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Optimizing to increase the utilization ratio of regenerative braking energy reduces energy consumption, and can be done without increasing the deviation of train running time in one circle. The latter entails that the train timetable is upheld, which guarantees that the demand for passenger transport services is met and the quality of services in t...
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... The model was solved using a non dominated sorting genetic algorithm -II. The research results show that the optimal energy-saving schedule reduces total energy consumption by 8.72%, but the deviation of one week train operation time increases by 728 seconds; The total energy consumption decreased by 6.09%, but the deviation of train operation time for one week did not increase [7]. Tang J et al. proposed a data-driven URT schedule optimization method to reduce the operating costs and improve service quality of URT company. ...
At present, the traditional urban public transportation system cannot meet people’s daily travel needs. Urban Rail Transit (URT) has been rapidly promoted in major cities due to its advantages such as low energy consumption, high frequency, and large traffic volume. To achieve a more excellent and energy-saving operation scheduling strategy, the research first combines the train dynamics model and the energy consumption model. Since the optimization problem of URT is a linear problem, the attraction model of the Firefly algorithm can determine the calculation time consumed by the algorithm, which is very suitable for the complex optimization problem of URT. Therefore, the FA based optimization algorithm for urban rail transit operation scheduling (FURTOSO) based on the Firefly algorithm is studied and designed. Therefore, based on the study of the four working conditions of traction, cruise, coasting, and braking, a Firefly Algorithm for Urban Rail Transit Operation Scheduling (FURTOSO) was designed. Finally, the study optimizes the operation scheduling of Chengdu Metro Line 8 from two aspects: driving strategy and train schedule. The research demonstrates that the FURTOSO algorithm only needs 76 iterations to reach a stable state, with a fitness value of 0.6827. In practical applications, the utilization rate of train RBE is 30.1%, the total energy consumption (TEC) is 2.661 * 1011J, and the energy saving rate is 13.03%. In summary, the FURTOSO algorithm proposed in the study has excellent performance and has better energy-saving effects in Chengdu Metro Line 8.
... To mitigate this, automatic receptivity units (ARU) are used to dissipate the RBE and prevent overvoltage. Recently, optimization of train schedules using artificial intelligence (AI) techniques such as genetic algorithm [22][23][24], particle swarm optimization [25], heuristic algorithm [26,27], and brute force algorithm [28] have partially resolved this issue. By synchronizing the acceleration and deceleration of neighboring trains using these techniques, the RBE of the decelerating train can be picked up by the accelerating train, thus preventing overvoltage issues [29][30][31]. ...
Regenerative braking energy (RBE) recovery of trains can improve energy efficiency as well as reduce the overall greenhouse gases emissions of electric rail systems. The train's speed limit, track elevation, track curvature, and headway time can affect the amount of RBE recovered. This study aims to investigate the impacts of these parameters on the RBE recovery for a DC third rail system. A DC third rail system has been modeled using the ETAP-eTraX software based on the Malaysia Mass Rapid Transit Line 2 (MRT Line 2) traction power system. The effects of the change of speed limit, track elevation, track curvature, and headway time of the trains on the RBE recovery have been evaluated under various scenarios. The results showed that the track’s elevation has the most significant influence on the overall energy consumptions of the train while the speed limit has the most significant influence on the amount of RBE recovery.
... Li et al., in [12], are concerned with the minimization of the total energy consumption in urban railways while maximizing service quality, by proposing a multi-objective optimization model with timetable optimization. It was applied for the case study of the Yizhuang urban railway line in Beijing, and it was shown that the optimal quality of the service timetable reduced the total energy consumption by 6.09%, without increasing the deviation of train running time. ...
Increasing energy efficiency in electric transportation systems is a topical issue, considering the worldwide concern for reducing CO2 emissions, and especially through the significant reduction in energy loss and energy consumption [...]
... The traction energy consumption is the sum of the work by the train applying traction during the running process from Equations (28) and (29). Additionally, the traction energy consumption caused by the change of train mass changes is greater than the rate of change of other factors besides train mass [12]. The following section of this paper uses the above-mentioned online learn- The deviation between the learning mean value and the actual value is shown in Table 3. ...
