Abstract
Understanding mechanism of daily activity trip chain especially multi-activity trip chain is significant for travel demand management and urban redevelopment. This paper explores the multi-activity trip chain behavior from both individual attributes and spatial attributes. Multiactivity trip chain is identified from people’s full-day travel diaries, then categorized into multi-activity intermittent trip chain and continuous trip chain to distinguish the characteristics and influencing factors of different chains. Using resident travel survey data of Xiaoshan District of Hangzhou, China, multinomial logistic regression model, transition probability matrix and activity analysis methods are employed to make analyses. Findings include: 1) making multi-activity trip chain can achieve multiple purposes with less average travel time for each purpose, but public transit is less used. 2) The choices of single or multi-activity trip chain and multi-activity intermittent or continuous trip chain are mainly affected by different individual attributes such as occupation, education, household income, etc. Moreover, household registration, driver’s license, gender, and household car ownership are related to the choice of activity sequence. 3) For typical trip chain with purposes of work, shopping/dining, and home, activity sequence is also obviously influenced by spatial distance between origin and destination, especially for the chain of work-shopping/dining-home.
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References
Bamberg S, Rölle D, Weber C (2003) Does habitual car use not lead to more resistance to change of travel mode? Transportation 30(1):97–108, DOI: https://doi.org/10.1023/A:1021282523910
Bautista-Hernández D (2020) Urban structure and its influence on trip chaining complexity in the Mexico City Metropolitan Area. Urban, Planning and Transport Research 8(1):71–97, DOI: https://doi.org/10.1080/21650020.2019.1708784
Ben-Akiva ME, Bowman JL (1998) Activity based travel demand model systems. In: Marcotte P, Nguyen S (eds) Equilibrium and advanced transportation modelling. Springer, Berlin, Germany, 27–46
Bhat CR (1997) Work travel mode choice and number of non-work commute stops. Transportation Research Part B: Methodological 31(1):41–54, DOI: https://doi.org/10.1016/S0191-2615(96)00016-1
Bowman JL, Ben-Akiva ME (2001) Activity-based disaggregate travel demand model system with activity schedules. Transportation Research Part A: Policy and Practice 35(1):1–28, DOI: https://doi.org/10.1016/S0965-8564(99)00043-9
Bowman JL, Bradley M (2017) Testing spatial transferability of activity-based travel forecasting models. Transportation Research Record 2669(1):62–71, DOI: https://doi.org/10.3141/2669-07
Chattopadhyay A (2010) Oral health epidemiology: Principles and practice. Jones & Bartlett Learning, Burlington, MA, USA
Chen Y, Akar G (2017) Using trip chaining and joint travel as mediating variables to explore the relationships among travel behavior, sociodemographics, and urban form. Journal of Transportation and Land Use 11(1):1–16, DOI: https://doi.org/10.5198/jtlu.2017.882
Cheng L, Chen X, Yang S (2016) An exploration of the relationships between socioeconomics, land use and daily trip chain pattern among low-income residents. Transportation Planning and Technology 39(4):358–369, DOI: https://doi.org/10.1080/03081060.2016.1160579
Currie G, Delbosc A (2011) Exploring the trip chaining behaviour of public transport users in Melbourne. Transport Policy 18(1):204–210, DOI: https://doi.org/10.1016/j.tranpol.2010.08.003
Dong X, Ben-Akiva ME, Bowman JL, Walker JL (2006) Moving from trip-based to activity-based measures of accessibility. Transportation Research Part A: Policy and Practice 40(2):163–180, DOI: https://doi.org/10.1016/j.tra.2005.05.002
Friedrichsmeier T, Matthies E, Klöckner CA (2013) Explaining stability in travel mode choice: An empirical comparison of two concepts of habit. Transportation Research Part F: Traffic Psychology and Behaviour 16:1–13, DOI: https://doi.org/10.1016/j.trf.2012.08.008
Garvill J, Marell A, Nordlund A (2003) Effects of increased awareness on choice of travel mode. Transportation 30(1):63–79, DOI: https://doi.org/10.1023/A:1021286608889
Gerber P, Ma T, Klein O, Schiebel J, Carpentier S (2017) Cross-border residential mobility, quality of life and modal shift: A Luxembourg case study. Transportation Research Part A: Policy and Practice 104:238–254, DOI: https://doi.org/10.1016/j.tra.2017.06.015
Hangzhou Bureau of Statistics (2016) Hangzhou statistical yearbook 2016. Retrieved September 23, 2016, http://tjj.hangzhou.gov.cn/art/2016/9/23/art_1653175_35022565.html (in Chinese)
Harding C, Miller EJ, Patterson Z, Axhausen KW (2015) Multiple purpose tours and efficient trip chaining: An analysis of the effects of land use and transit on travel behavior in Switzerland. Proceedings of transportation research board 94th annual meeting, January 11–15, Washington DC, USA
