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Investigation into train positioning systems for saving energy with optimised train trajectories
Abstract
One approach to reduce energy consumption in railway systems is to implement optimised train trajectories. These are speed profiles that reduce energy consumption without foregoing customer comfort or running times. This is achieved by avoiding unnecessary braking and running at reduced speed whilst maintaining planned arrival times. An optimised train trajectory can be realised using a driver advisory system (DAS). The optimal train trajectory approach needs a variety of input data, such as the train's position, speed, direction, gradient, maximum speed, dwell time, and station locations. Many studies assume the availability of a very accurate train position in real time. However, providing and using high precision positioning data is not always the most cost-effective solution. The aim of this research is to investigate the use of appropriate positioning systems, with regard to their performance and cost specifications, with optimised trajectories. This paper first presents a single train trajectory optimisation to minimise overall energy consumption. It then explores how errors in train position data affect the total consumed energy, with regard to the tractive force due to gradient when following the optimised trajectory. A genetic algorithm is used to optimise the train speed profile. The results from simulation indicate that a basic GPS system for specifying train position is sufficient to save energy via an optimised train trajectory. The authors investigate the effect of error in positioning data, to guarantee the reliability of employing the optimised solution for saving energy whilst maintaining an acceptable journey time.
... The real-world processes are simulated to calculate the actual force due to gradient and thus the actual power and energy consumed. Real-world process scheme of using DAS [132] The positioning deviation (sd) is the difference between the positioning system data (s) and the actual position (sr). As mentioned in Section 4.4.2, the train position model in this study mimics the positioning deviation (sd) which can be used by the simulator to read the gradient angle of the railway track, and therefore to calculate the Fgrad of each distance step. ...
... Genetic algorithm flowchart[132] ...
... 10 (a) The considered sections of the East Coast Main Line route (from Google map) Fig. 4.10 (b) Altitude of sections of the East Coast Main Line route[132] ...
Many studies propose new advanced railway subsystems, such as Driver Advisory System (DAS), Automatic Door Operation (ADO) and Traffic Management System (TMS), designed to improve the overall performance of current railway systems. Real time train positioning information is one of the key pieces of input data for most of these new subsystems. Many studies presenting and examining the effectiveness of such subsystems assume the availability of very accurate train positioning data in real time. However, providing and using high accuracy positioning data may not always be the most cost-effective solution, nor is it always available. The accuracy of train position information is varied, based on the technological complexity of the positioning systems and the methods that are used. In reality, different subsystems, henceforth referred to as 'applications', need different minimum resolutions of train positioning data to work effectively, and uncertainty or inaccuracy in this data may reduce the effectiveness of the new applications. However, the trade-off between the accuracy of the positioning data and the required effectiveness of the proposed applications is so far not clear. A framework for assessing the impact of uncertainties in train positions against application performance has been developed. The required performance of the application is assessed based on the characteristics of the railway system, consisting of the infrastructure, rolling stock and operational data. The uncertainty in the train positioning data is considered based on the characteristics of the positioning system. The framework is applied to determine the impact of the positioning uncertainty on the application's outcome. So, in that way, the desired position resolution associated with acceptable application performance can be characterised. In this thesis, the framework described above is implemented for DAS and TMS applications to understand the influence of positioning uncertainty on their fundamental functions compared to base case with high accuracy (actual position). A DAS system is modelled and implemented with uncertainty characteristic of a Global Navigation Satellite System (GNSS). The train energy consumption and journey time are used as performance measures to evaluate the impact of these uncertainties compared to a base case. A TMS is modelled and implemented with the uncertainties of an on-board low-cost low-accuracy positioning system. Preliminaries ii The impact of positioning uncertainty on the modelled TMS is evaluated in terms of arrival punctuality for different levels of capacity consumption. The implementation of the framework for DAS and TMS applications determines the following: which of the application functions are influenced by positioning uncertainty; how positioning uncertainty influences the application output variables; how the impact of positioning uncertainties can be identified, through the application output variables, whilst considering the impact of other railway uncertainties; what is the impact of the underperforming application, due to positioning uncertainty, on the whole railway system in terms of energy, punctuality and capacity.
... These algorithms are able to find the available solution with an acceptable cost arising from the consumed energy and elapsed time. The genetic algorithm (GA) is one of the most widely used methods in speed trajectory optimization ( [13,[15][16][17][18]43]). GA was applied to design a formal method to optimize the traction energy and to investigate the relationship between the journey time and energy consumption ( [13]). ...
... The authors in [18] used a GA to solve the speed trajectory optimization problem with special consideration of regeneration braking so that the net energy could be reduced. The authors in [43] investigated the influence of the error of train positioning in optimal speed trajectory obtained using a GA. The speed trajectory optimization is based on a simple case with a single speed limit and assumes that the operations of the train are designed in a preset sequence. ...
... Because 1/v i,ave and v 2 i,ave are both non-linear term in the model, another set of SOS2 variables denoted by β i for v i,ave are used to present 1/v i,ave and v 2 i,ave by v i,ave . Details are shown in Equations (43)-(47) 1 = β i,1 + β i,2 + · · · + β i,j + · · · + β i,50 (43) 0 ≤ β i,j ≤ 1 j = 1, 2, · · · , 50 (44) v i,ave = 50β i,1 + 49β i,2 + · · · + (51 − j)β i,j + · · · + 1β i,50 ...
Train speed trajectory optimization has been proposed as an efficient and feasible method for energy-efficient train operation without many further requirements to upgrade the current railway system. This paper focuses on an adaptive partial train speed trajectory optimization problem between two arbitrary speed points with a given traveling time and distance, in comparison with full speed trajectory with zero initial and end speeds between two stations. This optimization problem is of interest in dynamic applications where scenarios keep changing due to signaling and multi-train interactions. We present a detailed optimality analysis based on Pontryagin's maximum principle (PMP) which is later used to design the optimization methods. We propose two optimization methods, one based on the PMP and another based on mixed-integer linear programming (MILP), to solve the problem. Both methods are designed using heuristics obtained from the developed optimality analysis based on the PMP. We develop an intuitive numerical algorithm to achieve the optimal speed trajectory in four typical case scenarios; meanwhile, we propose a new distance-based MILP approach to optimize the partial speed trajectory in the same scenarios with high modeling precision and computation efficiency. The MILP method is later used in a real engineering speed trajectory optimization to demonstrate its high computational efficiency, robustness, and adaptivity. This paper concludes with a comparison of both methods in addition to the widely applied pseudospectral method and propose the future work of this paper.
