Train Stop Research Articles

Intelligent train stopping control for railways: A deep learning approach

AbstractStation parking accuracy is an important indicator for the automatic control of high‐speed trains; however, it is subject to many influencing factors, such as the characteristics of high nonlinearity and large time delays in the train control model, time‐variant humidity, and uncertain weather conditions, leading to unsatisfying performance with existing feedback control algorithms. This study first proposes an intelligent train stopping control approach based on deep learning for high‐speed railways. By collecting a large amount of historical train operation data from the Beijing–Shenyang high‐speed railway, three data‐driven models are developed for train stopping control. The first model is based on a deep‐layered feedforward neural network (DNN), which predicts the exact train stopping position with dynamic train running states (position, velocity etc.) as input. Taking advantage of the physical train control models used in practice, the DNN to a convolutional neural network (CNN) is then improved by customizing the convolutional layers of the CNN. To overcome the issues arising from the incompleteness of data samples, a few‐shot convolutional neural network (FSCNN) is further developed to enhance the prediction performance of the CNN. Compared with that of the current method used in practice, the simulation experiments show that the train station parking error can be decreased by 38.6%, 42.8%, and 49.2% by our developed DNN, CNN, and FSCNN, respectively.

A Conversation with Sergei Loznitsa

Abstract Film critic and scholar Jean-Michel Frodon interviews Ukrainian filmmaker about his career, beginning with his training in mathematics and science (and the surprising connections between math and movies), his training at Russian film school, his relationship with various of his teachers, how he finances his films, and of course the films themselves, including Train Stop, My Joy, Blockade, Mr. Landsbergis, The Natural History Of Destruction, Maidan, Reflection, Austerlitz, In the Fog, A Gentle Creature, Donbass, and Babi Yarr Context.

Collaborative optimization for train stop planning and train timetabling on high-speed railways based on passenger demand.

In recent years, with increasing passenger travel demand, high-speed railways have developed rapidly. The stop planning and timetabling problems are the core contents of high-speed railway transport planning and have important practical significance for improving efficiency of passenger travel and railway operation Dong et al. (2020). This study proposes a collaborative optimization approach that can be divided into two phases. In the first phase, a mixed-integer nonlinear programming model is constructed to obtain a stop plan by minimizing the total passenger travel time. The constraints of passenger origin-destination (OD) demand, train capacity, and stop frequency are considered in the first phase. In the second phase, the train timetable is optimized after the stop plan is obtained. A multiobjective mixed-integer linear optimization model is formulated by minimizing the total train travel time and the deviation between the expected and actual departure times from the origin station for all trains. Multiple types of trains and more refined headways are considered in the timetabling model. Finally, the approach is applied to China's high-speed railway, and the GUROBI optimizer is used to solve the models in the above two stages. By analyzing the results, the total passenger travel time and train travel time decreased by 2.81% and 3.34% respectively. The proposed method generates a more efficient solution for the railway system.

Multiobjective Collaborative Optimization Method for the Urban Rail Multirouting Train Operation Plan

The Train Operation Plan (TOP) of urban rail transit (URT) is a comprehensive plan for the operation of trains, the use of facilities and equipment, and the organization of other operational tasks. The TOP should not only be formulated in terms of time-varying passenger flow periods, but it should also be arranged to consider the substitutability of trains between multiple routes combined with the passenger choice. Based on the principle of “operating by the flow” and the requirement for precise allocation of transport capacity for multiple routes, this article constructs a multiobjective nonlinear integer programming model by taking the minimized generalized travel cost of passengers, total running mileage of trains, fluctuation of trains for each route (as optimization targets), and the combination of requirements of both headways and fully loaded rates as constraints. A multiobjective genetic-based algorithm is designed to simultaneously optimize the TOP and the two-way train stopping time in each period. Finally, the proposed model and algorithm are validated with the real data from the Guangzhou Metro Line 2. The results show that the Pareto optimal TOP and dynamic train stopping time are significantly improved compared to the original values.

