A Multiscale Spatial Prediction Model for Taxi OD Flow Based on Deep Gravity and Its Interpretability Research in Beijing
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.
urban internal mobilitytaxi trajectory dataOD flow predictionmulti-scaleDeep Gravityinter-pretable deep learningSHAP interpretabilityPoint of Interest
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