Can Machine Learning Uncover Insights into Vehicle Travel Demand from Our Built Environment?

• DOI: 10.48550/arxiv.2311.06321
• Publication Date: 2023-01-01
• Authors: Zixun Huang; Hao Zheng
• Keywords: Point of interest

In this paper, we propose a machine learning-based approach to address the lack of ability for designers optimize urban land use planning from perspective vehicle travel demand. Research shows that our computational model can help quickly obtain feedback on demand, which includes its total amount and temporal distribution based function designed by designers. It also assists in design optimization evaluation travel. We city information hours traveled (VHT) collecting point-of-interest (POI) data online data. The artificial neural networks (ANNs) with best performance prediction are selected. By using sets collected different regions mutual remapping predictions onto map visualization, evaluate extent sees across an attempt reduce workload future researchers. Finally, demonstrate application demand built environment combine it genetic algorithms current state provide recommendations