Scholarlink

<p>Recent attention to work activities during the COVID-19 pandemic has motivated researchers to revisit how workers manage and schedule activities. In this study, we consider the completion of daily travel-activity patterns from the perspective of a single-server queuing system with vacations, focusing on discretionary activities. From this perspective, travelers are analogous to a single server, completing activities generated, and witnessing varying queue lengths from limited resources such as time budgets for discretionary activities. We apply Larson’s (Manag Sci 36(5), 586–601, 1990) <i>Queue Inference Engine (QIE)</i> to infer discretionary activity queues using a dataset of observed one-day travel-activity patterns from Honolulu. Through this lens, we investigate the concepts of activity stress measured by queue length and its impact on completing discretionary activities across worker segments. We infer activity queue lengths with the <i>QIE</i> and include them in a choice model specification to investigate impacts on discretionary activity completion. The estimation results reveal a propensity towards completing more discretionary activities when faced with longer discretionary activity queues. The inferred mean discretionary activity queue lengths vary from 0.496 activities for travelers with disability status to 0.644 activities for workers with flexible schedules. Full-time students had longer estimated delays compared to homemakers with delays lasting 5.408&#xa0;h and 4.017&#xa0;h, respectively. Delay is measured from the time activities are generated to the time activities depart from the queue. Finally, daily discretionary activity queue profiles differ across worker segments. For students and workers with telework options, queues peaked in the afternoon. For others, queues peaked in the morning. Retirees and workers with fixed schedules had peaks early in the morning. </p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Personal activity queue inference: assessing activity stress and loads of travelers

  • 期刊论文

  • 发表日期：

    2026年1月19日

查看全文
  • Roger B. Chen,
  • Ahmadreza Soroush Azizzadeh

摘要

Recent attention to work activities during the COVID-19 pandemic has motivated researchers to revisit how workers manage and schedule activities. In this study, we consider the completion of daily travel-activity patterns from the perspective of a single-server queuing system with vacations, focusing on discretionary activities. From this perspective, travelers are analogous to a single server, completing activities generated, and witnessing varying queue lengths from limited resources such as time budgets for discretionary activities. We apply Larson’s (Manag Sci 36(5), 586–601, 1990) Queue Inference Engine (QIE) to infer discretionary activity queues using a dataset of observed one-day travel-activity patterns from Honolulu. Through this lens, we investigate the concepts of activity stress measured by queue length and its impact on completing discretionary activities across worker segments. We infer activity queue lengths with the QIE and include them in a choice model specification to investigate impacts on discretionary activity completion. The estimation results reveal a propensity towards completing more discretionary activities when faced with longer discretionary activity queues. The inferred mean discretionary activity queue lengths vary from 0.496 activities for travelers with disability status to 0.644 activities for workers with flexible schedules. Full-time students had longer estimated delays compared to homemakers with delays lasting 5.408 h and 4.017 h, respectively. Delay is measured from the time activities are generated to the time activities depart from the queue. Finally, daily discretionary activity queue profiles differ across worker segments. For students and workers with telework options, queues peaked in the afternoon. For others, queues peaked in the morning. Retirees and workers with fixed schedules had peaks early in the morning.