LoRa Network based Parking Dispatching System : Queuing Theory and Q-learning ApproachLoRa Network based Parking Dispatching System : Queuing Theory and Q-learning Approach
- Other Titles
- LoRa Network based Parking Dispatching System : Queuing Theory and Q-learning Approach
- Authors
- 조영호; 서영건; 정대율
- Issue Date
- 2017
- Publisher
- 한국디지털콘텐츠학회
- Keywords
- Parking; LoRa; IoT; Queuing Theory; Q-learning system; 주차; LoRa; 사물통신; 큐잉 이론; 큐러닝 시스템
- Citation
- 디지털콘텐츠학회논문지, v.18, no.7, pp 1443 - 1450
- Pages
- 8
- Indexed
- KCI
- Journal Title
- 디지털콘텐츠학회논문지
- Volume
- 18
- Number
- 7
- Start Page
- 1443
- End Page
- 1450
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/14334
- DOI
- 10.9728/dcs.2017.18.7.1443
- ISSN
- 1598-2009
2287-738X
- Abstract
- The purpose of this study is to develop an intelligent parking dispatching system based on LoRa network technology. During the local festival, many tourists come into the festival site simultaneously after sunset. To handle the traffic jam and parking dispatching, many traffic management staffs are engaged in the main road to guide the cars to available parking lots. Nevertheless, the traffic problems are more serious at the peak time of festival. Such parking dispatching problems are complex and real-time traffic information dependent. We used Queuing theory to predict inbound traffics and to measure parking service performance. Q-learning algorithm is used to find fastest routes and dispatch the vehicles efficiently to the available parking lots.
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