... By using their optimization method, the result showed that the energy consumption reduced from 79.5kWh to 67.3kWh [2]. Hamid et al. (2016) investigated the train positioning systems for saving energy with optimized train trajectories. They reduced the metro speed which has a significant effect on the traction energy consumption because the reduction of speed can reduce running resistance force of the metro [3]. ...
... Hamid et al. (2016) investigated the train positioning systems for saving energy with optimized train trajectories. They reduced the metro speed which has a significant effect on the traction energy consumption because the reduction of speed can reduce running resistance force of the metro [3]. Ahmadi and Dastfan (2016) obtained the energy consumption-time by using the non-dominated sorting algorithm. ...
... Furthermore, the optimized trajectory needs input data, such as the train's position, gradient, direction, speed and maximum speed, dwell time, and station locations. In [138] it is proposed a genetic algorithm for optimizing the train speed profile. The results obtained following the advice generated by the DAS when updating the system every meter, showed that the optimized trajectory could save energy up to around 25%. ...
... Hamid et al. [138] Genetic algorithms Design of an optimized train trajectory, energy by up to around 25% can be saved. ...
Nowadays, the railway industry is in a position where it is able to exploit the opportunities created by the IIoT (Industrial Internet of Things) and enabling communication technologies under the paradigm of Internet of Trains. This review details the evolution of communication technologies since the deployment of GSM-R, describing the main alternatives and how railway requirements, specifications and recommendations have evolved over time. The advantages of the latest generation of broadband communication systems (e.g., LTE, 5G, IEEE 802.11ad) and the emergence of Wireless Sensor Networks (WSNs) for the railway environment are also explained together with the strategic roadmap to ensure a smooth migration from GSM-R. Furthermore, this survey focuses on providing a holistic approach, identifying scenarios and architectures where railways could leverage better commercial IIoT capabilities. After reviewing the main industrial developments, short and medium-term IIoT-enabled services for smart railways are evaluated. Then, it is analyzed the latest research on predictive maintenance, smart infrastructure, advanced monitoring of assets, video surveillance systems, railway operations, Passenger and Freight Information Systems (PIS/FIS), train control systems, safety assurance, signaling systems, cyber security and energy efficiency. Overall, it can be stated that the aim of this article is to provide a detailed examination of the state-of-the-art of different technologies and services that will revolutionize the railway industry and will allow for confronting today challenges.
... This exclusion may compromise the effectiveness of the optimisation methods. Genetic algorithms have demonstrated success in optimising single-train trajectories for DC traction systems in the context of solving linear optimisation problems [30][31][32][33][34][35][36]. In comparison, dynamic programming has exhibited superior performance over genetic and ant colony optimisation algorithms, particularly when the solution space converges during the process of finding a solution [37]. ...
This paper introduces a novel optimisation algorithm for hybrid railway vehicles, combining a non‐linear programming solver with the highly efficient “Mayfly Algorithm” to address a non‐convex optimisation problem. The primary objective is to generate efficient trajectories that enable effective power distribution, optimal energy consumption, and economical use of multiple onboard power sources. By reducing unnecessary load stress on power sources during peak time, the algorithm contributes to lower maintenance costs, reduced downtime, and extended operational life of these sources. The algorithm's design considers various operational parameters, such as power demand, regenerative braking, velocity and additional power requirements, enabling it to optimise the energy consumption profile throughout the journey. Its adaptability to the unique characteristics of hybrid railway vehicles allows for efficient energy management by leveraging its hybrid powertrain capabilities.
... Train positioning technology is the core on-board technology in train control systems, and is the basic guarantee for the safe operation of urban rail transit [1][2][3]. Traditional train positioning methods include track circuits, transponders, and cross loops. Track circuits can only achieve occlusion zone positioning [4]. ...
We propose a communication positioning integrated signal (CPIS) to achieve sub-meter positioning of metro trains. However, the electromagnetic environment of metro trains is complex, and the interference of radio frequency signals will reduce the accuracy of train positioning and affect the safe operation of trains. In this paper, an interference mitigation method for metro train positioning based on Karhunen–Loeve decomposition is proposed. The signal is orthogonally expanded, and the eigenvalues of the interference signal are obtained according to the distribution characteristics of the eigenvalues. The reconstructed interference signal is then subtracted from the received signal to achieve interference suppression. The experimental results demonstrate the effectiveness of the method in interference scenarios.
... Com relação a Eficiência Energética, destacam-se três abordagens conhecidas como tabela de horário (do inglês, timetable) (YONG et al., 2011), (SU et al., 2013, reuso de energia (do inglês, regenerative energy) e perfil de velocidade (do inglês, speed profile) (HAMID et al., 2016), (SICRE et al., 2014), . Cada uma dessas abordagens possui características específicas, de forma que é possível utilizar uma ou uma combinação dessas abordagens a fim de diminuir o gasto energético. ...
... Tian et al. [18] proposed a multi-train traction power network modelling method to determine the system energy flow of the rail system with regenerating braking trains. Hamid et al. [19] explored how errors in train position data affect the overall energy consumption under a single train trajectory optimization scheme. Gomes et al. [20] evaluated the potentials of the energy efficiency from "fixed block" to the "moving block" type using the empirical data and the statistical approaches. ...
