Abstract
This paper aims to deeply analyze the regularity of residents’ daily activity-travel behaviors to help traffic management departments predict travel demand and evaluate transportation policies. Taking time use data in American as empirical analysis, the influencing factors such as residents’ activity pattern, duration and competition were discussed and analyzed based on the competing risk model. Results can be concluded that: a) people feel happy and meaningful during most of the activities, while they feel sad or pain in relatively rare occasions; b) there is no significant difference between young people and the elderly as for the total number of activities during one day; c) estimation results show that personal, household and activity characteristics have significant influence on activity duration and pattern, and the pattern of last activity (N) has a great influence on the occurrence of next activity (N+1); d) the competition between residents’ daily activities is confirmed based on the competing risk model, and travel is closely related to various activities and can be seen as the derived demand of various activities. In all combinations of activity-travel chain schedule, personal — household (30.0%), household — recreation (25.9%), work — travel (45.2%), purchase — travel (33.7%), recreation — travel (22.5%), volunteer — travel (55.3%) and travel — purchase (28.4%) have the highest proportion respectively.
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References
Alexander L, Jiang S, Murga M, González MC (2015) Origin-destination trips by purpose and time of day inferred from mobile phone data. Transportation Research Part C: Emerging Technologies 58:240250, DOI: https://doi.org/10.1016/j.trc.2015.02.018
Ali A, Kim J, Lee S (2016) Travel behavior analysis using smart card data. KSCE Journal of Civil Engineering 20(5):1532–1539, DOI: https://doi.org/10.1007/s12205-015-1694-0
Andersen PK, Geskus RB, de Witte T, Putter H (2012) Competing risks in epidemiology: Possibilities and pitfalls. International Journal of Epidemiology 41(3):861–870, DOI: https://doi.org/10.1093/ije/dyr213
Arentze TA, Timmermans HJP (2009) A need-based model of multi-day, multi-person activity generation. Transportation Research Part B: Methodological 43(2):251–265, DOI: https://doi.org/10.1016/j.trb.2008.05.007
Bhat CR (1998) A model of post home-arrival activity participation behavior. Transportation Research Part B: Methodological 32(6): 387–400, DOI: https://doi.org/10.1016/S0191-2615(98)00006-X
Bhatta BP, Larsen OI (2011) Errors in variables in multinomial choice modeling: A simulation study applied to a multinomial logit model of travel mode choice. Transport Policy 18(2):326–335, DOI: https://doi.org/10.1016/j.tranpol.2010.10.002
Bureau of Labor Statistics (2017) American time use survey user’s guide. Retrieved November 5, 2020, <https://www.bls.gov/tus/atususersguide.pdf>
Choo S, Mokhtarian PL (2012) Individual responses to congestion policies: Modeling the consideration of factor-based travel-related strategy bundles. KSCE Journal of Civil Engineering 16(7):822–834, DOI: https://doi.org/10.1007/s12205-012-1315-0
Chung Y, Yoon BJ (2012) Analytical method to estimate accident duration using archived speed profile and its statistical analysis. KSCE Journal of Civil Engineering 16(9):1064–1070, DOI: https://doi.org/10.1007/s12205-012-1632-3
Cox DR (1972) Regression models and life tables. Journal of the Royal Statistical Society 34:187–220
Dharmowijoyo DBE, Susilo YO, Karlstrom A (2016) Relationships among discretionary activity duration, its travel time spent and activity space indices in the Jakarta Metropolitan Area, Indonesia. Journal of Transport Geography 54:148–160, DOI: https://doi.org/10.1016/j.jtrangeo.2016.05.012
Dianat L, Miller EJ, Habib KN (2020) Investigating the influence of assigning a higher priority to scheduling work and school activities in the activity-based models on the simulated travel/activity patterns. Transportation 47:2109–2132, DOI: https://doi.org/10.1007/s11116-019-10003-z
