Abstract
Driving behavior optimization can not only reduce energy consumption and the probability of traffic accidents but also improve the riding experience of passengers. Unfortunately, the low estimation accuracy resulting from the poor performance of prediction models greatly influences bus service performance. In this paper, a time cycle neural network, the long short-term memory (LSTM) network, is used to evaluate real-time bus riding comfort and provide driving suggestions. To ensure the prediction accuracy, a series of preprocessing procedures, such as data filtering, GPS data processing, parameter calculation and road segmentation, are performed. Three indicators, velocity, longitudinal acceleration, and yaw rate, are selected, while a critical zone-oriented training process is performed. Simulation results show that the proposed method has rapid convergence and acceptable prediction accuracy while providing driving suggestions is reasonable.
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Acknowledgements
The authors especially thank the anonymous reviewers for their insightful comments that resulted in a significantly improved paper. This research was supported in part by Chongqing Vehicle & Research Institude and Chongqing Engineering Research Center of Reasearch and Testing for Automated Driving System and Intelligent Connected Vehicle under Project 20AKC17, in part by the National Natural Science Foundation of China under Project 61601066, and in part by the Key Research and Development Projects of Chongqing Special Industries for Technological Innovation and Application Demonstration under Grant cstc2018jszx-cyzdX0064.
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Zeng, L., Zhang, H., Han, Q. et al. An LSTM-based driving operation suggestion method for riding comfort-oriented critical zone. J Ambient Intell Human Comput 14, 755–771 (2023). https://doi.org/10.1007/s12652-021-03327-1
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DOI: https://doi.org/10.1007/s12652-021-03327-1