A spatial-temporal dependent short-term traffic flow prediction model for road networks
ZHANG Junxi
QU Shiru
ZHANG Zhiteng
BI Yang
Abstract：To effectively address the spatio-temporal feature mining problem in short-term traffic flow prediction for complex road networks, a traffic flow prediction model based on the Improved Long Short-Term Memory (ILSTM) is proposed. Firstly, an improved genetic algorithm optimizes the ini-tial parameters of the LSTM model, obtaining the optimal parameter combination and reducing the im-pact of initial parameter settings on output results. Secondly, to tackle the spatial feature extraction problem encountered in predicting multi-segment traffic flow in complex road networks, an ILSTM model is constructed by evaluating the influence of relevant road segments on the target road segment. This involves reconstructing the loss function of the LSTM model using the influence coefficients of relevant road segments in the network, and terminating the optimization when the loss function output reaches its minimum value. Finally, model validation experiments are conducted using traffic data from the California road network. The performance of the ILSTM model is compared against the Genetic Algorithm-LSTM (GA-LSTM) model, the standard LSTM model, and the Pearson Correlation Coefficient-LSTM (PCC-LSTM) model through multiple experiments with weekday and weekend data. The results demonstrate that the ILSTM model effectively captures the temporal and spatial characteristics of complex road network traffic flow, with an average prediction error of approxi-mately 1.16％. The ILSTM model outperforms other models in terms of both convergence efficiency and prediction accuracy.
Keywords：intelligent transportationshort-term traffic flow predictionspatial-temporal correlationLSTMloss function
Publication Date：2024-06-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 74-82 )