272 research outputs found
Feasibility evaluation and critical factor analysis for subway scheduling
In strategic subway scheduling stage, the conflict sometimes comes from different requirements of the subway operator. This study aims to investigate the significant factors concerning strategic subway scheduling problem and to develop an automatic procedure of feasibility analysis in subway scheduling. To this end, accurate simulation of train movement (via a simulator, named HAMLET) is applied first by considering the line geography, train performances, actual speed restrictions, etc. The critical elements of subway scheduling and their correlations are then studied and a bound structure of the critical factors is established. The feasibility of primary plan requirements is analysed with the restrictions of the bound structure. Infeasible aspects and possible adjustments are shortly discussed. Finally, the subsequent applications including schedule generation and optimization according to various objectives are indicated as well
A multiphase optimal control method for multi-train control and scheduling on railway lines
We consider a combined train control and scheduling problem involving multiple trains in a railway line with a predetermined departure/arrival sequence of the trains at stations and meeting points along the line. The problem is formulated as a multiphase optimal control problem while incorporating complex train running conditions (including undulating track, variable speed restrictions, running resistances, speed-dependent maximum tractive/braking forces) and practical train operation constraints on departure/arrival/running/dwell times. Two case studies are conducted. The first case illustrates the control and scheduling problem of two trains in a small artificial network with three nodes, where one train follows and overtakes the other. The second case optimizes the control and timetable of a single train in a subway line. The case studies demonstrate that the proposed framework can provide an effective approach in solving the combined train scheduling and control problem for reducing energy consumption in railway operations
An approach to Handling Irregular Oversaturation in Urban Subway Stations
Train timetable, Passenger waiting time, Oversaturated condition, Genetic algorithmThis Theses presents a data-based approach for a train scheduling that aims to minimize passenger waiting time by controlling train departure time and the number of skipped trains. In contrast to existing approaches that rely on a statistical model of passenger arrival, we develop a model based on real-world automated fare collection (AFC) data from a metro line in Daegu, a Korean city. The model consists of decomposing the travel time for each passenger into waiting, riding, and walking times, clustering of passengers by trains they ride and calculating the number of passengers in each train for any given time. Based on this, for a given train schedule, the passenger waiting time of each passenger for the entire AFC data period can be calculated. The problem