Developing Train Station Parking Algorithms: New Frameworks Based on Fuzzy Reinforcement Learning

Train station parking (TSP) accuracy is important to enhance the efficiency of train operation and the safety of passengers for urban rail transit. However, TSP is always subject to a series of uncertain factors such as extreme weather and uncertain conditions of rail track resistances. To increase the parking accuracy, robustness, and self-learning ability, we propose new train station parking frameworks by using the reinforcement learning (RL) theory combined with the information of balises. Three algorithms were developed, involving a stochastic optimal selection algorithm (SOSA), a Q-learning algorithm (QLA), and a fuzzy function based Q-learning algorithm (FQLA) in order to reduce the parking error in urban rail transit. Meanwhile, five braking rates are adopted as the action vector of the three algorithms and some statistical indices are developed to evaluate parking errors. Simulation results based on real-world data show that the parking errors of the three algorithms are all within the "mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"""mml:mrow""mml:mo"±"/mml:mo""/mml:mrow""/mml:math"30cm, which meet the requirement of urban rail transit. Document type: Articl

Riding Comfort Evaluation Based on Longitudinal Acceleration for Urban Rail Transit—Mathematical Models and Experiments in Beijing Subway

Riding comfort is an important index to measure the quality of service for railways, especially for congested urban rail transit systems where the majority of passengers cannot find a seat. Existing studies usually employ the value of longitudinal acceleration as the key indicator to evaluate the riding comfort of vehicles, while there is no validated mathematical models to evaluate the riding comfort of urban rail trains from the perspective of passengers. This paper aims to employ the collected longitudinal acceleration data and passengers’ feedback data in Beijing subway to qualitatively measure and validate the riding comfort of transit trains. First, we develop four regular fuzzy sets based comfort measurement models, where the parameters of the fuzzy sets are determined by experiences of domain experts and the field data. Then a combinational model is given by averaging the four regular fuzzy set models to elaborate a comprehensive measurement for the riding comfort. In order to verify the developed models, we conducted a questionnaire survey in Beijing subway. The surveyed riding comfort data from passengers and the measured acceleration data are used to validate and optimize the proposed models. Two key parameters are deduced to describe all parameters in the fuzzy set models and a meta-heuristic algorithm is applied to optimize the parameters and weight coefficients of the combinational model. Comparing the collected comfort data with the comfort levels and values calculated by different models shows that the averaging model is better than any regular fuzzy set model. Furthermore, the optimized model is better than the averaging model and provides the best accuracy and robustness for riding comfort measurement. The models provided in this paper offer an optional way to measure the riding comfort for further assessment and more comprehensively tuning of train control systems

A robust MPC approach with controller tuning for close following operation of virtually coupled train set

Virtual coupling, as an emerging concept in railways, expects successive trains in a virtually coupled train set (VCTS) to maintain a short following distance. However, this close following operation is still difficult to be achieved given uncertain resistances and nonlinear safety constraints. To solve this problem, this paper proposes a robust model predictive control (RMPC) approach for the close following operation of VCTS while satisfying a nonlinear safety constraint with relative braking principle. First, we construct a robust positively invariant set that bounds the tracking errors caused by uncertain resistances. Further, a semi-definite program-based controller tuning algorithm is proposed to reduce the following distance in the premise of the tightened constraint for robustly satisfying the nonlinear safety constraint. Then, by mathematically examining the future trajectories of successive trains, we create a terminal constraint set to ensure the recursive feasibility of the proposed RMPC. This closed-loop property guarantees the satisfaction of the safety constraint in any situation, even in the case of sudden deceleration of VCTS. Finally, numerical experiments are conducted to evaluate the following distance with respect to heterogeneous trains and verify the effectiveness of our approach. Experimental results demonstrate that the expected close following operation can be achieved while robustly satisfying the nonlinear safety constraint with uncertain resistances. Moreover, our approach further reduces the following distance in a VCTS by over 5%, compared with existing research

Processing Optimization of Shear Thickening Fluid Assisted Micro-Ultrasonic Machining Method for Hemispherical Mold Based on Integrated CatBoost-GA Model

