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Yuan Li

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Proceedings Article | 14 June 2023 Paper

Driver decision prediction model in dilemma zones based on BP neural network

Yuan Li, Xin Lv

Proceedings Volume 12708, 1270824 (2023) https://doi.org/10.1117/12.2683872

KEYWORDS: Neural networks, Artificial neural networks, Data modeling, Decision making, Binary data, Roads, Video, Statistical analysis, Analytical research, Reflection

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To study drivers’ driving behaviour in dilemma zones at signalized intersections, dilemma zones at signalized intersections are taken as the research objects. Based on analyzing the driving characteristics of motor vehicles. And collect the driving behavior parameters of video observations and road parameters of field observations, take these parameters as the factors which influence the drivers’ decision, conduct the significance analysis with SPSS(Statistical Product Service Solutions) for significant influencing factors, and then take these factors as the input value of BP(Back Propagation) neural network model, BP neural network model is set up and tested based on TensorFlow in Python. The prediction model of drivers’ decision-making in dilemma zone of signalized intersections under different speed limits is obtained, the research shows that the lower the speed limit, the better the accuracy of the intersection prediction. Further verified by comparison with the prediction model of drivers’ decision-making based on binary logistic, the accuracy of different prediction models is analyzed based on actual driver decisions.

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