Extracting the Complete Travel Trajectory of Subway Passengers Based on Mobile Phone Data
The usage of mobile phones has undergone tremendous growth in the past decades. Large amounts of mobile-phone signaling data (MSD) are generated while using various mobile phone applications. The large-scale MSD presents opportunities for transport planners to utilize it for better planning and mana...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2022/8151520 |
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| _version_ | 1832564635018985472 |
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| author | Junwei Zhang Wei Wu Qixiu Cheng Weiping Tong Anish Khadka Xiao Fu Ziyuan Gu |
| author_facet | Junwei Zhang Wei Wu Qixiu Cheng Weiping Tong Anish Khadka Xiao Fu Ziyuan Gu |
| author_sort | Junwei Zhang |
| collection | DOAJ |
| description | The usage of mobile phones has undergone tremendous growth in the past decades. Large amounts of mobile-phone signaling data (MSD) are generated while using various mobile phone applications. The large-scale MSD presents opportunities for transport planners to utilize it for better planning and management of the transportation system. In this paper, we use MSD to analyze subway passengers’ travel behavior and extract their complete travel trajectories. The complete travel trajectories of subway passengers include their trajectories, both inside and outside the subway system. In the first stage, the MSD from the subway base stations is selected, sorted by time, and the rough trajectory in the subway system is extracted. The ground base stations around the subway station are then considered to correct the boarding and alighting subway stations in order to obtain a more detailed trajectory. In the second stage, the service range of the base station is determined according to the Thiessen polygon, and a temporal dynamic threshold is proposed to extract the passenger’s stop point outside the subway system. Finally, the complete trajectories of subway passengers are obtained. The proposed algorithms are verified using a set of MSD collected in Suzhou, China. The results show that the proposed algorithms can effectively extract the complete travel trajectory of subway passengers. |
| format | Article |
| id | doaj-art-c893457e0e6d4197b1c26b9aa41c1ff8 |
| institution | Kabale University |
| issn | 2042-3195 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-c893457e0e6d4197b1c26b9aa41c1ff82025-02-03T01:10:37ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/8151520Extracting the Complete Travel Trajectory of Subway Passengers Based on Mobile Phone DataJunwei Zhang0Wei Wu1Qixiu Cheng2Weiping Tong3Anish Khadka4Xiao Fu5Ziyuan Gu6School of TransportationZhejiang Institute of Communications Co., Ltd.School of TransportationSchool of TransportationSchool of TransportationSchool of TransportationSchool of TransportationThe usage of mobile phones has undergone tremendous growth in the past decades. Large amounts of mobile-phone signaling data (MSD) are generated while using various mobile phone applications. The large-scale MSD presents opportunities for transport planners to utilize it for better planning and management of the transportation system. In this paper, we use MSD to analyze subway passengers’ travel behavior and extract their complete travel trajectories. The complete travel trajectories of subway passengers include their trajectories, both inside and outside the subway system. In the first stage, the MSD from the subway base stations is selected, sorted by time, and the rough trajectory in the subway system is extracted. The ground base stations around the subway station are then considered to correct the boarding and alighting subway stations in order to obtain a more detailed trajectory. In the second stage, the service range of the base station is determined according to the Thiessen polygon, and a temporal dynamic threshold is proposed to extract the passenger’s stop point outside the subway system. Finally, the complete trajectories of subway passengers are obtained. The proposed algorithms are verified using a set of MSD collected in Suzhou, China. The results show that the proposed algorithms can effectively extract the complete travel trajectory of subway passengers.http://dx.doi.org/10.1155/2022/8151520 |
| spellingShingle | Junwei Zhang Wei Wu Qixiu Cheng Weiping Tong Anish Khadka Xiao Fu Ziyuan Gu Extracting the Complete Travel Trajectory of Subway Passengers Based on Mobile Phone Data Journal of Advanced Transportation |
| title | Extracting the Complete Travel Trajectory of Subway Passengers Based on Mobile Phone Data |
| title_full | Extracting the Complete Travel Trajectory of Subway Passengers Based on Mobile Phone Data |
| title_fullStr | Extracting the Complete Travel Trajectory of Subway Passengers Based on Mobile Phone Data |
| title_full_unstemmed | Extracting the Complete Travel Trajectory of Subway Passengers Based on Mobile Phone Data |
| title_short | Extracting the Complete Travel Trajectory of Subway Passengers Based on Mobile Phone Data |
| title_sort | extracting the complete travel trajectory of subway passengers based on mobile phone data |
| url | http://dx.doi.org/10.1155/2022/8151520 |
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