... The traction energy consumption is the sum of the work by the train applying traction during the running process from Equations (28) and (29). Additionally, the traction energy consumption caused by the change of train mass changes is greater than the rate of change of other factors besides train mass [12]. The following section of this paper uses the above-mentioned online learning time-varying train mass and fixed experience train mass value to compare and analyze its impact on the traction energy consumption of the train. ...
The current train model of the train control system is unable to accurately reflect the influence of nonlinear running resistance, line conditions, the mutative train mass value, and external environment changes on the model in train dynamics, resulting in a defect of poor train model performance. The train basic model and additional resistances are discussed in this paper, a novel neural network online learning method of the time-varying dynamic train model is proposed, combined with the characteristics of rail transit lines, and a neural network learning algorithm is designed by categories and steps. This method can identify the train mass value that changes continuously with passengers during running. The energy savings resulting from using the actual varying train mass in the train control system are calculated. The results show that, when compared to the traditional model’s invariant approximate empirical parameters, the time-varying parameter model can follow changes in the train and line environment and obtain quantitative expressions of curve resistance and tunnel resistance with speed. The time-varying train model was used to conduct engineering tests on the Beijing Capital Airport Line; the online learning deviation of train mass was controlled within a margin of 3.08%, and at the same time, energy consumption decreased by 6.13%.
... It is mainly responsible for barrier-free and short-distance travel, with light electric multiple units (EMUs) or trams as the transportation carrier, effectively alleviating the traffic pressure of dense passenger flow in the city. Urban rail transit generally has the advantages of large volume, fast speed, dense frequency, security and comfort, high punctuality, all-day operation, low freight, energy saving, and environmental protection [2]. From an operational perspective, in the future, we should fully use advanced technologies such as the Internet, 5G, big data, and artificial intelligence to promote operational informatization and intelligence. ...
Based on a stated preference survey, we comprehensively analyze the travel psychology of residents and the advantages and disadvantages of rail transit and conventional buses, travel time, travel cost, travel security, and vehicle comfort and investigate the relationship between the relevant influencing factors and the transition probability from rail transit to buses. A stochastic utility theory is introduced to describe the transfer behavior pertaining to travel modes, and a binary Logit model for diversion transfer is constructed. The decision tree is also used to predict the diversion transfer. Then, based on the large amount of travel willingness data obtained through the stated preference survey, a maximum likelihood estimation method is used to calibrate the parameters of the binary Logit model. The performance of the binary Logit proves to be better than that of the decision tree. Results show that the travel time most notably affects the passenger flow transfer, followed by the vehicle comfort. Finally, Guangzhou Rail Transit Line 3 is considered an example, and the diversion route planning and design are performed according to the constructed diversion transfer probability model to verify the effectiveness and practicability of the model. The research provides an effective theoretical basis and technical reference for other cities to perform rail traffic diversion planning. Based on these results, the following suggestions can be made: (1) the organization of public transportation routes, delivery volume, and travel speed outside should be improved; (2) undertaking combined operation of bus and rail transportation and integrated development is preferred; (3) the transportation management should focus on the comprehensive function development and hardware support of public transportation stations. The convenience and comfort of rail transit are closely related to the facilities and functions of the stations and their connections, which should be highly valued.
... Method extensions and practical applications were also discussed. In addition, evaluation work can be further combined with other transportation organization or disruption management research to formulate collaborative optimization strategies, such as robust train timetabling [57], train platforming [58], rolling stock scheduling [59], and energy saving [60]. e method of parameter estimation needs to be improved. ...
The evaluation of the railway operation safety situation is important for managers to ensure transportation safety and make control decisions. In this study, first, six situation indicators are designed from a regional perspective based on the characteristics of railway operation accidents, and the quantitative methods of these indicators are determined. Second, an improved technique for order of preference by similarity to ideal solution (TOPSIS) method is developed to evaluate the railway operation safety situation against the situation indicators. Based on the set-pair analysis (SPA) theory and cosine similarity measure (CSM), the comprehensive evaluation values and rankings of the safety situation are first calculated from the distance and trend levels for each period. Game theory is then employed to determine a more reasonable combined weight, and the values of the parameters involved in the situation indicators are also estimated. The real-life statistical accident data in a regional area of China from 2016 to 2018 are chosen as a case study to verify the proposed method. A brief analysis is conducted, resulting in suggestions for the evaluation results. Two groups of comparative experiments are designed to demonstrate the feasibility and effectiveness of the method. Finally, the quality of the evaluation results is verified through actual conditions and expert scoring. Some extensions and potential practical applications of this work are discussed.
