The aim of this study is to construct an urban traffic flow prediction and route planning system based on deep learning models to solve the growing problem of urban traffic congestion. By analyzing and processing historical traffic data of a city and using a hybrid model combining Long Short-Term Memory Network (LSTM) and Graph Neural Network (GNN), we are able to accurately predict traffic conditions at a certain time and location in the future. To achieve this goal, we first preprocessed the traffic data, including converting dates to temporal features and One-Hot coding of intersection locations. Subsequently, we used deep learning techniques for training, during which an Adam optimizer as well as a categorical cross-entropy loss function were used to improve the accuracy and reliability of the predictions. On this basis, this study also develops an optimal path planning function based on the prediction results, which provides the optimal traffic jam avoidance routes by adjusting the weights of the road network and applying Dijkstra's algorithm to find the optimal paths from the start to the end points. The experimental results show that the system achieves good results in traffic flow prediction and path planning with an accuracy rate of 91%.
Deep-learning-based urban intelligent traffic flow prediction and optimization research
International Conference on Smart Transportation and City Engineering (STCE 2024) ; 2024 ; Chongqing, China
Proc. SPIE ; 13575
28.04.2025
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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