Energy conservation is attracting more attention to achieve a reduced lifecycle system cost level while enabling environmentally friendly characteristics. Conventional research mainly concentrates on energy-saving speed profiles, where the energy level evaluation of the timetable is usually considered separately. This paper integrates the train driving control optimization and the timetable characteristics by analyzing the achievable tractive energy conservation performance and the corresponding boundaries. A calculation method for energy efficient driving control solution is proposed based on the Bacterial Foraging Optimization (BFO) strategy, which is utilized to carry out batch processing with timetable. A boundary identification solution is proposed to detect the range of energy conservation capability by considering the relationships with average interstation speed and the passenger volume condition. A case study is presented using practical data of Beijing Metro Batong Line and two timetable schemes. The results illustrate that the proposed optimized energy efficient driving control approach is capable of saving tractive energy in comparison with the conventional traction calculation-based train operation solution. With the proposed boundary identification method, the capability space of the energy conservation profiles with respect to the energy reduction and energy saving rate is revealed. Moreover, analyses and discussions on effects from different passenger load conditions are given to both the weekday and weekend timetables. Results of this paper may assist the decision making of rail operators and engineers by enhancing the cost effectiveness and energy efficiency.
... The train motion module calculates the tractive effort required for train movement based on Lomonossoff's equation [35]. More details can be found in [36]. The RNS calculates the train speed and position based on a 1 s time-step. ...
Nowadays the railway industry is beginning to give serious consideration to using intelligent traffic management systems (TMSs) in order to improve railway performance regarding train and passenger delays and robust use of capacity. The TMS is responsible for handling railway traffic once a disturbance happens. A fundamental input parameter of a TMS is the train positions, to be used for traffic re‐planning purposes. Inaccuracy in the train positioning data could significantly influence the effectiveness of a TMS. In this study, the authors developed a framework to evaluate how inaccuracies in the train position reporting may affect the TMS performance. This is achieved by assessing the impact of adding inaccuracies to the train position reported to a simulated TMS as it handles operational disturbances in real‐time. The performance of the TMS is analysed by considering variability in overall delay outcomes after re‐planning based on using accurate/inaccurate positional data. They demonstrate the usefulness of their framework in determining the positional accuracy required for the effective application of a basic rescheduling system via an example on a bottleneck area. Results show how the positioning inaccuracies can affect TMS and thus the overall delay.
... Com relação a Eficiência Energética, destacam-se três abordagens conhecidas como tabela de horário (do inglês Timetable) [7] [8], reuso de energia (do inglês Regenerative Energy) [9] e perfil de velocidade(do inglês Speed Profile) [10][11][12]. Cada uma dessas abordagens possui características específicas, de forma que é possível utilizar uma ou uma combinação dessas abordagens a fim de diminuir o gasto energético. ...
In the context of Smart Cities, there are concerns regarding Urban Mobility, which consists of identifying alternatives for the reduction of traffic of individual vehicles, better occupation of urban space, among other aspects. An alternative is the adoption of electric trains. However, a problem concerning energy consumption arises. Thus, this work aims to propose a framework based on Genetic Algorithms (GAs), called SmartSubway, to assist specialists with insertion of domain information in the problem of energy efficiency in electric trains in order to identify energy efficient driving profiles. As proof of concept, a system inspired in GAs was implemented. To validate the system, the domain information of a real scenario was inserted, where it was possible to carry out six experiments and identify the ones that obtained the best results.
... Fig. 3 shows the space-time diagram representing the traffic in the studied area during that time period, where A refers to stations in the direction of Perth, B refers to stations in the direction of Ladybank and C refers to stations in the direction of Arbroath. Two classes of passenger trains are modelled: Class 150 "Sprinter" Diesel Multiple Unit (DMU) [15] and Class 375 "Electrostar" Electric Multiple Unit (EMU) [16]. Class 150 is used for (Train 1, 4 and 6) and Class 375 is used for (Train 2, 3, 5, 7 and 8). ...
There is growing demand for energy saving to prevent global warming. Thus, the railway industry is focusing on energy saving. Power consumption by running trains has a large variation of about 20% caused by drivers' operation. Therefore, improving the running profile will considerably reduce energy consumption. Some algorithms for generating energy saving running profile have been researched and developed. To extend the applicability of energy saving running profile to various railway, we have developed an algorithm for generating energy saving running profile which fulfills target passing time on line which has passing stations between departure and arrival station like limited express train, in addition to between only adjacent stations like local line. Conventional algorithms do not optimize the passing speed because it generates running profile without consideration after the passing station. As a result, there is inefficient acceleration on the next section, and the energy saving performance is degraded. To solve this issue, we propose an evaluation function that adds energy consumption and running time on the next section when generating an energy saving running profile. The proposed method was evaluated by a running simulation, and the results show that the energy saving is improved by at most 4.7%, compared with the conventional method.
Nowadays, the digitisation and automation of railway systems are being carried out all over the world, in order to increase the systems’ accuracy, efficiency and reduce maintenance costs. As part of this trend, intelligent traffic management systems (TMS) are under investigation as a way to increase punctuality and automatically return trains back to the original traffic plan when an operational disturbance happens. A TMS needs a variety of input data to consider the current traffic conditions, predict the future state and decide on a new traffic plan when necessary. Numerous studies have proposed TMS designs; all the proposed systems need to read the train positions in real-time for monitoring and analysis purposes. However, the accuracy of the train positions that can be reported in real-time varies; it depends mainly on the control system design and type of positioning sensor used. Train position uncertainty can significantly influence the performance of a proposed TMS, although the impact has rarely been assessed. In this study, TMS positional accuracy requirements for different railway services are investigated. The influence of train positioning uncertainty is studied with respect to TMS of urban, inter-city and high-speed and mixed-traffic services. This is achieved by simulating the characteristics of these railway services in terms of different trains, tracks, a local TMS and train positioning systems with their associated uncertainty. The experiment is carried out first using exact position data; then it is repeated using position data containing stochastic inaccuracies. The TMS outputs are compared with respect to the train order of the traffic plan and the trains’ total delay. The results show that a small positioning deviation can influence the TMS performance of an urban service, while the TMS of high-speed service is affected less by positioning deviation than the TMS of other services.
