Exploring Travel Patterns during the Holiday Season—A Case Study of Shenzhen Metro System During the Chinese Spring Festival

Jianxiao Liu · Wenzhong Shi · Pengfei Chen

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Human Mobility and Location-Based Analysis Transportation Planning and Optimization Urban Transport and Accessibility

Abstract

Research has shown that the growing holiday travel demand in modern society has a significant influence on daily travel patterns. However, few studies have focused on the distinctness of travel patterns during a holiday season and as a specified case, travel behavior studies of the Chinese Spring Festival (CSF) at the city level are even rarer. This paper adopts a text-mining model (latent Dirichlet allocation (LDA)) to explore the travel patterns and travel purposes during the CSF season in Shenzhen based on the metro smart card data (MSC) and the points of interest (POIs) data. The study aims to answer two questions—(1) how to use MSC and POIs inferring travel purpose at the metro station level without the socioeconomic backgrounds of the cardholders? (2) What are the overall inner-city mobility patterns and travel activities during the Spring Festival holiday-week? The results show that six features of the CSF travel behavior are found and nine (three broad categories) travel patterns and trip activities are inferred. The activities in which travelers engaged during the CSF season are mainly consumption-oriented events, visiting relatives and friends and traffic-oriented events. This study is beneficial to metro corporations (timetable management), business owners (promotion strategy), researchers (travelers’ social attribute inference) and decision-makers (examine public service).

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References

2012Discovering regions of different functions in a city using human mobility and POIs 2015Origin–destination trips by purpose and time of day inferred from mobile phone data 2012Social Media Use and Impact during the Holiday Travel Planning Process 2016Activity-Based Human Mobility Patterns Inferred from Mobile Phone Data: A Case Study of Singapore 2005The potential of public transport smart card data 2018Check-in behaviour and spatio-temporal vibrancy: An exploratory analysis in Shenzhen, China 2014Urban activity pattern classification using topic models from online geo-location data 2014Behavioural data mining of transit smart card data: A data fusion approach

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