Despite the nobody-at-home situation having induced several problems, including home burglaries and home-delivery failure, this situation is not comprehensively understood yet. A recent study has examined and developed temporal profiles of households with the situation of every member out-of-home (HEMO) or nobody-at-home, using household travel surveys. However, the variation of HEMO across different cities over several years remains unclear. This study performs a comparative analysis of the HEMO and individual out-of-home (IO) rates across Japanese cities over several years by using the Nationwide Person Trip Survey data. The result obtained by a single-year (2015) data revealed that the HEMO and IO rates were higher in cities than in towns and villages. The municipalities were then classified according to the temporal HEMO profiles. The results obtained by multi-year (1987, 1992, 2005, and 2015) data revealed that the variation of the HEMO rate was marginal in comparison to the reduction in the IO rate during 1987–2015. The minimal variation of HEMO was attributed to the opposing effects of the changes in the household size distribution and the changes in the HEMO rate for households of each size. The temporal profiles of the HEMO rates, obtained by the multi-year data, were also classified. Their transition patterns were demonstrated and analyzed. Household travel survey data can thus be used to plot temporal profiles, which can be useful for analyzing and understanding cities.

尽管无人在家的情况引发了入室盗窃、送货上门等多个问题，但目前还没有对这种情况进行全面了解。最近的一项研究使用家庭旅行调查，检查并开发了每个家庭成员外出 (HEMO) 或无人在家的情况的家庭时间概况。然而，几年来不同城市 HEMO 的变化尚不清楚。本研究使用全国个人旅行调查数据，对日本城市几年来的 HEMO 和个人外出 (IO) 率进行了比较分析。单年（2015 年）数据得出的结果显示，城市的 HEMO 和 IO 率高于城镇和村庄。然后根据时间 HEMO 配置文件对直辖市进行分类。多年（1987 年、1992 年、2005 年和 2015 年）数据获得的结果表明，与 1987-2015 年期间 IO 率的下降相比，HEMO 率的变化是微不足道的。HEMO 的最小变化归因于家庭规模分布的变化和每个规模家庭的 HEMO 率变化的相反影响。通过多年数据获得的 HEMO 率的时间分布也被分类。他们的过渡模式被证明和分析。因此，家庭旅行调查数据可用于绘制时间分布图，这对于分析和理解城市非常有用。HEMO 的最小变化归因于家庭规模分布的变化和每个规模家庭的 HEMO 率变化的相反影响。通过多年数据获得的 HEMO 率的时间分布也被分类。他们的过渡模式被证明和分析。因此，家庭旅行调查数据可用于绘制时间分布图，这对于分析和理解城市非常有用。HEMO 的最小变化归因于家庭规模分布的变化和每个规模家庭的 HEMO 率变化的相反影响。通过多年数据获得的 HEMO 率的时间分布也被分类。他们的过渡模式被证明和分析。因此，家庭旅行调查数据可用于绘制时间分布图，这对于分析和理解城市非常有用。