The current status of energy saving and environment protection in railway sector is analyzed in this paper. The passenger transport-related energy consumption has degraded dramatically, and the freight-related energy consumption on the decline in general, but the energy consumption per unit grows slightly in last two years, due to the shrinking of rail freight volume. Then, the key effective factors of energy saving and environment protection are presented from the aspects of key facilities and equipment and operation management. Finally, based on the development status of China railway, some new technologies and measures of energy saving and environment protection in four perspectives are proposed, such as life cycle management of railway system, lightweight cabin design and manufacture, comprehensive energy consumption control and energy recycle, and high-efficiency operation.
This paper describes an Enhanced Brute Force Algorithm application to optimise train trajectory (driving speed curve) for Automatic Train Operation (ATO) systems. A multi-train simulator was developed specifically for the study. It can be used to simulate the movement of railway vehicles and calculate the detailed power system energy consumption with different train trajectories when implemented on an AC or DC powered railway line operating with multiple trains. Results are presented using a practical train trajectory and an optimal train trajectory with a full day timetable and passenger flow on the Beijing Yizhuang Metro Line. Analysis of the results shows that by using an optimal train trajectory, the energy consumption around the power network can be significantly reduced within a constrained journey time. Furthermore, the results also show that the developed simulator is able to facilitate the understanding of the railway traction and power system, and that it provides guidance for adjusting the service timetable and driving strategy to minimise energy usage.
The rail industry in the UK is undergoing substantial changes in response to a modernisation vision for 2040. Development and implementation of these will lead to a highly automated and safe railway. Real-time regulation of traffic will optimise the performance of the network, with trains running in succession within an adjacent movable safety zone. Critically, maintenance will use intelligent trainborne and track-based systems. These will provide accurate and timely information for condition based intervention at precise track locations, reducing possession downtime and minimising the presence of workers in operating railways. Clearly, precise knowledge of trains’ real-time location is of paramount importance.The positional accuracy demand of the future railway is less than 2m. A critical consideration of this requirement is the capability to resolve train occupancy in adjacent tracks, with the highest degree of confidence. A finer resolution is required for locating faults such as damage or missing parts, precisely.Location of trains currently relies on track signalling technology. However, these systems mostly provide an indication of the presence of trains within discrete track sections. The standard Global Navigation Satellite Systems (GNSS), cannot precisely and reliably resolve location as required either.Within the context of the needs of the future railway, state of the art location technologies and systems were reviewed and critiqued. It was found that no current technology is able to resolve location as required. Uncertainty is a significant factor. A new integrated approach employing complimentary technologies and more efficient data fusion process, can potentially offer a more accurate and robust solution. Data fusion architectures enabling intelligent self-aware rail-track maintenance systems are proposed.
An energy-efficient train trajectory describing the motion of a single train can be used as an input to a driver guidance system or to an automatic train control system. The solution for the best trajectory is subject to certain operational, geographic, and physical constraints. There are two types of strategies commonly applied to obtain the energy-efficient trajectory. One is to allow the train to coast, thus using its available time margin to save energy. The other one is to control the speed dynamically while maintaining the required journey time. This paper proposes a distance-based train trajectory searching model, upon which three optimization algorithms are applied to search for the optimum train speed trajectory. Instead of searching for a detailed complicated control input for the train traction system, this model tries to obtain the speed level at each preset position along the journey. Three commonly adopted algorithms are extensively studied in a comparative style. It is found that the ant colony optimization (ACO) algorithm obtains better balance between stability and the quality of the results, in comparison with the genetic algorithm (GA). For offline applications, the additional computational effort required by dynamic programming (DP) is outweighed by the quality of the solution. It is recommended that multiple algorithms should be used to identify the optimum single-train trajectory and to improve the robustness of searched results.
The energy cost of a rail transport system is very big especially for the tractions of trains in its daily operation. Continual effort on decreasing the traction energy cost intensities of various rail transport modes has been made by many researchers and practitioners on different aspects for a long time. From the rail transport operation perspective, this study reviews such energy-saving research mainly focusing on the optimizations of train coasting schemes, the rational designs and utilizations of track alignments, the ameliorations of train attributes, and the improvements of system operations. According to the review work, it is confirmed that in sound responses to distinct track alignments, dynamically optimizing control programs of trains with their reasonably improved attributes ought to be further studied in view of the systematic transport operation of a rail line or network in an integrated manner as much as possible in the future research on traction energy saving of rail transport.
Energy saving and emission reduction for railway systems should not only be studied from a technical perspective but should also be focused on management and economics. On the basis of relevant train-scheduling models for train operation management, in this paper we introduce an extended multi-objective train-scheduling optimization model considering locomotive assignment and segment emission constraints for energy saving. The objective of setting up this model is to reduce the energy and emission cost as well as total passenger-time. The decision variables include continuous variables such as train arrival and departure time, and binary variables such as locomotive assignment and segment occupancy. The constraints are concerned with train movement, trip time, headway, and segment emission, etc. To obtain a non-dominated satisfactory solution on these objectives, a fuzzy multi-objective optimization algorithm is employed to solve the model. Finally, a numerical example is performed and used to compare the proposed model with the existing model. The results show that the proposed model can reduce the energy consumption, meet exhausts emission demands effectively by optimal locomotive assignment, and its solution methodology is effective.
This work is focused on the application of evolutionary algorithms to solve very complex real-world problems. For this purpose
a Genetic Algorithm is designed to solve the Train Timetabling Problem. Optimizing train timetables on a single line track
is known to be NP-hard with respect to the number of conflicts in the schedule. This makes it difficult to obtain good solutions
to real life problems in a reasonable computational time and raises the need for good heuristic scheduling techniques. The
railway scheduling problem considered in this work implies the optimization of trains on a railway line that is occupied (or
not) by other trains with fixed timetables. The timetable for the new trains is obtained with a Genetic Algorithm (GA) that
includes a guided process to build the initial population. The proposed GA is tested using real instances obtained from the
Spanish Manager of Railway Infrastructure (ADIF). The results of the computational experience, point out that GA is an appropriate
method to explore the search space of this complex problems and able to lead to good solutions in a short amount of time.
