智慧城轨车地一体化机电系统标准体系构建研究

With the rapid development and application of emerging information technologies such as cloud computing,big data,artificial intelligence,5G,satellite communication,and blockchain in urban rail transit,China’s urban rail transit has entered an era of intelligent transformation and upgrading.The development of intelligent systems and the construction of smart urban rail transit have formed a consensus in the industry.The electromechanical system is an important component of urban rail transit engineering,covering power supply stations,vehicles,stations,and lines.The depot and control center are important support for promoting the development of urban rail transit towards informatization and intelligence.However,research on the technical standards for the smart urban rail vehicle-ground integrated electromechanical system has just begun,and a technical standards system has not yet been formed,which cannot better support the electromechanical system.Therefore,it is necessary to conduct research on the technical standards system,propose the common criteria structure and standards list of the standards system,which can provide reference and guidance for the planning and establishment of China’s smart urban rail standards system.

A New System for Road Traffic Optimisation Using the Virtual Traffic Light Technology

Large cities suffer from traffic congestion,particularly at intersections,due to a large number of vehicles,which leads to the loss of time by increasing carbon emissions,including fuel consumption.Therefore,the need for optimising the flow of vehicles at different intersections and reducing the waiting time is a critical challenge.Conventional traffic lights have been used to control traffic flow at different intersections and have been improved to become more efficient by using different algorithms,sensors and cameras.However,they also face some challenges,such as high-cost installation,operation,and maintenance issues.This paper develops a new system based on the Virtual Traffic Light(VTL)technology to improve traffic flow at different intersections and reduce the encountered loss of time and vehicles’travel time.Additionally,it reduces the costs of installation,maintenance and operation over various conventional traffic light systems.Consequently,the system proposes algorithms for traffic scheduling and lane identification by using vehicle ID,priority and time of arrival.To evaluate the system,four scenarios were presented where each scenario uses a different number of vehicles consisting of three types(emergency vehicles,public buses and private vehicles),each given a different priority.The proposed system is evaluated by integrating two simulators,namely,(OMNeT++)and(SUMO),and two frameworks,namely,(VEINS)and(INET)to prepare an appropriate working environment.the results prove that an improvement in the average travel time for several vehicles reaches 44.43%–49.76%compared with conventional traffic lights.Further,it is proven from the obtained results that the average waiting time for emergency vehicles is enhanced by 96.63%–97.63%,while the average waiting time for public buses is improved by 94.81%–97.23%.On the other hand,the waiting time for private vehicles‘improved by 87.14%to 89.71%’.

智慧城轨车辆技术标准体系构建研究

With the rapid development and application of cloud computing,big data,artificial intelligence,5G,satellite communication,blockchain and other emerging information technologies,conditions have been provided for the intelligent development of urban rail vehicles.With the development of smart urban rail vehicles,the standards system of traditional urban rail vehicle cannot meet the development requirements,so it is necessary to study and reconstruct the standards system.To embody the intelligent level of urban rail vehicles,the paper conducts the research on the standards for the urban rail vehicle train control system,monitoring and diagnosis system,passenger service system,and then proposes the overall architecture and standard list of smart city rail vehicle technical standards system,providing reference for the planning and establishment of China's smart city rail standards system.

A novel forecasting approach to schedule aggregated electric vehicle charging

To be able to schedule the charging demand of an electric vehicle fleet using smart charging,insight is required into different charging session characteristics of the considered fleet,including the number of charging sessions,their charging demand and arrival and departure times.The use of forecasting techniques can reduce the uncertainty about these charging session characteristics,but since these characteristics are interrelated,this is not straightforward.Remarkably,forecasting frameworks that cover all required characteristics to schedule the charging of an electric vehicle fleet are absent in scientific literature.To cover this gap,this study proposes a novel approach for forecasting the charging requirements of an electric vehicle fleet,which can be used as input to schedule their aggregated charging demand.In the first step of this approach,the charging session characteristics of an electric vehicle fleet are translated to three parameter values that describe a virtual battery.Subsequently,optimal predictor variable and hyperparameter sets are determined.These serve as input for the last step,in which the virtual battery parameter values are forecasted.The approach has been tested on a real-world case study of public charging stations,considering a high number of predictor variables and different forecasting models(Multivariate Linear Regression,Random Forest,Artificial Neural Network and k-Nearest Neighbors).The results show that the different virtual battery parameters can be forecasted with high accuracy,reaching R^(2) scores up to 0.98 when considering 400 charging stations.In addition,the results indicate that the forecasting performance of all considered models is somehow similar and that only a low number of predictor variables are required to adequately forecast aggregated electric vehicle charging characteristics.