In order to solve the problems of insufficient consideration of the traffic flow characteristics and the low accuracy of the prediction, a short-term prediction method of urban road traffic flow based on spatio-temporal node selection and deep learning was proposed. Firstly, the characteristics of traffic flow were analyzed in theory and data representation to obtain its spatial characteristics, and temporal characteristics and candidate spatio-temporal nodes set. Secondly, the set of candidate spatio-temporal nodes was determined according to the reachable range of traffic flow, and the fitness was calculated by taking the inverse of the sum of squares of errors as the objective function. In the historical training set, genetic algorithm and Back Propagation Neural Network (BPNN) were used to select spatio-temporal nodes, and the final spatio-temporal nodes and BPNN structure were obtained. Finally, the measured values of the selected spatio-temporal nodes were taken as the input of BPNN in the working set to obtain the predicted values. The experimental results show that compared with only using data of adjacent spatio-temporal nodes, using other time node ranges, Support Vector Machine (SVM) and Gradient Boosting Decision Tree (GBDT), the proposed model has a slight reduction in Mean Absolute Error （MAE） and Mean Absolute Percentage Error (MAPE), which are 10.631 6 and 14.275 8%, respectively; and 0.257 3和0.999 1 percentage points lower than those by using adjacent spatio-temporal nodes.

In order to solve the problem that the existing repair methods for missing bus arrival information have little factors considered, low accuracy and poor robustness, a method to repair missing bus arrival data based on DBSCAN (Density-Based Spatial Clustering of Applications with Noise) algorithm and multi-source data was proposed. Bus GPS (Global Positioning System) data, IC (Integrated Circuit) card data and other source data were used to repair the missing arrival information. For the name, longitude and latitude data of the missing arrival station, the association analysis of complete arrival data and static line information were carried out to repair. For the missing arrival time data, the following steps were taken to repair. Firstly, for every missing data station and its nearest non-missing data station, the travel time and schedule in the historical complete arrival data between the two stations were clustered based on DBSCAN algorithm. Secondly, whether the two adjacent runs of the studied bus with complete data belonged to the same cluster was judged, and if they belonged to the same cluster, th cluster would not change, otherwise the two clusters would be merged. Finally, the maximum travel time corresponding to the cluster midpoint was used as the missing travel time to determine whether there was a passenger swiping his card to board the bus at this station or not, if so, the arrival time was calculated from the time of swiping cards, and if not, the mean of the maximum and minimum travel time corresponding to the cluster midpoint was used as the missing travel time to calculate the arrival time. Taking Xia'men bus arrival data as examples, in the repair of name, longitude and latitude of the missing arrival station, the clustering method based on GPS data, the maximum probability estimation method and the proposed method can repair the data by 100.00%. In the repair of missing arrival time, the mean relative error of the proposed method is 0.0301% and 0.0004% lower than that of two comparison methods respectively, and the correlation coefficient of the proposed method is 0.005 and 0.0075 higher than that of two comparison methods respectively. The simulation results show that the proposed method can effectively improve the accuracy of repair of missing bus arrival data, and reduce the impact of the number of missing stations on accuracy.