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An approach to Handling Irregular Oversaturation in Urban Subway Stations

An approach to Handling Irregular Oversaturation in Urban Subway Stations
Minji Kim
DGIST Authors
Kim, Minji; Park, Kyung-Joon; Son, Sang Hyuk
Kyung-Joon Park
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Train timetable, Passenger waiting time, Oversaturated condition, Genetic algorithm
This 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.
Table Of Contents
Ⅰ. 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 Passenger Waiting Time Minimization Problem 21 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 요약문 81
Information and Communication Engineering
Related Researcher
  • Author Park, Kyung-Joon CSI(Cyber-Physical Systems Integration) Lab
  • Research Interests Cyber-Physical Systems; 무선 센서-액츄에이터 네트워크; 스마트 팩토리
Department of Information and Communication EngineeringThesesMaster

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