As the core of the rail freight flow organization process, train formation problem (TFP) has attracted much attention. In Chinese practice, car flow routing, TFP, and train routing are usually optimized sequentially to reduce the complexity of computation, which may result in a local optimum, and even no feasible solution. To address this issue, this paper studies the integrated optimization of the three sub problems with aims to minimize the total cost of transportation cost, accumulation cost, and classification cost. An integer linear arc-based model incorporating the unitary and in-tree rules of a shipment is first formulated and solved by the state-of-the-art solver GUROBI. Since GUROBI can't deal with the large test cases, a path-based model is built and solved by a bespoken two-phase algorithm. The first phase of the algorithm is Benders-and-Price approach that combines Benders decomposition and column generation, and the second phase is to solve the arc-based model with some variables fixed as the corresponding values fetched from the first phase. The results show that the proposed algorithm outperforms GUROBI, and the acceleration techniques, i.e., trust region and Pareto-optimal cuts, can improve the convergence efficiency of Benders decomposition significantly.

This paper addresses the scheduled service network design problem for rail freight transportation. The proposed model integrates service selection and scheduling, and routing of time-dependent customer shipments based on a connection network representation of the associated operations and decisions along with their relations and time dimensions. Benefiting from the construction method of the connection network, the transfer restrictions of shipments between services can be easily incorporated into our models. We propose two model formulations based on arc variables and path variables respectively. A bespoken solution methodology that combines the Benders decomposition and column generation is proposed to solve the path-based model. Compared with the results of using GUROBI to solve the arc-based model, experimental results show that the proposed approach is effective, yielding high-quality solutions for all test instances, and the acceleration techniques, i.e., trust-region and Pareto-optimal cuts, can significantly improve the convergence efficiency of Benders decomposition.

The dynamic exit sign has been verified as an effective means to guide the pedestrian during evacuation. The most critical mechanism with dynamic exit sign guidance is to balance the pedestrian flow on each exit route by optimizing the direction of signs. This paper formulates a bi-level programming model for the direction optimization problem of dynamic signs in buildings. In the bi-level program, the upper-level model is a system optimal model, aiming to minimize the total travel time by optimizing the dynamic sign direction. The lower-level model is a pedestrian assignment model satisfying the dynamic user optimal principle that describes the evacuee exit/route choice behaviour to achieve a balanced pedestrian distribution on the route. A method based on the fundamental diagram, the cell transmission model, and the point-queuing theory is developed to estimate evacuation travel time considering congestion and queuing. A heuristic algorithm is extended to solve the bi-level program. Finally, the proposed methodology is validated with numerical examples. Results reveal that the proposed model can produce the optimal dynamic sign direction, significantly improving the evacuation efficiency.

The cross-line operation (CO) of trains in urban rail transit is an effective method to efficiently satisfy transfer passenger travel demand as well as relieve the pressure of transfer stations. The primary problem of CO is designing train services to satisfy travel demand with an uneven spatial distribution of passengers. This study constructs a nonlinear integer programming model with a novel train operation scheme, i.e., virtual coupling (VC) technology, which allows the coupling/decoupling of trains on different lines at both ends of each operation zone. This scheme makes the train capacity equitably distributed in each operation zone, thereby balancing train capacity utilization over the whole CO system. Regarding the nonlinear characteristics of the proposed model, an adaptive simulated annealing genetic algorithm (ASA-GA) was designed to quickly generate high-quality solutions. Based on real-world data from the Beijing Changping Line and Line 13, the effectiveness of the proposed model and algorithm were verified. The computation results show that in comparison to a single grouping train composition scheme without CO, a VC scheme with CO would reduce operation costs by 46.8%, with 80.6% savings of train capacity equity. Furthermore, the average passenger residence time would be reduced by 25.9%.

In the context of the network operation of urban rail transit systems, disruptions caused by signal interruptions influence not only the operation of the service at a single station but also the level of service of the whole network. Moreover, it is even possible to induce the cascading failure of the urban rail transit network. Therefore, it is essential to maintain the real-time dynamic monitoring of abnormal stations in urban rail transit systems for security reasons. Based on the large amounts of automated fare collection (AFC) data, a real-time calculation method to estimate the influence intensity of the passenger flow is presented, the spatiotemporal distribution of the influence characteristics is analyzed, and the propagation law of disruptions in the urban rail transit network is explored. First, the fluctuation threshold of passenger flow in a normal situation for all stations was calculated. Accordingly, abnormal stations influenced by the disruption were identified. Then, an evaluation method for calculating the influence intensity of the passenger flow was proposed. Finally, a real-world case study based on the Beijing rail transit system was conducted. All abnormal stations were identified dynamically and displayed in real time, and the distribution and propagation law of abnormal stations were constructed by spatiotemporal diagrams. The influence intensity of passenger flow was analyzed in detail from the perspective of the whole network and representative stations. The results revealed that transfer stations were more vulnerable to the effects of disruption, and the duration for which these stations were affected was longer than that of ordinary stations. Moreover, short-distance travelers were less affected by the disruption than long-distance travelers. The method proposed in this paper can provide a theoretical basis for rail management departments to grasp the characteristics of passenger flow in real time, formulate disposal measures dynamically, and provide more accurate information services for passengers.