ABSTRACT
This chapter discusses the use of smart card ticketing data to determine the distribution of waiting times at individual railway stations by frequency of service, time of day and day of week. Best practice suggests that if public transport services are frequent enough, then customers will no longer try to match their arrival time at the public transport stop to the departure time of the service and will simply ‘turn up and go’. But the question remains as to how frequent a service needs to be before passengers will simply turn up and go.
The distribution of waiting times is investigated through a case study in Sydney, Australia. Sydney’s Opal Card data is used to model individual passengers’ arrival times at railway stations compared to the frequency of the service to identify waiting times. The challenges in transforming the raw data and fitting relevant probability distributions are discussed. The chapter concludes with some suggestions for future research.
