Abstract
Accurate demand data are crucial in public transport planning. Bus operators increasingly are introducing smart cards to replace cash payments. Users load money onto their cards, the cards are tagged by a reading device on the vehicle, and the customer is charged accordingly. Smart cards give the public transport operator access to a vast amount of information on trips made by customers. However, not every bus operator has access to these data; instead they use ride and point checks or revenue counts to determine demand and the resulting loads on vehicles. These checks are costly and time-consuming and are prone to human error. The objective of this study was to examine how the quality of the resultant timetables differed when other methods were used. This study focused on the determination of the daily and hourly maximum load points (which can be determined by point checks) as well as individual maximum load points of the vehicles (which require either ride checks or an automated passenger count system). The timetables based on the different maximum load points were created with a multi-objective approach that had two simultaneous objectives: minimize the expected passenger waiting time and minimize the discrepancy from a desired occupancy level on the vehicles. The developed methodology was applied to a case study in Auckland, New Zealand. A detailed analysis of the timetables then revealed whether one method was superior to the others and, if so, to what extent.
Get full access to this article
View all access options for this article.