Spatiotemporal change in duration of households with every member out-of-home: a case in Kumamoto, Japan

References

• Akar, G., Clifton, K. J., & Doherty, S. T. (2011). Discretionary activity location choice: In-home or out-of-home? Transportation, 38(1), 101–122. doi:10.1007/s11116-010-9293-x
   Web of Science ®Google Scholar
• Anderson, B., & Torriti, J. (2018). Explaining shifts in UK electricity demand using time use data from 1974 to 2014. Energy Policy, 123, 544–557. doi:10.1016/j.enpol.2018.09.025
   Web of Science ®Google Scholar
• Aragon, V., Gauthier, S., Warren, P., James, P. A. B., & Anderson, B. (2019). Developing English domestic occupancy profiles. Building Research & Information, 47(4), 375–393. doi:10.1080/09613218.2017.1399719
   Web of Science ®Google Scholar
• Arroyo, R., Mars, L., & Ruiz, T. (2021). Activity participation and wellbeing during the COVID-19 lockdown in Spain. International Journal of Urban Sciences, 25(3), 386–415. doi:10.1080/12265934.2021.1925144
   Web of Science ®Google Scholar
• Barthelmes, V. M., Li, R., Andersen, R. K., Bahnfleth, W., Corgnati, S. P., & Rode, C. (2018). Profiling occupant behaviour in Danish dwellings using time use survey data. Energy and Buildings, 177, 329–340. doi:10.1016/j.enbuild.2018.07.044
   Web of Science ®Google Scholar
• Bernardo, C., Paleti, R., Hoklas, M., & Bhat, C. (2015). An empirical investigation into the time-use and activity patterns of dual-earner couples with and without young children. Transportation Research Part A: Policy and Practice, 76, 71–91. doi:10.1016/j.tra.2014.12.006
   Web of Science ®Google Scholar
• Bhat, C. R., Goulias, K. G., Pendyala, R. M., Paleti, R., Sidharthan, R., Schmitt, L., & Hu, H. H. (2013). A household-level activity pattern generation model with an application for Southern California. Transportation, 40(5), 1063–1086. doi:10.1007/s11116-013-9452-y
   Web of Science ®Google Scholar
• Bhat, C. R., & Misra, R. (1999). Discretionary activity time allocation of individuals between in-home and out-of-home and between weekdays and weekends. Transportation, 26(2), 193–209. doi:10.1023/A:1005192230485
   Web of Science ®Google Scholar
• Budnitz, H., Tranos, E., & Chapman, L. (2020). Telecommuting and other trips: An English case study. Journal of Transport Geography, 85, 102713. doi:10.1016/j.jtrangeo.2020.102713
   Web of Science ®Google Scholar
• Buldeo Rai, H., Verlinde, S., & Macharis, C. (2021). Unlocking the failed delivery problem? Opportunities and challenges for smart locks from a consumer perspective. Research in Transportation Economics, 87, 100753. doi:10.1016/j.retrec.2019.100753
   Web of Science ®Google Scholar
• Calastri, C., Hess, S., Daly, A., & Carrasco, J. A. (2017). Does the social context help with understanding and predicting the choice of activity type and duration? An application of the Multiple Discrete-Continuous Nested Extreme Value model to activity diary data. Transportation Research Part A: Policy and Practice, 104, 1–20. doi:10.1016/j.tra.2017.07.003
   Web of Science ®Google Scholar
• Calvo, F., Eboli, L., Forciniti, C., & Mazzulla, G. (2019). Factors influencing trip generation on metro system in Madrid (Spain). Transportation Research Part D: Transport and Environment, 67, 156–172. doi:10.1016/j.trd.2018.11.021
   Web of Science ®Google Scholar
• Cazabet, R., Jensen, P., & Borgnat, P. (2018). Tracking the evolution of temporal patterns of usage in bicycle-sharing systems using nonnegative matrix factorization on multiple sliding windows. International Journal of Urban Sciences, 22(2), 147–161. doi:10.1080/12265934.2017.1336468
   Web of Science ®Google Scholar
• Chen, L., & Jiang, S. (2021). Spatiotemporal polyrhythm characteristics of public bicycle mobility in urban chronotopes context. ISPRS International Journal of Geo-Information, 11(1), 6. doi:10.3390/ijgi11010006
