Day-to-day Similarity of Individual Activity Chain of Public Transport Passengers

Smart card data provides the data basis for the study of long-term travel regularity of public transport passengers. Based on the smart card data from April to May 2018 in Beijing, this study constructed the passenger activity chain in three steps, including extracting the passenger activity location, inferring the residence location, and identifying the activity type. The PrefixSpan algorithm was used to extract the frequent sequence patterns of activity chains for regular, senior and student card users. Levenshtein distance was applied to measure the day-to-day similarity of the three types of passengers' daily activity chain. The results show that about 70% of users in each type have a symmetrical pattern of frequent activity sequences. The similarity of regular card and student card users is higher than senior card users, with an average of 0.645, 0.649, and 0.530, respectively. For all the three types of users, the differences in day-to-day activity chain sequences between workdays and weekends are larger, but the similarities within workdays or within weekends are higher. This study helps to quantitatively analyze the regularity of passenger travel and activity and provides evidence for scientific optimization of public transport services. Copyright © 2020 by Science Press.