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Forecast on-street parking space vacancy with LSTM Model under Fog Computing
LSTM(Long Short-Term Memory)
IoT(Internet of Things)
|Issue Date: ||2019-08-07 17:07:45 (UTC+8)|
|Abstract: ||在許多大城市中，停車問題一直都是駕駛者最頭痛的事，當需要開車前往市中心或是鬧區附近時，常是遍尋不著停車位，駕駛只能不停地在周遭巡航，找尋可停車的位置。找尋停車位是造成道路交通壅塞的原因之一，駕駛不斷的巡航除了耗費多餘的時間與汽車燃料外，更會造成空氣的汙染。因此，在都市計畫中常會針對停車問題設法進行改善，除了規劃增建停車區域或是提高大眾交通工具的使用率外，更重要的是活用既有的停車位，透過制訂彈性的停車收費原則，提高車輛周轉率；或是讓空餘停車位的閒置時間縮短，增加停車位的使用效率。近年來，在物聯網及人工智慧的蓬勃發展下，透過感測器結合影像辨識技術，可以長時間觀察停車位的使用情況，並將觀測後的數據進行分析及運用，以解決此類問題。本研究依據臺北市松山區的路邊停車情形作為案例，並活用政府公開資料，透過物聯網感測器來進行實驗，我們採用LSTM（Long Short-Term Memory）模型，經由歷史資料的學習及訓練，進一步預測路邊停車位的可用數量；並利用iFogSim模擬器，模擬出停車預測模型分別擺放在雲端運算及霧計算架構下，在能源消耗、網路使用量、整體延遲上的差異。實驗結果顯示，於霧計算架構中的停車預測模型誤差值在MAPE（Mean Absolute Percentage Error）指標下平均可達13.6%，且整體延遲時間較雲端運算下降約9成、網路使用量下降約7成|
Drivers driving to urban neighborhoods suffer metropolis parking problems. Cruising down the street in search of parking spaces results in one of the leading causes of traffic congestion. Furthermore, continuous cruising along streets not only wastes time but also leads to excessive energy consumption and air pollution. Therefore, in urban planning, parking issues are crucial to be dealt with. Measures such as the expansion of parking space and increase of public transportation usage rate are taken. On top of that, shorten the idle time for available parking space and efficient parking pricing to increase turnover are also critical methods to solve the problems. With the aid of the rapid growth of IoT (Internet of Things) and AI (Artificial Intelligence), real-time on-street parking usage can be observed with data collected by sensors combined with image recognition technology, and the data is further analyzed to put into practical use. This paper aims at comparing efficiency in forecasting on-street parking vacancy with LSTM (Long Short-Term Memory) model under fog computing and under cloud computing. The data for computing was sourced from open data in Song Shan District of Taipei City, chosen as a case study. Learning from historical data, LSTM model is used to forecast parking availability with iFogSim system adopted to simulate the efficiency of energy consumption, network usage, and total delay when placed under cloud computing and fog computing architecture. MAPE (Mean Absolute Percentage Error) result of LSTM model is 13.6%. Total delay of LSTM under fog computing is about 90% lower than that under cloud computing; network usage, about 70% lower. However, with the increased number of fog nodes, energy consumption rises as well. Therefore, when the nodes are more than a threshold, the energy consumption of LSTM under fog computing is higher than that under cloud computing.
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|Source URI: ||http://thesis.lib.nccu.edu.tw/record/#G0105971022|
|Data Type: ||thesis|
|Appears in Collections:||[資訊科學系碩士在職專班] 學位論文|
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