|Table of Contents|

[1] Xu Dandan, Bian Yang, Shu Shinan, et al. The walking distance decay law of amenity selectionbased on binary logistic model [J]. Journal of Southeast University (English Edition), 2020, 36 (1): 88-97. [doi:10.3969/j.issn.1003-7985.2020.01.012]

The walking distance decay law of amenity selectionbased on binary logistic model()

Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

2020 1
Research Field:
Traffic and Transportation Engineering
Publishing date:


The walking distance decay law of amenity selectionbased on binary logistic model
Xu Dandan1 2 Bian Yang1 2 Shu Shinan3 Rong Jian1 2
1 College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China
2 Beijing Key Laboratory of Traffic Engineering, Beijing University of Technology, Beijing 100124, China
3 Beijing Municipal Institute of City Planning and Design, Beijing 100045, China
walkability walking distance distance decay amenity binary logistic model
The current measuring methods of walkability, such as the walk score, consider that walking distance decay laws for all amenities are the same, which is not applicable to typical communities in China with plentiful resources. Therefore, the walking distance decay laws of multi-type and multi-scale facilities are studied. Firstly, based on the residents’ amenity selection survey, the walking distance decay law of residents’ choice of amenity was studied from three aspects, including the law of all amenities, the laws of different types of amenities and the laws of different scales of amenities. It was proved that the walking distance decay laws of different kinds of amenities showed a significant difference. Secondly, different amenities’ acceptable walking distance and optimum walking distance were obtained according to previous studies and the decay curve. Amenities with higher attraction and/or a larger scale showed a longer acceptable walking distance and optimum walking distance. Finally, the binary logistic model was used to describe the relationships between walking distance, amenity type, amenity scale and the probability of one amenity being selected, the prediction accuracy of which reached 80.4%. The calculated probability obtained from the model can be used as the decay coefficient of amenities in the measurement of walkability, providing a reference for the site selection and evaluation of amenities.


