POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station
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 meth...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-05-01
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| Series: | ISPRS International Journal of Geo-Information |
| Subjects: | |
| Online Access: | http://www.mdpi.com/2220-9964/7/5/178 |
| _version_ | 1819044922058080256 |
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| author | Wei Yang Tinghua Ai |
| author_facet | Wei Yang Tinghua Ai |
| author_sort | Wei Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T10:20:22Z |
| format | Article |
| id | doaj.art-637fe42b01404899b37986edaa6aa201 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-12-21T10:20:22Z |
| publishDate | 2018-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-637fe42b01404899b37986edaa6aa2012022-12-21T19:07:28ZengMDPI AGISPRS International Journal of Geo-Information2220-99642018-05-017517810.3390/ijgi7050178ijgi7050178POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas StationWei Yang0Tinghua Ai1School of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaSchool of Resource and Environmental Sciences, Wuhan University, 129 Luoyu Road, Wuhan 430079, ChinaPoints 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.http://www.mdpi.com/2220-9964/7/5/178crowdsourcing trajectory datasocial media datadata enhancementgas stationmap update |
| spellingShingle | Wei Yang Tinghua Ai POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station ISPRS International Journal of Geo-Information crowdsourcing trajectory data social media data data enhancement gas station map update |
| title | POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station |
| title_full | POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station |
| title_fullStr | POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station |
| title_full_unstemmed | POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station |
| title_short | POI Information Enhancement Using Crowdsourcing Vehicle Trace Data and Social Media Data: A Case Study of Gas Station |
| title_sort | poi information enhancement using crowdsourcing vehicle trace data and social media data a case study of gas station |
| topic | crowdsourcing trajectory data social media data data enhancement gas station map update |
| url | http://www.mdpi.com/2220-9964/7/5/178 |
| work_keys_str_mv | AT weiyang poiinformationenhancementusingcrowdsourcingvehicletracedataandsocialmediadataacasestudyofgasstation AT tinghuaai poiinformationenhancementusingcrowdsourcingvehicletracedataandsocialmediadataacasestudyofgasstation |