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).
Highlights
Traditional traffic surveys mainly collected data on people’s workdays [1,2] and there were few surveys for holidays
Using a text mining technique to explore the travel patterns and trip activities of an important Chinese holiday season, the research aims to achieve the following contributions—(1) based on the various passenger groups, the overall inner-city mobility characteristics of the Spring Festival are described from three levels; (2) With the points of interest (POIs)-appended metro stations, travelers’ trip activities at the station level are inferred and passengers’ travel pattern difference between the holiday-week and the other two normal-weeks is revealed; (3) Latent Dirichlet allocation, a text mining technique is applied to explore the travel patterns and several policy implications are discussed
The overall mobility patterns are represented by the POIs-depicted travel purpose, which is estimated by the shift between two metro stations with the appended POIs attribute
Summary
Traditional traffic surveys mainly collected data on people’s workdays [1,2] and there were few surveys for holidays (e.g., the 1995 American Travel Survey). Using a text mining technique to explore the travel patterns and trip activities of an important Chinese holiday season, the research aims to achieve the following contributions—(1) based on the various passenger groups, the overall inner-city mobility characteristics of the Spring Festival are described from three levels; (2) With the POIs-appended metro stations, travelers’ trip activities at the station level are inferred and passengers’ travel pattern difference between the holiday-week and the other two normal-weeks is revealed; (3) Latent Dirichlet allocation, a text mining technique is applied to explore the travel patterns and several policy implications are discussed. Section 7: research limitations and lines of future research are identified
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