This project studies the driving factors of non-work travel trip chaining in the México City Metropolitan Area (MCMA). It has been argued that urban traveling is increasingly becoming more intricate given that travelers pursue more than one activity in their daily travel. Unlike commuting, non-work travel has been less studied, the literature being even scarcer if we consider the Latin American context as well. Non-work travel is represented by travel to study and other variety of destinations. Thus, using the 2017 Household Origin-Destination Survey (HODS17), this paper analyzes separately the groups of “Students” and the rest of travelers called “Others” in terms of patterns of trip chaining among three modes choices: Transit, Car, and Mixed transportation. Two regression models were applied to study the determinants of 1) number of stops in the daily travel (trip chaining) and 2) tour categories in increasing degree of complexity. A descriptive analysis identified that Car users and Mixed transportation had the highest shares of complex tours together with those in the “Others” group. The associations found between some socioeconomic variables (gender, age, and household size) with trip chaining and tour complexity were different among Students and Others, and discussed in relation to the existing literature. Moreover, most urban form variables supported the hypothesis that dense and diverse urban environments are associated with more trip chaining and tour complexity. These results were discussed in light of the implications to understand the transport disadvantage condition of peripheral low-income neighborhoods typical of big cities in the developing context.

该项目研究了墨西哥城都市区 (MCMA) 非工作旅行旅行链的驱动因素。有人认为，鉴于旅行者在日常旅行中追求的活动不止一项，城市旅行变得越来越复杂。与通勤不同，非工作旅行的研究较少，如果我们也考虑拉丁美洲的背景，文献甚至更少。非工作旅行以学习旅行和其他各种目的地的旅行为代表。因此，本文利用 2017 年家庭始发地-目的地调查 (HODS17)，分别分析了“学生”和其他被称为“其他”的旅行者群体在三种模式选择之间的旅行链模式：公交、汽车和混合运输。应用两个回归模型来研究 1) 日常旅行（旅行链）中的停靠站数量和 2) 复杂程度增加的旅游类别的决定因素。一项描述性分析发现，与“其他”组中的用户一起，汽车用户和混合交通在复杂旅行中所占的比例最高。一些社会经济变量（性别、年龄和家庭规模）与旅行链和旅行复杂性之间的关联在学生和其他人中有所不同，并结合现有文献进行了讨论。此外，大多数城市形态变量支持密集和多样化的城市环境与更多的旅行链和旅行复杂性相关的假设。