Data mining concepts are used frequently throughout the transportation research sector. This article examines the concept of the market basket technique as a means of gaining more insight into public transport users’ demands. The article 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 designed to maximize 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, new timetables, and strategic decisions in relation to satisfying public transport customer demands.
Tseytin, G., et al.
Tracing Individual Public Transport Customers from an Anonymous Transaction Database.
Journal of Public Transportation, 9 (4): 47-60.
Available at: https://scholarcommons.usf.edu/jpt/vol9/iss4/4