摘要
准确预测城市内部OD流对于优化城市交通运行效率、提高资源利用率以及促进城市可持续性发展具有重要作用.现有研究大多基于单一尺度利用地理位置之间大量的历史流量来预测未来的流量,尚未有研究充分探究不同空间尺度下OD流预测可能存在的重要特征或建模精度差异等问题.本研究以北京市出租车轨迹为例,采用深度重力模型(Deep Gravity)对不同空间尺度下的轨迹OD流进行预测.同时,引入SHAP值(SHapley Additive exPlanations)揭示不同尺度下影响OD流预测建模的重要特征.结果表明:①相比于重力模型和辐射模型,街道尺度下深度重力模型的OD流预测精度最高(CPC值高达0.83),且成功捕捉到了北京市早晚高峰时段的OD流网络整体结构,呈现出"环形散射状"特征;②在本研究所选各空间尺度下,对OD流预测精度影响最大的4个特征均为O、D点之间的出行距离,O、D点周围公司企业数量、餐饮服务数量以及购物服务数量;③同一特征对OD流预测模型的局部影响不同于全局,如科教文化和体育休闲类POI在全局尺度下对模型影响较小,但在局部尺度下却表现出极大的影响.
Abstract
The movement of people within urban areas serves as a driving force for the development of social phenomena.Accurate Origin-Destination(OD)flow data record spatial interaction patterns of individuals,goods,or information from their starting points(Origin[O])to their destinations(Destination[D]).Precise prediction of internal city OD flows is crucial for optimizing urban traffic operational efficiency,enhancing resource utilization,and fostering sustainable urban development.However,obtaining high-quality OD flow data is challenging due to issues such as privacy protection.There are significant hurdles,including high acquisition costs,limited coverage within large areas,and sparse spatial distribution,which hinder extensive research in urban computation.Current research often relies on a single scale,utilizing extensive historical traffic data between geographic locations to predict future flows.Yet,there has been limited exploration into crucial features and model accuracy for different spatial scales.This study addresses this gap by employing taxi trajectories in Beijing and leveraging the Deep Gravity model to predict OD flow at different spatial scales.Additionally,the integration of SHapley Additive exPlanations(SHAP)values sheds light on the pivotal features influencing OD flow predictions across diverse scales.Results show that:1)Compared to Gravity model and Radiation model,the Deep Gravity model at the street scale exhibits the highest accuracy in predicting OD flows,achieving an impressive Common Part of Commuters(CPC)value of 0.83.The Deep Gravity model effectively captures the overall structure of the OD flow network during peak morning and evening hours in Beijing,revealing a distinctive"circular dispersal"pattern;2)For the selected spatial scales,the four features with the most significant impact on OD flow prediction accuracy are the travel distance between O and D points,the number of businesses around O and D points,the quantity of dining establishments,and the number of shopping services;3)The local impact of the same feature on OD flow prediction models differs from its global impact.For instance,features related to education,science,and culture,as well as sports and leisure Points of Interest(POI),exhibit relatively minor effects on the model at a global scale.However,on a local scale,these features demonstrate a significant influence.This study has achieved high-precision prediction of OD flows at various spatial scales.Additionally,it quantitatively reveals the crucial factors influencing OD flow modeling at different spatial scales,thereby providing valuable insights into understanding population movements within urban areas.The findings of this research hold significant practical implications for urban planning,traffic management,and the development of smart cities.
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