대기행렬이론과 Q-러닝 알고리즘을 적용한 지역문화축제 진입차량 주차분산 시뮬레이션 시스템A Simulation of Vehicle Parking Distribution System for Local Cultural Festival with Queuing Theory and Q-Learning Algorithm
- Other Titles
- A Simulation of Vehicle Parking Distribution System for Local Cultural Festival with Queuing Theory and Q-Learning Algorithm
- Authors
- 조영호; 서영건; 정대율
- Issue Date
- 2020
- Publisher
- 한국정보시스템학회
- Keywords
- Queuing Theory; Q-learning; LoRa Network; IoT; Parking Distribution; Parking Probability; Local Cultural Festival
- Citation
- 정보시스템연구, v.29, no.2, pp 131 - 147
- Pages
- 17
- Indexed
- KCI
- Journal Title
- 정보시스템연구
- Volume
- 29
- Number
- 2
- Start Page
- 131
- End Page
- 147
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/7744
- ISSN
- 1229-8476
2733-8770
- Abstract
- Purpose: The purpose of this study is to develop intelligent vehicle parking distribution system based on LoRa network at the circumstance of traffic congestion during cultural festival in a local city. This paper proposes a parking dispatch and distribution system using a Q-learning algorithm to rapidly disperse traffics that increases suddenly because of in-bound traffics from the outside of a city in the real-time base as well as to increase parking probability in a parking lot which is widely located in a city.
Design/methodology/approach: The system get information on realtime-base from the sensor network of IoT (LoRa network). It will contribute to solve the sudden increase in traffic and parking bottlenecks during local cultural festival. We applied the simulation system with Queuing model to the Yudeung Festival in Jinju, Korea. We proposed a Q-learning algorithm that could change the learning policy by setting the acceptability value of each parking lot as a threshold from the Jinju highway IC (Interchange) to the 7 parking lots. LoRa Network platform supports to browse parking resource information to each vehicle in realtime. The system updates Q-table periodically using Q-learning algorithm as soon as get information from parking lots. The Queuing Theory with Poisson arrival distribution is used to get probability distribution function. The Dijkstra algorithm is used to find the shortest distance.
Findings: This paper suggest a simulation test to verify the efficiency of Q-learning algorithm at the circumstance of high traffic jam in a city during local festival. As a result of the simulation, the proposed algorithm performed well even when each parking lot was somewhat saturated. When an intelligent learning system such as an O-learning algorithm is applied, it is possible to more effectively distribute the vehicle to a lot with a high parking probability when the vehicle inflow from the outside rapidly increases at a specific time, such as a local city cultural festival.
- Files in This Item
- There are no files associated with this item.
- Appears in
Collections - College of Business Administration > Department of Management Information Systems > Journal Articles

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.