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.