[1] Tabaei B P, Rundle A G, Wu W Y, et al. Associations of residential socioeconomic, food, and built environments with glycemic control in persons with diabetes in New York City from 2007—2013[J].American Journal of Epidemiology, 2018, 187(4): 736-745. DOI:10.1093/aje/kwx300.
[2] Sallis J F, Cerin E, Conway T L, et al. Physical activity in relation to urban environments in 14 cities worldwide: A cross-sectional study[J].The Lancet, 2016, 387(10034): 2207-2217. DOI:10.1016/S0140-6736(15)01284-2.
[3] Al Shammas T, Escobar F. Comfort and time-based walkability index design:A GIS-based proposal[J]. International Journal of Environmental Research and Public Health, 2019, 16: 2850-1-2850-22. DOI:10.3390/ijerph16162850.
[4] Sung H, Lee S. Residential built environment and walking activity: Empirical evidence of Jane Jacobs’ urban vitality[J].Transportation Research Part D: Transport and Environment, 2015, 41: 318-329. DOI:10.1016/j.trd.2015.09.009.
[5] Smart M J. Walkability, transit, and body mass index:A panel approach[J]. Journal of Transport & Health, 2018, 8: 193-201. DOI:10.1016/j.jth.2017.12.012.
[6] Qureshi S, Jamshoro D, Shaikh J M, et al. Residents’s subjective assessment of walkability attributes in objectively assessed neighbourhoods[J].Mehran University Research Journal of Engineering and Technology, 2018, 37(3): 673-680. DOI:10.22581/muet1982.1803.20.
[7] McCormack G R, Mardinger C. Neighbourhood urban form and individual-level correlates of leisure-based screen time in Canadian adults[J].BMJ Open, 2015, 5(11): e009418. DOI:10.1136/bmjopen-2015-009418.
[8] Lee E, Dean J. Perceptions of walkability and determinants of walking behaviour among urban seniors in Toronto, Canada[J].Journal of Transport & Health, 2018, 9: 309-320. DOI:10.1016/j.jth.2018.03.004.
[9] Li T F, Sun H J, Wu J J, et al. Household residential location choice equilibrium model based on reference-dependent theory[J].Journal of Urban Planning and Development, 2020, 146(1): 04019024. DOI:10.1061/(asce)up.1943-5444.0000534.
[10] Taleai M, Taheri Amiri E. Spatial multi-criteria and multi-scale evaluation of walkability potential at street segment level: A case study of Tehran[J].Sustainable Cities and Society, 2017, 31: 37-50. DOI:10.1016/j.scs.2017.02.011.
[11] Larsen J, El-Geneidy Y A, Yasmin F. Beyond the quarter mile: Re-examining travel distances by active transportation[J]. Canadian Journal of Urban Research, 2010, 19(1):70-88.
[12] National Research Council. Highway capacity manual(HCM)[M]. National Research Council: Washington, DC, USA, 2010:782-793.
[13] Dixon L B. Bicycle and pedestrian level-of-service performance measures and standards for congestion management systems[J].Transportation Research Record: Journal of the Transportation Research Board, 1996, 1538(1): 1-9. DOI:10.1177/0361198196153800101.
[14] Jaskiewicz F, Jackson G, Anglin K, et al.Pedestrian level of service based on trip quality[C]// Transportation Research Board. Washington, DC, USA, 1999:14p.
[15] Rundle A G, Chen Y, Quinn J W, et al. Development of a neighborhood walkability index for studying neighborhood physical activity contexts in communities across the US over the past three decades[J].Journal of Urban Health, 2019, 96(4): 583-590. DOI:10.1007/s11524-019-00370-4.
[16] Millington C, Ward Thompson C, Rowe D, et al. Development of the Scottish walkability assessment tool(SWAT)[J].Health & Place, 2009, 15(2): 474-481. DOI:10.1016/j.healthplace.2008.09.007.
[17] Smith L. Walkability audit tool[J].Workplace Health & Safety, 2015, 63(9): 420. DOI:10.1177/2165079915595307.
[18] Lerner M. City and neighborhood ranking methodology[EB/OL].(2014)[2019-06-09]. http://blog.walkscore.com/2013/11/2014-rankings-methodology.
[19] Rundle A G, Sheehan D M, Quinn J W, et al. Using GPS data to study neighborhood walkability and physical activity[J].American Journal of Preventive Medicine, 2016, 50(3): e65-e72. DOI:10.1016/j.amepre.2015.07.033.
[20] Eldridge J D, Jones J P III. Warped space:A geography of distance decay[J]. The Professional Geographer, 1991, 43(4): 500-511. DOI:10.1111/j.0033-0124.1991.00500.x.
[21] Wu J F, Bao H S. Research on the distance decay of the tourist flow[J].Human Geography, 2005, 20(2): 62-65. DOI:10.13959/j.issn.1003-2398.2005.02.014. (in Chinese)
[22] Fujii S, Taniguchi Y, Hasegawa G, et al. Pedestrian counting with grid-based binary sensors based on Monte Carlo method[J].SpringerPlus, 2014, 3: 299-1-299-10. DOI:10.1186/2193-1801-3-299.
[23] Lu Y T, Wang D. Walkability measuring in America and its enlightenment[J].Urban Planning International, 2012, 27(1): 10-15.(in Chinese)
[24] Goel R. Distance-decay functions of travel to work trips in India[J].Data in Brief, 2018, 21: 50-58. DOI:10.1016/j.dib.2018.09.096.
[25] Xia Z L, Li H, Chen Y H. Assessing neighborhood walkability based on usage characteristics of amenities under Chinese metropolises context[J].Sustainability, 2018, 10(11): 3879-1-3879-18. DOI:10.3390/su10113879.
[26] He J L, Zhang R Z, Huang X J, et al. Walking access distance of metro passengers and relationship with demographic characteristics: A case study of Nanjing metro[J].Chinese Geographical Science, 2018, 28(4): 612-623. DOI:10.1007/s11769-018-0970-6.
[27] Jia P, Wang F H, Xierali I M. Using a huff-based model to delineate hospital service areas[J].The Professional Geographer, 2017, 69(4): 522-530. DOI:10.1080/00330124.2016.1266950.
[28] Zhang J, Yang Y, Mao Q Z. A residential amenities research based on household preference[J]. Community Design, 2013(2): 142-147.(in Chinese)
[29] China City Designing Studying Institute. GB 50180—2018 Urban community planning and design criterion[S]. Beijing: China Building Industry Press, 2018.(in Chinese)
[30] Editorial Board of China Highway and Transportation Society. Traffic engineering manual[M]. Beijing: China Communications Press, 1998:583-590.(in Chinese)
[31] Liu X Z, Wu J X. Investigation of the supporting facilities of the residential quarters in Beijing[J]. Architectural Creation, 2006(2): 78-81.(in Chinese)
[32] Jia B, Jiang C Y. Service facilities and their impact on travel needs in an urban residential district in Beijing, China[C] //The 16th CIB World Building Congress: Building for the Future. Toronto, Canada, 2004:1-10.
[33] Yi X X. A study on community retail and service facilities based on consumption theories: The case of Shanghai[J]. Urban Planning Forum, 2012(3): 44-52.(in Chinese)
[34] Guan H Z. Disaggregate model—a tool of traffic behavior analysis[M]. Beijing: China Communications Press, 2004:51-60.(in Chinese)


Biographies: Xu Dandan(1989—), female, doctor;Bian Yang(corresponding author), female, doctor, associate professor, bianyang@bjut.edu.cn.
Citation: Xu Dandan, Bian Yang, Shu Shinan, et al.The walking distance decay law of amenity selection based on binary logistic model[J].Journal of Southeast University(English Edition), 2020, 36(1):88-97.DOI:10.3969/j.issn.1003-7985.2020.01.012.
Last Update: 2020-03-20