Ding HY, Yang M, Wang W, Xu CC (2018) Simulating and analyzing the effect on travel behavior of residential relocation and corresponding traffic demand management strategies. KSCE Journal of Civil Engineering 22(2):837–849, DOI: https://doi.org/10.1007/s12205-017-0798-0
Fu X, Juan Z (2016) Empirical analysis and comparisons about time-allocation patterns across segments based on mode-specific preferences. Transportation 43(1):37–51, DOI: https://doi.org/10.1007/s11116-014-9561-2
Garikapati VM (2014) A tour level stop scheduling framework and a vehicle type choice model system for activity-based travel forecasting. PhD Thesis, Arizona State University, Phoenix, AZ, USA
Hafezi MH, Liu L, Millward H (2019) A time-use activity-pattern recognition model for activity-based travel demand modeling. Transportation 46(4):1369–1394, DOI: https://doi.org/10.1007/s11116-017-9840-9
Han Y, Li WY, Wei SS, Zhang TT (2018) Research on passenger’s travel mode choice behavior waiting at bus station based on SEM-logit integration model. Sustainability 10(6):1996, DOI: https://doi.org/10.3390/su10061996
Hassan MN, Rashidi TH, Nassir N (2019) Consideration of different travel strategies and choice set sizes in transit path choice modelling. Transportation 48:723–746, DOI: https://doi.org/10.1007/s11116-019-10075-x
Jenelius E (2012) The value of travel time variability with trip chains, flexible scheduling and correlated travel times. Transportation Research Part B: Methodological 46(6):762–780, DOI: https://doi.org/10.1016/j.trb.2012.02.003
Ji YJ, Liu Y, Liu QY, He B, Cao Y (2018) How household roles influence individuals’ travel mode choice under intra-household interactions? KSCE Journal of Civil Engineering 22(11):4635–4644, DOI: https://doi.org/10.1007/s12205-018-1821-9
Juan ZC, Xianyu JC (2010) Daily travel time analysis with duration model. Journal of Transportation Systems Engineering and Information Technology 10(4):62–67, DOI: https://doi.org/10.1016/S1570-6672(09)60053-2
Kleinbaum DG, Klein M (2011) Survival analysis: A self-learning text, second edition. World Publishing Corporation, Beijing, China
Lam WHK, Huang HI (2003) Combined activity/travel choice models: Time-dependent and dynamic versions. Networks & Spatial Economics 3(3):323–347, DOI: https://doi.org/10.1023/A:1025346318490
Li L, Cao M, Yin J, Wang Y, Mishra S (2020a) Observing the characteristics of multi-activity trip chain and its influencing mechanism. KSCE Journal of Civil Engineering 24(11):3447–3460, DOI: https://doi.org/10.1007/s12205-020-1927-8
Li WY, Guan HZ, Han Y, Zhu HY (2020b) Research on travelers’ daily activity-travel behavior based on competing risk model. International Conference on Transportation and Development 2020: Planning and Development 2020:201–204
Li WY, Han Y, Wang PF, Guan HZ (2019) Invulnerability analysis of traffic network in tourist attraction under unexpected emergency events based on cascading failure. IEEE Access 7:47383–147398, DOI: https://doi.org/10.1109/ACCESS.2019.2946737
Liao F (2016) Modeling duration choice in space-time multi-state supernetworks for individual activity-travel scheduling. Transportation Research Part C: Emerging Technologies 69:16–35, DOI: https://doi.org/10.1016/j.trc.2016.05.011
Liu P, Liao F, Tian Q, Huang HJ, Timmermans H (2019) Day-to-day needs-based activity-travel dynamics and equilibria in multi-state supernetworks. Transportation Research Part B: Methodological 38:503–523, DOI: https://doi.org/10.1016/j.trpro.2019.05.027
Liu C, Susilo YO, Karlstrom A (2015) Investigating the impacts of weather variability on individual’s daily activity-travel patterns: A comparison between commuters and non-commuters in Sweden. Transportation Research Part A 82:47–64, DOI: https://doi.org/10.1016/j.tra.2015.09.005
Machin D, Cheung YB, Parmar M (2006) Survival analysis: A practical approach, 2nd edition. John Wiley & Sons, Hoboken, NJ, USA
Millward H, Hafezi MH, Daisy NS (2019) Activity travel of population segments grouped by daily time-use: GPS tracking in Halifax, Canada. Travel Behaviour and Society 16:161–170, DOI: https://doi.org/10.1016/j.tbs.2019.05.005
