Spatial Distribution of Travel Activities and its Relationship with Points of Interest
Abstract
This study explores the spatial distribution characteristics of travel activities and their relationship with land use, using data from the resident travel survey in 2015 of Xiaoshan District of Hangzhou City, China. A new classification method is proposed to classify the travel activity patterns into three groups: single-activity travel, multi-activity intermittent travel, and multi-activity continuous travel. The main findings are: (a) the length of activity chain and the proportion of multi-activity travels increase with the distance between residence and activity centre; (b) the non-home destinations of single-activity travel, multi-activity intermittent travel and multi-activity continuous travel agglomerate towards the activity centre, and the degree of agglomeration increases in this order; (c) the distribution density of Point Of Interest (POI) and activity destinations have strong positive correlations in space; (d) some attributes of POIs and demographics have significant influence on multi-activity continuous travels. These findings are useful in inducing the activities through reasonable combinations and spatial interconnections of POIs in urban planning.
References
Farooq D, Moslem S, Duleba S. Evaluation of driver behaviour criteria for evolution of sustainable traffic safety. Sustainability. 2019;11(11): 3142. DOI: 10.3390/su11113142
Moslem S, Farooq D, Ghorbanzadeh O, Blaschke T. Application of the AHP-BWM model for evaluating driver behaviour factors related to road safety: A case study for Budapest. Symmetry. 2020;12(2): 243. DOI: 10.3390/sym12020243
Ratner KA, Goetz AR. The reshaping of land use and urban form in Denver through transit-oriented development. Cities. 2013;30: 31-46. DOI: 10.1016/j.cities.2012.08.007
Pacione M. (ed.) The City: Land use, structure, and change in the Western city. London, UK: Routledge; 2002.
Pacione M. Urban geography: A global perspective. 3rd Ed. Abingdon, UK: Routledge; 2009.
Pacione M. Urban environmental quality and human wellbeing—a social geographical perspective. Landscape and Urban Planning. 2003;65(1-2): 19-30. DOI: 10.1016/S0169-2046(02)00234-7
Wang D, Zhou M. The built environment and travel behaviour in urban China: A literature review. Transportation Research Part D: Transport and Environment. 2017; 52: 574-585. DOI: 10.1016/j.trd.2016.10.031
Wang D, Cao X. Impacts of the built environment on activity-travel behaviour: Are there differences between public and private housing residents in Hong Kong? Transportation Research Part A: Policy and Practice. 2017;103: 25-35. DOI: 10.1016/j.tra.2017.05.018
Jiang Y, Gu P, Chen Y, He D, Mao Q. Influence of land use and street characteristics on car ownership and use: Evidence from Jinan, China. Transportation Research Part D: Transport and Environment. 2017;52: 518-534. DOI: 10.1016/j.trd.2016.08.030
Jones P, Koppelman F, Orfueil, JP. Activity analysis: state-of-the-art and future directions. In: Jones P. (ed.) Developments in Dynamic and Activity Based Approaches to Travel Analysis. Aldershot, UK: Gower Publishing; 1990. p. 34-55.