   Web of Science ®Google Scholar
• De Lauretis, S., Ghersi, F., & Cayla, J. M. (2017). Energy consumption and activity patterns: An analysis extended to total time and energy use for French households. Applied Energy, 206, 634–648. doi:10.1016/j.apenergy.2017.08.180
   Web of Science ®Google Scholar
• Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F. R., Gaughan, A. E., … Tatem, A. J. (2014). Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences of the United States of America, 111(45), 15888–15893. doi:10.1073/pnas.1408439111
   PubMed Web of Science ®Google Scholar
• Diao, L., Sun, Y., Chen, Z., & Chen, J. (2017). Modeling energy consumption in residential buildings: A bottom-up analysis based on occupant behavior pattern clustering and stochastic simulation. Energy and Buildings, 147, 47–66. doi:10.1016/j.enbuild.2017.04.072
   Web of Science ®Google Scholar
• Enam, A., & Konduri, K. C. (2018). Time allocation behavior of twentieth-century American generations: GI generation, silent generation, baby boomers, generation X, and millennials. Transportation Research Record: Journal of the Transportation Research Board, 2672(49), 69–80. doi:10.1177/0361198118794710
   Web of Science ®Google Scholar
• Enam, A., Konduri, K. C., Eluru, N., & Ravulaparthy, S. (2018). Relationship between well-being and daily time use of elderly: Evidence from the disabilities and use of time survey. Transportation, 45(6), 1783–1810. doi:10.1007/S11116-017-9821-Z/TABLES/4
   Web of Science ®Google Scholar
• Fatmi, M. R., Thirkell, C., & Hossain, M. S. (2021). COVID-19 and travel: How our out-of-home travel activity, in-home activity, and long-distance travel have changed. Transportation Research Interdisciplinary Perspectives, 10, 100350. doi:10.1016/J.TRIP.2021.100350
   PubMedGoogle Scholar
• Feng, J., Chuai, X., Lu, Y., Guo, X., & Yuan, Y. (2020). Who will do more? The pattern of daily out-of-home activity participation in elderly co-residence households in urban China. Cities, 98, 102586. doi:10.1016/j.cities.2019.102586
   Web of Science ®Google Scholar
• Fu, X. (2020). How do out-of-home workers spend their daily time? An empirical comparison across five cities in China. Travel Behaviour and Society, 21, 203–213. doi:10.1016/j.tbs.2020.07.001
   Web of Science ®Google Scholar
• Fukahori, T., & Maruyama, T. (2021). Evolutions of households with every member out-of-home across Japanese cities from 1987 to 2015. Computers, Environment and Urban Systems, 89, 101683. doi:10.1016/j.compenvurbsys.2021.101683
   Web of Science ®Google Scholar
• Garikapati, V. M., Pendyala, R. M., Morris, E. A., Mokhtarian, P. L., & McDonald, N. (2016). Activity patterns, time use, and travel of millennials: A generation in transition? Transport Reviews, 36(5), 558–584. doi:10.1080/01441647.2016.1197337
   Web of Science ®Google Scholar
• Hossain, M. (2020). Sharing economy: A comprehensive literature review. International Journal of Hospitality Management, 87, 102470. doi:10.1016/J.IJHM.2020.102470
   Web of Science ®Google Scholar
• Hossain, M. S., Haque, K., & Fatmi, M. R. (2022). COVID-19: Modeling out-of-home and in-home activity participation during the pandemic. Transportation Research Record: Journal of the Transportation Research Board, 036119812110677. doi:10.1177/03611981211067790
   Web of Science ®Google Scholar
• Kelobonye, K., McCarney, G., Xia, J. (Cecilia), Swapan, M. S. H., Mao, F., & Zhou, H. (2019). Relative accessibility analysis for key land uses: A spatial equity perspective. Journal of Transport Geography, 75, 82–93. doi:10.1016/J.JTRANGEO.2019.01.015
   Web of Science ®Google Scholar
• Kelobonye, K., Zhou, H., McCarney, G., & Xia, J. (Cecilia) (2020). Measuring the accessibility and spatial equity of urban services under competition using the cumulative opportunities measure. Journal of Transport Geography, 85, 102706. doi:10.1016/J.JTRANGEO.2020.102706
