Feb. 23, 2024, 5:43 a.m. | Qingyi Wang, Shenhao Wang, Yunhan Zheng, Hongzhou Lin, Xiaohu Zhang, Jinhua Zhao, Joan Walker

cs.LG updates on arXiv.org arxiv.org

arXiv:2303.04204v2 Announce Type: replace
Abstract: Classical demand modeling analyzes travel behavior using only low-dimensional numeric data (i.e. sociodemographics and travel attributes) but not high-dimensional urban imagery. However, travel behavior depends on the factors represented by both numeric data and urban imagery, thus necessitating a synergetic framework to combine them. This study creates a theoretical framework of deep hybrid models with a crossing structure consisting of a mixing operator and a behavioral predictor, thus integrating the numeric and imagery data into …

abstract analysis arxiv behavior behavior analysis computer computer vision cs.cv cs.lg data demand econ.gn hybrid low modeling q-fin.ec satellite travel type urban vision

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