Mode choice analysis of school trips using random forest technique

Q2 Engineering Archives of Transport Pub Date : 2022-06-30 DOI:10.5604/01.3001.0015.9175
Jinit J. M. D’Cruz, A. Alex, V. Manju
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引用次数: 1

Abstract

Mode choice analysis of school trips becomes important due to the fact that these trips contribute to the second largest share of peak hour traffic. This scenario is more relevant in India, which has almost 265 million students enrolled in different accredited urban and rural schools of India, from Class I to XII as per the UDISE report of 2019-20. Thus, it becomes necessary to understand what mode of transport will be mostly used for school trips in order to design an efficient transportation system. Modal attributes and socio-economic characteristics are mostly considered as explana-tory variables in travel mode choice models. Multinomial Logit (MNL) model is one of the classic models used in the development of mode choice models. These logistic regression models predict outcomes based on a set of independent variables. With the recent advances in machine learning, transportation problems are getting a wide arena of methods and solutions. Among them the method of ensemble learning is finding a prominent place in contemporary modelling. This study explores the potential of using ensembles of random decision trees in mode choice analysis by Random Forest Technique with a comparative analysis on conventional method. It was observed that Random Forest method outperforms MNL method in predicting the mode choice preference of students. The high accuracy of machine learning models is mainly due to its ability to consider complex nonlinear relationship between socio-economic attributes and travel mode choice. These models can learn and identify pattern characteristics extracted from sample data and form adaptive structures through computational process thereby offering insights into the relationships between variables that random utility models cannot recognize. This study considered activity -travel information, personal data and household characteristics of students as attributes for model development and observed that the age of the student and distance of school from home plays a significant role in deciding the mode choice of school trips.
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基于随机森林技术的学校旅行模式选择分析
学校旅行的模式选择分析变得很重要,因为这些旅行在高峰时段的交通流量中占第二大份额。这种情况在印度更有意义,根据UDISE 2019- 2020年的报告,印度有近2.65亿学生在印度不同的认可城市和农村学校就读,从1级到12级。因此,有必要了解哪种交通方式将主要用于学校旅行,以设计一个有效的交通系统。在出行方式选择模型中,模式属性和社会经济特征通常被视为解释变量。多项Logit (MNL)模型是模式选择模型开发中使用的经典模型之一。这些逻辑回归模型基于一组独立变量来预测结果。随着机器学习的最新进展,交通问题正在获得广泛的方法和解决方案。其中,集成学习方法在当代建模中占有突出地位。本文通过与传统方法的比较分析,探讨了随机决策树集合在随机森林模式选择分析中的应用潜力。随机森林方法在预测学生的模式选择偏好方面优于MNL方法。机器学习模型的高精度主要是由于它能够考虑社会经济属性与出行方式选择之间复杂的非线性关系。这些模型可以学习和识别从样本数据中提取的模式特征,并通过计算过程形成自适应结构,从而深入了解随机实用新型无法识别的变量之间的关系。本研究将学生的活动-旅行信息、个人数据和家庭特征作为模型开发的属性,发现学生的年龄和学校离家的距离对学校旅行模式的选择起着重要作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Archives of Transport
Archives of Transport Engineering-Automotive Engineering
CiteScore
2.50
自引率
0.00%
发文量
26
审稿时长
24 weeks
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