Incorporating Smart Card Data in Spatio-Temporal Analysis of Metro Travel Distances
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Hui Bi | Chen Enhui | Zhirui Ye | Z. Ye | Hui Bi | Chen Enhui
[1] Jonathan Corcoran,et al. Examining the spatial–temporal dynamics of bus passenger travel behaviour using smart card data and the flow-comap , 2014 .
[2] Peng Zhou,et al. Understanding the determinants of travel mode choice of residents and its carbon mitigation potential , 2018 .
[3] Chuan Ding,et al. Influences of built environment characteristics and individual factors on commuting distance: A multilevel mixture hazard modeling approach , 2017 .
[4] Catherine Morency,et al. Smart card data use in public transit: A literature review , 2011 .
[5] Jinhyun Hong,et al. Does public transit improvement affect commuting behavior in Beijing, China? A spatial multilevel approach , 2017 .
[6] Xinyu Cao,et al. Effects of metro transit on the ownership of mobility instruments in Xi’an, China , 2017 .
[7] Yunpeng Wang,et al. Investigating the impacts of built environment on vehicle miles traveled and energy consumption: Differences between commuting and non-commuting trips , 2017 .
[8] A. El-geneidy,et al. The effect of neighbourhood characteristics, accessibility, home–work location, and demographics on commuting distances , 2010 .
[9] Arefeh A. Nasri,et al. The analysis of transit-oriented development (TOD) in Washington, D.C. and Baltimore metropolitan areas , 2014 .
[10] Keechoo Choi,et al. Land use characteristics of subway catchment areas and their influence on subway ridership in Seoul , 2015 .
[11] M. Batty,et al. Measuring variability of mobility patterns from multiday smart-card data , 2015, J. Comput. Sci..
[12] Meng Zhou,et al. The built environment and travel behavior in urban China: A literature review , 2017 .
[13] T. Toivonen,et al. Modelling the potential effect of shared bicycles on public transport travel times in Greater Helsinki: An open data approach☆ , 2013 .
[14] Qing Shen,et al. How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales , 2014 .
[15] Kwangyul Choi. The influence of the built environment on household vehicle travel by the urban typology in Calgary, Canada , 2018 .
[16] Harry Timmermans,et al. Using metro smart card data to model location choice of after-work activities : An application to Shanghai , 2017 .
[17] Panagiotis Ch. Anastasopoulos,et al. Hazard-Based Analysis of Travel Distance in Urban Environments: Longitudinal Data Approach , 2012 .
[18] Chandra R. Bhat,et al. Quantifying the relative contribution of factors to household vehicle miles of travel , 2018, Transportation Research Part D: Transport and Environment.
[19] Alireza Ermagun,et al. Built environmental impacts on commuting mode choice and distance: evidence from Shanghai , 2017 .
[20] Bo Wu,et al. Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices , 2010, Int. J. Geogr. Inf. Sci..
[21] Xinyu Cao,et al. Examining the effect of the Hiawatha LRT on auto use in the Twin Cities , 2019, Transport Policy.
[22] Joseph Ferreira,et al. Vehicle Miles Traveled and the Built Environment: Evidence from Vehicle Safety Inspection Data , 2014 .
[23] H. Timmermans,et al. A causal model relating urban form with daily travel distance through activity/travel decisions , 2009 .
[24] Thomas Klinger,et al. Moving between mobility cultures: what affects the travel behavior of new residents? , 2016 .
[25] Y. Chai,et al. Understanding job-housing relationship and commuting pattern in Chinese cities: Past, present and future , 2017 .
[26] W. Deng,et al. What influences Metro station ridership in China? Insights from Nanjing , 2013 .
[27] Xiaohong Chen,et al. Vehicle kilometers traveled reduction impacts of Transit-Oriented Development: Evidence from Shanghai City , 2017 .
[28] Min Yang,et al. Exploring the impact of residential relocation on modal shift in commute trips:Evidence from a quasi-longitudinal analysis , 2017 .
[29] B. Loo,et al. Rail-Based Transit-Oriented Development: Lessons from New York City and Hong Kong , 2010 .
[30] Yongxi Gong,et al. Exploring the spatiotemporal structure of dynamic urban space using metro smart card records , 2017, Comput. Environ. Urban Syst..
[31] Chao Liu,et al. Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance , 2017 .
[32] Yunpeng Wang,et al. Understanding commuting patterns using transit smart card data , 2017 .
[33] J. Scheiner,et al. Travel Distances in Daily Travel and Long-Distance Travel: What Role is Played by Urban Form? , 2014 .
[34] Frank Witlox,et al. Car ownership as a mediating variable in car travel behaviour research using a structural equation modelling approach to identify its dual relationship , 2010 .
[35] Yingling Fan,et al. Do built environment effects on travel behavior differ between household members? A case study of Nanjing, China , 2017, Transport Policy.
[36] S. Bekhor,et al. Evaluating long‐distance travel patterns in Israel by tracking cellular phone positions , 2013 .
[37] R. Cervero,et al. Effects of Built Environments on Vehicle Miles Traveled: Evidence from 370 US Urbanized Areas , 2010 .
[38] Jie Bao,et al. Exploring Bikesharing Travel Patterns and Trip Purposes Using Smart Card Data and Online Point of Interests , 2017 .
[39] Azad Abdulhafedh,et al. How to Detect and Remove Temporal Autocorrelation in Vehicular Crash Data , 2017 .
[40] Mika Ristimäki,et al. Relationships between commuting distance, frequency and telework in Finland , 2007 .
[41] Seungil Lee,et al. Urban structural hierarchy and the relationship between the ridership of the Seoul Metropolitan Subway and the land-use pattern of the station areas , 2013 .
[42] Cheng Shi,et al. What determines rail transit passenger volume? Implications for Transit oriented development planning , 2017 .