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
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ISPRS Int. J. Geo-Inf. 2018, 7(5), 178; https://doi.org/10.3390/ijgi7050178
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)
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.
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Keywords:
crowdsourcing trajectory data; social media data; data enhancement; gas station; map update
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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. https://doi.org/10.3390/ijgi7050178
AMA Style
Yang W, Ai T. POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station. ISPRS International Journal of Geo-Information. 2018; 7(5):178. https://doi.org/10.3390/ijgi7050178
Chicago/Turabian StyleYang, Wei; Ai, Tinghua. 2018. "POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station" ISPRS Int. J. Geo-Inf. 7, no. 5: 178. https://doi.org/10.3390/ijgi7050178
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