Simulation of pedestrian accessibility to assess the spatial distribution of urban amenities
A city can be perceived as a framework for the everyday activities of its residents, whose movements create complex network patterns as consequences of their individual decisions. Given that there are apparent differences in the use of urban amenities among residents of different ages, we examined the spatial distribution of urban amenities with regard to the preferences of various age groups and the pedestrian accessibility of amenities. In this paper, we propose an algorithm for detecting the most favorable combinations for the spatial distribution of urban amenities, in order to minimize the total walking distances and maximum frequencies of pedestrians of different age groups. The proposed method focuses on the parametric interpretation of various age groups, their preferences for urban amenities, the mutual proximity between residential and non-residential areas, and crowd intensity. Since residents act as agents whose individual decisions are not predictable, we used agent-based modeling to simulate pedestrian movement in order to optimize the spatial distribution of amenities. The digital environment, which allows the parameterization of different types of data, is used for simulation performance. The simulation outcome is quantitatively presented through two criteria of pedestrian accessibility, whose mutual relationship is used to detect the final, optimized combination for the spatial distribution of amenities. This approach can assist with a better understanding of pedestrian dynamics and support pedestrian-friendly choices in urban systems. Finally, the algorithm is applied to the case study of real space in a brownfield location.
Ardeshiri, A., Willis, K., Ardeshiri, M. (2018). Exploring preference homogeneity and heterogeneity for proximity to urban public services, Cities, Vol. 81, pp. 190-202. https://doi.org/10.1016/j.cities.2018.04.008
Arnberger, A., Allex, B., Eder, R., Ebenberger, M., Wanka, A., Kolland, F., Hutter, H. (2017). Elderly resident’s uses of and preferences for urban green spaces during heat periods. Urban Forestry and Urban Greening, Vol. 21, pp. 102–115. https://doi.org/10.1016/j.ufug.2016.11.012
Back, K.-J. & Parks, S. C. (2003). A Brand Loyalty Model Involving Cognitive, Affective, and Conative Brand Loyalty and Customer Satisfaction, Journal of Hospitality & Tourism Research, Vol. 27, pp. 419-435. https://doi.org/10.1177/10963480030274003
Beames, A., Broekx, S., Schneidewind, U., Landuyt, D., Van Der Meulen, M., Heijungs, R. Seuntjens, P. (2018). Amenity proximity analysis for sustainable brownﬁeld redevelopment planning, Landscape and Urban Planning, Vol. 171, pp. 68-79. https://doi.org/10.1016/j.landurbplan.2017.12.003
Benenson, I. (1998). Multi-agent simulations of residential dynamics in the city. Computers, Environment and Urban Systems, Vol. 22, No. 1, pp. 25-42. https://doi.org/10.1016/S0198-9715(98)00017-9
Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems, Proceedings of the National Academy of Sciences of the United States of America, Vol 99 (Suppl 3), pp. 7280-7287. https://doi.org/10.1073/pnas.082080899
Byrne, J., Wolch, J., Zhang, J. (2009). Planning for environmental justice in an urban national park, Journal of Environmental Planning and Management, Vol. 52, pp. 365-392. https://doi.org/10.1080/09640560802703256
Crooks, A., Castle, C., Batty, M. (2008). Key challenges in agent-based modelling for geo-spatial simulation. Computers, Environment and Urban Systems, Vol. 32, No. 6, pp. 417-430. https://doi.org/10.1016/j.compenvurbsys.2008.09.004
Epstein, J. M., Axtell, R. (1996). Growing artificial societies: Social science from the bottom up. Cambridge, MA: MIT Press.
Ettema, D., De Jong, K., Timmermans, H., Bakema, A. (2007). PUMA: Multi-agent modelling of urban systems. In Koomen, E., Stillwell, J. Modelling land-use change. Springer. Dordrecht, pp. 237-258. http://doi.org/doi:10.1007/1-4020-5648-6_14
Eurostat (2017). Being young in Europe today - demographic trends, Luxembourg: Eurostat [online].http://ec.europa.eu/eurostat/statistics-explained/index.php?title=Being_young_in_Europe_today_-_demographic_trends [Accessed: 2 Jul 2018].
Evans, T. P., Kelley, H. (2004). Multi-scale analysis of a household level agent-based model of landcover change, Journal of Environmental Management, Vol. 72, No. 1, pp. 57-72. https://doi.org/10.1016/j.jenvman.2004.02.008
Gebel, K., Bauman, A. E. Sugiyama, T., Owen, N. (2011). Mismatch between perceived and objectively assessed neighborhood walkability attributes: prospective relationships with walking and weight gain, Health & Place, Vo. 17, No. 2, pp. 519-524. https://doi.org/10.1016/j.healthplace.2010.12.008
Geurs, K.T., Östh, J. (2016). Advances in the measurement of transport impedance in accessibility modelling, European Journal of Transport and Infrastructure Research, Vol. 16, No. 2, pp. 294-299. https://doi.org/10.18757/ejtir.2016.16.2.3138
Gould, P. R. (1969). Spatial Diffusion (Resource Paper No. 4). Washington, DC: Association of American Geographers.