is formulated using the model under realistic constraints such as headway, the number of available trains, and train capacity. To find the optimal solution, we employed a genetic algorithm (GA). The results demonstrate that the average waiting time is reduced up to 56% in the highly congested situation. Moreover, letting the trains directly go to the congested station by skipping previous stations further reduces the maximum waiting time by up to 19%. The effect of the optimization varies depending on the passenger arrival pattern of highly congested stations. This approach will improve the quality of the subway services by reducing passenger waiting time.openβ
. INTRODUCTION 1
II. RELATED WORK 4
2.1. Passenger Volume Estimation 4
2.2. Train Scheduling Optimization 5
III. PROPOSED APPROACH 6
3.1. Overview 6
3.2. Dataset 8
3.3. Scenario Analysis 9
3.3.1 Peak Hours Scenario 10
3.3.2 Congested Off-Peak Hours Scenario 10
IV. PROBLEM FORMULATION 13
4.1. Assumptions 13
4.2. Train Capacity 15
4.3. Passenger Volume Estimation 15
4.3.1. Passenger Volume on the Train 16
4.3.2. Passenger Volume on the Platform 20
4.4. Timetable Optimization Model 20
4.4.1. Train Departure Time Control 21
4.4.1.1. Passenger Waiting Time Minimization Problem 21
4.4.1.2. Oversaturation Time Minimization Problem 24
4.4.2. Train Skip Plan Control 24
4.5. Genetic Algorithm 27
V. EVALUATION 29
5.1. Peak Hours Scenario 30
5.2. Congested Off-peak Hours Scenario 32
5.2.1 Single Peak Oversaturation 32
5.2.2 Double Peak Oversaturation 36
5.2.3 Box-shaped Peak Oversaturation 40
5.3. Discussion 43
VI. CONCLUSION AND FUTURE WORK 44
REFERENCES 46
APPENDIX A. Optimization Results 48
μμ½λ¬Έ 81λμ μ§νμ² μ λλ‘κ΅ν΅ μν©μ μν₯μ ν¬κ² λ°μ§ μμΌλ©° λμ©λμ κ΅ν΅ μμλ₯Ό μ²λ¦¬ν μ μμ΄ λ§μ μΉκ°λ€μκ² μ΄μ©λλ€. νΌμ‘ν μ§νμ² μ μΉκ°λ€μκ² λΆνΈμ μΌκΈ°νλ©°, μΉκ°λ€μ μΉκ°μ₯μμμ λκΈ°μκ°μ μ¦κ°μν¨λ€. λ³Έ λ
Όλ¬Έμ μ΄μ°¨ μΆλ° μκ°κ³Ό μλ€μ 건λ λ΄ μ΄μ°¨ μλ₯Ό μ‘°μ νμ¬ μΉκ° λκΈ° μκ°μ μ΅μννλ κ²μ λͺ©νλ‘ ν μ΄μ°¨ μκ°ν μ΅μ ν λ°©μμ μ μνλ€. μΉκ° λμ°© ν΅κ³ λͺ¨λΈμ μμ‘΄νλ κΈ°μ‘΄μ μ κ·Ό λ°©μκ³Ό λ¬λ¦¬, μ΄ μ°κ΅¬λ λꡬμ μ§νμ² μμ μμ§λ κ΅ν΅μΉ΄λ λ°μ΄ν°λ€μ κΈ°λ°μΌλ‘ νλ μ΅μ ν λͺ¨λΈμ λ§λ λ€. λͺ¨λΈμ κ° μΉκ°μ μ¬ν μκ°μ μ°¨λ λκΈ° μκ°, μ°¨λ νμΉ μκ° λ° λ³΄ν μκ°μΌλ‘ ꡬλΆνκ³ , νμΉν κΈ°μ°¨μ λ°λΌ μΉκ°λ€μ κ΅°μ§ν μν¨ ν κ° μ°¨λλ§λ€ μΉκ° μλ₯Ό μΆμ νλ κ²μΌλ‘ ꡬμ±λλ€. μ΄λ₯Ό λ°νμΌλ‘ μ£Όμ΄μ§ μ΄μ°¨ μ€μΌμ€μ λν΄ λͺ¨λ μΉκ° κ°κ°μ λκΈ° μκ°λ€μ κ³μ°ν μ μλ€. μ΅μ ν λ¬Έμ λ μ΄μ© κ°λ₯ν μ΄μ°¨ μ, μ΄μ°¨κ° μμ© κ°λ₯ν μ΅λ μΉκ° μ, νμꡬκ°κ³Ό κ°μ νμ€μ μΈ μ μ½ μ‘°κ±΄ νμμ ꡬμ±λλ€. μ΅μ μ μκ°νλ₯Ό μ°ΎκΈ° μν λ°©λ²μΌλ‘ μ μ μ μκ³ λ¦¬μ¦μ΄ μ¬μ©λμλ€. κ·Έ κ²°κ³Ό μΉκ° νκ· λκΈ° μκ°μ μ΅λ 56%κΉμ§ λ¨μΆλμμΌλ©°, μ΄μ°¨ μΆλ°μκ° λΏλ§ μλλΌ μΌλΆ μμ 건λλ°λ μ΄μ°¨μ μκΉμ§ μ΅μ ννλ©΄ λ§€μ° νΌμ‘ν μν©μμ μΉκ°μ μ°¨λ λκΈ° μκ°μ λμ± μ€μΌ μ μμλ€. νΌμ‘ν μν©μμ κΈ°μ°¨κ° μΌλΆ μμ 건λλ°μμ λ, κ·Έλ μ§ μμ λλ³΄λ€ μΉκ° μ΅λ λκΈ° μκ°μ 19%, μΉκ° νκ· λκΈ° μκ°μ 15% μ λ λμ± λ¨μΆλμλ€. λν νΌμ‘ν μν©μμ μΉκ° λμ°© ν¨ν΄μ λ°λΌ μ΅μ νμ ν¨μ¨μ΄ λ¬λΌμ§λ€λ κ²μ νμΈνμλ€. λ³Έ λ°©μμ μΉκ° νκ· λκΈ°μκ°μ κ°μμν΄μΌλ‘μ¨ μ§νμ² μλΉμ€λ₯Ό ν₯μμν¬ κ²μ΄λ€.MasterdCollectio
Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review
Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities
- β¦