Next Article in Journal
2DPR-Tree: Two-Dimensional Priority R-Tree Algorithm for Spatial Partitioning in SpatialHadoop
Next Article in Special Issue
A Simple Line Clustering Method for Spatial Analysis with Origin-Destination Data and Its Application to Bike-Sharing Movement Data
Previous Article in Journal / Special Issue
Agent-Based Modeling of Taxi Behavior Simulation with Probe Vehicle Data
Article Menu
Issue 5 (May) cover image

Export Article

Open AccessArticle
ISPRS Int. J. Geo-Inf. 2018, 7(5), 178; https://doi.org/10.3390/ijgi7050178

POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station

School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Received: 26 March 2018 / Revised: 30 April 2018 / Accepted: 7 May 2018 / Published: 8 May 2018
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
Full-Text   |   PDF [3689 KB, uploaded 8 May 2018]   |  

Abstract

Points of interest (POIs) such as stores, gas stations, and parking lots are particularly important for maps. Using gas station as a case study, this paper proposed a novel approach to enhance POI information using low-frequency vehicle trajectory data and social media data. First, the proposed method extracted spatial information of the gas station from sparse vehicle trace data in two steps. The first step proposed the velocity sequence linear clustering algorithm to extract refueling stop tracks from the individual trace line after modeling the vehicle refueling stop behavior using movement features. The second step used the Delaunay triangulation to extract the spatial information of gas stations from the collective refueling stop tracks. Second, attribute information and dimension sentiment semantic information of the gas station were extracted from social media data using the text mining method and tripartite graph model. Third, the gas station information was enhanced by fusing the extracted spatial data and semantic data using a matching method. Experiments were conducted using the 15-day vehicle trajectories of 12,000 taxis and social media data from the Dazhongdianping in Beijing, China, and the results showed that the proposed method could extract the spatial information, attribute information, and review information of gas stations simultaneously. Compared with ground truth data, the automatically enhanced gas station was proved to be of higher quality in terms of the correctness, completeness, and real-time. View Full-Text
Keywords: crowdsourcing trajectory data; social media data; data enhancement; gas station; map update crowdsourcing trajectory data; social media data; data enhancement; gas station; map update
Figures

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).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Yang, W.; Ai, T. POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station. ISPRS Int. J. Geo-Inf. 2018, 7, 178.

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

1

Comments

[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