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• W4366149061 abstract "The inclusion of human behavioral factors in travel demand analysis is a new trend in transportation planning. Perceived security (PS) is one of the crucial behavioral factors governing urban travelers' choices and demand analysis, exclusively for active modes of transportation like bicycles and vulnerable road users like women. However, no scientific attempt has been made to explain and quantify PS for women cyclists. We estimate women cyclists’ PS by considering individual, social, and built environment features from the data collected in Tehran, Iran. Fifty-two women were recruited as the test participants in a VR-based bicycle experimental system (VRBES). For analysis, we focused on the tree-based machine learning (ML) methods besides an econometric model. In the end, the random forest (RF) algorithm is chosen and improved for the best performance (with test-R2=0.69) on the dataset, and feature importance showed population, a social feature, is the most important." @default.
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• W4366149061 date "2023-01-01" @default.
• W4366149061 modified "2023-10-18" @default.
• W4366149061 title "Exploration of women cyclists’ perceived security using tree-based machine learning algorithms" @default.
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• W4366149061 doi "https://doi.org/10.1016/j.procs.2023.03.079" @default.
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