The train operation plan plays an essential role in metro systems and directly affects transportation organization efficiency and passenger service level. In metro systems, passengers have paid more attention to the travel time reliability (TTR), reflecting the reliability of metro operation management. This article proposes an analysis method of train operation plan based on TTR in the station dimension. First, an automated fare collection (AFC) data-driven framework is established to calculate the station travel time reliability (STTR) and analyze the train operation plan at different periods. The framework structure consists of four steps: AFC data preprocessing, STTR calculation and assignment, clustering algorithm design based on SOM neural network, and train operation plan analysis and optimization. Second, the proposed method is applied to the Beijing metro network as a case study. Several promising results are analyzed that allow the optimization of the existing train operation plan. Our research shows that STTR is a good supplement for the existing metro operation assignment studies, which can help analyze and optimize the train operation plan effectively. This study is also applicable to other metro networks with AFC systems.
In this study, we developed a method for coordinating and optimizing the train connection plans of different lines under the conditions of urban rail transit (URT) network operation. The method allows trains of different lines to form good connections at transfer stations, which can shorten the waiting time of passengers for transfers and reduce passenger retention. A mathematical model was developed to simulate the interaction between passengers and trains. Two optimization models were developed for the train connection plan of network transfer stations based on different optimization objectives during peak and off-peak hours. Subsequently, a corresponding solution method based on a genetic algorithm and simulation was designed. Finally, the Suzhou URT network was used as a case study, and the passenger flow of the transfer station was simulated and calculated using relevant automatic fare collection (AFC) data. The results indicated that the average waiting time and the number of passengers stranded were reduced using the proposed method. The calculation example demonstrated the effectiveness of the model and algorithm, which can guide the coordinated preparation of a network train connection plan.
Regional integrated energy system research is the current trend of energy development. Electric vehicle is an important part of the integrated energy system. Electric vehicle can realize the optimal allocation of energy by participating in automatic demand response (ADR). Therefore, this paper proposes based on regional integrated energy automatic demand response optimization scheduling of electric vehicle with based on the conditional value at risk with the utility function (UCVaR-based) electric vehicle response intention decision model under integrated energy system. Primarily, given both the electric vehicle’s advantage and the network load fluctuation, a based on regional integrated energy automatic demand response optimization scheduling of electric vehicle model is proposed. The price-based DR stage aims to minimize the charge/discharge cost. Minimizing the gap at the incentive-based demand response stage between the peak and valley load. Then, to quantify the risk attitude of an EV group, the UCVaR-based EV owner response willingness decision model is adopted. Specifically, prospect theory is used to calculate the utility value of electric vehicle owners. Finally, to check the feasibility of the proposed process, a case study is given. Results show that compared to the price-based demand response model, the model, which is proposed in this paper, decreased the average charging expense of electric vehicles by 49 percent and raised the gain by 19 percent.
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