An artificial neural network (ANN) has been proposed in this paper to determine the optimal coasting speed of train operation for the Kaohsiung Mass Rapid Transit (KMRT) system to achieve the cost minimization of energy consumption and passenger traveling time. A train performance simulation (TPS) is applied to solve the energy consumption and the traveling time required to complete the journey between stations with various riderships to create the data set for ANN training. The ANN model for the determination of the optimal coasting speed is then derived by performing the ANN training. To demonstrate the effectiveness of the proposed ANN model, the annual ridership forecast of the KMRT system over the project concession period from 2007 to 2035 has been used to determine the optimal coasting speed of train sets for each study year according to the distance between stations and the passenger ridership. The power consumption profile of train sets and the traveling time of passengers have been solved by TPS to verify the reduction of social cost for KMRT system operation with the optimal coasting speed derived.
The artificial neural network (ANN) has been proposed in this paper to determine the optimal coasting speed of the train set for a mass rapid transit system to achieve the maximization of social welfare. The energy consumption and the traveling time to complete the journey between stations with various riderships are calculated by executing the train performance simulation to generate the data set for ANN training. The objective function is formulated by considering the cost of energy consumption and the cost of passenger traveling time. The ANN model is obtained after performing the ANN training, which can be applied to solve the optimal coasting speed of train sets according to the distance between stations and the ridership of passengers. To demonstrate the effectiveness of the proposed ANN model, the forecasted annual ridership of train sets for Kaohsiung mass rapid transit (KMRT) system is used to determine the optimal coasting speed of train sets operating between stations for each study years. The corresponding profile of power consumption and the traveling time cost of passengers for the train operation have been solved to illustrate the social cost of MRT systems operation by applying the optimal coasting speed derived.
Improved fuel consumption and lower emissions are two of the key objectives for future transportation. Hybrid electric vehicles (HEVs), in which two or more power systems are combined, can meet these objectives through the capture and reuse of regenerated braking energy and through optimized use of the prime mover. However, more complicated power-management strategies are required for such vehicles. This paper explores the potential of applying to advanced power-management strategies for a diesel multiple-unit (DMU) train. These types of vehicles have multiple diesel engines that are commonly operated in a homogenous manner. This paper analyzes the potential energy savings that may be obtained through the independent operation of the engines. Two widely investigated power-management strategies that have been developed for HEVs have been applied to a typical DMU railroad vehicle. Dynamic programming (DP) strategies have been applied to the results produced by a single-train motion simulator to identify the optimal instant power distribution between the engines. An adaptive rule-based online strategy based on the optimization results from the DP solution is then proposed and realized using a nonlinear programming optimization algorithm. Both strategies indicate acceptable agreement and show a fuel cost reduction of around 7%, in comparison with the evenly split engine operation.
This paper describes an analytical process that computes the optimal operating successions of a rail vehicle to minimize energy consumption. Rising energy prices and environmental concerns have made energy conservation a high priority for transportation operations. The cost of energy consumption makes up a large portion of the Operation and Maintenance (O&M) costs of transit especially rail transit systems. Energy conservation or reduction in energy cost may be one of the effective ways to reduce transit operating cost, therefore improve the efficiency of transit operations.
For pt.I see ibid., vol.49, no.2, p.467-75 (2000). In this paper,
applications of the map matching algorithm proposed in part I are
presented. In particular, steady-state Kalman filters are proposed and
applied for filtering the yaw rate and tachometer signals. In addition,
experimental results, using a quartz yaw rate sensor and axle encoders
aboard a freight train, are included to show the performance of the
proposed map matching algorithm
Train simulation software is conventionally validated by checking simulation results against equivalent data collected from real train runs. It is typically expected that these results will be within 5–10% accuracy of the recorded data. However, such a large margin could allow errors in the programming to be overlooked, resulting in an inaccurate model. This paper presents a method for error checking and validating the kinematics of train simulators based on comparison with calculated results, which are found by solving the fundamental equations governing train motion. A typical train run comprises of a combination of two or more of the four stages: accelerating, cruising, coasting and braking. Each stage is considered as a separate scenario for which the equations must be solved, in order to find the running time, distance travelled and energy consumption of the vehicle. This validation method is applied to two train movement simulators currently used for research. Certain specific scenarios for which analytical solutions are available are run in each simulator. The differences from the analytical solution in each test case are quantified, allowing the simulators to be compared to each other and the exact solution.
This paper assesses solution alternatives for railway driver advice systems. To do so a two stage assessment procedure is adopted. First, a wide range of existing systems is identified, using a basis of scientific literature and input from a field survey. Next, the reviewed systems are evaluated using a set of criteria, like: distribution of intelligence, processing unit integration, driver interface, positioning system and communication requirements. The above provides a clear structure for the assessment of DAS, aiming to identify which systems should be investigated in more detail as potential components of real-world deployment. The results highlight major differences in the way that intelligence and processing capabilities are distributed between the control center and the train. They also highlight different approaches to the integration of driver interface, train positioning systems and communication technologies that facilitate the exchange of information between the track and the train. The decision to embark on one of the various approaches depends not only on algorithmic issues but also on human factors considerations, the limits of technology and the costs of upgrading it. Practical aspects such as technical and spatial characteristics of the driver’s cabin, context and format of the advisory information are also of importance.
Rail is becoming an increasingly popular choice to satisfy transportation demands locally, nationally and internationally, due to its inherent efficiency and high capacity. Despite this, operators are facing pressure to reduce rail energy consumption to meet efficiency targets, whilst still maintaining service quality and managing increased demand. A number of individual measures have been proposed to reduce energy in the rail sector, often showing good results on specific case studies. It is generally agreed that the attainable savings of a given measure change dependant on the route, vehicle and service characteristics. However, there is little information in the literature specifically regarding which measures are most suitable for given network types, or how they interact. This paper therefore aims to begin evaluating the available measures in terms of their suitability for different systems. Firstly, networks are defined in terms of their distinguishing features. As traction accounts for the majority of all energy use in the rail sector, the traction flow through a vehicle is considered as the starting point for an evaluation of measures. Current technologies and procedures are reviewed and categorised based on which area of traction use they target. Thought is given to the factors that affect implementation and the networks where they are applied. A key output of this paper is a comparison of the achievable energy savings of different measures dependent on the network type. It is hoped that this will provide a good starting point for identifying which measures are most applicable for a given network, and the characteristics that affect their success.