   Web of Science ®Google Scholar
• Kim, K., Han, S.-L., Jang, Y.-Y., & Shin, Y.-C. (2020). The effects of the antecedents of “buy-online-pick-up-in-store” service on consumer’s BOPIS choice behaviour. Sustainability, 12(23), 9989. doi:10.3390/su12239989
   Web of Science ®Google Scholar
• Kim, S. N., Choo, S., & Mokhtarian, P. L. (2015). Home-based telecommuting and intra-household interactions in work and non-work travel: A seemingly unrelated censored regression approach. Transportation Research Part A: Policy and Practice, 80, 197–214. doi:10.1016/j.tra.2015.07.018
   Web of Science ®Google Scholar
• Kim, W., & Wang, X. C. (2022). The adoption of alternative delivery locations in New York City: Who and how far? Transportation Research Part A: Policy and Practice, 158, 127–140. doi:10.1016/J.TRA.2022.02.006
   Web of Science ®Google Scholar
• Kitamura, R. (1984). A model of daily time allocation to discretionary out-of-home activities and trips. Transportation Research Part B, 18(3), 255–266. doi:10.1016/0191-2615(84)90036-5
   Google Scholar
• Kleiminger, W., Mattern, F., & Santini, S. (2014). Predicting household occupancy for smart heating control: A comparative performance analysis of state-of-the-art approaches. Energy and Buildings, 85, 493–505. doi:10.1016/j.enbuild.2014.09.046
   Web of Science ®Google Scholar
• KMTC. (2016). Report on Kumamoto Metropolitan Area Urban Transportation Master Plan. Kumamoto Metropolitan Area Transportation Council.
   Google Scholar
• Kuhnimhof, T., Armoogum, J., Buehler, R., Dargay, J., Denstadli, J. M., & Yamamoto, T. (2012). Men shape a downward trend in car use among young adults—evidence from six industrialized countries. Transport Reviews, 32(6), 761–779. doi:10.1080/01441647.2012.736426
   Web of Science ®Google Scholar
• Lai, M., Cai, X., & Hu, Q. (2021). Market design for commute-driven private parking lot sharing. Transportation Research Part C: Emerging Technologies, 124, 102915. doi:10.1016/J.TRC.2020.102915
   Web of Science ®Google Scholar
• Lai, X., Lam, W. H. K., Su, J., & Fu, H. (2019). Modelling intra-household interactions in time-use and activity patterns of retired and dual-earner couples. Transportation Research Part A: Policy and Practice, 126, 172–194. doi:10.1016/j.tra.2019.05.007
   Web of Science ®Google Scholar
• Liu, J., Gross, J., & Ha, J. (2021). Is travel behaviour an equity issue? Using GPS location data to assess the effects of income and supermarket availability on travel reduction during the COVID-19 pandemic. International Journal of Urban Sciences, 25(3), 366–385. doi:10.1080/12265934.2021.1952890
   Web of Science ®Google Scholar
• Liu, S., Yamamoto, T., Yao, E., & Nakamura, T. (2021). Examining public transport usage by older adults with smart card data: A longitudinal study in Japan. Journal of Transport Geography, 93, 103046. doi:10.1016/j.jtrangeo.2021.103046
   Web of Science ®Google Scholar
• Maruyama, T., & Fukahori, T. (2020). Households with every member out-of-home (HEMO): Comparison using the 1984, 1997, and 2012 household travel surveys in Kumamoto, Japan. Journal of Transport Geography, 82, 102632. doi:10.1016/J.JTRANGEO.2019.102632
   Web of Science ®Google Scholar
• Meloni, I., Guala, L., & Loddo, A. (2004). Time allocation to discretionary in-home, out-of-home activities and to trips. Transportation, 31(1), 69–96. doi:10.1023/B:PORT.0000007228.44861.ae
   Web of Science ®Google Scholar
• Miwa, T., Yamamoto, T., & Morikawa, T. (2009). Inter-temporal and inter-regional comparative analyses on household shared time. Journal of the City Planning Institute of Japan, 44(3), 745–750. doi:10.11361/journalcpij.44.3.745
   Google Scholar
• Morris, E. A. (2015). Should we all just stay home? Travel, out-of-home activities, and life satisfaction. Transportation Research Part A: Policy and Practice, 78, 519–536. doi:10.1016/j.tra.2015.06.009
