NDLI: Bayesian models with spatial autocorrelation for bike sharing ridership variability based on revealed preference GPS trajectory data

Content Provider	IET Digital Library
Author	Christian, Kapuku Cho, Shin Hyung Kho, Seung Young Kim, Dong Kyu
Abstract	Increasing bike sharing ridership to reduce traffic congestion and air pollution has become the major goal of policy makers and transportation planners. In this study, aggregated macro-level models are developed to investigate and quantify correlations of bike sharing ridership variability in the city of Seoul. The bike sharing kilometres travelled (BSKT) is introduced as the ridership measurement, and a revealed preference method for its estimation is proposed using more than five million trip trajectories of global positioning system (GPS) collected from the bike sharing system of Seoul city. Six different models grouped into three categories are estimated, including linear regression, Bayesian model without spatial effect and Bayesian models with spatial autocorrelation. Five categories of variables are used to explain the variability of BSKT in Seoul. The contribution of these variables in intra-zonal BSKT and inter-zonal BSKT are also modelled and compared. The findings suggest a strong correlation among BSKT, bike sharing stations and bicycle network variables. The model comparison results indicate that the performances of the models with spatial autocorrelation are considerably improved.
Starting Page	1658
Ending Page	1667
Page Count	10
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 11, Nov (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/11
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2019.0159
Journal	IET Intelligent Transport Systems
Publisher Date	2019-09-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Aggregated Macro-level Model Air Pollution Bayes Method Bayesian Model Bicycle Network Variables Bicycles Bike Sharing Kilometres Bike Sharing Ridership Bike Sharing Stations Bike Sharing System BSKT Global Positioning System Million Trip Trajectories Model Comparison Result Regression Analysis Revealed Preference GPS Trajectory Data Revealed Preference Method Ridership Measurement Ridership Variability Road Traffic Seoul City Spatial Autocorrelation Statistics System Theory Application in Transportation Traffic Congestion Transportation
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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