Search

To provide personalized and precise guidance services to subway passengers, a route recommendation method for frequent subway riders based on passenger preference ranking is proposed. Firstly, a data-driven analysis method of travel preference is proposed to identify the travel preference of frequent passengers in different origin-destinations (ODs) and time periods. Secondly, a route recommendation model of dictionary order preference based on just noticeable difference (JND) is proposed to solve the problem of different passenger perception in route attributes. Thirdly, the model of depth deterministic strategy gradient (DDPG) is proposed to consider dynamic parameters of JND. A case study of Guangzhou Metro is illustrated to verify the accuracy of the route recommendation model. The results show that the individual passenger recommendation in the proposed method is more accurate than the traditional logit model and the JND model without parameter optimization by 30% and 20%. The group recommendation in the proposed method is more accurate than the comparative models by 37% and 11%, respectively. Specifically, the accuracy of this method in group recommendation fluctuates less, and its accuracy variance is much smaller than the traditional logit model. Therefore, the method proposed in this study performs better in reflecting personalized need of passenger travel. [ABSTRACT FROM AUTHOR]

The accurate prediction of short-term passenger flow is of high importance to efficiently manage the passenger flow of metro systems and adjust timetable accordingly. However, the existing methods of passenger flow prediction cannot achieve adequate accurate results due to its complex nonlinear spatiotemporal characteristics. To improve the accuracy of short-term passenger flow prediction, this paper proposes a deep learning model based on a spatiotemporal framework. Firstly, the graph convolutional network, which incorporates prior domain knowledge (such as travel time and origin–destination demand), is used to extract spatial features of passenger flow. Secondly, the attention mechanism is integrated into the gated recurrent unit to extract the time correlation of passenger flow. Finally, external factors are introduced to capture their impact on passenger flow as well. A case study of the Beijing Subway system is illustrated to verify the performance of the proposed model. The results show that compared with the existing models, the proposed model achieves the highest prediction accuracy and strong robustness. Furthermore, we demonstrate that the adjacency matrix based on travel time outperforms the one based on OD demand, especially during evening peak hours. In addition, it is also verified that the attention mechanism and external factors can improve the prediction performance of the proposed model. [ABSTRACT FROM AUTHOR]

Urban mobility pattern recognition has great potential in revealing human travel mechanism, discovering passenger travel purpose, and predicting and managing traffic demand. This paper aims to propose a data-driven method to identify metro passenger mobility patterns based on Automatic Fare Collection (AFC) data and geo-based data. First, Point of Information (POI) data within 500 meters of the metro stations are captured to characterize the spatial attributes of the stations. Especially, a fusion method of multisource geo-based data is proposed to convert raw POI data into weighted POI data considering service capabilities. Second, an unsupervised learning framework based on stacked auto-encoder (SAE) is designed to embed the spatiotemporal information of trips into low-dimensional dense trip vectors. In detail, the embedded spatiotemporal information includes spatial features (POI categories around the origin station and that around the destination station) and temporal features (start time, day of the week, and travel time). Third, a density-based clustering algorithm is introduced to identify passenger mobility patterns based on the embedded dense trip vectors. Finally, a case of Beijing metro network is used to verify the feasibility of the above methodology. The results show that the proposed method performs well in recognizing mobility patterns and outperforms the existing methods. [ABSTRACT FROM AUTHOR]