   Web of Science ®Google Scholar
• Rovira, Y. L., Imani, A. F., Sivakumar, A., & Pawlak, J. (2022). Do in-home and virtual activities impact out-of-home activity participation? Investigating end-user activity behaviour and time use for residential energy applications. Energy and Buildings, 257, 111764. doi:10.1016/J.ENBUILD.2021.111764
   Web of Science ®Google Scholar
• Rummens, A., Hardyns, W., & Pauwels, L. (2017). The use of predictive analysis in spatiotemporal crime forecasting: Building and testing a model in an urban context. Applied Geography, 86, 255–261. doi:10.1016/j.apgeog.2017.06.011
   Web of Science ®Google Scholar
• Sekar, A., Williams, E., & Chen, R. (2018). Changes in time use and their effect on energy consumption in the United States. Joule, 2(3), 521–536. doi:10.1016/j.joule.2018.01.003
   Web of Science ®Google Scholar
• Shabanpour, R., Golshani, N., Fasihozaman Langerudi, M., & Mohammadian, A. (Kouros). (2018a). Planning in-home activities in the ADAPTS activity-based model: A joint model of activity type and duration. International Journal of Urban Sciences, 22(2), 236–254. doi:10.1080/12265934.2017.1313707
   Web of Science ®Google Scholar
• Shabanpour, R., Golshani, N., Tayarani, M., Auld, J., & Mohammadian, A. (Kouros) (2018b). Analysis of telecommuting behavior and impacts on travel demand and the environment. Transportation Research Part D: Transport and Environment, 62, 563–576. doi:10.1016/J.TRD.2018.04.003
   Web of Science ®Google Scholar
• Shamshiripour, A., Rahimi, E., Shabanpour, R., & Mohammadian, A. K. (2020). How is COVID-19 reshaping activity-travel behavior? Evidence from a comprehensive survey in Chicago. Transportation Research Interdisciplinary Perspectives, 7, 100216. doi:10.1016/J.TRIP.2020.100216
   PubMedGoogle Scholar
• Shao, C., Yang, H., Zhang, Y., & Ke, J. (2016). A simple reservation and allocation model of shared parking lots. Transportation Research Part C: Emerging Technologies, 71, 303–312. doi:10.1016/J.TRC.2016.08.010
   Web of Science ®Google Scholar
• Shi, Z., Pun-Cheng, L. S. C., Liu, X., Lai, J., Tong, C., Zhang, A., … Shi, W. (2020). Analysis of the temporal characteristics of the elderly traveling by bus using smart card data. ISPRS International Journal of Geo-Information, 9(12), 751. doi:10.3390/ijgi9120751
   Web of Science ®Google Scholar
• Spinney, J. E. L., Newbold, K. B., Scott, D. M., Vrkljan, B., & Grenier, A. (2020). The impact of driving status on out-of-home and social activity engagement among older Canadians. Journal of Transport Geography, 85, 102698. doi:10.1016/j.jtrangeo.2020.102698
   Web of Science ®Google Scholar
• Spissu, E., Pinjari, A. R., Bhat, C. R., Pendyala, R. M., & Axhausen, K. W. (2009). An analysis of weekly out-of-home discretionary activity participation and time-use behavior. Transportation, 36(5), 483–510. doi:10.1007/s11116-009-9200-5
   Web of Science ®Google Scholar
• Srinivasan, S., & Bhat, C. R. (2005). Modeling household interactions in daily in-home and out-of-home maintenance activity participation. Transportation, 32(5), 523–544. doi:10.1007/s11116-005-5329-z
   Web of Science ®Google Scholar
• Stiles, J., & Smart, M. J. (2021). Working at home and elsewhere: Daily work location, telework, and travel among United States knowledge workers. Transportation, 48(5), 2461–2491. doi:10.1007/s11116-020-10136-6
   PubMed Web of Science ®Google Scholar
• Stopher, P. R., & Greaves, S. P. (2007). Household travel surveys: Where are we going? Transportation Research Part A: Policy and Practice, 41(5), 367–381. doi:10.1016/j.tra.2006.09.005
   Web of Science ®Google Scholar
• Tan, S. Y., Jacoby, M., Saha, H., Florita, A., Henze, G., & Sarkar, S. (2022). Multimodal sensor fusion framework for residential building occupancy detection. Energy and Buildings, 258, 111828. doi:10.1016/j.enbuild.2021.111828
