Abstract
In the current urban rail transit systems, nearly 15% of passengers are noncommuter travelers who use single-trip ticket cards (ticket cards). Accordingly, the effective management of ticket cards is of great importance. This article suggests a time series model for use in predicting ticket card storage based on the characteristics of ticket cards collected by an automatic fare collection (AFC) system. The distribution cycle, station types, and distribution volume of each station are also determined. Then, drawing on small package transportation feasibility theory, an unbalanced distribution model between production and demand (unbalanced distribution model), as well as a hybrid distribution model of loading and unloading (hybrid distribution model), is established. Application of these models to the Beijing Subway system is used to verify the efficiency and feasibility of such a hybrid distribution model. The analysis and results offer insights into usage patterns of urban rail transit ticket cards, providing solid evidence for a relative decision-making process.
Highlights
One notable barrier to effective management of ticket cards is the geographical imbalance of ticket card storage among subway system’s stations
After ticket cards are sold by an automatic fare collection (AFC) system, they are collected by an automatic gate machine (AGM) as passengers exit the station
Uncertain passenger flows inevitably result in geographical imbalances of ticket cards
Summary
One notable barrier to effective management of ticket cards is the geographical imbalance of ticket card storage among subway system’s stations. The present approach to managing ticket cards is not sufficient to effectively respond to growing traffic demand In response to these issues, this paper proposes a set of positive management methods for forecasting and dynamically distributing ticket cards among stations so as to reduce total storage demand for ticket cards, improve ticket card turnover rate, and ensure better travel experiences for passengers. Precise short-term predictions of passenger flows are the basis for determining distribution cycle, distribution volume, and other basic statistics of ticket cards. Us, the time series model has been proved to be an effective approach with which to predict short-term passenger flows in an urban transit system. Is paper is organized as follows: Section 2 proposes a storage prediction model based on a time series model.
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