預測交通需求之分佈與數量—基於多重式注意力 機制之AR-LSTMs 模型;Predicting Transportation Demand based on AR-LSTMs Model with Multi-Head Attention

NCUIR > College of Electrical Engineering & Computer Science > Software Engineer > Electronic Thesis & Dissertation > Item 987654321/80948

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/80948

Title:	預測交通需求之分佈與數量—基於多重式注意力機制之AR-LSTMs 模型;Predicting Transportation Demand based on AR-LSTMs Model with Multi-Head Attention
Authors:	徐志榮;Hsu, Chih-Jung
Contributors:	軟體工程研究所
Keywords:	計程車需求預測;深度學習;遞歸神經網絡;長短期記憶模型;注意力模型;Taxi Demand Prediction;Deep Learning;Recurrent Neural Networks;Long Short-Term Memory Work;Attention
Date:	2019-07-02
Issue Date:	2019-09-03 15:20:50 (UTC+8)
Publisher:	國立中央大學
Abstract:	智慧交通儼然成為智慧城市的重要一環，計程車需求預測是智慧交通中一項重要課題。有效地預測下個時間點載客需求的分布可以減少司機空車時間、降低乘客等待時間及增加獲利載客次數，將計程車產業獲利最大化並解決車輛巡迴攬客所造成的能源消耗及汙染。本文利用計程車行車紀錄資料結合深度學習的架構提出有效的計程車載客需求預測模型，使用善於處理時間序列架構的短中長期記憶模型(LSTM) 為基礎，交通議題的資料與長時間周期變化有關，過去的方式難以克服於尖峰與離峰間變化的預測，因此我們使用注意力機制 (Attention) 加強長時間週期的交通問題資訊處理，並設計多層的深度學習網路架構來提高預測準確率。我們並自訂了一個同時考慮均方損失誤差及平均百分比誤差的損失函數，因為均方損失誤差通常會低估低需求區域的叫車數，而平均百分比誤差則容易錯估高需求區域的叫車數。為驗證模型的一般性，我們使用兩組資料集，分別為紐約市計程車的行車紀錄資料與台灣大車隊在台北的計程車叫車資料進行驗證。在實驗中我們比較了傳統的預測方式、淺層機器學習、及深度學習模型等方式預測計程車需求分佈，實驗結果顯示我們提出的多重式AR-LSTMs 預測模型能有效的提高預測的準確度。;Smart transportation is a crucial issue for a smart city, and the forecast for taxi demand is one of the important topics in smart transportation. If we can effectively predict the taxi demand in the near future, we may be able to reduce the taxi vacancy rate, reduce the waiting time of the passengers, increase the number of trip counts for a taxi, expand driver’s income, and diminish the power consumption and pollution caused by vehicle dispatches. This paper proposes an efficient taxi demand prediction model based on state-of-the-art deep learning architecture. Specifically, we use the LSTM model as the foundation, because the LSTM model is effective in predicting time-series datasets. We enhance the LSTM model by introducing the attention mechanism such that the traffic during the peak hour and the off-peak period can better be predicted. We leverage a multi-layer architecture to increase the predicting accuracy. Additionally, we design a loss function that incorporates both the absolute mean-square-error (which tends under-estimate the low taxi demand areas) and the relative meansquare-error (which tends to misestimate the high taxi demand areas). To validate our model, we conduct experiments on two real datasets — the NYC taxi demand dataset and the Taiwan Taxi’s taxi demand dataset in Taipei City. We compare the proposed model with non-machine learning based models, traditional machine learning models, and deep learning models. Experimental results show that the proposed model outperforms the baseline models.
Appears in Collections:	[Software Engineer] Electronic Thesis & Dissertation

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