   Web of Science ®Google Scholar
• Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26(1), 24–36. doi:10.2307/1907382
   Web of Science ®Google Scholar
• Tseloni, A., Wittebrood, K., Farrell, G., & Pease, K. (2004). Burglary victimization in England and Wales, the United States and the Netherlands: A cross-national comparative test of routine activities and lifestyle theories. British Journal of Criminology, 44(1), 66–91. doi:10.1093/bjc/44.1.66
   Web of Science ®Google Scholar
• Van Duin, J. H. R., De Goffau, W., Wiegmans, B., Tavasszy, L. A., & Saes, M. (2016). Improving home delivery efficiency by using principles of address intelligence for B2C deliveries. Transportation Research Procedia, 12, 14–25. doi:10.1016/j.trpro.2016.02.006
   Google Scholar
• Van Duin, J. H. R., Wiegmans, B. W., Van Arem, B., & Van Amstel, Y. (2020). From home delivery to parcel lockers: A case study in Amsterdam. Transportation Research Procedia, 46, 37–44. doi:10.1016/J.TRPRO.2020.03.161
   Google Scholar
• Wang, H., Huang, H., Ni, X., & Zeng, W. (2019). Revealing spatial-temporal characteristics and patterns of urban travel: A large-scale analysis and visualization study with taxi GPS data. ISPRS International Journal of Geo-Information, 8(6), 257. doi:10.3390/ijgi8060257
   Web of Science ®Google Scholar
• Washington, S., Karlaftis, M., Mannering, F., & Anastasopoulos, P. (2020). Statistical and econometric methods for transportation data analysis. Chapman and Hall/CRC. doi:10.1201/9780429244018.
   Google Scholar
• Watanabe, H., Chikaraishi, M., & Maruyama, T. (2021). How different are daily fluctuations and weekly rhythms in time-use behavior across urban settings? A case in two Japanese cities. Travel Behaviour and Society, 22, 146–154. doi:10.1016/j.tbs.2020.09.004
   Web of Science ®Google Scholar
• Wilcox, P., Madensen, T. D., & Tillyer, M. S. (2007). Guardianship in context: Implications for burglary victimization risk and prevention. Criminology; An interdisciplinary Journal, 45(4), 771–803. doi:10.1111/j.1745-9125.2007.00094.x
   Web of Science ®Google Scholar
• Xu, S. X., Cheng, M., Kong, X. T. R., Yang, H., & Huang, G. Q. (2016). Private parking slot sharing. Transportation Research Part B: Methodological, 93, 596–617. doi:10.1016/J.TRB.2016.08.017
   Web of Science ®Google Scholar
• Yamamoto, T., & Kitamura, R. (1999). An analysis of time allocation to in-home and out-of-home discretionary activities across working days and non-working days. Transportation, 26(2), 231–250. doi:10.1023/a:1005167311075
   Web of Science ®Google Scholar
• Yamamoto, T., Miwa, D., & Morikawa, T. (2009). Analysis of household joint activity engagement using person trip survey data and time use and leisure activity survey data. Proceedings of 39th JSCE IP Conference.
   Google Scholar
• Yilmazkuday, H. (2021). Unequal welfare costs of staying at home across socioeconomic and demographic groups. International Journal of Urban Sciences, 25(3), 347–365. doi:10.1080/12265934.2021.1951822
   Web of Science ®Google Scholar
• Zhang, C., & Chen, J. (2021). Evaluation of residential parking spots sharing effects based on practical experience. Journal of Advanced Transportation, 2021, 1–13. doi:10.1155/2021/6222813
   Web of Science ®Google Scholar
• Zhang, F., Liu, W., Wang, X., & Yang, H. (2020). Parking sharing problem with spatially distributed parking supplies. Transportation Research Part C: Emerging Technologies, 117, 102676. doi:10.1016/J.TRC.2020.102676
   Web of Science ®Google Scholar
• Zhang, W., Ji, C., Yu, H., Zhao, Y., & Chai, Y. (2021). Interpersonal and intrapersonal variabilities in daily activity-travel patterns: A networked spatiotemporal analysis. ISPRS International Journal of Geo-Information, 10(3), 148. doi:10.3390/ijgi10030148
   Web of Science ®Google Scholar