Application of Machine Learning Classifiers for Mode Choice Modeling for Movement-Challenged Persons
Application of Machine Learning Classifiers for Mode Choice Modeling for Movement-Challenged Persons
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DOI：
10.3390/futuretransp2020018
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发表时间：
2022-06-01
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影响因子：
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通讯作者：
Mohiuddin, Hossain
中科院分区：
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作者：
Bhuiya, Md Musfiqur Rahman;Hasan, Md Musleh Uddin;Mohiuddin, Hossain
在这项研究中，我们旨在评估各种机器学习（ML）分类器的性能，以预测运动障碍人士（mcp）的模式选择，该分类器是基于对孟加拉国达卡384名受访者进行问卷调查收集的数据。模式选择集由cng驱动的机动人力车、公交车、步行、机动人力车和非机动人力车组成，其中机动人力车是mcp使用的最突出的模式。年龄、性别、收入、出行时间和mcp使用的辅助工具（作为残疾水平的指标）作为预测变量进行了探讨。通过10倍交叉验证比较不同分割比的结果来评估模型结果。分割比为60%，显示出最佳的精度。结果表明，多标称逻辑回归（MNL）、k近邻回归（KNN）和线性判别分析（LDA）在分割率为60%的情况下具有较高的准确率。发现公交和步行作为出行方式的过拟合是分类误差的来源。在MNL、KNN和LDA中，出行时间是影响步行、CNG和人力车选择的最重要因素。LDA和KNN将辅助仪器描述为比MNL更重要的模式选择因素。人力车作为一种模式的选择遵循一个相对正态的概率分布，而其他三种模式的概率分布是负偏态的。
In this study, we aimed to evaluate the performance of various machine learning (ML) classifiers to predict mode choice of movement-challenged persons (MCPs) based on data collected through a questionnaire survey of 384 respondents in Dhaka, Bangladesh. The mode choice set consisted of CNG-driven auto-rickshaw, bus, walking, motorized rickshaw, and non-motorized rickshaw, which was found as the most prominent mode used by MCPs. Age, sex, income, travel time, and supporting instrument (as an indicator of the level of disability) utilized by MCPs were explored as predictive variables. Results from the different split ratios with 10-fold cross-validation were compared to evaluate model outcomes. A split ratio of 60% demonstrates the optimum accuracy. It was found that Multi-nominal Logistic Regression (MNL), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA) show higher accuracy for the split ratio of 60%. Overfitting of bus and walking as a travel mode was found as a source of classification error. Travel time was identified as the most important factor influencing the selection of walking, CNG, and rickshaw for MNL, KNN, and LDA. LDA and KNN depict the supporting instrument as a more important factor in mode choice than MNL. The selection of rickshaw as a mode follows a relatively normal probability distribution, while probability distribution is negatively skewed for the other three modes.