Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data,such as weather,time,and points of interest(POI).This study proposes a destinati...Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data,such as weather,time,and points of interest(POI).This study proposes a destination prediction method based on multi-source data,and a multi-input neural network model is established.In terms of the coding of vehicle trajectory data,a GeoHash to vector(Geo2vec)model is proposed to realize the characterization of the trajectory.As for the coding of temporal features,a cyclic coding model is proposed based on trigonometric functions.For the coding of POI,an origin-destination POI matrix(OD-POI)model is proposed based on the state transition probability.Experimental results show that in terms of the average distance and root-mean-square distance deviations,Geo2vec reveals reductions of 4.51%and 5.63%compared to word to vector(Word2vec),and cyclic encoding shows reductions of 6.35%and 6.67%compared to label encoding;further,the method of OD-POI state transition probability is reduced by 5.85%and 6.4%,and the model based on multi-source data is 17.29%and 17.65%lower than the model based on trajectory data only.Finally,the cyclic encoding is reduced by 48.60%in the data dimension compared to one-hot encoding.Accurate destination prediction will help improve the efficiency of automotive human-computer interaction.展开更多
基金This study was financially supported by the National Natural Science Foundation of China(Grant No.51775393)Liuzhou Science and Technology Planning Project(Grant No.2018BC20501,2018B0301b003)+1 种基金Innovative Research Team Development Program of Ministry of Education of China(Grant No.IRT_17R83)China and 111 Project(Grant No.B17034).
文摘Research on vehicle travel destinations mostly only consider vehicle trajectory data and ignore the influence of other multi-source data,such as weather,time,and points of interest(POI).This study proposes a destination prediction method based on multi-source data,and a multi-input neural network model is established.In terms of the coding of vehicle trajectory data,a GeoHash to vector(Geo2vec)model is proposed to realize the characterization of the trajectory.As for the coding of temporal features,a cyclic coding model is proposed based on trigonometric functions.For the coding of POI,an origin-destination POI matrix(OD-POI)model is proposed based on the state transition probability.Experimental results show that in terms of the average distance and root-mean-square distance deviations,Geo2vec reveals reductions of 4.51%and 5.63%compared to word to vector(Word2vec),and cyclic encoding shows reductions of 6.35%and 6.67%compared to label encoding;further,the method of OD-POI state transition probability is reduced by 5.85%and 6.4%,and the model based on multi-source data is 17.29%and 17.65%lower than the model based on trajectory data only.Finally,the cyclic encoding is reduced by 48.60%in the data dimension compared to one-hot encoding.Accurate destination prediction will help improve the efficiency of automotive human-computer interaction.
