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Exploring Best-Fit Hazard Functions and Lifetime Regression Models for Urban Weekend Activities: Case Study

机译:探索适合于城市周末活动的最佳危害功能和终生回归模型:案例研究

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Activity-based travel demand forecasting consists of modeling activity type, location, and duration with a view to improving transportation planning and creating effective traffic management systems. Research to date has focused primarily on weekday activity patterns, but given its steady increase, weekend activities and related travel demand also deserve attention. Limited research studied weekend activities, and none of them was found to provide detailed specifications with respect to best-fit hazard functions and lifetime regression models. This study, which took place in Calgary, Alberta (a Canadian city of 1,000,000+), is meant to address that gap. Ten activity patterns of eight demographic groups were assessed and nearly 13,000 observations analyzed. Results affirm that most weekend activities are neither work nor school related and tend to begin mid-day or later; analysis of activity participation by demographic group shows that adults (19-64 years old) are the most active components of our society. Likelihood ratio tests confirm that a two-level modeling exercise is required to handle the heterogeneity within the data: first, analysis by activity type and second, analysis by demographic group. Eleven candidate hazard functions were examined for 10 weekend activities such as shopping or entertainment, then best-fit hazard functions and lifetime regression models were determined. The results show a high degree of fit. It was found that the best-fit parametric models for demographic subgroups are generally consistent with those based on activity type at the aggregate level, a discovery that should simplify future applications. Lifetime regression models show that the starting time of a given activity and personal mobility are the most significant factors influencing activity duration. The applicability of fully parametric, nonparametric, and semipara-metric model is discussed and addressed at various points within the paper. The rounding problem of reported durations is also noticed and discussed during the process of identifying best-fit hazard functions and lifetime regression models.
机译:基于活动的旅行需求预测包括对活动类型,位置和持续时间进行建模,以改善运输计划并创建有效的交通管理系统。迄今为止,研究主要集中在工作日的活动方式上,但是鉴于其稳定增长,周末的活动和相关旅行需求也值得关注。有限的研究对周末活动进行了研究,但没有发现有关最佳拟合危害函数和寿命回归模型的详细规范。这项研究是在艾伯塔省卡尔加里(加拿大拥有1,000,000多个城市)进行的,旨在解决这一差距。评估了八个人口群体的十个活动模式,并分析了近13,000个观察值。结果表明,大多数周末活动既与工作无关,也不与学校有关,并且往往在中午或以后开始。通过按人口群体进行的活动参与分析表明,成年人(19-64岁)是我们社会中最活跃的组成部分。似然比测试确认需要两个级别的建模练习才能处理数据中的异质性:首先是按活动类型进行分析,其次是按人口统计学组进行分析。针对10个周末活动(例如购物或娱乐活动)检查了11种候选危害函数,然后确定了最适合的危害函数和寿命回归模型。结果显示高度拟合。结果发现,针对人口子组的最适合参数模型通常与基于活动级别的总体模型一致,这一发现应该简化未来的应用。终生回归模型显示,给定活动的开始时间和个人活动能力是影响活动持续时间的最重要因素。全参数,非参数和半参数模型的适用性已在本文中的各个点进行了讨论和讨论。在确定最佳拟合危害函数和寿命回归模型的过程中,也注意到并讨论了报告持续时间的四舍五入问题。

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