Estimating Sectional Volume of Travelers Based on Mobile Phone Data
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 146, Issue 10
Abstract
The sectional volume of travelers refers to the number of travelers crossing a section boundary (e.g., river, mountain, railway line, etc.) within a certain time period. Mobile phone data provides continuous and large-scale mobility pattern information without compromising the comprehensiveness of travel modes. We propose a three-stage framework to estimate the sectional volume of travelers using the base station trajectory from massive mobile phone data. In the first two stages, the spatial and temporal uncertainties of trajectories are explicitly addressed by a hybrid filtering algorithm and a cell-to-cell trajectory inference algorithm, respectively. In the third stage, the sectional volume of travelers is estimated using aggregated trajectories. The proposed framework is validated using a sampled dataset with annotated ground truth and a city-scale dataset. The results show that the proposed framework is effective in dealing with spatial and temporal uncertainties of trajectories. The sectional volume estimation method performs stably with a low average error rate and is applicable to section boundaries of different scales.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are proprietary or confidential in nature and may only be provided with restrictions, including a sampled dataset of Nanjing, China collected in April 2016 and a dataset of Kunshan, China from August 2–9, 2019.
Acknowledgments
The work was jointly supported by the National Key Research and Development Program of China (Grant No. 2018YFB1600900), the National Natural Science Foundation of China (Grant No. 71601045) and the Social Science Foundation of Jiangsu Province (Grant No. 16GLC008). Zhichen Liu and Xiao Fu contributed equally to this paper and should be considered co-first authors.
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History
Received: Jan 8, 2020
Accepted: May 14, 2020
Published online: Jul 20, 2020
Published in print: Oct 1, 2020
Discussion open until: Dec 20, 2020
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