Volume 39 Issue 2
Apr.  2021
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Article Navigation >  Journal of Transport Information and Safety  > 2021  >  39(2): 101-108
ZUO Jingli, WANG Qiuping, CHEN Ju. A Fusion Algorithm Based on Spatiotemporal Characteristics of the GPS Data and IC Card Data in Urban Public Transportation[J]. Journal of Transport Information and Safety, 2021, 39(2): 101-108. doi: 10.3963/j.jssn.1674-4861.2021.02.013
Citation: ZUO Jingli, WANG Qiuping, CHEN Ju. A Fusion Algorithm Based on Spatiotemporal Characteristics of the GPS Data and IC Card Data in Urban Public Transportation[J]. Journal of Transport Information and Safety, 2021, 39(2): 101-108. doi: 10.3963/j.jssn.1674-4861.2021.02.013
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A Fusion Algorithm Based on Spatiotemporal Characteristics of the GPS Data and IC Card Data in Urban Public Transportation

doi: 10.3963/j.jssn.1674-4861.2021.02.013

  • School of Civil Engineering, Xi'an University of Architecture & Technology, Xi'an 710055, China

  • Received Date: 2020-07-27
    Abstract
  • Since there is no direct connection between the GPS data and IC card data of some urban buses, it is difficult to correlate and obtain the passenger boarding data. The situation becomes more difficult when the two sets of data have irregular time deviations. The paper analyzes the fast matching data fusion of spatiotemporal characteristics, containing the following steps. Firstly, the bus timetable is obtained according to the bus GPS data and stop location matching. Then, the time similarity curve is drawn between the timetable and time-corrected IC card data through tra versal calculation. The corresponding relationship is found and verified by the curve of time-average deviation. Finally, the time correction value between the two systems is determined. The relevant three-day data is calculated on 195 buses in 5 routes in Xi'an city, where 191 vehicles have obvious identification characteristics. Besides, the algorithm is verified through 344 vehicles with known correspondences in 16 routes in Nanning City. The exact correspondence between 336 vehicles is obtained, with an average time corrected error of 16.5 s. The results show that the matching rate of the algorithm is 97.67%. For the widely existing bus GPS data and IC data belonging to different systems, it is difficult to judge the situation of bus stops by swiping the card. The proposed method expands the application scope of the original imperfect bus data and lays a foundation for analyzing individual micro travel behaviors in public transportation.

     

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