预测拉丁美洲的出行选择和社区连通性

IF 2.4 Q3 TRANSPORTATION
Eduardo Bilbao Pavón , Luis Alonso Pastor , Alejandro Padilla , Mayra Gamboa , Kent Larson
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引用次数: 0

摘要

本研究的重点是解决拉丁美洲发展中地区在数据收集和正式和非正式交通构成方面面临的流动性挑战。在本文中,使用机器学习(ML)模型开发了一个工具,该模型能够根据瓜达拉哈拉大学(UdeG)的学生调查学习和预测选择一种移动选择的模式。这项研究有助于了解哪些因素是影响拉丁美洲最大的大学之一的出行选择的最相关因素,其中旅行时间和家庭车辆数量是最具决定性的因素。该工具的有效性通过创建两个场景来验证,这两个场景通过将个人重新安置到离目的地更近的地方来模拟移动选择的变化。所进行的实验表明,通过将人们搬迁到离目的地更近的地方,人们倾向于步行,私家车的使用也显著减少。该工具旨在帮助公共机构做出更好的决策,以建设一个更美好的社会,减少污染,增强社会影响和气候变化的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting mobility choice and community connectivity in Latin America
This study focuses on addressing the mobility challenges faced by developing regions of Latin America as data collection and the composition of formal and informal transportation. In this article, a tool is developed using a Machine Learning (ML) model that is able to learn and predict the patterns for choosing one mobility choice over another based on a student survey of the University of Guadalajara (UdeG). The study helps to understand which are the most relevant factors influencing mobility choice at one of the largest universities in Latin America, with travel time and number of household vehicles being the most determinant factors. The tool effectiveness is validated by the creation of two scenarios that simulate changes in mobility choices by relocating individuals closer to their destinations. The conducted experiment demonstrates a tendency towards walking and a significant decrease in private auto usage by relocating people closer to their destinations. The creation of this tool aims to help public institutions in making better decisions to develop a better society with reduced pollution, enhanced social impacts and climate change effects.
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来源期刊
CiteScore
5.00
自引率
12.00%
发文量
222
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