The decision making involved behind the mode choice is critical for
transportation planning. While statistical learning techniques like discrete
choice models have been used traditionally, machine learning (ML) models have
gained traction recently among the transportation planners due to their higher
predictive performance. However, the black box nature of ML models pose
significant interpretability challenges, limiting their practical application
in decision and policy making. This study utilised a dataset of $1350$
households belonging to low and low-middle income bracket in the city of
Bengaluru to investigate mode choice decision making behaviour using
Multinomial logit model and ML classifiers like decision trees, random forests,
extreme gradient boosting and support vector machines. In terms of accuracy,
random forest model performed the best ($0.788$ on training data and $0.605$ on
testing data) compared to all the other models. This research has adopted
modern interpretability techniques like feature importance and individual
conditional expectation plots to explain the decision making behaviour using ML
models. A higher travel costs significantly reduce the predicted probability of
bus usage compared to other modes (a $0.66\%$ and $0.34\%$ reduction using
Random Forests and XGBoost model for $10\%$ increase in travel cost). However,
reducing travel time by $10\%$ increases the preference for the metro ($0.16\%$
in Random Forests and 0.42% in XGBoost). This research augments the ongoing
research on mode choice analysis using machine learning techniques, which would
help in improving the understanding of the performance of these models with
real-world data in terms of both accuracy and interpretability.

决策行为是交通规划中的关键问题，传统的统计学习方法在预测性能方面存在局限性，机器学习模型在交通规划者中备受关注。然而，机器学习模型的黑箱特性限制了其在决策和政策制定中的实际应用。本研究使用了低收入和低中等收入家庭的数据集，采用了多项式逻辑模型和机器学习分类器，通过特征重要性和个体条件期望图解释了决策行为。研究结果表明，机器学习模型中的随机森林模型具有最佳的准确性，而旅行成本的增加会显著降低公交出行的概率，而旅行时间的减少则增加对地铁的偏好。这项研究扩展了使用机器学习技术进行模式选择分析的研究，并有助于提高对这些模型在真实数据方面的理解，包括准确性和可解释性。

评估使用统计和机器学习技术确定出行方式选择的因素 —— 以印度孟加拉城市为例

Evaluating the Determinants of Mode Choice Using Statistical and Machine  Learning Techniques in the Indian Megacity of Bengaluru

Various forms of disruption in transport systems perturb urban mobility in
different ways. Passengers respond heterogeneously to such disruptive events
based on numerous factors. This study takes a data-driven approach to explore
multi-modal demand dynamics under disruptions. We first develop a methodology
to automatically detect anomalous instances through historical hourly travel
demand data. Then we apply clustering to these anomalous hours to distinguish
various forms of multi-modal demand dynamics occurring during disruptions. Our
study provides a straightforward tool for categorising various passenger
responses to disruptive events in terms of mode choice and paves the way for
predictive analyses on estimating the scope of modal shift under distinct
disruption scenarios.

本文采用数据驱动方法，研究交通运输系统中各种形式的干扰对城市移动性的不同影响，并通过对历史性小时出行需求数据进行异常检测和聚类，分类各种干扰情况下的多模式需求动态，为衡量不同干扰情景下的方式转移范围提供了参考。