In railway operations, if the journey of a preceding train is disturbed, the service interval between it and the following trains may fall below the minimum line headway distance. If this occurs, train interactions will happen, which will result in extra energy usage, knock-on delays, and penalties for the operators. This paper describes a train trajectory (driving speed curve) optimization study to consider the tradeoff between reductions in train energy usage against increases in delay penalty in a delay situation with a fixed block signaling system. The interactions between trains are considered by recalculating the behavior of the second and subsequent trains based on the performance of all trains in the network, apart from the leading train. A multitrain simulator was developed specifically for the study. Three searching methods, namely, enhanced brute force, ant colony optimization, and genetic algorithm, are implemented in order to find the optimal results quickly and efficiently. The result shows that, by using optimal train trajectories and driving styles, interactions between trains can be reduced, thereby improving performance and reducing the energy required. This also has the effect of improving safety and passenger comfort.
This paper demonstrates an enhanced Brute Force algorithm application for optimising the driving speed curve by trading off reductions in energy usage against increases in delay penalty. A simulator is used to compare the train operation performance with different train control system configurations when implemented on a section of high-speed line operating with two trains, including differences in journey time and train energy consumption. Results are presented using six different train control system configurations combined with three different operating priorities. Analysis of the results shows that the operation performance can be improved by eliminating the interactions between trains using advanced control systems or optimal operating priorities. The algorithm is shown to achieve the objectives efficiently and accurately. Control system configurations with intermediate levels of complexity (e.g. European Train Control System Levels 2 and 1 with in-fill) when coupled with the optimisation process have been shown to have similar performance to the more advanced control system.
Fuzzy Modeling and Genetic Algorithms for Data Mining and Exploration is a handbook for analysts, engineers, and managers involved in developing data mining models in business and government. As youll discover, fuzzy systems are extraordinarily valuable tools for representing and manipulating all kinds of data, and genetic algorithms and evolutionary programming techniques drawn from biology provide the most effective means for designing and tuning these systems. You dont need a background in fuzzy modeling or genetic algorithms to benefit, for this book provides it, along with detailed instruction in methods that you can immediately put to work in your own projects. The author provides many diverse examples and also an extended example in which evolutionary strategies are used to create a complex scheduling system.Written to provide analysts, engineers, and managers with the background and specific instruction needed to develop and implement more effective data mining systems.Helps you to understand the trade-offs implicit in various models and model architectures.Provides extensive coverage of fuzzy SQL querying, fuzzy clustering, and fuzzy rule induction.Lays out a roadmap for exploring data, selecting model system measures, organizing adaptive feedback loops, selecting a model configuration, implementing a working model, and validating the final model.In an extended example, applies evolutionary programming techniques to solve a complicated scheduling problem.Presents examples in C, C++, Java, and easy-to-understand pseudo-code.Extensive online component, including sample code and a complete data mining workbench.
In this study, an optimal algorithm for a train speed profile using GA (genetic algorithm) to obtain optimum energy efficiency is presented, and its effectiveness is shown by simulation results. Based on a method that simplifies the train driving modes between stations (maximum-power driving, coasting, maximum braking) and by controlling the coasting point, the fitness function is set so that the train can travel the distance between two stations within the defined target travelling time. Then the coasting points that satisfy both constraint elements (distance between stations and target travelling time) are determined by applying GA. Train performance simulation blocks and GA blocks are designed using Simulink.
This paper presents a simulation-based model for manual driving strategies that will minimize energy consumption for high-speed trains. Specific characteristics of both high-speed lines (HSLs) and manual driving strategies are considered in order to obtain achievable designs that can be tested on commercial services. The proposed design model calculates a list of efficient high-level commands to be systematically executed by the driver on an HSL along the trip. The design is based on a detailed simulation model of the train's motion (taking into account track and train characteristics and operational constraints), combined with a genetic algorithm to select the best driving. Continuous control solution by mathematical optimization is avoided, as it is not an appropriate reference for manual driving in HSL. The validation of the simulation model is focused on running resistance, tractive/braking efficiencies, and consumption of auxiliary equipment, and shows differences between real measurements and simulated results which are lower than 2% both in run time and energy consumption. Finally, a real case is presented in which the proposed model was used to design efficient driving strategies that were subsequently implemented on commercial services along the Spanish HSL Madrid–Barcelona in both directions, measuring average energy savings of 23 and 18%, respectively, when the efficient driving strategies were compared with measured standard manual driving. The future scope will be the application of this model to online recalculation of driving commands. © 2012 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
One of the strategies for the reduction of energy consumption in railways systems is to execute efficient drivings (eco-driving). This eco-driving is the speed profile that requires the minimum energy consumption without degrading commercial running times or passenger comfort. When the trains are equipped with Automatic Train Operation systems (ATO) additional difficulties are involved. Their particular features make it necessary to develop accurate models that optimize the combination of the ATO commands of each speed profile to be used by the traffic regulation system. These commands are transmitted to the train via encoded balises on the track with little channel capacity (bandwidth). Thus, only a few and discrete values of the commands can be sent and the solution space of every interstation is made up of a relatively small set of speed profiles. However, the new state-of-the-art of signalling technologies permit a better bandwidth resulting in an exponential solution space. This calls for new methods for the optimal design of the ATO speed profiles without an exhaustive simulation of all the combinations. A MOPSO algorithm (Multi Objective Particle Swarm Optimization) to obtain the consumption/time Pareto front based on the simulation of a train with a real ATO system is proposed. The algorithm is able even to take into account only the comfortable speed profiles of the solution space. The fitness of the Pareto front is verified by comparing it with a NSGA-II algorithm (non-dominated sorting genetic algorithm II) and with the real Pareto front. Further, it has been used to obtain the optimal speed profiles in a real line of the Madrid Underground.
