Refining Sparse Cell-ID Trajectory of Public Service Vehicles by Spatiotemporal Modelling

Kemin Zhu,Weifeng Wang,Junli Liu,Xianfeng Song,Hao Chen,Rocío De Oña

doi:10.1155/2021/1586010

Abstract

Mobile phone data have become a critical data source for transportation research. While a cell-id trajectory was routinely reorganized by International Mobile Subscriber Identity (IMSI), it potentially allows to analyze transportation behaviors and social interaction of total population, with a full temporal coverage at low cost. However, cell-id trajectory is often sparse due to low reporting frequency and uncertainness of mobile holders’ position. So, the cell-id trajectory refinement has been recognized as challenging work to further facilitate trajectory data mining. This paper presents a comprehensive approach to identify cell-id trajectories of public service vehicles (PSVs) from large volume of trajectories and further refines these cell-id trajectories by a heuristic global optimization approach. The modified longest common subsequence (LCSS) method is used to match a cell-id trajectory and a public transportation route (PTR) and correspondingly calculates their similarities for determining whether the trajectory is PSV mode or not. Taking full advantages of the nature of a PSV tends to move on the PTR in uniform motion to meet a prescript visit to stops, a heuristic global optimization approach is deployed to build a spatiotemporal model of a PSV motion, which estimates new locations of cell-id trajectories on the PTR. The approach was finally tested using Beijing cellular network signaling datasets. The precision of PSV trajectory detection is 90%, and the recall is 88%. Evaluated by our GNSS-logged trajectories, the mean absolute error (MAE) of refined PSV trajectories is 144.5 m and the standard deviation (St. Dev) is 81.8 m. It shows a significant improvement in comparison of traditional interpolation methods.

Highlights

Cellular network-based data are emerging as a great data source for urban transportation application due to the advantage in the large geographic coverage of cellular networks and the comprehensive penetration in a population [1,2,3]
E location in cell-id trajectory at a particular time is assigned with the coordinate of an occupied base transceiver station (BTS). e spatial resolution of cell-id trajectory data depends on the service radius of each BTS, which varies in different areas, e.g., of hundred meters in metropolitan cities, and several kilometers in rural areas [5]
A public service vehicles (PSVs) cell-id trajectory often includes several round-trips along a public transportation route (PTR) route, each alternating with a long-time stay at terminal stations as drivers have access to toilet facilities at rest, fuel, and food establishments. e stop time cannot be directly obtained from original data, so we introduced a spatiotemporal kernel density estimation (STKDE) method to identify these stays and enable to partition trips

Summary

Introduction

Cellular network-based data are emerging as a great data source for urban transportation application due to the advantage in the large geographic coverage of cellular networks and the comprehensive penetration in a population [1,2,3]. Existing refinement method based on mobile phone location data mainly. Trajectory points are calculated based on the distances and time spans between each missing point and its contextual points by an estimation function (e.g., nearest-neighbor function, linear function, or Gaussian function). Ficek and Kencl [12] proposed an intercall mobility model which combines Gaussian mixtures to refine CDRs. Hoteit et al [13, 14] compared the reconstruction performance of various interpolation methods (linear, cubic, nearest, and spline interpolations) on trajectories with different sampling interval and radius of gyration. Yu et al [15] used a spatially-linear-interpolated method to estimate exposure in air pollution of cell phone user. Perera et al [17] provided a method to compute the location of phone user within a cell, but it requires extra speed information which is generally unavailable in cell-id data

Methods

Results

Conclusion