Next Article in Journal
Task-Oriented Visualization Approaches for Landscape and Urban Change Analysis
Next Article in Special Issue
Are the Poor Digitally Left Behind? Indications of Urban Divides Based on Remote Sensing and Twitter Data
Previous Article in Journal
Water Level Reconstruction Based on Satellite Gravimetry in the Yangtze River Basin
Previous Article in Special Issue
Using High-Performance Computing to Address the Challenge of Land Use/Land Cover Change Analysis on Spatial Big Data
Open AccessArticle

Utilizing MapReduce to Improve Probe-Car Track Data Mining

School of Geodesy and Geomatics, Wuhan University, Wuhan 430072, China
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430072, China
Wuhan Kotei Infomatics Co., Ltd., Wuhan 430072, China
Department of Geography and GeoInformation Sciences, College of Science, George Mason University, Fairfax, VA 22030, USA
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(7), 287;
Received: 29 May 2018 / Revised: 1 July 2018 / Accepted: 19 July 2018 / Published: 23 July 2018
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
PDF [2080 KB, uploaded 23 July 2018]


With the rapidly increasing popularization of the automobile, challenges and greater demands have come to the fore, including traffic congestion, energy crises, traffic safety, and environmental pollution. To address these challenges and demands, enhanced data support and advanced data collection methods are crucial and highly in need. A probe-car serves as an important and effective way to obtain real-time urban road traffic status in the international Intelligent Transportation System (ITS), and probe-car technology provides the corresponding solution through advanced navigation data, offering more possibilities to address the above problems. In addition, massive spatial data-mining technologies associated with probe-car tracking data have emerged. This paper discusses the major problems of spatial data-mining technologies for probe-car tracking data, such as true path restoration and the close correlation of spatial data. To address the road-matching issue in massive probe-car tracking data caused by the strong correlation combining road topology with map matching, this paper presents a MapReduce-based technology in the second spatial data model. The experimental results demonstrate that by implementing the proposed spatial data-mining system on distributed parallel computing, the computational performance was effectively improved by five times and the hardware requirements were significantly reduced. View Full-Text
Keywords: Probe-car track; spatial data-mining; big data; MapReduce Probe-car track; spatial data-mining; big data; MapReduce

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Zheng, L.; Sun, M.; Luo, Y.; Song, X.; Yang, C.; Hu, F.; Yu, M. Utilizing MapReduce to Improve Probe-Car Track Data Mining. ISPRS Int. J. Geo-Inf. 2018, 7, 287.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top