Concerns over future energy security, energy costs, and competitiveness with other modes have prompted the railway industry to search for cost-effective energy efficient traction solutions which will ensure continuing business feasibility. For non-electrified routes, where the business case for electrification is unfavourable, traction is usually provided by diesel fuel combustion. Hybridization offers the potential to achieve a step change in energy efficiency. This article presents an analysis of the potential benefits of hybridization for rail vehicles. The performance requirements of the energy storage device in a hybrid rail vehicle which is storage device dominant are derived. A rail vehicle simulator has been developed in order to compute the drive train duty cycle in typical high-speed and commuter passenger services. The outputs from the simulator have been inputted into a series hybrid model, which has been optimized to preserve the state of charge of the energy storage device over a single typical rail journey. The analysis suggests the energy savings of up to 28 per cent for high-speed intercity vehicles and 35 per cent for commuter vehicles are achievable with practical system components. A sensitivity analysis exploring the effect of the inherent efficiency of the regenerative braking capability and the energy storage device revealed that primary energy savings are only realized with in/out storage efficiencies of greater than ∼40 per cent.
In railway network, trains running through stations may be disturbed by trains in other lines. This paper investigates energy saving train operation under disturbance condition. According to the mathematical description of the problem and dynamic equation for train movement, the authors propose an optimization model of energy saving train operation. A changeable chromosome length genetic algorithm (GA) and table of control mode list are applied to solve the optimization problem. The simulation is carried out, and the speed-position curve of disturbed train is compared with that of train that moves normally. The variation of speed with time is analyzed. Some principles for energy saving are revealed in speed-position curve and energy-position curve. At last, the simulation result is compared with that obtained by other methods. The comparison shows that the changeable chromosome length GA is an effective method to be applied in energy saving train operation.
This paper presents a method to optimize the train-speed trajectory and control between successive stations for mass rapid transit systems with the cable signaling system. The MAX–MIN ant system is utilized to search for the optimal speed codes of each section while taking track gradient, average speed, restriction of train speed, acceleration and jerk into consideration. The train acceleration is further regulated by a fuzzy-PID gain scheduler to meet the speed commands determined. Satisfactory simulation results show applicability and effectiveness of the proposed approach as a tool for designing an energy-saving mass rapid transit system.
Energy efficiency is an important concern in for railway administrations and operators. Strategies focused on traffic operation can achieve energy savings in short term and with associated low investments. For that purpose the main strategies are the design of efficient timetables and driving (ecodriving). The ecodriving applies coasting commands (null traction force) to reduce energy consumption, taking into account downhill slopes, speed reductions, etc. (Acikbas and Soylemez, 2008). However, timetable models in literature do not typically consider energy minimization as a goal, and punctuality requirements under uncertainty. In this paper a model for the joint design of ecodriving and timetable under uncertainty for high speed lines is proposed where the railway operator and administrator requirements are incorporated. Uncertainty in delays is modeled as fuzzy numbers and punctuality constraints, and the timetable optimization model is a fuzzy linear programming model, in which the objective function includes the consumptions of delayed scenarios and the behavioral response of the driver that will affect the consumption. The ecodriving design is based on a Genetic Algorithm that makes use of a detailed simulation model, taking into account the specific characteristics of high speed lines and trains. The proposed method is applied to a real Spanish high speed line to optimize the operation and it is compared to the current commercial service in order to evaluate the potential energy savings.
Focusing on solving critically important train operation problems on a railway network, this paper investigates a mathematical model for finding optimal trains movements under the consideration of operational interactions. With the predetermined routing and traversing order plan, we explicitly consider the optimization of energy consumption and travel time as the objective based on the coasting control methods. To reduce the calculation difficulties, simulation-based methodologies are proposed to compute the energy consumption and traversing time through using specific performance of the involved trains. A genetic algorithm integrated with simulation is designed to seek the approximate optimal coasting control strategies on the railway network. The numerical experiments investigate the effectiveness of the proposed model and algorithm.
The railway service is now the major transportation means in most of the countries around the world. With the increasing population and expanding commercial and industrial activities, a high quality of railway service is the most desirable. Train service usually varies with the population activities throughout a day and train coordination and service regulation are then expected to meet the daily passengers' demand. Dwell time control at stations and fixed coasting point in an inter-station run are the current practices to regulate train service in most metro railway systems. However, a flexible and efficient train control and operation is not always possible. To minimize energy consumption of train operation and make certain compromises on the train schedule, coast control is an economical approach to balance run-time and energy consumption in railway operation if time is not an important issue, particularly at off-peak hours. The capability to identify the starting point for coasting according to the current traffic conditions provides the necessary flexibility for train operation. This paper presents an application of genetic algorithms (GA) to search for the appropriate coasting point(s) and investigates the possible improvement on fitness of genes. Single and multiple coasting point control with simple GA are developed to attain the solutions and their corresponding train movement is examined. Further, a hierarchical genetic algorithm (HGA) is introduced here to identify the number of coasting points required according to the traffic conditions, and Minimum-Allele-Reserve-Keeper (MARK) is adopted as a genetic operator to achieve fitter solutions.
This paper aims to give an overview of the European Rail Traffic Management System (ERTMS) which is currently being implemented in Europe and other parts of the world. It provides some background on this system, the objectives behind the development of the ERTMS, its architecture, different application levels of t he E RTMS and brief information on its implementation in Europe and worldwide. The paper assumes the readers have a little or no knowledge of the ERTMS.
Halcrow and Unipart Rail have developed and are trialling a simple in-cab system which continually advises train drivers of the train's time with respect to a pre-calculated time-distance profile. The profile is set up to save energy by making use of unused recovery time in the timetable. The recovery time is used up at the end of each inter-station journey by “coasting”, i.e. reducing power to idle and allowing the train to decelerate gradually. An off-train performance calculator is used to generate the profiles for each stopping pattern, route and rolling stock type. The profile fully respects the stops and important passing points in the operating timetable.