Hatch, K., Dragicevic, S. (2018). Urban geosimulations with the Logic Scoring of Preference method for agent-based decision-making, Habitat International, Vol. 72, pp. 3-17. https://doi.org/10.1016/j.habitatint.2017.09.006
Huang, Q., Parker, D.C., Filatova, T., Sun, S. (2014). A Review of Urban Residential Choice Models Using Agent-Based Modeling, Environment and Planning B: Urban Analytics and City Science, Vol. 41, No. 4, pp. 661-689. https://doi.org/10.1068/b120043p
Karbovskii, V., Voloshin, D., Karsakov, A., Bezgodov, A., Gershenson, C. (2018). Multimodel agent-based simulation environment for mass-gatherings and pedestrian dynamics. Future Generation Computer Systems, Vol. 79, No. 1, pp. 155-165. https://doi.org/10.1016/j.future.2016.10.002
Kokx, A., Van Kempen, R. (2010). A fact is a fact, but perception is reality: stakeholders’ perceptions on urban policies in the process of urban restructuring, Environment and Planning C: Government and Policy, Vol. 28, pp. 335–348. https://doi.org/10.1068/c0932
Kwan, M. P. (1998). Space-time and integral measures of individual accessibility: A comparative analysis using a point-based framework, Geographical Analysis, Vol. 30, pp. 191- 216. https://doi.org/10.1111/j.1538-4632.1998.tb00396.x
Kwan, M.P., Murray, A. T., O'kelly, M. E., Tiefelsdorf, M. (2003). Recent advances in accessibility research: Representation, methodology and applications, Journal of Geographical Systems, Vol. 5, pp. 129-138. https://doi.org/10.1007/s101090300107
La Rosaa, D., Takatorib, C., Shimizub, H., Priviteraa, R. (2018). A planning framework to evaluate demands and preferences by diﬀerent social groups for accessibility to urban greenspaces, Sustainable Cities and Society, Vol. 36, pp. 346-362. https://doi.org/10.1016/j.scs.2017.10.026
Lawlor, J. A., McGirr, S. (2017). Agent-based modeling as a tool for program design and evaluation, Evaluation and Program Planning, Vol. 65, pp. 131-138. https://doi.org/10.1016/j.evalprogplan.2017.08.015
Lee, G., Hong, I. (2013). Measuring spatial accessibility in the context of spatial disparity between demand and supply of urban park service, Landscape and Urban Planning, Vol. 119, pp. 85-90. https://doi.org/10.1016/j.landurbplan.2013.07.001
Ligtenberg, A., Bregt, A. K., Van Lammeren, R. (2001). Multi-actor-based land use modelling: Spatial planning using agents, Landscape and Urban Planning, Vol. 56, No. 1-2, pp. 21-33. http://doi.org/10.1016/j.jenvman.2004.02.007
Loukaitou-Sideris, A., Levy-Storms, L., Chen, L., Brozen, M. (2016). Parks for an aging population: Needs and preferences of low-income seniors in Los Angeles, Journal of the American Planning Association, Vol. 82, No. 3, pp. 236–251. http:// doi.org/10.1080/01944363.2016.1163238
McNeel Europe (2019). PedSim (by Gradient12). Food4Rhino [online]. https://www.food4rhino.com/app/pedsim [Accessed: 26 May 2020].
Neutens, T., Schwanen, T., Witlox, F., De Maeyer, P. (2010). Equity of urban service delivery: a comparison of different accessibility measures, Environment and Planning A, Vol. 42, pp. 1613-1635. https://doi.org/10.1068/a4230
Pirie, G. H. (1979). Measuring accessibility: a review and proposal, Environment and Planning A, Vol. 11, pp. 299-312. https://doi.org/10.1068/a110299
Profil zajednice Grada Novog Sada (2011). Novi Sad: Kancelarija za lokalni ekonomski razvoj (in Serbian).
Službeni list Grada Novog Sada (br. 13/2006). Plan detaljne regulacije dela radne zone ”Zapad” u Novom Sadu (in Serbian).
Službeni list Grada Novog Sada (br. 52/2009). Plan detaljne regulacije stare ranžirne stanice u Novom Sadu (in Serbian).
Tao, Z., Cheng, Y. (2018). Modelling the spatial accessibility of the elderly to healthcare services in Beijing, China. Environment and Planning B: Urban Analytics and City Science, Vol. 46, No. 6, pp. 1132-1147. https://doi.org/10.1177/2399808318755145
Tiznado-Aitken, I., Muñoz, J. C., Hurtubia, R. (2021). Public transport accessibility accounting for level of service and competition for urban opportunities: An equity analysis for education in Santiago de Chile, Journal of Transport Geography, Vol. 90, pp. 1-14. https://doi.org/10.1016/j.jtrangeo.2020.102919
Waddell, P., Borning, A., Noth, M., Freier, N., Becke, M., Ulfarsson, G. (2003). Microsimulation of urban development and location choices: Design and implementation of UrbanSim, Networks and Spatial Economics, Vol. 3, No. 1, pp. 43-67. https://doi.org/10.1023/A:1022049000877
Weber, J. (2006). Reflections on the future of accessibility, Journal of Transport Geography, Vol. 14, pp. 399-400. https://doi.org/10.1016/j.jtrangeo.2006.06.005
Yamu, C., Frankhauser, P. (2016). Spatial accessibility to amenities, natural areas and urban green spaces: using a multiscale, multifractal simulation model for managing urban sprawl, Environment and Planning B: Urban Analytics and City Science, Vol. 42, No. 6, pp. 1054-1078. https://doi.org/10.1068/b130171p
Zondag, B., Pieters, M. (2005). Influence of accessibility on residential location choice, Transportation Research Record, Vol. 1902, pp. 63-70. https://doi.org/10.1177/0361198105190200108