This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.Th...This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.展开更多
为更好地调度出租车运力,缓解热点载客区域出租车供需不平衡现象,需探究出租车需求的时空分布特征及其影响因素。鉴于此,基于出租车GPS数据、计价器数据、公共交通刷卡数据和兴趣点(Point of Interesting,POI)数据等多源异构数据,结合...为更好地调度出租车运力,缓解热点载客区域出租车供需不平衡现象,需探究出租车需求的时空分布特征及其影响因素。鉴于此,基于出租车GPS数据、计价器数据、公共交通刷卡数据和兴趣点(Point of Interesting,POI)数据等多源异构数据,结合相关性分析法对区域出租车出行需求影响因素进行筛选,建立多维度的影响因素集,构建基于地理加权回归的区域出租车出行需求影响模型。以北京市1 398个交通小区的数据为例,分析不同时空条件下各影响因素对出租车出行需求的影响程度。结果表明:出租车出行需求空间分布具有空间集聚效应,影响因素对出租车需求的影响程度具有空间非稳态特征;各中心区域住宅密度、周边且公司密集区域办公密度及城市外围区域的休闲娱乐服务密度对出租车出行需求有很强的正影响;城市外围区域住宅密度、各中心区域办公密度与出租车出行需求呈负相关;非工作日休闲娱乐服务密度对出租车出行需求促进作用明显大于工作日;区域公共交通产生量对出租车出行需求的影响早、晚高峰差异显著。通过模型对比分析可知,所建模型具有较高的精度,适用于解释各影响因素对出租车出行需求影响的时空差异性。展开更多
基金supported by the Surface Project of the National Natural Science Foundation of China(No.71273024)the Fundamental Research Funds for the Central Universities of China(2021YJS080).
文摘This study proposes a prediction model considering external weather and holiday factors to address the issue of accurately predicting urban taxi travel demand caused by complex data and numerous influencing factors.The model integrates the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)and Convolutional Long Short Term Memory Neural Network(ConvLSTM)to predict short-term taxi travel demand.The CEEMDAN decomposition method effectively decomposes time series data into a set of modal components,capturing sequence characteristics at different time scales and frequencies.Based on the sample entropy value of components,secondary processing of more complex sequence components after decomposition is employed to reduce the cumulative prediction error of component sequences and improve prediction efficiency.On this basis,considering the correlation between the spatiotemporal trends of short-term taxi traffic,a ConvLSTM neural network model with Long Short Term Memory(LSTM)time series processing ability and Convolutional Neural Networks(CNN)spatial feature processing ability is constructed to predict the travel demand for urban taxis.The combined prediction model is tested on a taxi travel demand dataset in a certain area of Beijing.The results show that the CEEMDAN-ConvLSTM prediction model outperforms the LSTM,Autoregressive Integrated Moving Average model(ARIMA),CNN,and ConvLSTM benchmark models in terms of Symmetric Mean Absolute Percentage Error(SMAPE),Root Mean Square Error(RMSE),Mean Absolute Error(MAE),and R2 metrics.Notably,the SMAPE metric exhibits a remarkable decline of 21.03%with the utilization of our proposed model.These results confirm that our study provides a highly accurate and valid model for taxi travel demand forecasting.
文摘为更好地调度出租车运力,缓解热点载客区域出租车供需不平衡现象,需探究出租车需求的时空分布特征及其影响因素。鉴于此,基于出租车GPS数据、计价器数据、公共交通刷卡数据和兴趣点(Point of Interesting,POI)数据等多源异构数据,结合相关性分析法对区域出租车出行需求影响因素进行筛选,建立多维度的影响因素集,构建基于地理加权回归的区域出租车出行需求影响模型。以北京市1 398个交通小区的数据为例,分析不同时空条件下各影响因素对出租车出行需求的影响程度。结果表明:出租车出行需求空间分布具有空间集聚效应,影响因素对出租车需求的影响程度具有空间非稳态特征;各中心区域住宅密度、周边且公司密集区域办公密度及城市外围区域的休闲娱乐服务密度对出租车出行需求有很强的正影响;城市外围区域住宅密度、各中心区域办公密度与出租车出行需求呈负相关;非工作日休闲娱乐服务密度对出租车出行需求促进作用明显大于工作日;区域公共交通产生量对出租车出行需求的影响早、晚高峰差异显著。通过模型对比分析可知,所建模型具有较高的精度,适用于解释各影响因素对出租车出行需求影响的时空差异性。
