Mobility Crowdsourcing: Toward Zero-Effort Carpooling on Individual Smartphone
In current carpooling systems, drivers and passengers offer and search for their trips through available mediums, for example, accessing carpool website by smartphone, for finding a possible match of the journey. While efforts have been made to achieve fast matching for known trips, the need for acc...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2013-02-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2013/615282 |
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| _version_ | 1832547217475371008 |
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| author | Nianbo Liu Yong Feng Feng Wang Bang Liu Jinchuan Tang |
| author_facet | Nianbo Liu Yong Feng Feng Wang Bang Liu Jinchuan Tang |
| author_sort | Nianbo Liu |
| collection | DOAJ |
| description | In current carpooling systems, drivers and passengers offer and search for their trips through available mediums, for example, accessing carpool website by smartphone, for finding a possible match of the journey. While efforts have been made to achieve fast matching for known trips, the need for accurate mobile tracking for individual users still remains a bottleneck. For example, drivers feel impatient to input their routes before driving, or centralized systems haves difficulties to track a large number of vehicles in real time. In this paper, we present the idea of Mobility Crowdsourcing (MobiCrowd), which leverages private smartphone to collect individual trips for carpooling, without any explicit effort on the part of users. Our scheme generates daily trips and mobility models for each user, and then makes carpooling zero-effort by enabling travel data to be crowdsourced instead of tracking vehicles or asking users to input their trips. With prior mobility knowledge, one user's travel routes and positions for carpooling can be predicted according to the location of the time and other mobility context. Based on a realistic travel survey and simulation, we prove that our scheme can provide efficient and accurate position estimation for individual carpools. |
| format | Article |
| id | doaj-art-484e8ae7c6794f97b93ae0e88932effb |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2013-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-484e8ae7c6794f97b93ae0e88932effb2025-02-03T06:45:33ZengWileyInternational Journal of Distributed Sensor Networks1550-14772013-02-01910.1155/2013/615282Mobility Crowdsourcing: Toward Zero-Effort Carpooling on Individual SmartphoneNianbo Liu0Yong Feng1Feng Wang2Bang Liu3Jinchuan Tang4 School of Computer Science and Engineering, University of Electronic Science and Technology of China, North Jianshe Road, Chengdu 611731, China Yunnan Key Laboratory of Computer Technology Applications, Kunming University of Science and Technology, Kunming 650500, China School of Computer Science and Engineering, University of Electronic Science and Technology of China, North Jianshe Road, Chengdu 611731, China School of Computer Science and Engineering, University of Electronic Science and Technology of China, North Jianshe Road, Chengdu 611731, China School of Computer Science and Engineering, University of Electronic Science and Technology of China, North Jianshe Road, Chengdu 611731, ChinaIn current carpooling systems, drivers and passengers offer and search for their trips through available mediums, for example, accessing carpool website by smartphone, for finding a possible match of the journey. While efforts have been made to achieve fast matching for known trips, the need for accurate mobile tracking for individual users still remains a bottleneck. For example, drivers feel impatient to input their routes before driving, or centralized systems haves difficulties to track a large number of vehicles in real time. In this paper, we present the idea of Mobility Crowdsourcing (MobiCrowd), which leverages private smartphone to collect individual trips for carpooling, without any explicit effort on the part of users. Our scheme generates daily trips and mobility models for each user, and then makes carpooling zero-effort by enabling travel data to be crowdsourced instead of tracking vehicles or asking users to input their trips. With prior mobility knowledge, one user's travel routes and positions for carpooling can be predicted according to the location of the time and other mobility context. Based on a realistic travel survey and simulation, we prove that our scheme can provide efficient and accurate position estimation for individual carpools.https://doi.org/10.1155/2013/615282 |
| spellingShingle | Nianbo Liu Yong Feng Feng Wang Bang Liu Jinchuan Tang Mobility Crowdsourcing: Toward Zero-Effort Carpooling on Individual Smartphone International Journal of Distributed Sensor Networks |
| title | Mobility Crowdsourcing: Toward Zero-Effort Carpooling on Individual Smartphone |
| title_full | Mobility Crowdsourcing: Toward Zero-Effort Carpooling on Individual Smartphone |
| title_fullStr | Mobility Crowdsourcing: Toward Zero-Effort Carpooling on Individual Smartphone |
| title_full_unstemmed | Mobility Crowdsourcing: Toward Zero-Effort Carpooling on Individual Smartphone |
| title_short | Mobility Crowdsourcing: Toward Zero-Effort Carpooling on Individual Smartphone |
| title_sort | mobility crowdsourcing toward zero effort carpooling on individual smartphone |
| url | https://doi.org/10.1155/2013/615282 |
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