This study proposed a machine learning-based classification method to accurately predict mode choice in response to potential strategies for transit promotion in a sprawling region. The method consists of a machine learning classifier, a genetic feature selection process, and statistical analysis process. The Random Undersampling Boosting Algorithm is adopted for imbalanced datasets in sampling. The genetic algorithm is applied to optimize the combination of independent variables grounded on the principle of maximum relevance and minimum redundancy. The 2017 National Household Travel Surveys and the add-on samples data for the Houston metropolitan statistical area in Texas, USA, were utilized to build the mode choice classifier, which shows 99.22% classification accuracy for auto mode and 98.90% for transit mode. Based on a comprehensive study of commuters’ trip characteristics and socio-demographics of the study region, bus transit network expansion and bus rapid transit strategies were proposed to stimulate the predominant single occupancy vehicle mode to be shifted to public transit. Results show that the bus rapid transit, providing higher trip speeds for medium- and long-distance commuters, can significantly increase transit mode share by 8.24% and 8.95%, respectively. When the bus rapid transit is available to all the medium- and long-distance commuters, the total mode shift can increase to 15.96% in the study region. The walking distance to the nearest transit access is linearly associated with the mode shift to transit; up to 2.4% of current auto trips shifted to transit mode for those within a 5-min walking distance in the urban area.

这项研究提出了一种基于机器学习的分类方法，以准确地预测模式选择，以响应在广阔区域内促进过境的潜在策略。该方法包括机器学习分类器，遗传特征选择过程和统计分析过程。对于采样中的不平衡数据集，采用随机欠采样增强算法。应用遗传算法，以最大相关性和最小冗余度为基础，对自变量的组合进行优化。利用美国得克萨斯州休斯顿都会统计区的2017年全国家庭旅行调查和附加样本数据来构建模式选择分类器，该模型显示自动模式的分类准确度为99.22％，公交模式的分类准确度为98.90％。在对研究区域通勤者出行特征和社会人口学特征进行全面研究的基础上，提出了公交网络扩展和公交快速公交策略，以刺激主要的单人乘车模式向公共交通的转变。结果表明，快速公交为中长途通勤者提供更高的出行速度，可以分别显着提高公交模式的份额8.24％和8.95％。当所有中长途通勤者都可以使用公交车快速运输时，研究区域内的总模式转换可以增加到15.96％。到最近的公交通道的步行距离与公交的模式转换成线性关系。对于那些在市区步行5分钟以内的人来说，当前多达2.4％的自动旅行转换为公交模式。