Characterizing evolution of extreme public transit behavior using smart card data

Abstract

Existing studies have extensively used temporal-spatial data to mine the mobility patterns of different kinds of travelers. Smart Card Data (SCD) collected by the Automated Fare Collection (AFC) systems can reflect a general view of the mobility pattern of the whole bus and metro riders in urban area. Most existing work focusing on mobility pattern usually ignore a special group of people who travel in abnormal patterns or mechanisms. In this paper, we focus on the evolution extreme transit behaviors of travelers in urban area by using SCD in 2010 and 2014. We have several aspects of descriptive statistics of the SCD with a view to better understanding the dynamic process and evolution of the extreme transit behavior. By combining the SCD's temporal information with the amount of travel behavior, we also propose a concept of Extreme Index (EI) based on the mixture Gaussian model to depict the extreme level of the passengers' travel pattern. According to our analysis, the normal transit behavior of the two years have nearly the same temporal distribution. Although the EI models of the two years have similar distributions, the EI model of 2010 with two peaks is more scattered than that of 2014, which has only one peak. The EI model, which assigns an EI attribute for each SCD, can be applied in further analysis of urban transit or passengers' behavior.