Efficient and accurate fine grained trip identification for metro passengers from cellular trajectory data

Guanyao Li; Ruyu Xu; Xingdong Deng; Yang Liu; Chenghong Zheng; Tingyan Shi; Chuanbao Zhao; Jiuping Zha

doi:10.1117/12.3054489

20 December 2024 Efficient and accurate fine grained trip identification for metro passengers from cellular trajectory data

Guanyao Li, Ruyu Xu, Xingdong Deng, Yang Liu, Chenghong Zheng, Tingyan Shi, Chuanbao Zhao, Jiuping Zha

Author Affiliations +

Proceedings Volume 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024); 1342144 (2024) https://doi.org/10.1117/12.3054489
Event: Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 2024, Dalian, China

Abstract

Gaining a comprehensive understanding of individuals' fine-grained metro trips within a city is crucial for effective urban planning, optimizing public transport network design, and facilitating the arrangement of public transport timetables. As cellular data allow us to analyze travel behaviors of the whole population, we study the problem of identify metro trip from cellular trajectory data in this work. Existing identification approaches designed for GPS trajectories often lack applicability to cellular data due to their lower spatial-temporal granularity compared to GPS data. Consequently, most approaches for cellular data focus on identifying coarse-grained transportation modes. To address the challenges of location noise and irregular data sampling for cellular data, we propose an efficient and accurate approach for fine-grained metro trip identification. Our approach employs a series of data processing operation to enhance the quality of cellular data, mitigating the effects of noise and irregular sampling. Moreover, considering the spatial-temporal information in the trajectory and metro network, we propose an effective spatial-temporal Hidden Markov Model to jointly split trajectories and identify metro trips from cellular data. Extensive experiments on two real datasets show that our proposed approach is significantly accurate and efficient for metro trip identification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Guanyao Li, Ruyu Xu, Xingdong Deng, Yang Liu, Chenghong Zheng, Tingyan Shi, Chuanbao Zhao, and Jiuping Zha "Efficient and accurate fine grained trip identification for metro passengers from cellular trajectory data", Proc. SPIE 13421, Eighth International Conference on Traffic Engineering and Transportation System (ICTETS 2024), 1342144 (20 December 2024); https://doi.org/10.1117/12.3054489

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KEYWORDS

Transportation

Global Positioning System

Denoising

Tunable filters

Data modeling

Cell phones

Data processing

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