Buffer stops behavior due to rail impact loads with LISA FEA

The buffer stop also offers a gradual deceleration to the train, lowering the chance of passenger harm and railway damage. The buffer stop's conduct is critical for the train system's safety and integrity. The researcher conducted an analysis of the behavior of the buffer stop after being hit by an oncoming train and experienced failure in braking by using solid steel materials and with an additional layer of rubber on the head end of the train stop buffer. With reference to the impact force parameters that have been calculated in previous studies so that it can be viewed from the stress that occurs and the value of the lateral movement that occurs at the buffer stops.From the results of the analysis of this study it is known that an increase in the ability of the buffer stops by replacing steel material with rubber does not provide a significant increase, however, thickening the flange and web of the buffer stops provides a significant increase in capability with a ratio of 3,582 to the applied stress. and increased ability to reduce the length of the drift by a ratio of 2,478.

Collaborative Optimization Method for Multi-Train Energy-Saving Control with Urban Rail Transit Based on DRLDA Algorithm

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.

Fusion Swarm-Intelligence-Based Decision Optimization for Energy-Efficient Train-Stopping Schemes

To solve the decision problem of train stopping schemes, this paper introduces the static game into the optimal configuration of stopping time to realize the rational decision of train operation. First, a train energy consumption model is constructed with the lowest energy consumption of train operation as the optimization objective. In addition, a Mustang optimization algorithm based on cubic chaos mapping, the population hierarchy mechanism, the golden sine strategy, and the Levy flight strategy was designed for solving the problem of it being easy for the traditional population intelligence algorithm to fall into a local optimum when solving complex problems. Lastly, simulation experiments were conducted to compare the designed algorithm with PSO, GA, WOA, GWO, and other cutting-edge optimization algorithms in cross-sectional simulations, and the results show that the algorithm had excellent global optimization finding and convergence capabilities. The simulation results show that the research in this paper can provide effective decisions for the dwell time of trains at multiple stations, and promote the intelligent operation of the train system.

Integrated Optimization of Stop Planning and Timetabling for Demand-Responsive Transport in High-Speed Railways

The high-speed railways have made rapid developments in recent years. Fulfilling passenger demand and providing precise train services are the core problems to be solved in railway operation. This paper proposes an optimization strategy for demand-responsive transport to integrate train-stop planning and timetabling in high-speed railways. Passenger travel information, including their origins, destinations and expected departure times is taken as input. A mixed integer linear programming model is established to obtain an effective service plan, which consists of train stop pattern, passenger ride plan and train arrival/departure times at all stations. The optimization objective is to minimize the remaining passenger demand and train travel time. Finally, the proposed method is applied to a real-world case, and a series of several experiments are conducted to prove the efficiency and validity of the proposed model. The results suggest that the proposed approach could generate efficient service plans which are responsive to passenger demand.

Integrated optimization of train stop planning and ticket pricing with equity concerns of passengers for high-speed railways

Ticket pricing, which is critical to increasing the revenue for high-speed railways, is typically optimized with a given train stop plan. However, the passenger distributions are restricted by fixed train stop plans, and the optimal ticket price scheme may not be achieved. Therefore, a mixed-integer linear programming model is proposed to optimize collaboratively train stop planning and ticket pricing in this study. The objective is to maximize the total ticket revenue, while the total number of train stops is limited to guarantee service quality. Furthermore, equity concerns of passengers are addressed by ensuring that passengers have equitable travel costs and opportunities to access high-speed railways. Finally, numerical experiments are conducted on a hypothetical railway line and the Zhengzhou-Xi’an high-speed railway corridor to demonstrate the application of the proposed approach. The computational results indicate that the proposed method can increase the total ticket revenue by 27% through adding 14 train stops and formulating a new ticket price scheme compared to the traditional method. In addition, the equity performance can be kept at a level of 1.5.

