Paper Info
Title
Spatio-temporal trajectory estimation based on incomplete Wi-Fi probe data in urban rail transit network
Abstract
This study presents a methodology for estimating passenger’s spatio-temporal trajectory with personalization and timeliness by using incomplete Wi-Fi probe data in urban rail transit network. Unlike the automatic fare collection data that only records passenger’s entries and exits, the Wi-Fi probe data can capture more detailed passenger movements, such as riding a train or waiting on a platform. However, the estimation of spatio-temporal trajectories remains as a challenging task because a few unfavorable situations could result into deficient data. To address this problem, we first describe the Wi-Fi probe data and summarize their common defects. Then, the n-gram method is developed to infer missing spatio-temporal location information. Next, an estimation algorithm is designed to generate feasible spatio-temporal trajectories for each individual passenger by integrating multiple data sources, i.e., urban rail transit network topology, Wi-Fi probe data, train schedules, etc. This proposed method is tested on both simulated data in blind experiments and real-world data from a complex urban rail transit network. The results of case study show that 93% of passengers’ unique physical routes can be estimated. Then, for 80% of passengers, the number of feasible spatio-temporal trajectories can be reduced to one or two. Potential applications of the trajectory estimation approach are also identified.
Year
DOI
Venue
2021
10.1016/j.knosys.2020.106528
Knowledge-Based Systems
Keywords
DocType
Volume
Urban rail transit,Trajectory estimation,Spatio-temporal network,n-gram method,Wi-Fi probe data
Journal
211
ISSN
Citations 
PageRank 
0950-7051
1
0.63
References 
Authors
10
6
Name
Order
Citations
PageRank
Jinjing Gu110.63
Z. B. Jiang224236.08
Yanshuo Sun310.63
Min Zhou410.63
Shenmeihui Liao510.63
Jingjing Chen610.63