Mining smart card data to estimate transfer passenger flow in a metro network

Abstract

Metro systems play an important role in reducing urban traffic congestion and promoting the sustainable development of urban transport in megacities. With the expansion of a metro network, transfer stations are necessary for increasing the service connectivity of a metro network. An accurate estimation of transfer passenger flow can help improve the operations management of a metro system. This study proposes a data-driven methodology for estimating the transfer passenger flow volume of each transfer station in a metro network by mining smart card data. The estimated transfer passenger flow data are visualized to show the spatial-temporal distribution characteristics of metro transfer passenger flow. The case study results of the Shenzhen Metro network demonstrate that the proposed data-driven methodological framework is very effective in estimating different types of transfer passenger flows, such as total transfer passenger flow, hourly transfer passenger flow, and inbound and outbound transfer flows at each transfer station. The spatial-temporal distribution characteristics of transfer passenger flow can be very useful for designing effective and efficient passenger flow management measures to ensure the safe and efficient operation of a metro system.