Long-term equilibrium relationship analysis and energy-saving measures of metro energy consumption and its influencing factors based on cointegration theory and an ARDL model

The energy consumption of metros has widely been concerned with respect to their economy and environmental protection. To analyze the complex dynamic relationship between metro energy consumption and its influencing factors and provide a reference for metro energy conservation control, this paper uses the monthly energy consumption, passenger flow and operating distance statistical data for Wuhan Metro Line 2 from 2018 to 2019. First, metro energy consumption and its influencing factors are qualitatively analyzed and identified. Then, based on cointegration theory and an autoregressive distributed lag (ARDL) model, a correlation hypothesis between metro energy consumption and its influencing factors is constructed, and a method for analyzing the influencing factors of metro energy consumption is proposed. The total energy consumption of a metro (TEC), train traction energy consumption (TTEC), environmental control energy consumption (ECEC), station lighting energy consumption (SLEC), station equipment energy consumption (SEEC) and the operating distance (OD) and passenger flow (PF) variables are analyzed. Using cointegration and an impulse response function, the dynamic relationships between the various energy consumption factors and operating distance and passenger flow are evaluated. The results show that there are substantial differences in the effects of OD and PF and their degree of influence on metro energy consumption. (1) OD affects mainly TTEC and TEC. The degree of influence of OD on TTEC reaches 97.8%, and the degree of influence of OD on TEC reaches 65.9%. (2) PF affects mainly ECEC and SEEC, and the degrees of influence of PF on ECEC and SEEC are 32.2% and 41.3%, respectively. (3) Considering that OD is the key factor affecting TTEC and TEC, train marshaling schemes, train running intervals and train stopping scheme optimization countermeasures are proposed, which can provide decision support for metro energy consumption management and control.

Verified Train Controllers for the Federal Railroad Administration Train Kinematics Model: Balancing Competing Brake and Track Forces

Automated train control improves railroad operation by safeguarding the motion of trains while increasing efficiency by enabling motion within a safe envelope. Train controllers decide when to slow trains down to avoid collisions with other trains on the track, stay inside movement authorities, and navigate slopes, curves, and tunnels safely. These systems must base their decisions on detailed motion models to guarantee the absence of overshoot of the movement authority (safety) and limit undershoot (efficiency). This article is the first to formally verify the safety of the Federal Railroad Administration freight train kinematics model with all its relevant forces and parameters, including track slope and curvature, air brake propagation, and resistive forces as computed by the Davis equation. Due to the significant competing influence of these parameters on train stopping distances, even designing train controllers is a nontrivial control challenge, which we solve using formal verification. For increased generality at reduced verification effort, we verify symbolic mathematical generalizations of the train control models and subsequently apply efficient uniform substitutions to obtain verification results for physical train control models.

The effect of ventilation system with and without under-platform exhaust on the concentration of braking micro-particles inside the subway system

Thermal comfort has been the main target of the ventilation in subway systems. However, pollutant concentration and aerosol dispersion could be the leading health issues in underground metro stations. This study numerically simulated a train movement inside a subway system using the Dynamic Mesh Technique for a 3-D computational domain consisting of four stations and connecting tunnels. The effects of both the ventilation system and the train-induced fluid flow inside the subway system were investigated. Then, the particle generation and dispersion due to train braking are considered, and the impact of the ventilation system on reducing the particle concentration inside the station was investigated. It is shown that the airflow inside the subway system is entirely affected by the piston effect. The airflow generated by the train movement is much higher than that generated by the operation of the ventilation system when only one train passes through the tunnel. The results show that the ventilation system, consisting of the supply and exhaust fans inside the tunnel and supply grilles inside the platform, can reduce the particle concentration by half, except for the platform beside the stopped train when the train enters the station and during half of the train stop time. The other design concept demonstrates that the under-platform exhaust system considerably reduces the concentration of the particles released by the train braking system on the trackside platform.

Deep learning based domain adaptation for mitochondria segmentation on EM volumes

Background and Objective: Accurate segmentation of electron microscopy (EM) volumes of the brain is essential to characterize neuronal structures at a cell or organelle level. While supervised deep learning methods have led to major breakthroughs in that direction during the past years, they usually require large amounts of annotated data to be trained, and perform poorly on other data acquired under similar experimental and imaging conditions. This is a problem known as domain adaptation, since models that learned from a sample distribution (or source domain) struggle to maintain their performance on samples extracted from a different distribution or target domain. In this work, we address the complex case of deep learning based domain adaptation for mitochondria segmentation across EM datasets from different tissues and species. Methods: We present three unsupervised domain adaptation strategies to improve mitochondria segmentation in the target domain based on (1) state-of-the-art style transfer between images of both domains; (2) self-supervised learning to pre-train a model using unlabeled source and target images, and then fine-tune it only with the source labels; and (3) multi-task neural network architectures trained end-to-end with both labeled and unlabeled images. Additionally, to ensure good generalization in our models, we propose a new training stopping criterion based on morphological priors obtained exclusively in the source domain. The code and its documentation are publicly available at https://github.com/danifranco/EM_domain_adaptation. Results: We carried out all possible cross-dataset experiments using three publicly available EM datasets. We evaluated our proposed strategies and those of others based on the mitochondria semantic labels predicted on the target datasets. Conclusions: The methods introduced here outperform the baseline methods and compare favorably to the state of the art. In the absence of validation labels, monitoring our proposed morphology-based metric is an intuitive and effective way to stop the training process and select in average optimal models.