Micro-electro-mechanical systems (MEMS) hemispherical resonant gyroscopes are used in a wide range of applications in defense technology, electronics, aerospace, etc. The surface roughness of the silicon micro-hemisphere concave molds (CMs) inside the MEMS hemispherical resonant gyroscope is the main factor affecting the performance of the gyroscope. Therefore, a new method for reducing the surface roughness of the micro-CM needs to be developed. Micro-ultrasonic machining (MUM) has proven to be an excellent method for machining micro-CMs; shear thickening fluids (STFs) have also been used in the ultra-precision polishing field due to their perfect processing performance. Ultimately, an STF-MUM polishing method that combines STF with MUM is proposed to improve the surface roughness of the micro-CM. In order to achieve the excellent processing performance of the new technology, a Categorical Boosting (CatBoost)-genetic algorithm (GA) optimization model was developed to optimize the processing parameters. The results of optimizing the processing parameters via the CatBoost-GA model were verified by five groups of independent repeated experiments. The maximum absolute error of CatBoost-GA is 7.21%, the average absolute error is 4.69%, and the minimum surface roughness is reduced by 28.72% compared to the minimum value of the experimental results without optimization

Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach

In a heavily congested metro line, unexpected disturbances often occur to cause the delay of the traveling passengers, infeasibility of the current timetable and reduction of the operational efficiency. Due to the uncertain and dynamic characteristics of passenger demands, the commonly used method to recover from disturbances in practice is to change the timetable and rolling stock manually based on the experiences and professional judgements. In this paper, we develop a stochastic programming model for metro train rescheduling problem in order to jointly reduce the time delay of affected passengers, their total traveling time and operational costs of trains. To capture the complexity of passenger traveling characteristics, the arriving ratio of passengers at each station is modeled as a non-homogeneous poisson distribution, in which the intensity function is treated as time-varying origin-to-destination passenger demand matrices. By considering the number of on-board passengers, the total energy usage is modeled as the difference between the tractive energy consumption and the regenerative energy. Then, we design an approximate dynamic programming based algorithm to solve the proposed model, which can obtain a high-quality solution in a short time. Finally, numerical examples with real-world data sets are implemented to verify the effectiveness and robustness of the proposed approaches

Ridesharing Problem with Flexible Pickup and Delivery Locations for App-Based Transportation Service: Mathematical Modeling and Decomposition Methods

App-based transportation service system, such as Uber and Didi, has brought a new transportation mode to users, who are able to make reservations using mobile apps conveniently. However, one of the fundamental challenges in app-based transportation system is the inefficiency and unreliability of the vehicle routing plans caused by complex topology of urban road network and unpredictable traffic conditions. A common way to tackle this problem is repositioning pickup or delivery locations via the coordination between drivers and passengers. This paper studies an on-demand ridesharing problem that determines the optimal ride-share matching strategy and vehicle routing plan with respect to flexible pickup and delivery locations. By introducing the concept of space-time windows, the problem is formulated as the pickup and delivery problem with space-time windows (PDPSW) in space-time network. To solve the model efficiently and accurately, we particularly develop a customized solution approach based on Lagrangian relaxation. Numerical examples are conducted to demonstrate the performance of the proposed framework and draw some managerial insights into the optimal system operation. The results indicate that adopting the serving strategy of flexible pickup and delivery locations will evidently reduce the system cost and improve the service quality in app-based transportation service systems

Synchronization of train timetables in an urban rail network: A bi-objective optimization approach

As urban rail networks in big cities tend to expand, the synchronization of trains has become a key issue for improving the service quality of passengers because most urban rail transit systems in the world involve more than one connected line, and passengers must transfer between these lines. In contrast to most existing studies that focus on a single line, in this study, we focus on synchronized train timetable optimization in an urban rail transit network, considering the dynamic passenger demand with transfers as well as train loading capacity constraints. First, we propose a mixed-integer programming (MIP) formulation for the synchronization of training timetables, in which we consider the optimization of two objectives. The first objective is to minimize the total waiting time of passengers, involving arriving and transfer passengers. Our second objective is a synchronization quality indicator (SQI) with piecewise linear formulation, which we propose to evaluate the transfer convenience of passengers. Subsequently, we propose several linearization techniques to handle the nonlinear constraints in the MIP formulation, and we prove the tightness of our reformulations. To solve large-scale instances more efficiently, we also develop a hybrid adaptive large neighbor search algorithm that is compared with two benchmarks: the commercial solver CPLEX and a metaheuristic. Finally, we focus on a series of real-world instances based on historical data from the Beijing metro network. The results show that our algorithm outperforms both benchmarks, and the synchronized timetable generated by our approach reduces the average waiting time of passengers by 1.5% and improves the connection quality of the Beijing metro by 14.8%