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A time-use activity-pattern recognition model for activity-based travel demand modeling

机译：基于活动的出行需求建模的时间使用活动模式识别模型

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摘要

This study develops a new comprehensive pattern recognition modeling framework that leverages activity data to derive clusters of homogeneous daily activity patterns, for use in activity-based travel demand modeling. The pattern recognition model is applied to time use data from the large Halifax STAR household travel diary survey. Several machine learning techniques not previously employed in travel behavior analysis are used within the pattern recognition modeling framework. Pattern complexity of activity sequences in the dataset was recognized using the FCM algorithm, and resulted in identification of twelve unique clusters of homogeneous daily activity patterns. We then analysed inter-dependencies in each identified cluster and characterized the cluster memberships through their socio-demographic attributes using the CART classifier. Based on the socio-demographic characteristics of individuals we were able to correctly identify which cluster individuals belonged to, and also predict various information related to their activities, such as start time, duration, travel distance, and travel mode, for use in activitybased travel demand modeling. To execute the pattern recognition model, the 24-h activity patterns are split into 288 three dimensional 5 min intervals. Each interval includes information on activity types, duration, start time, location, and travel mode if applicable. Results from aggregated statistical evaluation and Kolmogorov-Smirnov tests indicate that there is heterogeneous diversity among identified clusters in terms of temporal distribution, and substantial differences in a variety of socio-demographic variables. The homogeneous clusters identified in this study may be used to more accurately predict the scheduling behavior of specific population groups in activity-based modeling, and hence to improve prediction of the times and locations of their travel demands. Finally, the results of this study are expected to be implemented within the activity-based travel demand model, Scheduler for Activities, Locations, and Travel (SALT).
机译：这项研究开发了一个新的综合模式识别建模框架，该框架利用活动数据来导出同类的日常活动模式集群，以用于基于活动的旅行需求建模。模式识别模型应用于来自大型Halifax STAR家庭旅行日记调查的时间使用数据。模式识别建模框架中使用了几种之前未在出行行为分析中使用的机器学习技术。使用FCM算法识别数据集中活动序列的模式复杂度，并导致识别出十二个独特的均质日常活动模式簇。然后，我们分析了每个已识别集群中的相互依赖性，并使用CART分类器通过其社会人口统计学属性对集群成员进行了表征。基于个体的社会人口统计学特征，我们能够正确识别个体所属的集群，并预测与他们的活动有关的各种信息，例如开始时间，持续时间，出行距离和出行方式，以用于基于活动的出行需求建模。为了执行模式识别模型，将24小时活动模式分为288个三维5分钟间隔。每个时间间隔包括有关活动类型，持续时间，开始时间，位置和出行方式的信息（如果适用）。汇总统计评估和Kolmogorov-Smirnov检验的结果表明，就时间分布而言，已识别集群之间存在异质多样性，并且各种社会人口统计学变量之间存在实质性差异。在这项研究中确定的同质集群可用于在基于活动的建模中更准确地预测特定人群的调度行为，从而改善对他们出行需求的时间和位置的预测。最后，这项研究的结果有望在基于活动的旅行需求模型，活动，位置和旅行计划程序（SALT）中实施。

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