Exploring human mobility is essential for urban applications. To observe human mobility, various data-driven techniques based on different data sources, such as cell phone and transportation data, have been proposed. This paper investigates human mobility through the emerging vehicular Internet service on public bus system. The key idea is that if a passenger is using WiFi on the bus or his/her WiFi device is activated in the background, we know that the passenger is traveling on the bus. By fusing the network events generated by WiFi devices with the data from the automatic fare collection (AFC) system, and the bus GPS information, we exploit not only the origin but also the destination of a passenger. Based on this idea, we develop a novel system called ViFi-MobiScanner which consists of about 4, 800 mobile routers distributed in a city with 1, 992 KM2 urban area. We develop an ID matching algorithm that matches part of the users' network identities and their smartcard identities anonymously. As a result, we have built a set of labeled samples with the reference of observation from smartcard data and use them to train a classifier to infer users mobility from their network activities. We evaluate ViFi-MobiScanner with both field tests and collected datasets associated with 168 million network events, 3.6 million trips, and 1.4 million users. The evaluation results show that ViFi-MobiScanner increases the observability on the passengers and trips by about 53.9% and 48.1% over the smartcard observations. ViFi-MobiScanner also helps to estimate the passengers' destination that cannot be observed by current smartcard systems and the estimation can be accomplished in minutes. Thus it expands the observability of mobility in object, temporal and spatial dimensions and provides unique insights on human mobility at metropolitan scales.
|Original language||English (US)|
|Number of pages||13|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|State||Published - Jan 2021|
Bibliographical noteFunding Information:
Manuscript received January 9, 2018; revised August 19, 2018, August 13, 2019, and October 28, 2019; accepted November 18, 2019. Date of publication December 5, 2019; date of current version December 24, 2020. This work was supported in part by the China National Key Research and Development Program under Grant 2018YFB2100300, and in part by the National Natural Science Foundation of China under Grant 6167219 and Grant BK20190336. The Associate Editor for this article was C. F. Mecklenbräuker. (Corresponding author: Lai Tu.) L. Tu is with the School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China (e-mail: email@example.com).
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- Human mobility observation
- multi-source data mining
- vehicular internet service