Tracing individual public transport customers from an anonymous transaction database

Abstract

Data mining concepts are used frequently throughout the transportation research sector. This paper uses the concept of the market basket technique on public transport users as a means of gaining more insight into their transport 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 attempts to track the same customer from week to week. The general idea behind the measure is that, if two 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 and this aims to maximise the percentage of positive ticket identification over a number of weeks. Once similarity has been established, the travel patterns of customers can be useful in helping the operator identify new routes, new timetables and strategic decisions in relation to satisfying public transport customer demands.