It is known that for a single DC powered train, energy savings can be obtained by a combination of motoring, braking and coasting during a journey. However, this does not necessarily yield all of the net energy savings that are possible if other trains are running within the same electrical section. Further savings may be available during motoring by using energy regenerated by other trains while they are braking. This paper first presents a single train trajectory optimisation to obtain minimum energy consumption with maximum regenerated energy, and then considers net energy reduction between two adjacent DC substations when the optimised trajectories are used. Each trajectory is optimised individually using a genetic algorithm to search for the best possible compromise between energy consumption and journey time requirements. A weighted combination of these two is used as the objective function and the rates of train acceleration, braking and coasting form a set of variables that define a driving strategy. In order to estimate the benefits and effects of optimised trajectories on net energy consumption, multi-train simulation was then performed for both fastest and optimised journeys. Both qualitatively and quantitatively, the results suggest that further considerable reductions of net energy consumption may be achieved by the adjustment of schedules for both the up and down direction so as to increase the receptivity of those trains within each subsection, or by the recalculation of single train trajectories with different optimisation criteria. Finally, consideration is given to the possible application of the technique on a real railway traction system. Although demonstrated here on a DC system, the method could equally be applied to AC electrified railways.
This paper shows the form of the optimal solution and how to
minimize energy of the train driving control that can be included into
automatic train operation (ATO) systems. We consider the case where a
train is to be driven by automatic operation mode along a nonconstant
gradient curve and with speed limits. Using the genetic algorithms (GA),
we constructed an optimal train driving strategy. The results are
compared with P. Howlett's optimization method using the constrained
optimal technique (Lagrange function and Kuhn-Tucker equations) in view
of energy cost benefit. For the case studies, we used a railway track of
Seoul City MRT system. As a result of the test, we verified that the
proposed algorithm could be of effective energy cost benefit
Energy consumption of a rail transit system depends on many parameters. One of the most effective methods of reducing energy consumption in a rail transit system is optimising the speed profile of the trains along the route. A new efficient method will be presented for the optimisation of the coasting points for trains in a global manner. The proposed approach includes realistic system modelling using multi-train, multi-line simulation software and application of artificial neural networks (ANN) and genetic algorithms (GA). The simulation software used can model regenerative braking and train performance at low voltages. Using ANN and GA together, optimal coasting points for long line sections covering five stations and two lines are achieved. Simulation software is used for creating training and test data for the ANN. These data are used for training of the ANN. Trained ANNs are then used for estimating energy consumption and travel time for new sets of coasting points. Finally, the outputs of the ANN are optimised to find optimal train coasting points. For this purpose, a fitness function with target travel time, energy consumption and weighting factors is proposed. An interesting observation is that the use of ANN increases the speed of optimisation. The proposed method is used for optimising coasting points for minimum energy consumption for a given travel time on the first 5 km section of Istanbul Aksaray-Airport metro line, where trains operate every 150 s. The section covers five passenger stations, which means four coasting points for each line. It has been demonstrated that an eight input ANNs can be trained with acceptable error margins for such a system.
Energy saving on electrified railways has been studied for many years and the technical solution is usually provided by a combination of driving strategy (e.g. coasting), regenerative braking and energy storage systems. An alternative approach is for the driver (or automatic train operation system if fitted) to manage energy consumption more efficiently. A formal method for optimising traction energy consumption during a single-train journey by trading-off reductions in energy against increases in running time has been demonstrated. The balance between saving energy and running faster has been investigated by designing a fitness function with variable weightings. Energy savings were found, both qualitatively and quantitatively, to be affected by acceleration and braking rates, and, by running a series of simulations in parallel with a genetic algorithm search method, the fitness function was used to identify optimal train trajectories. The influence of the fitness function representation on the search results was also explored.
This paper presents an approach to identify a fuzzy control model
for determining an economical running pattern for a high-speed railway
through an optimal compromise between trip time and energy consumption.
Since the linguistic model is intuitive and informative to railway
operators, they can easily implement a control strategy for saving
energy. The approach includes structure identification and parameter
identification. It is proposed to utilize a fuzzy c-means clustering and
a GA hybrid scheme to identify the structure and parameters of a fuzzy
model, respectively. To evaluate the advantages and the effectiveness of
the suggested approach, numerical examples are presented. Comparison
shows that the proposed approach can produce a fuzzy model with higher
accuracy and smaller number of rules than previously achieved in other
works. To show the global optimization and local convergence of the GA
hybrid-scheme, an optimization problem having a few local minima and
maxima is considered
With daily commercial and social activity in cities, regulation of train service in mass rapid transit railways is necessary to maintain service and passenger flow. Dwell-time adjustment at stations is one commonly used approach to regulation of train service, but its control space is very limited. Coasting control is a viable means of meeting the specific run-time in an inter-station run. The current practice is to start coasting at a fixed distance from the departed station. Hence, it is only optimal with respect to a nominal operational condition of the train schedule, but not the current service demand. The advantage of coasting can only be fully secured when coasting points are determined in real-time. However, identifying the necessary starting point(s) for coasting under the constraints of current service conditions is no simple task as train movement is governed by a large number of factors. The feasibility and performance of classical and heuristic searching measures in locating coasting point(s) is studied with the aid of a single train simulator, according to specified inter-station run times.
A genetic algorithm (GA) is proposed to optimise train movements
using appropriate coast control that can be integrated within automatic
train operation (ATO) systems. The coast control output for a train
changes with the interstation distances and gradient profiles, and the
current operating conditions of the mass rapid transit (MRT) system,
namely: (i) train schedules; (ii) expected passenger loads; and (iii)
expected track voltages. The algorithm generates an optimum coast
control based on evaluation of the punctuality, riding comfort and
energy consumption. Before the train sets off to the designated station,
a coast control table is generated that will be referenced by the train
at runtime for deciding when to initiate coasting or resume motoring
control. Each coast control table is encoded into variable length
chromosomes with each gene representing the relative position between
stations where coasting should be initiated or terminated. Each
generation is evolved from mating of the paired equal-length chromosomes
with possibilities of crossover, mutations, gene duplications and gene
deletions. The key feature of this method is that it has a solid
mathematical foundation. Effectively, the implementation provides good,
credible and reasonably fast solutions for this variable dimensional and
multiobjective optimisation problem. The algorithm has the potential for
online implementation for producing a coast control lookup table for
each interstation run before the train sets off. The results, although
preliminary, suggest that the method is promising
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