NDLI: Improved algorithms for trip-chain estimation using massive student behaviour data from urban transit systems

Content Provider	IET Digital Library
Author	Cong, Jiamin Gao, Linjie Juan, Zhicai
Abstract	With bus global positioning system data and smart-card data, this study puts forward improved algorithms to further increase the success rate of alighting stop identification for the student group, so that trip chains and origin-destination (OD) matrixes of students can be obtained with the high estimated rate. Aimed at the student group, after using and generalising conventional alighting algorithms, this study innovatively utilises resident and non-resident students’ typical trip patterns to further increase the success estimated rate. As a result, the algorithms are verified with a correct estimation of 74.9%, where the success rate steeply increases by 8.6% through the method based on students’ typical trip patterns. The empirical analysis and application have shown that the methodology can observe the trip chains of students and optimise the bus service for students. In the future, OD matrixes obtained should be validated with bus OD surveys, and traffic system of metro and shared bikes as well as other datasets such as land use data will be taken into consideration.
Starting Page	435
Ending Page	442
Page Count	8
ISSN	1751956X
Volume Number	13
e-ISSN	17519578
Issue Number	Issue 3, Mar (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/13/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2018.5183
Journal	IET Intelligent Transport Systems
Publisher Date	2018-09-07
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Algebra Alighting Stop Identification Bus Global Positioning System Data Bus OD Surveys Bus Service Optimisation Data Analysis Data Handling Technique Global Positioning System Land Use Data Massive Student Behaviour Data Matrix Algebra Metro Bikes Nonresident Students Typical Trip Pattern OD Matrixes Resident Students Typical Trip Pattern Road Traffic Road Vehicle Shared Bikes Smart Cards Smart-card Data Student Group Traffic Engineering Computing Traffic System Trip-chain Estimation Urban Transit System
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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