Inference Method of Passenger Age Attributes in Rail Transit Based on Deep Learning
Passenger age attributes in rail transit are missing and difficult to obtain.To address these issues,an inference model of passenger age attributes combining deep neural network(DNN)and automatic encoder(AE)was proposed by integrating multi-source data mining such as automatic fare collection(AFC)data of subways and urban land use data to reflect the travel characteristics of passengers at different ages.Firstly,multi-source data were used to analyze and extract six travel characteristics related to passenger age attributes from the perspective of time and space(travel frequency,travel time distribution,first/last trip time,OD latitude and longitude,travel time consumption,and POI of destination),and passenger travel feature matrix was constructed as the model input.In particular,a weighted POI of service capability was considered to enhance the accuracy of the characterization of destination attraction intensity.Secondly,the travel frequency of passengers at different ages to each station was analyzed,and the sparse matrix of passenger travel was constructed,which was used as the supplementary input of spatial information of the model.To learn and extract the relationship and temporal correlation between passenger travel features,DNN was used to capture the relationship between features.To learn the spatial relationship implicit in the sparse matrix of passenger travel,AE was used to compress the sparse matrix and encode and decode it.Finally,Guangzhou Metro was selected for case analysis.The research results show that compared with support vector machine(SVM),decision tree(DT),multilayer perceptron(MLP),and AdaBoost,the accuracy of the DNN+AE model is improved by 13.83%,8.01%,5.66%,and 4.98%,respectively.Among them,the inference accuracy of age attributes for the elderly is the highest,reaching 77.51%.The inference accuracy of age attributes of students and adults reaches 74.69%and 68.89%respectively.The weighted POI,which takes service capability into account,significantly improves the results of passenger age attribute inference.The proposed method can realize the age attribute inference of passengers in urban rail transit and provide support for smart subway operation.
Urban Rail TransitAge Attribute InferenceDeep Neural NetworkAutoencoderMulti-Source Data