Abstract
Crowds are an important feature of high-dense Mass Rail Transit (MRT), assessing its crowding status is a critical step in crowd management. In this chapter, a pedestrian crowd classification method based on an improved ant colony clustering algorithm (ACCA) is developed for MRT systems. First, survey data from Automatic Fare Collection (AFC) regarding three statuses (check-in/check-out and sum). Second, the PCI-influenced factors were also considered in the method, which included average daily ridership intensity, the duration of crowd, and the scope of crowd influence. Third, to classify the pedestrian crowd, an improved ant colony clustering model and its solving algorithm were presented. The results show that, for the two types of time scale, the passengers’ time–space characteristics present a clear image of M, the variation trend of morning and evening peak hour is obvious in the MRT.
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