Applying a random forest method approach to model travel mode choice behavior
Applying a random forest method approach to model travel mode choice behavior
复制标题
应用随机森林方法对出行模式选择行为进行建模
DOI：
10.1016/j.tbs.2018.09.002
复制
发表时间：
2019-01-01
影响因子：
5.2
通讯作者：
Witlox, Frank
中科院分区：
文献类型：
--
作者：
Cheng, Long;Chen, Xuewu;Witlox, Frank
出行方式选择分析是交通规划和政策制定的重要内容，是了解和预测出行需求的重要依据。机器学习领域的研究一直在探索使用随机森林作为框架，在该框架内可以研究许多交通和运输问题。随机森林是构造随机决策树集成的一种有效方法。它通过随机化对集合中的决策树进行去相关，从而改善预测，并在对树进行平均时减少方差。然而，RF对出行方式选择行为的有用性在很大程度上尚未得到探索。本文提出了一个强大的随机森林方法来分析出行方式选择的预测能力和模型的可解释性。利用2013年中国南京的旅行日记数据，丰富了建筑环境的变量，研究了不同模型参数对预测性能的影响。结果表明，随机森林方法在出行方式选择预测中具有更高的精度和更少的计算量。此外，所提出的方法估计解释变量的相对重要性，以及它们如何与模式选择。这对于更好地理解人们的旅行行为并对其进行有效建模至关重要。
The analysis of travel mode choice is important in transportation planning and policy-making in order to understand and forecast travel demands. Research in the field of machine learning has been exploring the use of random forest as a framework within which many traffic and transport problems can be investigated. The random forest (RF) is a powerful method for constructing an ensemble of random decision trees. It de-correlates the decision trees in the ensemble via randomization that leads to an improvement of forecasting and reduces the variance when averaged over the trees. However, the usefulness of RF for travel mode choice behavior remains largely unexplored. This paper proposes a robust random forest method to analyze travel mode choices for examining the prediction capability and model interpretability. Using the travel diary data from Nanjing, China in 2013, enriched with variables on the built environment, the effects of different model parameters on the prediction performance are investigated. The comparison results show that the random forest method performs significantly better in travel mode choice prediction for higher accuracy and less computation cost. In addition, the proposed method estimates the relative importance of explanatory variables and how they relate to mode choices. This is fundamental for a better understanding and effective modeling of people's travel behavior.