With the development of big data and deep learning, bus passenger flow prediction considering real-time data becomes possible. Real-time traffic flow prediction helps to grasp real-time passenger flow dynamics, provide early warning for a sudden passenger flow and data support for real-time bus plan changes, and improve the stability of urban transportation systems. To solve the problem of passenger flow prediction considering real-time data, this paper proposes a novel passenger flow prediction network model based on long short-term memory (LSTM) networks. The model includes four parts: feature extraction based on Xgboost model, information coding based on historical data, information coding based on real-time data, and decoding based on a multi-layer neural network. In the feature extraction part, the data dimension is increased by fusing bus data and points of interest to improve the number of parameters and model accuracy. In the historical information coding part, we use the date as the index in the LSTM structure to encode historical data and provide relevant information for prediction; in the real-time data coding part, the daily half-hour time interval is used as the index to encode real-time data and provide real-time prediction information; in the decoding part, the passenger flow data for the next two 30 min interval outputs by decoding all the information. To our best knowledge, it is the first time to real-time information has been taken into consideration in passenger flow prediction based on LSTM. The proposed model can achieve better accuracy compared to the LSTM and other baseline methods.

To alleviate traffic congestion and traffic-related environmental pollution caused by the increasing numbers of private cars, public transport (PT) is highly recommended to travelers. However, there is an obvious contradiction between the diversification of travel demands and simplification of PT service. Customized bus (CB), as an innovative supplementary mode of PT service, aims to provide demand-responsive and direct transit service to travelers with similar travel demands. But how to obtain accurate travel demands? It is passive and limited to conducting online surveys, additionally inefficient and costly to investigate all the origin-destinations (ODs) aimlessly. This paper proposes a methodological framework of extracting potential CB routes from bus smart card data to provide references for CB planners to conduct purposeful and effective investigations. The framework consists of three processes: trip reconstruction, OD area division and CB route extraction. In the OD area division process, a novel two-step division model is built to divide bus stops into different areas. In the CB route extraction process, two spatial-temporal clustering procedures and one length constraint are implemented to cluster similar trips together. An improved density-based spatial clustering of application with noise (DBSCAN) algorithm is used to complete these procedures. In addition, a case study in Beijing is conducted to demonstrate the effectiveness of the proposed methodological framework and the resulting analysis provides useful references to CB planners in Beijing.

The customized bus (CB) is an innovative and environmental supplementary mode of public transport, providing demand-responsive and user-oriented service to specific passenger groups with similar travel demands, especially commuters, based on online reservations. However, sufficient travel demand is essential for the successful operation of CB. The purpose of this study is to analyze the factors influencing the attraction of CB to commuters, which is tied to the ordered mode shift decisions, do no transfer to CB, remain undecided, and transfer to CB. A combination of revealed preference (RP) survey and stated preference (SP) survey is conducted among commuters in Beijing through online and offline questionnaire, collecting 1304 valid commuting demands. The ordered logit (OL) model and two-level mixed-effect ordered logit (MEOL) model are used to estimate the variable effects and the difference in five commute modes, including car, taxi, bus, rail, bus + rail, is considered. Common variables significantly influencing the transfer decision in all groups are specified in models, including familiarity to CB, seat availability, and gender. Meanwhile, travel cost, travel time, and transfer time of the current travel mode have positive effects on the attraction of CB. In addition, car ownership and accessibility to bus stations also influence the attraction of CB to certain group commuters. This paper can provide references to CB operators for formulating differentiation strategies and attracting more passengers in Beijing.