Stopher PR, Hartgen DT, Li Y. SMART: Simulation model for activities, resources and travel. Transportation. 1996;23(3): 293-312. DOI: 10.1007/BF00165706
Krizek KJ. Neighborhood services, trip purpose, and tour-based travel. Transportation. 2003;30(4): 387-410. DOI: 10.1023/A:1024768007730
Hedau AL, Sanghai S. Development of trip generation model using activity based approach. International Journal of Civil, Structural, Environmental and Infrastructure Engineering Research and Development. 2014;4(3): 61-78. Available from: https://www.researchgate.net/publication/264122534
Molla MM, Stone ML, Motuba D. Developing an activity-based trip generation model for small/medium size planning agencies. Transportation Planning and Technology. 2017;40(5): 540-555. DOI: 10.1080/03081060.2017.1314505
Ye X, Pendyala RM, Gottardi G. An exploration of the relationship between mode choice and complexity of trip chaining patterns. Transportation Research Part B: Methodological. 2007;41(1): 96-113. DOI: 10.1016/j.trb.2006.03.004
Schlich R, Axhausen KW. Habitual travel behaviour: Evidence from a six-week travel diary. Transportation. 2003;30(1): 13-36. DOI: 10.1023/A:1021230507071
Cervero R. Built environments and mode choice: Toward a normative framework. Transportation Research Part D: Transport and Environment. 2002;7(4): 265-284. DOI: 10.1016/S1361-9209(01)00024-4
Zhou S, Yan X. The relationship between urban structure and traffic demand in Guangzhou. Acta Geographica Sinica. 2005;60(1): 131-142. Available from: http://en.cnki.com.cn/Article_en/CJFDTotal-DLXB200501014.htm
Zhao Y, Chai YW. Tour-based travel decision making and related factors of urban residents. Urban Studies. 2010;17(10): 96-101. Available from: http://en.cnki.com.cn/Article_en/CJFDTotal-CSFY201010022.htm
Ma J, Mitchell G, Heppenstall A. Daily travel behaviour in Beijing, China: An analysis of workers’ trip chains, and the role of socio-demographics and urban form. Habitat International. 2014;43: 263-273. DOI: 10.1016/j.habitatint.2014.04.008
Pitombo C, Sousa A, Filipe L. Classification and regression tree, principal components analysis and multiple linear regression to summarize data and understand travel behaviour. Transportation Letters: International Journal of Transportation Research. 2009;1(4): 295-308. DOI: 10.3328/TL.2009.01.04.295-308
João de Abreu e Silva, Martinez L, Goulias K. Using a multi equation model to unravel the influence of land use patterns on travel behaviour of workers in Lisbon. Transportation Letters: International Journal of Transportation Research. 2012:4(4): 193-209. DOI: 10.3328/TL.2012.04.04.193-209
Chen YJ, Akar G. Using trip chaining and joint travel as mediating variables to explore the relationships among travel behaviour, socio-demographics, and urban form. Journal of Transport and Land Use. 2017;11(1): 573-588. DOI: 10.5198/jtlu.2017.882
Millward H, Spinney J, Scott D. Active-transport walking behaviour: Destinations, durations, distances. Journal of Transport Geography. 2013;28: 101-110. DOI: 10.1016/j.jtrangeo.2012.11.012
Harding C, Miller EJ, Axhausen K. Multiple purpose tours and efficient trip chaining: An analysis of the effects of land use and transit on travel behaviour in Switzerland. Proceedings of 94th Annual Meeting of the Transportation Research Board, 11-15 January 2015, Washington, DC, USA. Available from: https://www.researchgate.net/publication/281455283
Vale DS, Pereira M. Influence on pedestrian commuting behaviour of the built environment surrounding destinations: A structural equations modeling approach. International Journal of Sustainable Transportation. 2016;10(8): 730-741. DOI: 10.1080/15568318.2016.1144836
Wang D, Zhang J. The analysis of consumer trip characteristics and spatial structure of commercial facilities in Shanghai. City Planning Review. 2001;25(10): 6-14. Available from: http://en.cnki.com.cn/Article_en/CJFDTotal-CSGH200110001.htm
Dai D, Yao D, Duan J. Comparison and reconstruction——A study on typical development patterns of community centre. Urban Planning Forum. 2013;31(6): 112-118. Available from: http://en.cnki.com.cn/Article_en/CJFDTotal-CXGH201306016.htm
Duleba S, Moslem S. Examining Pareto optimality in analytic hierarchy process on real Data: An application in public transport service development. Expert Systems with Applications. 2019;116: 21-30. DOI: 10.1016/j.eswa.2018.08.049
Dong H, Lu J. A study on the city synthesis acting as organizing form of compact cities. Urban Planning Forum. 2009;(1): 54-61. Available from: http://en.cnki.com.cn/Article_en/CJFDTotal-CXGH200901012.htm
Gao S, Janowicz K, Couclelis H. Extracting urban functional regions from points of interest and human activities on location-based social networks. Transactions in GIS. 2017;21(3): 446-467. DOI: 10.1111/tgis.12289
Gan Z, Feng T, Wu Y, Yang M, Timmermans H. Station-based average travel distance and its relationship with urban form and land use: An analysis of smart card data in Nanjing City, China. Transport Policy. 2019;79: 137-154. DOI: 10.1016/j.tranpol.2019.05.003
Long Y, Shen Z. Discovering functional zones using bus smart card data and points of interest in Beijing. In: Long Y, Shen Z. (ed.) Geospatial Analysis to Support Urban Planning in Beijing. Cham, Switzerland: Springer Publishing; 2015. p. 193-217.
