Railway infrastructure maintenance consumes very large budgets, is complicated to organize and has numerous challenging planning problems. Specifically, the coordination with train traffic operatio ...

http://liu.diva-portal.org/smash/get/diva2:857144/FULLTEXT01

Railway Infrastructure Maintenance - A Survey of Planning Problems and Conducted Research

Metropolitan railway operators' strategic plans include nowadays actions to reduce energy consumption. The application of ecodriving initiatives in lines equipped with automatic train operation (ATO) systems can provide important savings with low investments. Previous studies carried out under the ATO framework have not considered the main uncertainties in the traffic operation: the train load and delays in the line. This paper proposes a method to design robust and efficient speed profiles to be programmed in the ATO equipment of a metro line. First, an optimal Pareto front for ATO speed profiles that are robust to changes in train load is constructed. There are two objectives: running time and energy consumption. A robust optimization technique and an alternative method based on the conservation of the shape of the speed profiles (pattern robustness) are compared. Both procedures make use of a multi objective particle swarm optimization algorithm. Then, the set of speed profiles to be programmed in the ATO equipment is selected from the robust Pareto front by means of an optimization model. This model is a particle swarm optimization algorithm (PSO) to minimize the total energy consumption considering the statistical information about delays in the line. This procedure has been applied to a case study. The results showed that the pattern robustness is more restrictive and meaningful than the robust optimization technique as it provides information about shapes that are more comfortable for passengers. In addition, the use of statistical information about delays provides additional energy savings between 3% and 14%.

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Design of Robust and Energy-Efficient ATO Speed Profiles of Metropolitan Lines Considering Train Load Variations and Delays

Train Delay Prediction Systems: A Big Data Analytics Perspective ☆

Current train delay (TD) prediction systems do not take advantage of state-of-the-art tools and techniques for handling and extracting useful and actionable information from the large amount of endogenous (i.e., generated by the railway system itself) and exogenous (i.e., related to railway operation but generated by external phenomena) data available. Additionally, they are not designed in order to deal with the intrinsic time varying nature of the problem (e.g., regular changes in the nominal timetable, etc.). The purpose of this paper is to build a dynamic data-driven TD prediction system that exploits the most recent tools and techniques in the field of time varying big data analysis. In particular, we map the TD prediction problem into a time varying multivariate regression problem that allows exploiting both historical data about the train movements and exogenous data about the weather provided by the national weather services. The performance of these methods have been tuned through the state-of-the-art thresholdout technique, a very powerful procedure which relies on the differential privacy theory. Finally, the performance of two efficient implementations of shallow and deep extreme learning machines that fully exploit the recent in-memory large-scale data processing technologies have been compared with the current state-of-the-art TD prediction systems. Results on real-world data coming from the Italian railway network show that the proposal of this paper is able to remarkably improve the state-of-the-art systems.

Dynamic Delay Predictions for Large-Scale Railway Networks: Deep and Shallow Extreme Learning Machines Tuned via Thresholdout

Optimal design of energy-efficient ATO CBTC driving for metro lines based on NSGA-II with fuzzy parameters

With the increasing energy consumption in Chinese mainline railways amid the worldwide carbon emission concerns, the need for energy-efficient locomotive operation becomes urgent. Locomotive operation is directly linked to speed limits imposed by the train ahead through signaling. In China's mainline railways, speed limits for locomotive operation change frequently because of relatively short headways in a highly congested network. Whenever the speed limit changes, the locomotive operation must be determined again quickly to adapt to the new speed limit. As a result, the energy-efficient locomotive operation is a real-time optimization problem with time-varying constraints, in which the tradeoff between solution optimality and computational time is essential, but it has not been considered adequately in previous studies. This study develops a fuzzy predictive control approach, continuously providing locomotive operation instructions, with respect to the prevailing speed limits, to reduce energy consumption of train movement. The proposed approach is implemented in an onboard decision support system to assist drivers. The system is tested on the Ning'xi line in China. The results indicate that energy consumption on train operations is reduced by 4%, without increasing the runtime between stations, while the computational requirement satisfies the demand of real-time solutions. Extensive simulations show that the proposed approach is able to provide sufficient solution optimality in reasonable computational time under different operation settings.

Energy-Efficient Locomotive Operation for Chinese Mainline Railways by Fuzzy Predictive Control

High reliability is the crucial requirement in railway operation and a power supply system is one of the key components of electrified railways. The cost-effectiveness of the maintenance works is also the concern of the railway operators while the time window on trackside maintenance is often limited. Maintenance scheduling is thus essential to uphold reliability and to reduce operation cost. It is however difficult to formulate the optimal schedule to meet both reliability and maintenance cost for a railway power supply system as a whole because of its functional complexity and demanding operation conditions. Maintenance scheduling models to achieve reliability and maintenance cost are proposed in this study. Optimisation algorithms are then developed to attain the solutions of the model. The applicability of the models and efficiency of the solution algorithms are demonstrated in an example. The proposed methods are vitally important for the railway engineers and operators to assure the service quality ...

Maintenance schedule optimisation for a railway power supply system

The invention discloses an energy-saving advancing method for a tramcar. The method includes: dividing a tramcar advancing route into a plurality of subareas according to signal intersections and stations; determining the time range needed by the tramcar to pass each subarea according to the green wave band and advancing route of the tramcar; calculating the minimum traction energy consumption value of the tramcar passing each subarea at optional time in the time range needed by the tramcar to pass each subarea; calculating the total minimum traction energy consumption value of the tramcar passing all the subareas according to the minimum traction energy consumption value of the tramcar passing each subarea, and determining the advancing time, corresponding to the total minimum energy consumption value, needed by the tramcar to pass each subarea. The method has the advantages that the problem that the tramcar meets the red light due to the fact that the time loss of starting and stopping operation is not considered is avoided, the traction energy consumption during tramcar operation is lowered on the basis that green wave passing is guaranteed, and support is provided for the achieving of the traction energy saving of the tramcar.

Shaokuan Chen

Papers

Energy-Efficient Locomotive Operation for Chinese Mainline Railways by Fuzzy Predictive Control

Maintenance schedule optimisation for a railway power supply system

Energy-saving advancing method for tramcar