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

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

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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.

http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2018.5183
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