Spatiotemporal Evolution of Travel Pattern Using Smart Card Data
Abstract
:1. Introduction
- We built individual subway trip chains (i.e., the sequence of trips generated during the day, with the information of O-D times and locations) and explored individual travel patterns based on individual trip frequency.
- We proposed a user clustering scheme to unveil the distribution of trip frequency over the hour of the day for each user, employing the GMM with EM algorithm for clustering and integrated Pareto principle method to decide the number of clusters.
- We revealed the evolution of residents’ personal travel patterns from 2011 to 2017, as well as the spatial and temporal distribution of each cluster.
2. Methods
2.1. Data Source and Preliminary Analysis
2.2. Vector of Individual Trip Features
2.3. Gaussian Mixture Model
2.4. Expectation-Maximization Algorithm
2.5. Parameter Choice
3. Results and Discussion
3.1. Clustering Results of Gaussian Mixture Model
3.2. Passenger Structures and Travel Characteristics
3.3. Spatio-Temporal Evolution of Cluster
4. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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Lin, M.; Huang, Z.; Zhao, T.; Zhang, Y.; Wei, H. Spatiotemporal Evolution of Travel Pattern Using Smart Card Data. Sustainability 2022, 14, 9564. https://doi.org/10.3390/su14159564
Lin M, Huang Z, Zhao T, Zhang Y, Wei H. Spatiotemporal Evolution of Travel Pattern Using Smart Card Data. Sustainability. 2022; 14(15):9564. https://doi.org/10.3390/su14159564
Chicago/Turabian StyleLin, Mu, Zhengdong Huang, Tianhong Zhao, Ying Zhang, and Heyi Wei. 2022. "Spatiotemporal Evolution of Travel Pattern Using Smart Card Data" Sustainability 14, no. 15: 9564. https://doi.org/10.3390/su14159564