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This paper studies the problem of rescheduling trains with speed management in uncertain disruptions that lead to temporary speed restrictions (TSRs) in high-speed railway systems. The disruption uncertainties are that the coverage and speed limit value of TSRs change randomly over time, and the start and end times of TSRs are unknown in advance. First, a mixed-integer linear programming model is formulated to reduce the total train traveling times and improve the passenger comfortability. The solutions of the model provide simultaneously the optimal train rescheduling strategies and train speed control strategies. Second, a rolling horizon algorithm is applied in consideration of the disruption uncertainties. The rolling horizon algorithm updates and solves the model in every time horizon, according to the disruption information newly detected. Therefore, the train rescheduling orders and speed control orders can be adjusted according to the real-time situation to guarantee effectiveness. The rolling horizon approach is terminated automatically once the disruption ends. Finally, based on the practical data of the Beijing-Tianjin intercity high-speed railway line, numerical experiments are carried out to test the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

• The paper proposes a novel dynamic graph recurrent convolutional neural network model, named Dynamic-GRCNN, to deeply capture the spatio-temporal traffic flow features for more accurately predicting urban passenger traffic flows. • The paper presents incidence dynamic graph structures based on historically passenger traffic flows to model traffic station relationships. Different from existing traffic transportation network topological structures based graph relationships between stations, the incidence dynamic graph structures firstly model the traffic relationships from historical passenger flows. • For real urban passenger traffic flows, the paper demonstrates that dynamic spatial-temporal incidence graphs are more suitable to model external changes and influences. • The paper compares Dynamic-GRCNN with state-of-the-art deep learning approaches on three benchmark datasets which contain different types of passenger traffic flows for evaluation. The results show that Dynamic-GRCNN significantly outperforms all the baselines in both effectiveness and efficiency in urban passenger traffic flows prediction. Accurate and real-time traffic passenger flows forecasting at transportation hubs, such as subway/bus stations, is a practical application and of great significance for urban traffic planning, control, guidance, etc. Recently deep learning based methods are promised to learn the spatial-temporal features from high non-linearity and complexity of traffic flows. However, it is still very challenging to handle so much complex factors including the urban transportation network topological structures and the laws of traffic flows with spatial and temporal dependencies. Considering both the static hybrid urban transportation network structures and dynamic spatial-temporal relationships among stations from historical traffic passenger flows, a more effective and fine-grained spatial-temporal features learning framework is necessary. In this paper, we propose a novel spatial-temporal incidence dynamic graph neural networks framework for urban traffic passenger flows prediction. We first model dynamic traffic station relationships over time as spatial-temporal incidence dynamic graph structures based on historically traffic passenger flows. Then we design a novel dynamic graph recurrent convolutional neural network, namely Dynamic-GRCNN, to learn the spatial-temporal features representation for urban transportation network topological structures and transportation hubs. To fully utilize the historical passenger flows, we sample the short-term, medium-term and long-term historical traffic data in training, which can capture the periodicity and trend of the traffic passenger flows at different stations. We conduct extensive experiments on different types of traffic passenger flows datasets including subway, taxi and bus flows in Beijing. The results show that the proposed Dynamic-GRCNN effectively captures comprehensive spatial-temporal correlations significantly and outperforms both traditional and deep learning based urban traffic passenger flows prediction methods. [ABSTRACT FROM AUTHOR]

High-precision short-term inbound passenger flow prediction is of great significance to the daily crowd management and line rescheduling in urban rail systems. Although current models have been applied to prediction, most methods need optimization to meet refined passenger flow management demand. In order to better predict the passenger flow, a novel Wave-LSTM model, based on long short-term memory network (LSTM) and wavelet, is introduced in this paper. In an empirical study with practical passenger flow data of Dongzhimen Station in the Beijing Subway system, the hybrid model exhibited more effective performance in terms of prediction accuracy than the existing algorithms, e.g., autoregressive integrated moving average (ARIMA), nonlinear regression (NAR), and traditional LSTM model. The study illustrates that our newly adopted model is a promising approach for predicting high-precision short-term inbound passenger flow. [ABSTRACT FROM AUTHOR]

• GPS and IC card data are used to simulate the time-dependent travel time and passenger demand. • An improved passenger selection model is proposed by introducing a preference coefficient. • A working hours constraint is first formulated in bus timetabling problem. • The model is transformed into an integer linear program, followed by a model contraction approach. • A two-stage solution method is proposed for large-scale bus timetabling problems. Bus lines are suffering from serious decline in passenger volume due to the rapid development of urban rail transit and shared transport, and big data intelligence may help them change the status quo. However, the tremendous amount of travel data collected in recent years have not got effectively utilization. In order to improve passenger volume for bus lines, this paper devotes to develop a data-driven bus timetable to substitute the existing experience-based bus timetable, which is now widely used by bus lines. Driven by the bus GPS data and IC card data, a timetable optimization model with time-dependent passenger demand and travel time among stops is proposed. The objective of maximizing passenger volume is based on a new preference-based passenger selection model. The working hours constraint is initially formulated, and the headway constraint and departure time constraints are also taken into account. For handling the step functions in both objective and constraints, we introduce a set of 0–1 variables to transform the proposed model into an integer linear programming. A model contraction approach is provided for solving the medium-scale problems and a two-stage solution method is proposed for the large-scale problems. The proposed model and methodology are tested on a real-world bus line in Beijing. The results show that it is able to produce a satisfactory timetable that outperforms the previously used experience-based one in terms of raising the average passenger volume by 8.2%. [ABSTRACT FROM AUTHOR]