... ird, the train movements at the stations can be viewed as a set of space-time paths, and thus more efficient dual decomposition methods could be developed accordingly [7,44,45]. Fourth, more efficient solution algorithms can be developed for the simultaneous reoptimization of several interconnected railway stations in a railway line or even a regional railway network with more complicated structure so that the joint optimization of delay situations and train timetabling problems can be achieved [8,19,27,[46][47][48][49]. Finally, the integration of energy-efficient train movement with the reoptimization of train platforming can be considered for more system-wide benefits [50,51]. ...
Train platforming is critical for ensuring the safety and efficiency of train operations within the stations, especially when unexpected train delays occur. This paper studies the problem of reoptimization of train platforming in case of train delays, where the train station is modeled using the discretization of the platform track time-space resources. To solve the reoptimization problem, we propose a mixed integer linear programming (MILP) model, which minimizes the weighted sum of total train delays and the platform track assignment costs, subject to constraints defined by operational requirements. Moreover, we design an efficient heuristic algorithm to solve the MILP model such that it can speed up the reoptimization process with good solution precision. Furthermore, a real-world case is taken as an example to show the efficiency and effectiveness of the proposed model and algorithm. The computational results show that the MILP model established in this paper can describe the reoptimization of train platforming accurately, and it can be solved quickly by the proposed heuristic algorithm. In addition, the model and algorithm developed in this paper can provide an effective computer-aided decision-making tool for the train dispatchers in case of train delays.
Urban rail transit systems play an essential role in improving mobility and efficiency. A complex rail transit network serves the Boston metropolitan area, U.S., which costs $38 million for the 422 GWh of system electricity consumed annually. With the aim of developing a tool for energy and cost reduction decision support, we propose a comprehensive machine learning framework to investigate line-specific contributions to energy. This effort builds on prior work in estimating a system-wide energy model for the Boston network. By introducing line-specific train movement and operation variables, we obtain a higher-performing model [Formula: see text]. Furthermore, the model better explains the relationship between energy and train movement, ridership, and weather variables. Most importantly, the model facilitates analyses of how each line contributes to system consumption at the hour level. We found that the non-line-specific variables made a contribution of −2.7% to the average hourly energy of consumption of −5.4 MWh with a baseline energy consumption of 39 MWh. The Red Line dominates the energy consumption among line-specific variables, contributing 2.3% to the hourly average. Our model could be further enhanced to evaluate the energy and cost impacts of line-specific strategies that may be required for future planning and disaster response, as well as for real-time energy monitoring by line.
The rise in the service economy has been fueled by breakthroughs in technology, globalization, and evolving consumer patterns. However, this sector faces various challenges, such as issues related to service quality, innovation, efficiency, and sustainability, as well as macro-level challenges such as globalization, geopolitical risks, failures of financial institutions, technological disruptions, climate change, demographic shifts, and regulatory changes. The impacts of these challenges on society and the economy can be both significant and unpredictable, potentially endangering sustainability. Therefore, it is crucial to comprehensively study services and service economies at both holistic and local levels. To this end, the objective of this study is to develop and validate an artificial-intelligence-based methodology to gain a comprehensive understanding of the service sector by identifying key parameters from the academic literature and public opinion. This methodology aims to provide in-depth insights into the creation of smarter, more sustainable services and economies, ultimately contributing to the development of sustainable future societies. A software tool is developed that employs a data-driven approach involving the use of word embeddings, dimensionality reduction, clustering, and word importance. A large dataset comprising 175 K research articles was created from the Scopus database, and after analysis, 29 distinct parameters related to the service sector were identified and grouped into 6 macro-parameters: smart society and infrastructure, digital transformation, service lifecycle management, and others. The analysis of over 112 K tweets collected from Saudi Arabia identified 11 parameters categorized into 2 macro-parameters: private sector services and government services. The software tool was used to generate a knowledge structure, taxonomy, and framework for the service sector, in addition to a detailed literature review based on over 300 research articles. The conclusions highlight the significant theoretical and practical implications of the presented study for autonomous capabilities in systems, which can contribute to the development of sustainable, responsible, and smarter economies and societies.
