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With the traffic congestion problem deteriorating, people increasingly choose urban rail transit (URT) to travel. Although URT alleviates traffic congestion, the long-term operation of a large number of trains leads to huge energy consumption. In order to adapt the major social development concept of "Low carbon", a multi-train energy-saving control collaborative optimization method is proposed in this paper. First, the composition of single train operating conditions is determined by the conversion of operating conditions between stations and the force changes under the premise of ensuring safe and on-time train operation. A single-train energy consumption calculation combinatorial optimization model with the dual control objectives of reducing passengers' average waiting time as well as train traction energy consumption is established. The energy saving control strategy of a single train is investigated by ARMA-Radial Basis Function Neural Network (ARMA-RBFNN) and Genetic Algorithm (GA). Next, the queuing theory is introduced to analyze the variation in passenger waiting time for multiple trains at different arrival intervals. A Deep Reinforcement Learning (DRL) algorithm is designed to obtain the correlation among passenger waiting time, arrival interval and train stopping time. The optimization objective is to minimize the multi-train traction energy consumption and the average passenger waiting time while considering conditions such as train operating safety interval, speed limit, multiple operating state and single train energy-saving models, etc. Then, a multi-train cooperative energy-saving control model is proposed based on the Dragonfly Algorithm (DA). Finally, a case study of Beijing Metro Line 4 is conducted to illustrate the effectiveness of the proposed method. The results demonstrate that the total traction energy consumption and passenger waiting time are reduced by 3.1% and 5 s, respectively, compared with the method of independently optimizing the single-train control strategy. The findings can aid in the development of energy-saving strategies and also provide a basis for energy-saving operation control of multiple trains. [ABSTRACT FROM AUTHOR]

Featured Application: This study purposes a synergy approach to generate the IFC model of very large quantities of data along with its graph database. Giving the identifier to each tile, following database syntax, the historic information and metadata are merged into the graph database. This synergy approach is developed on Grasshopper and Neo4j but is also applicable to other platforms. The investigation of cultural heritage is a continuous process, in which a large scale of metadata is gradually accumulated, i.e., condition data, intervention data, and the comprehension of building techniques. We take the roof tile of Yangxindian Palace as an example. The total number of roof tiles is 17,154. Their positions are various, as they are equally distributed on double-curved irregular surfaces. Their metadata are multisource and heterogeneous. Therefore, the integration between the model and the data can be complex. This paper aims to generate the M1 (metalevel) model with its graph database based on the M3 (metalevel) model through algorithm modeling. This synergy approach consists of three parts. First is the data collection. The unique identifier is used as the index while investigating onsite, which eventually forms the metadata repository. Second is the algorithm modeling. All the roof tiles are rebuilt in Grasshopper with identifiers. Their metadata are merged through Cypher syntax, which can generate the graph database in Neo4j. Third is the delivery. The IFC model and knowledge graph are connected in real time during the whole process. The significance of this paper is to provide a synergy approach and discuss its efficiency for the integration between a model and data of very large quantities. The results show that it is efficient to express the semantic relationships of both conditions and interventions. It is practicable to lightweight the IFC model and to associate metadata into the graph database, enabling online browsing and querying. [ABSTRACT FROM AUTHOR]