基于深度CNN-LSTM-ResNet组合模型的出租车需求预测

利用海量的离线GPS数据进行出租车需求预测是智能城市与智能交通系统的重要组成部分.本文提出了一种基于深度学习的出租车需求预测方法(CNN-LSTM-ResNet)，将出租车GPS数据和天气数据等转化为栅格数据，输入模型获得预测结果.该模型先使用卷积神经网络(CNN)提取城市范围交通流量的空间特征，然后引入残差单元加深网络层数，并利用长短期记忆网络(LSTM)提取GPS数据的临近性、周期性和趋势性，最后通过权重融合以上3个分量，并与外部因素(天气、节假日和空气质量指数)进一步融合，从而预测城市特定区域的出租车需求.采用西安市出租车GPS数据进行实验验证，结果表明，该模型与传统预测模型(如ARIMA，CNN，LSTM)相比具有更高的预测精度.

Taxi Demand Prediction Based on CNN-LSTM-ResNet Hybrid Depth Learning Model

To forecast the demand of the taxi, it is significant part of smart city and intelligent traffic system to use large amount of off-line GPS data. A deep learning-based, CNN-LSTM-ResNet, is proposed for the demand of taxi in this paper. We converted GPS data of taxi and weather data into raster data, and put them into the model as input to obtain the predictions. Firstly, Convolutional Neural Network (CNN) is used to extract the spatial features of urban traffic flow, and Residual Units to deepen the layers of network, then to extract the temporal proximity, periodicity and tendency of the GPS data, Long Short-Term Memory (LSTM) is used. Finally, to predict the demand of taxi in specific areas of the city, three components are fused by the corresponding weights, and the syncretic result is combined with external factors, like the weather, holiday and air quality index. The experiments are conducted on taxi GPS data of Xi’an, and the result shows that prediction accuracy of proposed model is much more higher than the traditional models such as ARIMA, CNN and LSTM.