Train Operation Optimization Research Articles

Optimization of the cruising speed for high-speed trains to reduce energy consumed by motion resistances

As one of the effective measures to reduce energy consumption of high-speed railway, train operation control has gained much attention because of its cost-effectiveness. Conventional approaches to eco-driving generally rely on single-mass train models and lack a thorough investigation into the vehicle cruising process, which can overlook real-world operation conditions and hinder further improvements in energy efficiency. To address these issues, this paper proposes a comprehensive framework for optimizing the cruising speed of high-speed trains (HSTs) based on track conditions to reduce energy consumed by motion resistances (ECMR). Firstly, a multibody dynamics simulation model of the HST is developed and validated to accurately evaluate ECMR under a range of operation scenarios. Simulation results show that the impact of speed on ECMR varies considerably according to track characteristics. Subsequently, a neural network is constructed to derive the regressive relationship between operation conditions and ECMR. On this basis, an optimization model for the cruising speed under different track conditions is formulated to minimize the ECMR of a railway segment with a given operation time. Finally, the effectiveness of the model is validated through case studies, which demonstrate that ECMR can be reduced by up to 27.85% compared to traditional control strategies. The proposed optimization method can be integrated with current train speed profiles to further enhance energy efficiency. This framework has broad applicability for addressing engineering problems related to train operation optimization.

Simulation and analysis of optimal energy-saving mode in microfield of underground rail transit

Underground rail transit has become one of the most popular transportation modes because of its advantages such as fast running speed, large passenger flow and punctuality. This mode of transportation can alleviate urban traffic congestion to a certain extent, so underground rail transit has been vigorously developed, and the number of rail lines has increased exponentially. However, with the continuous development of underground rail transit, the energy consumption of train operation is also increasing, resulting in a waste of energy. To solve this problem, this paper proposes to improve the energy-saving technology of train operation by using train autopilot control strategy. Firstly, a train operation optimization model considering train position and speed limit is established by using automatic train driving strategy, and the nonlinear problem of the optimization model is solved by genetic algorithm. The micro-field theory is introduced into the control strategy of automatic train driving, and the energy-saving model of underground rail transit is established. The energy consumption is optimized from three aspects: train energy-saving technology, line planning and overall operation planning of rail transit. The simulation results show that the average impact rate and energy consumption of trains under the proposed energy-saving model are significantly reduced compared with the other two groups of trains, and the average impact rate and energy consumption are significantly reduced compared with the other two groups of trains. The running time and energy consumption of the energy-saving model are lower than those of the experimental group. To sum up, the energy-saving model of underground rail transit proposed in this paper not only reduces the operating energy consumption, but also improves the passenger comfort, which can provide low-cost energy-saving technology for underground transportation field, and has positive significance for urban low-carbon development.

Research on Train Operation Optimization Model Based on Linear Programming and Kinematics

Research on Train Operation Optimization Model Based on Linear Programming and Kinematics

Driving Strategy Using an Improved Ant Colony System for Energy-Efficient Train

Optimal energy-efficient train operation optimization is one of the widely studied areas in transportation science, which can significantly reduce energy consumption that accounts for a large proportion of operating costs. In order to adapt to the complex and changeable railway line conditions such as gradient, slope length, and speed limit and avoid the error in tracking speed curve, an optimal driving strategy decision-making (ODSD) model is proposed in this paper. The model considers the non-fixed sequence of driving regimes, and the regimes are directly selected in the discrete micro-subsegments of an equal time-division pattern. To solve this model efficiently, an improved ant colony system algorithm with the difference edges (ACSd) is proposed, which takes the heuristic effect of the difference between the best solutions of two adjacent iterations, i.e., “the difference edges,” into account. Additionally, energy-efficient heuristic factor and speed heuristic factor are presented to balance energy saving and speed. The results demonstrate that ACSd performs better than the basic ant colony system algorithm in solving traveling salesman problem (TSP) and provides more flexible driving strategies for the ODSD model.