• Spatiotemporal features of passenger flow under station closures is considered. • Residual time series due to station closures is checked and corrected. • The changes in passenger flow during station closures is captured. • EDFM-SVR hybrid approach outperforms regular approach. Passenger flow prediction is critical for subway managers to efficiently organize passenger flow and assign capacity resources. Station closures caused by economic conferences and political events alter the topology of subway networks, resulting in large-scale passenger flows with irregular trends. Thus, predicting subway passenger flow during station closures is a challenging task. In this study, we propose a hybrid model for predicting short-term passenger flow during subway station closures. First, a spatiotemporal ensemble prediction model is used to capture the temporal characteristics of passenger flow at each station and the spatial characteristics of passenger flow among distinct groups of stations. Then, to address the effects of station closures, a support vector regression method is devised to investigate the residual time series from the ensemble model. Finally, a case study of Beijing subway is examined to validate the performance of the proposed model. The results indicate that the proposed model is superior to other state-of-the-art models in terms of accuracy and reliability in estimating the effects of station closures. Furthermore, the residuals from station closures are thoroughly converted into white noise and the proposed model is more interpretable than those in previous studies. [ABSTRACT FROM AUTHOR]

This paper learns the route choice behavior of passengers from Auto Fare Collection, timetable, and train loading data using a method combined with Bayesian inference and Metropolis-Hasting sampling. First, the influential factors of route choice such as in-vehicle travel time, transfer time, and in-vehicle crowding are given. Next, formulations are established based on AFC, timetable and train loading data, which are merged into a logit model of route choice behavior of subway passengers. Next, an algorithm integrating Bayesian inference and Metropolis-Hasting sampling is designed to calibrate parameters of the logit model. Finally, a case study of Beijing subway is applied to verify the validity of the model and algorithm. A detailed discussion shows that in-vehicle crowding plays a crucial role in passenger route choice behavior. [ABSTRACT FROM AUTHOR]

Short-term passenger flow prediction and passenger flow control are essential for managing congestion in metros. This paper proposes a new dynamic radial basis function (RBF) neural network to forecast outbound passenger volumes and improve passenger flow control. First, we adopt a train timetable to model passenger flow propagation and identify potential stations that substantially impact the outbound volumes of the target stations. As a result, we incorporate inbound volume, outbound volume, and the train timetable to avoid overfitting. Second, passenger flow control is considered to improve the prediction accuracy by adding passenger flow control coefficients to our model, which then attempts to specify the true influence of these potential stations during crowded times. Finally, we construct a dynamic input radial basis function neural network whose performance is illustrated by the following three scenarios: large passenger flow under passenger flow control during morning peak hours, evening peak hours under passenger flow control, and normal passenger flow without passenger flow control. Compared with the backpropagation neural network, the wavelet support vector machine and the K-nearest neighbor, the proposed method achieves the best prediction performance at a half-hour prediction time lag. The proposed method can also identify crucial stations and time periods 30 min in advance, which contributes when considering proactive passenger flow control to alleviate congestion during peak hours in metro networks. • Passenger flow demand under congestion is predicted with big data. • A radial basis function neural network with dynamic input layer is introduced. • The proposed method outperforms other models illustrated by three scenarios. [ABSTRACT FROM AUTHOR]

It is critical to improve the effectiveness of demand management in metro systems with passenger departure time choice exactly learned during peak hours. In this study, a practical framework is developed to model departure time choice of metro passengers during peak hours. First, various attributes that influence departure time choice of metro passengers are investigated and the technique for order preference by similarity to ideal solutions (TOPSIS) is used to identify these main attributes. Then, a mixed logit (ML) model of departure time choice that accounts for price endogeneity is developed. To calibrate the model, a stated preference (SP) survey based on D-efficient design is conducted in the Beijing metro system. It is proved that the corrected ML model outperforms the uncorrected ML model according to the collected 1152 sample data. An elasticity analysis of these main attributes is further conducted, which indicates that metro fare and departure time change influence passenger departure time choice more than crowdedness in Beijing metro. Knowledge of these preferences assists traffic managers in balancing passenger departure time to mitigate overcrowding during peak hours. Heterogeneity of passenger socioeconomic and trip characteristics is also concerned taking advantage of ML model. Finally, a ML-based fare discount strategy to ease the crowdedness in Batong Line of Beijing metro is presented and evaluated via an existing simulation tool. [ABSTRACT FROM AUTHOR]