RESEARCH ARTICLE


Improving Population Demand Estimation with Transit Chaining Breaks



Jin Haitao1, 2, 3, *, Jin Fengjun1, 3, Ni Yong1, 3, 4, Huang Jianling2, Du Yong2
1 Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2 Beijing Transportation Information Center, Beijing 100161, China
3 University of Chinese Academy of Sciences, Beijing 100049, China
4 China National Environmental Monitoring Centre, Beijing 100012, China


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© 2018 Haitao et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China; Tel: 0086-13911172325; E-mail: jinht.16b@igsnrr.ac.cn


Abstract

Background:

Data mining of smart card data collected through AFC systems have proved useful in estimations of public transport demand. Whereas most estimations of demand are made by analyzing transit orientations or destinations of unchained transits. However, organization of bus or metro routes compels riders to make a lot of unnecessary transfers, and the transfer points are neither reflective of population’s actual orientations nor of their destinations.

Aims and Objectives:

The objective of this paper is to improve estimations of population demand by identifying transfer activities of riders using public transportation. Durations and displacements of transit chaining breaks are to be check in judging the transfer activities.

Boarding stops for making transfers are ruled out as transportation demand estimation. The effectiveness of the new approach entailing the use of transit chaining breaks is also to be evaluated based on the calculation of Pearson product-moment correlation coefficients for assessing the correlation between transportation estimation and population distribution.

Result and Conclusion:

Durations and displacements of transit chaining breaks could be used to identify transfer activities. The use of the transit chaining approach reduces the occurrence of false demand, resulting in the estimation being more objective in relation to the population.

The results of the study indicated that the inclusion of transit chaining breaks leads to more accurate estimations of public transport demand within a population.

Keywords: Public transport, Urban transportation, Transport demand estimation, Transit chaining breaks, Smart card data, Population equity.