Urban road travel speed prediction based on multi-feature data fusion.

Urban road speed prediction is helpful to guide drivers to choose unimpeded routes, reduce waiting time and improve travel efficiency. Urban traffic conditions are affected by many factors. Based on the consideration of various traffic flow characteristic data and weather data, a combined model of road travel speed prediction based on long short-term memory (LSTM) cyclic neural network is established. The Didi floating car data in the area around the South Second Ring Road of Xi'an city are selected to predict the road travel speed by extracting the traffic flow characteristics (speed, flow, acceleration and stopping times) and weather characteristics (temperature, humidity, weather and wind speed) of the data set. The results show that compared with LSTM model, BP neural network model and SVR model without external features, the mean absolute error, mean square error and determination coefficient of the combined model with multi-feature data are 2. 695, 13. 838 and 0. 771, and the confidence interval of (-1. 235, 1. 795) is better than other models. The combined model has higher accuracy and stability. [ABSTRACT FROM AUTHOR]