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.
Highlights
Reliable estimations of transit demands can facilitate improvements in public transport [1]
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
320 The Open Transportation Journal, 2018, Volume 12 made by analyzing transit orientations or destinations [3], in practice, transfer points are neither reflective of transit riders’ actual orientations nor of their destinations
Summary
Reliable estimations of transit demands can facilitate improvements in public transport [1]. The use of smart cards enabling Automated Fare Collection (AFC) is becoming increasingly popular [4], and data collected through AFC systems have proved useful in transit planning [5]. 320 The Open Transportation Journal, 2018, Volume 12 made by analyzing transit orientations or destinations [3], in practice, transfer points are neither reflective of transit riders’ actual orientations nor of their destinations. The practical organization of bus or metro routes compels riders to make unnecessary transfers. Data mining of smart card data collected through AFC systems have proved useful in estimations of public transport demand. 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
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