Two‐objective train operation optimization based on eco‐driving and timetabling

AbstractIn urban railway systems, the timetable guides the section operation of the single train and the arrangement of the train group to meet the dual needs of cost and passengers. This paper proposes a two‐objective train operation optimization based on eco‐driving and timetabling to restore a more realistic scene, including a method level and an objective level. For the method level, the speed curve optimization of the single train and the timetable optimization of the train group are adopted jointly. For the objective level, both the total energy consumption of the train group and the consuming time of passengers are considered. A hybrid solution strategy based on quadratic programming and improved artificial bee colony algorithm is proposed. A hardware‐in‐the‐loop platform is built to carry out validation experiments. Both the cases in general hours and special hours are verified based on the actual data from Beijing Metro Line 15. The results show that both the energy consumption and the passenger consuming time are reduced simultaneously. Correspondingly, the speed curve and the time distribution of the timetable are individually optimized based on the fluctuating passenger flow.

Optimization for Metro Operation Scheme of Suburban Lines: A New Method for Dealing with the Imbalanced Passenger Flow

The development of some central cities tends to be saturated, so some mega cities try to adopt the express and local mode of the metro. Suburban lines with imbalanced passenger flow between stations will lead to extremely crowded passenger flow in some stations, while scarce passenger flow in other stations. To balance the train’s carrying capacity with the imbalanced passenger flow, this study explores the collaborative optimization of train operation under the new mode of express and local. First, a time-stamped data cropping strategy based on fine-grained time zones as the grid index is developed, which is used to deeply explore the OD spatiotemporal representation of passenger flow correlation mapping mechanism based on multisource data; then, the calculation model of the proportion of trains matching with the OD temporal and spatial characteristics of suburban passenger flow are constructed, and an efficient solution algorithm is developed to solve the problem of interest. Finally, a set of numerical experiments with operation data from Shanghai Metro Line 16 are conducted to verify the performance and effectiveness of the proposed model and algorithm. The experimental results show that the proposed approach can effectively realize the collaborative optimization of passenger OD prediction, train proportion, stop scheme, and travel time, so as to provide decision-making support and method guidance for the optimization of metro organizations in megacities.

Optimization of Train Operation Planning with Full-Length and Short-Turn Routes of Virtual Coupling Trains

Virtual coupling uses wireless communication instead of mechanical coupling to ensure that trains are easily reconnected or disconnected. This technology can shorten the interval time between trains, give full play to the carrying capacity of lines, and improve the service level of urban rail transit. This paper optimizes the train operation plan with full-length and short-turn routes of virtual coupling trains by establishing a two-level optimization model. The upper model is used to minimize passenger travel time and enterprise operation cost, and the lower model to optimize the equilibrium of train load rate on short-turn routes. Meanwhile, a method based on the genetic algorithm is designed to solve the model. A case study of the Metro Line M has been carried out. The results can verify the efficiency and feasibility of the proposed method. The full-length and short-turn routes of virtual coupling trains can effectively reduce passenger travel time, enterprise operating cost and the number of vehicles, and improve the average load factor of the trains. Finally, sensitivity analyses are performed using three parameters which include departure frequency of the full-length train and short-turn train, starting and terminal station of short-turn route, and number of marshalled vehicles of the full-length train and short-turn train.

Train operation optimization with adaptive differential evolution algorithm based on decomposition

The train operation is a multi‐objective optimization process including several operating indexes, such as safety, punctuality, precision stop, comfort, and low‐energy consumption. The speed profile and timetable for trains should be adjusted correspondingly to satisfy the passengers' demands while minimizing energy consumption. To solve this problem, we propose a multi‐objective optimization approach for train operation. First, we develop a single‐particle model of the train and a multi‐objective optimization model for the train operation, which is subject to the constraints such as safety requirement, passenger comfort, and low energy consumption. Second, to obtain the Pareto frontier of train operation, a uniform design multi‐objective adaptive differential evolution algorithm based on decomposition (UMADE/D) is studied and applied to solve the multi‐objective optimization model for the train operation. Finally, we present numerical examples based on the real‐life operation data from the Nanjing Metro Line 1 in Nanjing, China. Three searching methods, namely, the proposed UMADE/D, Non‐Dominated Sorting Genetic Algorithm, and Multi‐Objective Evolutionary Algorithm based on Decomposition, are implemented to find the Pareto frontier of train operation quickly and efficiently. The simulation results show the efficiency of the proposed UMADE/D algorithm, which can provide corresponding operation strategies and satisfy the multi‐objective demands when the train is in different operation state. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Energy consumption optimization of train operation for railway systems: Algorithm development and real-world case study

