Tracing Individual Public Transport Customers from an Anonymous Transaction Database

Data mining concepts are frequently used throughout the transportation research sector. This paper examines the concept of the market basket technique as a means of gaining more insight into public transit users’ demands. The paper proposes a method that uses various data attributes of passenger records to infer the same customer in a different week (i.e., track the same customer from week to week). The general idea behind the measure is that if 2 records are considered similar, ideally every trip in one customer record should have a close counterpart in the other record. The research develops a similarity function aimed at maximizing the percentage of positive ticket identification over a number of weeks. Once similarity has been established, customer travel patterns can be useful in helping the operator identify new routes and timetables and strategic decisions in relation to satisfying public transit customer demands.