Driving Strategy Optimization and Field Test on an Urban Rail Transit System

In reality, the risk in urban rail transit system usually happens. In this paper, we construct the risk chain network in urban rail transit system, where the different factors are considered in detail. Moreover, in order to describe the characteristic of the risk chain network, the propagation risk is proposed. Based on these, the controllability of risk chain network is calculated in four kinds of systems in urban rail transit system. In addition, we analyze the key node, which has a serious impact on the controllability. Our paper may be useful to control the risk in urban rail transit system and improve the safety of urban rail transit.

With the accelerated urbanization in China, passenger demand has dramatically increased in large cities, and traffic congestion has become serious in recent years. Developing public urban rail transit systems is an indispensable approach to overcome these problems. However, the high energy consumption of daily operations is an emerging issue due to increased rail transit networks and passenger demands. Thus, reducing the energy consumption and operational cost by using advanced optimization methodologies is an urgent task for operation managers. This work systematically introduces energy-saving approaches for urban rail transit systems in three aspects, namely, train speed profile optimization, utilization of regenerative energy, and integrated optimization of train timetable and speed profile. Future research directions in this field are also proposed to meet increasing passenger demands and network-based urban rail transit systems.

Establishing simulation models is a widely used and effective approach for analysing passenger flow distribution in urban rail transit systems. Recently, multi-agent and discrete event-based simulation models have shown exceptional performance in studying passenger flow information within urban rail transit systems. While simulations of passengers and trains often yield satisfactory results, few models capture the overall operational status of urban rail transit systems. The complex interactions among stations, trains and passengers make it challenging to integrate these elements into a unified system framework. In this paper, we introduce a triple simulation framework that integrates stations, trains and passengers as foundational elements to comprehensively simulate the entire urban rail transit system and observe overall passenger flow distribution. Experimental results demonstrate that our system surpasses existing advanced simulation models, achieving an accuracy rate of 88.44% with a tolerance for a 30% deviation. To further illustrate the effectiveness of our framework in analysing passenger flows, we conducted experiments using the Nanjing Metro AFC dataset, analysing passenger flow distributions at stations and on trains.

An Approach to Calculate Overall Efficiency of Rolling Stock for an Urban Rail Transit System

Wireless Local Area Network (WLAN) is currently used in the train-ground communication for the urban rail transit systems. Due to the high speed and frequency band limitations, WLAN is unable to meet the demand of future city and intercity rail transit. Time Division-Long Term Evolution (TD-LTE) technology allows to construct a flexible service environment compared with current WLAN based system. In this paper, a TD-LTE based train-ground communication system for the urban rail transit system is given. It integrates all the applications in urban rail transit system. An outdoor testing environment is set up to test the performance of the integrated TD-LTE based train-ground communication system. The packet loss rate, response time, throughput of the TD-LTE system are analyzed. Extensive testing results show that the Quality of Service (QoS) of the integrated TD-LTE based train-ground communication system satisfies the requirements of urban rail transit system.

In order to improve the energy efficiency of urban rail transit system, it is necessary to figure out the key factor(s) that restrain(s) train rapidity and energy conservation and understand the inner complex relationships among them. We first define energy-saving and rapid train control/operation, then simulate and analyze the microscopic train behavior between two successive stations. The result reflects that due to the constraint of the short inter-station distance, the train has no space and time to take advantage of its excellent performance in speed and has to stop frequently at every station, which leads to the increase of energy consumption and the prolongation of running time. Based on the above energy consumption analysis of the microscopic train behavior, a hierarchical Pareto 0-1 integer programming with considering passenger flow distribution and public transport characteristics, is presented to organically integrate microscopic train control with macroscopic train organization. The case study shows that the presented method can greatly promote the energy-efficient and rapid train operation of urban rail transit system. This study is also helpful to the planning and construction of future cities and urban rail transit systems.

Energy intensity is one of the most important energy feature that has a dramatic value in energy system of Iran. Indeed, Iran is one of the most energy intensive countries in the world and its main reason is related to high energy consumption in household section. In this article, we present a non-linear model that considers three scenarios in management of household energy demand reducing. Therefore, a rational percentage of energy consumption reduction in the household sector, which firstly eliminates the imbalance between energy production and consumption, and secondly, derives a rational amount of profit from various reduction scenarios, will be presented. The mentioned rational profits in this article are obtained from three scenarios. In the first scenario, it is assumed that the percentage reduction in household energy consumption will be allocated to reducing energy demand in the industrial sector, resulting in profits from value-added creation in this sector. In the second scenario, all benefits from reducing energy consumption in the household sector will be devoted to energy exports, yielding profits from this source. Finally, in the third scenario, the reduction in energy consumption will lead to a decrease in energy supply and consequently a reduction in energy supply costs. To conduct a comprehensive study, a combination of the mentioned scenarios has also been modeled and investigated. The model results indicate that with a 25% reduction in household energy consumption in the 2024-2034 timeframe, the energy imbalance will be eliminated, and allocating 5% of this reduction entirely to the industrial sector will result in profits equivalent to $164.18 billion. However, it should be noted that in the considered combined scenarios, the greater the share of the first scenario, the higher the resulting profit, and the optimal point is achieved in the first scenario.

In urban rail transit systems, the intelligent driving system is gradually replacing manual driving for its high safety, punctuality, and stopping accuracy. With the development of big data analytics, the data-driven intelligent driving system becomes a research focus. Traditional datadriven intelligent driving systems suffer from inadequate data. Due to lacking effective incentives and trust, data from different urban rail operators cannot be shared directly. In this paper, we propose a framework that uses blockchain technology to realize sharing and collaborative training of intelligent driving models between operators. In this framework, we use blockchain-based distributed federated reinforcement learning methods to complete intelligent driving calculations. We use smart contracts to implement the management of the entire federal reinforcement learning. Operators use local historical data to participate in intelligent driving training based on Q-network by exchanging encrypted model parameters, and optimize the safety distance, energy consumption, and punctuality of urban rail transit systems. Simulation results show that our proposed distributed federated reinforcement learning method can significantly improve the intelligent driving system performance.