Joint Optimization of Ticket Pricing Strategy and Train Stop Plan for High-Speed Railway: A Case Study

In this study, we examined ticket pricing and train stop planning for the high-speed railway (HSR), which integrates two key aspects of railway operation and organization. We considered that passenger demand is sensitive to the generalized travel cost (depending on the ticket price and the travel time) and that the train stop plan can affect the travel time and passenger distribution. Then, a mixed-integer non-linear optimization model was proposed for the joint problem of ticket pricing and train stop planning to maximize HSR’s transport revenue and minimize passengers’ travel time. Based on the high similarity between combinatorial optimization problems and the solid annealing principle, we designed a combined simulated annealing (CSA) algorithm to solve practical problems. The results of a numerical example in the real HSR network showed that the proposed method can improve transport revenue by 5.1% and reduce passengers’ travel time loss by 11.15% without increasing transport capacity.

Precise Stop Control and Experimental Validation for Metro Train Overcoming Delays and Nonlinearities

Automatic Train Operator (ATO) is a system equipped on metro trains and controls train operation. One of the main functions of ATO is precise stop control, which aims to ensure that a train stops at a designated position on the platform with errors within a predefined specification. Precise stop control is an important function because trains stopping outside of the specification adversely affect passenger safety and punctuality of operation schedule. It is difficult to achieve high accuracy and precision for an ATO since trains are heavy, train control feedback loops involve several steps that cause various levels of time delay, and the actuation has nonlinear characteristics. Moreover, although existing studies propose algorithms to overcome some of the said challenges, few include experimental validations because access to the experimental environment is extremely limited. In this work, we address each one of the challenges and propose a mitigation algorithm, respectively. Moreover, unlike other studies, we experimental validate on an actual train the proposed algorithms. The precise stop control performance is shown to be superior to that of existing system.

Optimization of urban rail transit station spacing for minimizing passenger travel time

Urban rail transit, regarded as the backbone of urban public transportation, can effectively solve the problems of urban traffic congestion and automobile exhaust pollution. However, due to improper planning, unreasonable station location often appears, which seriously affects the service access and mobility of rail transit. In order to improve the station spacing and passenger transport efficiency, a calculation model of station spacing for minimizing passenger travel time was established. In order to enhance the adaptability of the model in different cities, the grid road network and radial road network are considered when establishing the model. The results show that the station spacing is related to the passenger access/egress speed, the passenger travel distance, the maximum train running speed, the train stop time and the train acceleration. The influence of these parameters on the station spacing is obtained through the control variate method, and there are indeed obvious differences under different road network conditions. The research results can provide theoretical reference for station planning of new lines. Since the station spacing of the operated lines cannot be changed, this study can provide reference for skip-stop operation.

A Study on the Influence of Rail Top Smoke Exhaust and Tunnel Smoke Exhaust on Subway Fire Smoke Control

