# Spatiotemporal Evolution of Travel Pattern Using Smart Card Data

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## Abstract

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## 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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**Figure 7.**The configurations of travel behaviors in clusters 1–7. (

**a**) Temporal profile of cluster 1: 549,327 passengers (2.9%); (

**b**) temporal profile of cluster 2: 2,239,660 passengers (11.8%); (

**c**) temporal profile of cluster 3: 3,798,313 passengers (20.0%); (

**d**) temporal profile of cluster 4: 2,234,894 passengers (11.8%); (

**e**) temporal profile of cluster 5: 2,825,129 passengers (14.9%); (

**f**) temporal profile of cluster 6: 4,521,905 passengers (23.8%); and (

**g**) temporal profile of cluster 7: 2,772,880 passengers (14.6%).

**Figure 10.**Spatial distribution of daily station volume for cluster 1 (2011–2017). (

**a**) cluster 1 in 2011; (

**b**) cluster 1 in 2012; (

**c**) cluster 1 in 2013; (

**d**) cluster 1 in 2014; (

**e**) cluster 1 in 2015; (

**f**) cluster 1 in 2016; and (

**g**) cluster 1 in 2017.

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**MDPI and ACS Style**

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

**AMA Style**

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 Style**

Lin, 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