基于张量分解的救援车辆行程时间预测模型OACSCD

Rescue vehicles have the right of way when driving on urban roads,and predicting their travel time in the urban road network can provide support for rescue activities according to the driving characteristics of rescue vehi-cles,which can effectively improve rescue efficiency.This paper proposes a model for predicting the travel time of rescue vehicles based on tensor decomposition considering congestion,termed the rescue vehicles travel time predic-tion model based on tensor decomposition(RTPT).The RTPT model integrates tensor decomposition algorithm,travel characteristics extraction,and a travel time prediction algorithm,all considering road congestion states.The tensor decomposition algorithm fused with congestion state constructs an urban road travel time tensor based on ve-hicle trajectory data,applying congestion-informed Tucker tensor decomposition to complete missing data.The trav-el characteristics extraction method examines the distinct driving patterns of rescue vehicles in contrast to social ve-hicles,constructing a travel time tensor for rescue vehicles in the urban road network.In the travel time prediction algorithm,a congestion probability tensor is constructed to weight the road congestion probabilities for predicting rescue vehicles travel time across varying data sparsity and time intervals.Experimental results show that RTPT achieves a substantial reduction in average absolute error,outperforming traditional methods:driver-based road trip time estimation(DRTE),moving average(MA),and historical average(HA)by 32.44%,70.66%and 74.50%,re-spectively.Additionally,the model reduces the root mean square error by 24.28%,69.73%and 74.67%,compared to DRTE,MA,and HA,respectively,exhibiting minimal error across all prediction scenarios and data conditions.With the increase of data sparsity and prediction period,the variation of the prediction error range of RTPT is basically kept within 1 s,showing its good stability and robustness.The integration of the congestion probability tensor signif-icantly enhances the model ability to reflect the unique driving characteristics of rescue vehicles while incorporating comprehensive traffic network information,resulting in improved prediction accuracy.