Minnen J, Glorieux I, Van Tienoven TP (2015) Transportation habits: Evidence from time diary data. Transportation Research Part A 76:25–37, DOI: https://doi.org/10.1016/j.tra.2014.12.013
Pawlak J, Polak JW, Sivakumar A (2017) A framework for joint modelling of activity choice, duration, and productivity while travelling. Transportation Research Part B: Methodological 106:153–172, DOI: https://doi.org/10.1016/j.trb.2017.10.010
Ruiz T, Timmermans H (2008) Changing the timing of activities in resolving scheduling conflicts. Transportation Research Part A: Policy and Practice 42(2):347–359, DOI: https://doi.org/10.1016/j.tra.2007.10.007
Seo T, Kusakabe T, Gotoh H, Asakura Y (2019) Interactive online machine learning approach for activity-travel survey. Transportation Research Part B: Methodological 123:362–373, DOI: https://doi.org/10.1016/j.trb.2017.11.009
Susilo YO, Axhausen KW (2014) Repetitions in individual daily activity-travel-location patterns: A study using the Herfindahl-Hirschman index. Transportation 41(5):995–1011, DOI: https://doi.org/10.1007/s11116-014-9519-4
Timmermans HJ, Zhang J (2009) Modeling household activity travel behavior: Examples of state of the art modeling approaches and research agenda. Transportation Research Part B: Methodological 43(2):187–190, DOI: https://doi.org/10.1016/j.trb.2008.06.004
Wang P, Guan H, Liu P, Zhao PF, Han Y, Yue H (2020) Mechanisms of allocation/pricing/revenue distribution for shared parking lots. China Journal of Highway and Transport 33(2):158–169+180, DOI: https://doi.org/10.19721/j.cnki.1001-7372.2020.02.015 (in Chinese)
Wang B, Shao C, Li J, Weng J, Ji X (2015) Holiday travel behavior analysis and empirical study under integrated multimodal travel information service. Transport Policy 39:21–36, DOI: https://doi.org/10.1016/j.tranpol.2014.12.005
Wang Y, Wong YD, Goh K (2019) Perceived importance of inclusive street dimensions: A public questionnaire survey from a vision(ing) perspective. Transportation 48:699–721, DOI: https://doi.org/10.1007/s11116-019-10071-1
Xianyu JC, Juan ZC (2012) Research on activity-travel choice behavior interactions between household heads. Journal of Systems & Management 21(2):252–257, DOI: https://doi.org/10.3969/j.issn.1005-2542.2012.02.015
Xu WQ, Han DF, Liu, MJ (2014) Stochastic user equilibrium analysis on activity-based transportation network. Journal of Transportation Systems Engineering and Information Technology 14(3):174–178, DOI: https://doi.org/10.3969/j.issn.1009-6744.2014.03.027
Xu C, Wang C (2019) Analysis of e-bike trip duration and frequency by Bayesian duration and zero-inflated count models. KSCE Journal of Civil Engineering 23(5):1806–1818, DOI: https://doi.org/10.1007/s12205-019-0674-1
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: https://doi.org/10.1023/A:1005167311075
Yang L, Zheng G, Zhu X (2013) Cross-nested logit model for the joint choice of residential location, travel mode, and departure time. Habitat International 38:157–166, DOI: https://doi.org/10.1016/j.habitatint.2012.06.002
Zhong M, Hunt JD (2010) Exploring best-fit hazard functions and lifetime regression models for urban weekend activities: Case study. Journal of Transportation Engineering 136(3):255–266, DOI: https://doi.org/10.1061/(ASCE)0733-947X(2010)136:3(255)
Zhu J, Fan Y (2018) Daily travel behavior and emotional well-being: Effects of trip mode, duration, purpose, and companionship. Transportation Research Part A: Policy and Practice 118:360–373, DOI: https://doi.org/10.1016/j.tra.2018.09.019
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This work was supported in part by the National Natural Science Foundation of China under Grant 71971005, Grant 51308015, and in part by the Beijing Municipal Natural Science Foundation under Grant 8202003.
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Li, W., Guan, H., Han, Y. et al. Exploring Resident’s Daily Activity-Travel Behavior: Activity Pattern, Duration and Competition. KSCE J Civ Eng 25, 3121–3135 (2021). https://doi.org/10.1007/s12205-021-2013-6
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DOI: https://doi.org/10.1007/s12205-021-2013-6