The special emergency plan for subway fires in China stipulates that when a fire occurs at a train stop, it is necessary to confirm whether the fire mode of the tunnel ventilation system is implemented. Because of the setting mode of tunnel ventilation and smoke exhaust in the station track area, the smoke exhaust at the rail top and tunnel cannot operate at the same time. To study the influence of rail top smoke exhaust and tunnel smoke exhaust on subway fire smoke control when a train stops at a station, we take an island station as an example. A 1:1 full-scale numerical model is established to study the smoke spread area, temperature field distribution, and carbon monoxide concentration. The results show that when a train fire occurs in a subway station, the rail top smoke exhaust mode has the best smoke exhaust effect compared with the other three smoke exhaust modes. In this mode, the smoke diffusion in the carriage is the slowest and the available escape time of personnel is the longest. Therefore, it is recommended to adopt the rail top smoke exhaust mode in case of train fire in the subway station; that is, open the smoke exhaust outlet on the rail top for smoke exhaust, and organize personnel to evacuate to the safe position of the platform through the connecting channel and escape exit. If conditions permit, local small fans can also be added to meet the requirements of smoke exhaust. The research results can provide guidance for the emergency plan and provide strong support for promoting the improvement of the fire emergency plan.

Joint optimization of high-speed train timetables, speed levels and stop plans for increasing capacity based on a compressed multilayer space-time network.

With the steady increase in passenger volume of high-speed railways in China, some high-speed railway sections have faced a difficult situation. To provide more transport services, it is necessary to add as many trains as possible in a section to increase capacity. To solve this problem, a compressed multilayer space-time network model is constructed with the maximum number of trains that can be scheduled in the train timetable as the objective. The combination of the train stop plan and speed level is represented by the layer of network where the train is located, and constraints such as train selection, train safety, train overtake and cross-line trains are considered. A method based on timing-cycle iterative optimization is designed to decompose the original problem into multiple subproblems, and the solving order of the subproblems is determined by a heuristic greedy rule. Taking the Beijing-Jinan section of the Beijing-Shanghai high-speed railway as an example, the maximum number of trains was increased by 12.5% compared with the timetable before optimization. The saturated timetables provide detailed schedules, which helps decision-makers better adjust the timetable to run more trains.

Basic Research for the Development of a Vegetation Biofilter Operation Algorithm in a Subway Station

Background and objective We measured the particulate matter (PM) reduction rate when the vegetation biofilter operated for 1 hour every 6 hours. As a result of data analysis, PM concentrations in the subway station showed a high correlation with the train stop duration depending on the service frequency of train operations, and the reduction rate was high in the section where PM concentrations as well as the change rates were high. Therefore, in order to maximize PM reduction using the biofilter, it is necessary to optimize the performance of the biofilter in various environments by designing an intelligent algorithm that takes the frequency of train operations into account. Methods We collected PM10 and PM2.5 data for 2 weeks in position 5 (POS5) and POS6. The multi-module vegetation biofilter was installed in POS6, whereas it was not in POS5. The average hourly data for PM concentrations were obtained, and the change in concentrations was analyzed every 24 hours. Results PM in subway stations is known to spread from the tracks to waiting areas as the train moves. Therefore, the change in PM concentrations by time zone has a high correlation of about 0.8 with the sum of the stop duration depending on the frequency of train operations. The vegetation biofilter is operated 4 times a day for 1 hour every 6 hours. The reduction rate reached up to 30% in 6–8 hours, where the PM concentrations increased sharply. Conclusion We came up with an algorithm to maximize the PM reduction effect by identifying the effect using the vegetation biofilter as a means for air purification in public use facilities such as subway stations. To this end, there is a need for a method that can dynamically select the section where the PM concentrations increase rapidly and set the biofilter operation time.

Experimental analysis of boarding and alighting behavior in urban public transport network: A case study

This paper investigates the characteristics of passengers’ behavior and its effect on the metro stopping time at stations. A field survey was conducted on two mega stations in Cairo metro by video cameras photographing and a stated preference survey was performed based on design of experiments approach. The level of passenger non-compliance behavior, defined as the passengers start boarding the metro while others alighting is analyzed and its relationship with deferent affecting factors is fitted by a two-factor interaction model with a determination coefficient of 0.9234. It is found that some factors have a significant impact on passenger’s non-compliance behavior such as the number of alighting and boarding passengers, applying a penalty when not comply with the rules and the door opening shift (i.e. the train stop away from the place designated for it on the platform). Factor interactions show that alighting process does not affect passenger behavior in case of door opening shift. This may be because the alighting passengers are ready to alight from their position inside the train and have nothing to do. Delay from work is the most influential factor and has interaction with all affecting factors. Results may help for verifying passenger behavior simulation to improve the operational capacity of the metro network.