Travel Time Prediction of Urban Agglomeration Significance Channel: A Case Study on the Cross-Hangzhou Bay Channel - Wang Yu; Hu Xiaowei; Cui Shu; Rao Zonghao (Journal of Advanced Transportation)
The Yangtze River Delta is one of the most economically dynamic urban agglomerations in China, with the Hangzhou Bay Bridge and Jiashao Bridge serving as crucial sea-crossing transportation corridors. This study proposes a novel travel time prediction framework that integrates a genetic algorithm–based section travel time calculation with a long short-term memory (GA-LSTM) neural network. The genetic algorithm enhances the segmentation of travel time across different road sections, ensuring refined input for the GA-LSTM model, which effectively captures spatiotemporal dependencies in travel patterns. Unlike conventional methods that rely on aggregated traffic data or simpler regression models, our approach leverages real-world toll data to provide highly accurate travel time predictions for different corridors and time periods. The case study on the Hangzhou Bay Bridge and Jiashao Bridge demonstrates that the proposed model significantly improves prediction accuracy compared to traditional methods. These findings offer valuable insights for optimizing traffic management, informing infrastructure planning, and enhancing the efficiency of major transportation corridors in urban agglomerations.