Most of the existing urban rail CBTC train ground communication systems are based on Wireless Local Networks (WLAN) technologies. With frequency interference and the increase of train speed, WLAN is incapable of meeting the requirements of CBTC train ground communication systems. Time Division-Long Term Evolution (TD-LTE) technology begins to be considered in urban rail transit systems. In this paper, we first design a TD-LTE based urban rail CBTC train ground communication systems, the handover procedure and handover requirements according to CBTC system principle are analyzed next. For verifing whether the designed TD-LTE based system meet the handover performance demands when trains travel in high speed CBTC systems, a laboratory test environment and a real field test environment are set up. Massive test results show that the handover performance of TD-LTE based urban rail CBTC train ground communication systems meets the demands of urban rail transit systems.

The imbalances between supply and demand in an urban rail transit system have received extensive attention. Addressing these imbalances by controlling the movements of passengers in the system is a real challenge. To complete the peak passenger flow control scheme, a novel discrete event simulation (DES) optimization model based on queuing theory is proposed to minimize the urban rail transit company losses and the passenger time delays considering effects of congestion propagation among facilities at busy stations. The proposed approach can generate a control scheme that consists of the controlling number of entering passengers and the controlling parameters of facilities inside the station. The first stage of the method models the subway network using queuing theory and then builds a DES model that is based on an urban rail transit queuing network. In the second stage, a service-security-economic-oriented optimization model is established and combined with a queuing network simulation model to implement the simulation optimization. We present three numerical experiments to compare the optimization results in different passenger flow scenarios. The results demonstrate that the DES optimization method in this paper can provide a reasonable and reliable control scheme in daily operations.

In an urban rail transit system, issues regarding headway regulation have aroused wide attention. The assurance of headway regularity can decrease train delay times and average passenger waiting times. An integrated control method is proposed to optimize train headway by adjusting the train arrival time at stations. The adjustment of train arrival time is achieved by using an analytical method, and then the speed profile for each train is calculated by a suboptimal method, which has been applied in a practical system. Through simulation, the CPU time for calculating optimal train arrival time and speed profile is analyzed, respectively. The analysis demonstrates that the proposed method satisfies the real-time requirements for solving the headway regulation problem. By adopting the proposed method, the average passenger waiting time and the energy consumption can be decreased. In particular, the proposed method has better performance when the dispatch headway is large.

With the increasing energy consumption in urban rail transit systems, energy-efficient train operation has been paid significant attention. Considering the application of regenerative braking technology, many studies focus on offline energy-saving train trajectory optimization, in which an accelerating regime is inserted into the train trajectory when there is regenerative braking energy (RBE) that can be utilized. However, in practical operation, train states are dynamic and scheduled trajectory might be useless. This paper proposes a real-time cooperative train control method based on Train-to-Train (T2T) communication technology. According to the train states transmitted through T2T communication, whether there is a train in braking regime is judged. Besides, an accelerating regime is inserted by changing train running modes when there is another train in the braking regime. A cooperative control algorithm is developed to achieve the energy-efficient cooperative control strategy. Besides, simulations based on a real-life metro line demonstrate that the proposed cooperative control method can reduce substation energy consumption and improve the utilization of RBE.

Current urban rail transit system must be drastically improved to accommodate the predicted traffic growth. The urban rail transit network of signalized intersections can be suitably modeled as a discrete event system (DES), in which the train flow behavior is described by means of a time-driven model and the traffic dynamics are represented by a discrete event model. In this paper, we propose a max-plus general modeling framework adapted to the optimal control of traffic flow. The open-loop and closed-loop control models of train flow were established firstly. The max-plus model of such a network is used to state and solve the problem of coordinating several traffic flows with the aim of improving the performance of urban rail transit system. Starting from the state evolution of the urban rail transit system, the system stability and periodic steady-state analysis were presented. The main focus is to obtain key features of urban rail transit system and extend the framework by introducing new analysis techniques.

Due to the wide application of computer, communication and control technologies in urban rail transit systems, amount of security threat are introduced, which can increase the security risks. Based on the safety- critical features of urban rail transits systems, lots of redundancy, fault-tolerant architectures are adopted, and fail-safe principles are also embedded into the operation mechanisms of the urban rail transit signalling systems. As a result, determining effects of security risks on safety of signalling systems is an urgent task. In the paper, based on the similarities and differences between the propagation principles of security risks and safety risks, the extended fault tree is proposed to perform the safety-security assessment for urban rail transit signalling systems, where security events could be taken as one kind of hazard of fault tree describing the evolution of safety risks. Considering some critical scenarios of signalling systems, the proposed approach demonstrates that security risks could significantly affect the operation efficiency of signalling systems due to the fail-safe designs while safety can be guaranteed based on the same fail-safe designs.

Urban rail transit such as subway is important means of transportation for people to travel. With the acceleration of urbanization construction throughout the country, urban rail transit has developed rapidly. More attention is paid to the interchange means between urban rail transit and other urban transportation systems, such as public transport, public bicycle, and private car, and the interchange means has gradually become an important indicator for assessing the level of urban transportation system. This paper analyzes the interchange means between urban rail transit and other urban transportation systems, and puts forward relevant suggestions on the interchange between between urban rail transit and private cars to contribute to the promotion of urbanization.