Improving Population Demand Estimation with Transit ChainingBreaks

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 reflectiveof 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 publictransportation. 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 approachentailing the use of transit chaining breaks is also to be evaluated based on the calculation of Pearson product-moment correlationcoefficients 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 chainingapproach 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 transportdemand within a population