), the National Natural Science Foundation of China (Nos. 61100220 and U1401258), and the US National Science Foundation (No. CCF-1016966);#A preliminary version of this paper was presented at the 2nd International Conference on Internet of Vehicles (IOV 2015), Chengdu, China, Dec. 19, 2015
- DOI：10.1631/FITEE.1600027
  中图分类号： TP399;U495
- 收稿：2016-01-19，
  
  录用：2016-4-11，
  
  纸质出版：2016-12
- Accepted：
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基于云计算的自适应可调节GPS轨迹地图匹配策略[J]. 信息与电子工程前沿（英文）, 2016,17(12):1305-1319.

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, 17(12): 1305-1319.
基于云计算的自适应可调节GPS轨迹地图匹配策略[J]. 信息与电子工程前沿（英文）, 2016,17(12):1305-1319. DOI： 10.1631/FITEE.1600027.

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, 17(12): 1305-1319. DOI： 10.1631/FITEE.1600027.

摘要

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。

Abstract

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 (

) 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-world datasets.

关键词

Keywords

references

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