Citywide Metro-to-Bus Transfer Behavior Identification Based on Combined Data from Smart Cards and GPS

Abstract

The aim of this study is to develop a fast data fusion method for recognizing metro-to-bus transfer trips based on combined data from smart cards and a GPS system. The method is intended to establish station- and time-specific elapsed time thresholds for overcoming the limitations of one-size-fits-all criterion which is not sufficiently convincing for different transfer pairs and personal characteristics. Firstly, a data fusion method with bus smart card data and GPS data is proposed to supplement absent bus boarding information in the smart card data. Then, a model for identifying metro-to-bus interchange trips is derived based on two rules about maximal allowable transfer distance and elapsed transfer time threshold. Finally, in tests that used half-monthly field smart card data and GPS data from Shenzhen, China, the results recognized by the proposed method were more consistent with the actual surveyed group transfer time with a P value of 0.17 determined by Mann–Whitney U test. The comparison analysis showed that the proposed method can be widely applied to successfully identify and interpret metro-to-bus interchange behavior beyond a static transfer time threshold of 30 min.

How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method

“…We also record the travel time of all passengers every hour from the bus stop to a candidate metro station that is less than the upper bound of 50 min. Specifically, the upper bounds of 40 min and 50 min are mainly set in accordance with the literature related to recognizing transfer methods [10,57]. Existing survey studies show that 95% of the transfer ridership of the metro-to-bus mode has transfer times within 40 min, while 95% of the transfer ridership of the bus-to-metro mode has transfer times within 50 min [58].…”

“…The method for identifying transfer time and transfer ridership is derived from the literature [10,55,56] and has been improved based on the following aspects. First, we record the travel time of all passengers every hour from the metro station to a candidate bus station, which is less than the upper bound of 40 min.…”

“…In this paper, the transfer ridership of the metro-to-bus mode is measured in terms of the number of transfer passengers from metro to bus per hour, and the transfer ridership of the bus-to-metro mode is measured in terms of the number of transfer passengers from bus to metro per hour. A complicated transfer system usually connects a metro network with a feeder bus network and can facilitate travelers, increase the accessibility of metros, and reduce congestion and environmental pollution [10,11]. However, the transfer is unpopular among passengers because it increases travel time and reduces travel efficiency [12,13].…”

Integration of Automated Vehicle Location, Fare Control, and Schedule Data for Improved Public Transport Trip Definition

“…• Tracking SCs along metro and bus to identify transfer behavior in Shenzhen (China), making use of bus AFC records that only show card id and sweeping time [55].…”

A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting

“…In recent years, with the rapid improvement in computer capabilities, many prediction methods based on deep learning algorithms have emerged. With good performance in other fields, many deep learning methods (such as convolutional neural network (CNN) models [5], recurrent neural network (RNN) models, and long short-term memory (LSTM) models [6][7][8][9]) have been introduced to predict short-term traffic flow and have achieved better prediction performance than traditional forecasting methods. In addition, the combined model often has a better predictive effect than the single model [10][11][12][13].…”