Traction energy is the main component of railway operation energy, and a timetable that predefines the running time of train operation can be used to determine the traction energy consumption. This study proposes a bi-level model that optimizes timetables to achieve the energy-saving control of railway systems. The upper level of the model ensures the relative stability of the timetable while maintaining railway safety constraints, which makes train operations more convenient for the railway sector as well as passengers; while the lower level of the model optimizes the arrival and departure time among intermediate stations to minimize the energy consumption of each train. Then, a unified iterative optimization algorithm combining particle swarm is developed to solve the model, and a timetable that ensures energy consumption optimizations is thus obtained. A case study using actual operation data from the Beijing-Shanghai high-speed railway is developed to illustrate the proposed method. Results show that the total energy consumption is reduced by more than 7.6%, and the average adjustment time for each distance interval is approximately 1 min, which maintains the stability of the original timetable.

Energy-Efficient Train Operation Using Nature-Inspired Algorithms

A train operation optimization by minimizing its traction energy subject to various constraints is carried out using nature-inspired evolutionary algorithms. The optimization process results in switching points that initiate cruising and coasting phases of the driving. Due to nonlinear optimization formulation of the problem, nature-inspired evolutionary search methods, Genetic Simulated Annealing, Firefly, and Big Bang-Big Crunch algorithms were employed in this study. As a case study a real-like train and test track from a part of Eskisehir light rail network were modeled. Speed limitations, various track alignments, maximum allowable trip time, and changes in train mass were considered, and punctuality was put into objective function as a penalty factor. Results have shown that all three evolutionary methods generated effective and consistent solutions. However, it has also been shown that each one has different accuracy and convergence characteristics.

Optimization of Train Operation in Multiple Interstations with Multi-Population Genetic Algorithm

Subway systems consume a large amount of energy each year. How to reduce the energy consumption of subway systems has already become an issue of concern in recent years. This paper proposes an energy-efficient approach to reduce the traction energy by optimizing the train operation for multiple interstations. Both the trip time and driving strategy are considered in the proposed optimization approach. Firstly, a bi-level programming model of multiple interstations is developed for the energy-efficient train operation problem, which is then converted into an integrated model to calculate the driving strategy for multiple interstations. Additionally, the multi-population genetic algorithm (MPGA) is used to solve the problem, followed by calculating the energy-efficient trip times. Finally, the paper presents some examples based on the operation data of the Beijing Changping subway line. The simulation results show that the proposed approach presents a better energy-efficient performance than that with only optimizing the driving strategy for a single interstation.

High-speed automatic train operation optimization algorithm

In order to achieve the high efficiency in automatic train operation,on the basis of the analysis of the train at different stages of operation,taking parking as the key stage,analytic hierarchy process was used to get quantitative description of the importance between each performance indexes and evaluation function of parking control's comprehensive performance in this stage,then the fuzzy manipulation rules of the online control were got.The offline operation of train under the rules was simulated for several times,the different schemes in sub-regional division and start braking point selection were scored to get the parking manipulation scheme which performance indexes are the best.Finally the simulation system was designed based on VC + + platform,and it has verified that the practical effect of the train running under the control algorithm has good parking precision,comfort and time saving.

Study on the Train Operation Optimization of Passenger Dedicated Lines Based on Satisfaction

The passenger transport demands at a given junction station fluctuate obviously in different time periods, which makes the rail departments unable to establish an even train operation schedule. This paper considers an optimization problem for train operations at the junction station in passenger dedicated lines. A satisfaction function of passengers is constructed by means of analyzing the satisfaction characteristics and correlative influencing factors. Through discussing the passengers’ travel choice behavior, we formulate an optimization model based on maximum passenger satisfaction for the junction and then design a heuristic algorithm. Finally, a numerical example is provided to demonstrate the application of the method proposed in this paper.

Optimization of Train Operation

Optimization of Train Operation