Yu B, Wang Z, Mu H, Sun L, Hu F. Identification of urban functional regions based on floating car track data and POI data. Sustainability. 2019;11(23): 6541. DOI: 10.3390/su11236541
Yuan N J, Zheng Y, Xie X. Discovering Functional Zones in a City Using Human Movements and Points of Interest. In: Thill JC. (ed.) Spatial Analysis and Location Modeling in Urban and Regional Systems. Berlin, Heidelberg: Springer Publishing; 2018. p. 33-62.
Chen Y, Chen X, Liu Z, Li X. Understanding the spatial organization of urban functions based on co-location patterns mining: A comparative analysis for 25 Chinese cities. Cities. 2020;97: 102563. DOI: 10.1016/j.cities.2019.102563
Zhao X, Du H, Zhao P. 2013. Research on building database of urban POI based on normalization rules——taking Jinan as a case study. Urban Geotechnical Investigation & Surveying. 2013;4: 21-24. Available from: http://en.cnki.com.cn/Article_en/CJFDTotal-CSKC201304009.htm
Yao M, Wang D. Mobility and travel behaviour in urban China: The role of institutional factors. Transport Policy. 2018;69: 122-131. DOI: 10.1016/j.tranpol.2018.05.012
Wang N, Du Y. Resident walking distance threshold of community. Transport Research. 2015;1(2): 20-24. Available from: http://www.cnki.com.cn/Article/CJFDTotal-JTBH201502004.htm
Pan H, Yang T, Wu J, Lu Y, Zhang Y. Spatial planning strategy for "low carbon cities" in China. Urban Planning Forum. 2008;6: 57-64. Available from: http://en.cnki.com.cn/Article_en/CJFDTotal-CXGH200806015.htm
Chen J, Yang ST, Li HW, Zhang B, Lv JR. Research on geographical environment unit division based on the method of natural breaks (Jenks). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2013;XL-4/W3: 47-50. Available from: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-4-W3/47/2013/isprsarchives-XL-4-W3-47-2013.pdf
Hangzhou Bureau of Statistics. 2016 Hangzhou Statistical Yearbook. Available from: http://tjj.hangzhou.gov.cn [Accessed 23 September 2016].
Yin J. Study on characteristic of concentration and guidance of resident’s activity. Dissertation. Tongji University, Shanghai, China; 2017.
Tan J, Xu R. 2009. Analysis of multi-factors influencing trip chain buildup. Journal of Tongji University (Natural Science). 2009;37: 1340-1344. Available from: http://en.cnki.com.cn/Article_en/CJFDTotal-TJDZ200910013.htm
Hensher DA, Reyes AJ. Trip chaining as a barrier to the propensity to use public transport. Transportation. 2000;27(4): 341-361. DOI: 10.1023/A:1005246916731
Copyright (c) 2021 Linbo Li, Mengfei Cao, Jiajun Yin, Yanli Wang, Yahua Zhang
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).