Hedau AL, Sanghai S (2014) Development of trip generation model using activity based approach. International Journal of Civil, Structural, Environmental and Infrastructure Engineering Research and Development 4(3):61–78
Huang A, Levinson D (2017) A model of two-destination choice in trip chains with GPS data. Journal of Choice Modelling 24:51–62, DOI: https://doi.org/10.1016/j.jocm.2016.04.002
Jones P (1983) New approaches to understanding travel behaviour: The human activity approach. PhD Thesis, University of London, London, UK
Krizek KJ (2003) Neighborhood services, trip purpose, and tour-based travel. Transportation 30(4):387–410, DOI: https://doi.org/10.1023/A:1024768007730
Krygsman S, Arentze T, Timmermans H (2007) Capturing tour mode and activity choice interdependencies: A co-evolutionary logit modelling approach. Transportation Research Part A: Policy and Practice 41(10):913–933, DOI: https://doi.org/10.1016/j.tra.2006.03.006
Lee H, Choo S, Kim J (2014) Analyzing the characteristics of trip chaining activities of the elderly in Seoul Metropolitan Area. The Journal of the Korea Institute of Intelligent Transport Systems 13(2):68–79, DOI: https://doi.org/10.12815/kits.2014.13.2.068 (in Korean)
Luan K, Juan Z, Zong F (2010) Research on commuter’s choice behavior between travel mode and trip chain. Journal of Highway and Transportation Research and Development 27(6):107–111, DOI: https://doi.org/10.3969/j.issn.1002-0268.2010.06.019 (in Chinese)
Ma J, Mitchell G, Heppenstall A (2014) Daily travel behaviour in Beijing, China: An analysis of workers’ trip chains, and the role of socio-demographics and urban form. Habitat International 43:263–273, DOI: https://doi.org/10.1016/j.habitatint.2014.04.008
Madhu B, Ashok NC, Balasubramanian S (2014) Multinomial logistic regression predicted probability map to visualize the influence of socio-economic factors on breast cancer occurrence in Southern Karnataka. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40(8):193–196, DOI: https://doi.org/10.5194/isprsarchives-XL-8-193-2014
Molla MM, Stone ML, Motuba D (2017) Developing an activity-based trip generation model for small/medium size planning agencies. Transportation Planning and Technology 40(5):540–555, DOI: https://doi.org/10.1080/03081060.2017.1314505
Ratner KA, Goetz AR (2013) The reshaping of land use and urban form in Denver through transit-oriented development. Cities 30:31–46, DOI: https://doi.org/10.1016/j.cities.2012.08.007
Scheiner J, Holz-Rau C (2017) Women’s complex daily lives: A gendered look at trip chaining and activity pattern entropy in Germany. Transportation 44(1):117–138, DOI: https://doi.org/10.1007/s11116-015-9627-9
Schlich R, Axhausen KW (2003) Habitual travel behaviour: Evidence from a six-week travel diary. Transportation 30(1):13–36, DOI: https://doi.org/10.1023/A:1021230507071
Seneta E (1996) Markov and the birth of chain dependence theory. International Statistical Review 64(3):255–263
Shifan Y (1998) Practical approach to model trip chaining. Transportation Research Record 1645(1):17–23, DOI: https://doi.org/10.3141/1645-03
Starkweather J, Moske AK (2011) Multinomial logistic regression. Retrieved April 26, 2020, http://www.unt.edu/rss/class/Jon/Benchmarks/MLR_JDS_Aug2011.pdf
Stopher PR, Hartgen DT, Li Y (1996) SMART: Simulation model for activities, resources and travel. Transportation 23(3):293–312, DOI: https://doi.org/10.1007/BF00165706
Xianyu J (2016) A model for the joint choice of commute mode and trip chaining pattern. Journal of Transportation Systems Engineering and Information Technology 16(5):143–148, DOI: https://doi.org/10.3969/j.issn.1009-6744.2016.05.022 (in Chinese)
Xianyu J, Juan Z (2010) Research on the interdependencies between trip chaining behavior and travel mode. Journal of Shanghai Jiaotong University 44(6):792–795 (in Chinese)
Yang L, Shen Q, Li Z (2016) Comparing travel mode and trip chain choices between holidays and weekdays. Transportation Research Part A: Policy and Practice 91:273–285, DOI: https://doi.org/10.1016/j.tra.2016.07.001
Zhao Y, Chai Y (2010) Tour-based travel decision making and related factors of urban residents. Urban Studies 17(10):96–101, DOI: https://doi.org/10.3969/j.issn.1006-3862.2010.10.018 (in Chinese)
Acknowledgements
The research is supported by the National Natural Science Foundation of China (No. 71804127), the 2017 Tongji University the Fundamental Research Funds for the Central Universities (No. 22120170164), and the Shanghai Philosophy and Social Science Planning Project (No.2017BGL029). Thanks to Dr. Shi Cheng for providing the data.
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Li, L., Cao, M., Yin, J. et al. Observing the Characteristics of Multi-Activity Trip Chain and Its Influencing Mechanism. KSCE J Civ Eng 24, 3447–3460 (2020). https://doi.org/10.1007/s12205-020-1927-8
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DOI: https://doi.org/10.1007/s12205-020-1927-8