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.

Keywords: Spatio-temporal network; Trajectory estimation; Urban rail transit; Wi-Fi probe data; n-gram method.

Fig. 1

Ideal case of passenger’s location identification based on Wi-Fi probe data.

Fig. 2

STT estimation process.(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 3

A simple URT network and one STT.(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 4

Spatio-temporal location inference results.

Fig. 5

Probability distribution of transfer time in one transfer station.

Fig. 6

Set of available routes.

Fig. 7

Typical feasible route-choice estimation results about circumstance (2).(For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Fig. 8

Typical feasible route-choice estimation results about circumstance (3).

Fig. 9

Sum of route and trajectory estimation results.