=Paper=
{{Paper
|id=Vol-3762/537
|storemode=property
|title=Sustainable walkability in inner areas of Italy: a research proposal on AI-based simulation for older adults
|pdfUrl=https://ceur-ws.org/Vol-3762/537.pdf
|volume=Vol-3762
|authors=Frida Milella,Eleonora Clarizia,Alessio De Pellegrin,Stefania Bandini
|dblpUrl=https://dblp.org/rec/conf/ital-ia/MilellaCPB24
}}
==Sustainable walkability in inner areas of Italy: a research proposal on AI-based simulation for older adults==
https://ceur-ws.org/Vol-3762/537.pdf
Sustainable walkability in inner areas of Italy: a research
proposal on AI-based simulation for older adults
Frida Milella1,* , Eleonora Clarizia1 , Alessio De Pellegrin1 and Stefania Bandini1
1
Department of Informatics, Systems and Communication (DISCo), University of Milano-Bicocca, viale Sarca 336, Milano, 20126, Italy
Abstract
This paper aims at discussing the ongoing research activities that are being conducted on the use of AI-based solutions
to promote walkability indexes in the Italian inner areas. Although social sustainability is an expected outcome of the
walkability concept, the literature has focused on urban social sustainability and the development of socially sustainable
urban communities, while attention to pedestrian-friendly rural areas is lacking. The paper examines existing research on the
subject and emphasises the potential of agent-based simulation to create indicators that promote service accessibility and
inclusion, specifically in terms of sustainable walkability in rural areas with a high density of older people.
Keywords
social sustainability, sustainable walkability, inner areas, artificial intelligence, agent-based simulation
1. Introduction proving pedestrian mobility is essential for promoting
active ageing and enhancing the quality of life of older
The significance of walking and the concept of walk- adults by facilitating social engagement and indepen-
ability are becoming increasingly important in urban dence [13], as well as fostering a sense of community and
planning, partly as a result of the increasing public de- reducing social isolation among older population [14, 15].
mand and the goal of sustainable urban development [1]. Furthermore, the incorporation of high-quality mobil-
The concepts of walkability and walking are strongly ity conditions and accessibility in urban public spaces
interconnected with the notions of the livability of local is essential in ageing, in order to ensure convenient ac-
communities, as well as sustainability and its three fun- cess to services and promote conducive environments
damental dimensions: economic, social, and environmen- for social interaction [16, 17, 13]. In particular, it is im-
tal [1]. Owing to a long-standing focus on tackling en- perative to guarantee that older people have easy acces-
vironmental issues, the field of sustainability has placed sibility to healthcare services [18, 19], as to effectively
considerable emphasis on the convergence of environ- improving and safeguarding their general state of health
mental and economic factors [2]. In contrast, the social and well-being [19]. Currently, the issue of accessibility
aspects of sustainability are frequently acknowledged to healthcare is being studied from a user-centered ap-
but receive limited analysis [2, 3], often being perceived proach and is being integrated with a personal mobility
as the least strong and least clearly defined element [2]. aspect [20]. Indeed, recently, there has been a rise in
Social sustainability is concerned with promoting de- the number of studies examining the quality of public
velopment that encourages social engagement, social spaces and their impact on the capacity of older adults
inclusion, and cultural enrichment [3], and walkability is to walk and access services and activities [13], acknowl-
an essential component in the pursuit of social sustain- edging accessibility as a contributing component in ur-
ability [4]. The concept of a walkable neighbourhood is ban social sustainability [5]. Nevertheless, the accessi-
indeed frequently discussed in literature as a key element bility to services is contingent upon the geographical
in promoting urban social sustainability and the devel- aspect [21], with rural communities need to face chal-
opment of socially sustainable urban communities [5, 6] lenges in terms of accessibility to services as a result of
(e.g., [7, 8, 9, 10, 11]). their frequent disconnection from main centres of in-
The importance of social sustainability is becoming frastructure [22, 21]. The existing body of research on
increasingly important in an ageing population [12]. Im- walkability is predominantly concerned with urban and
Ital-IA 2024: 4th National Conference on Artificial Intelligence, orga- metropolitan areas [23, 24, 25] making it less applicable to
nized by CINI, May 29-30, 2024, Naples, Italy rural, small urban, or semi-urban communities which do
*
Corresponding author not have access to pedestrian spaces of the same nature
$ frida.milella@unimib.it (F. Milella); e.clarizia@campus.unimib.it as major cities [26, 25]. The geographical characteristics
(E. Clarizia); a.depellegrin@campus.unimib.it (A. D. Pellegrin);
and sizes of rural areas differ from those of urban areas,
stefania.bandini@unimib.it (S. Bandini)
0000-0002-0522-2804 (F. Milella); 0000-XXXX-XXXX-XXXX which makes it difficult to measure the walkability of
(E. Clarizia); 0000-XXXX-XXXX-XXXX (A. D. Pellegrin); rural areas using the neighborhood-level geography typi-
0000-0002-7056-0543 (S. Bandini) cally used in research [25]. This implies that research on
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
the influence of walkability on socially sustainable com- demographic fragility caused by an ageing population,
munities should take into account the local conditions physical and ecological instability resulting from insuf-
of accessibility, i.e. broaden the focus to include rural ficient handling of semi-natural resources, and under-
communities as well. utilization of a significant amount of land resources in
Furthermore, a recent systematic review of the liter- numerous localities.
ature [27] on the role of AI-powered and sensor-based Walkability metrics gauge the level of pedestrian-
technologies in assisting informal carers (often volun- friendly in a neighborhood’s built environment, and walk-
teers or relatives) who provide care for older adults, re- ability evaluation is employed to evaluate this friendli-
vealed a lack in the reviewed literature concerning the ap- ness [35]. Several walkability indices have been created
plication of location-based technologies for older adults to objectively assess the features of the built environment
residing in remote regions, suggesting to focus on ex- that promote walking habits [36]. These indices range
ploring the integration of emerging technologies, such from ones that concentrate on urban form factors at the
as AI and Geographic Information System (GIS) [28], to neighbourhood level (such as population density, land
improve healthcare for informal carers and older adults use diversity, and street connectivity) to those that focus
in rural regions. Similarly, a second systematic review on urban design factors at the street level (such as the
by the authors [29] determined that employing innova- size and layout of streets, the design and condition of
tive strategies to create outdoor spaces that cater to the buildings, and street furniture) [36]. However, walkabil-
needs of adults in need of care, while also providing addi- ity in rural communities presents a unique challenge as
tional support to their informal carers, has the potential its theoretical and practical basis has not been thoroughly
to provide significant assistance to these carers. examined in this specific context [25]. This presents a
This article aims to discuss the ongoing research on substantial opportunity for the Italian territory, as in-
the use of AI-based technologies to enhance a sustain- ner areas comprise over 4,000 municipalities and over
able walkability in inner areas of Italy. This research is a 20 percent of the country’s resident population, or ap-
component of the ongoing project called “Care provision proximately 60 percent of the national territory [37]. On
across different territorial contexts”1 . Its objective is tothe other hand, there is a clear need for more research
address the current lack of walkability indexes for inner focused on rural older adults and their connection to the
areas by investigating the potential of AI-based tech- community, as the current evidence is insufficient [38, 39].
nologies and promoting the achievement of a sustainable The high occurrence of residential proximity, with family
walkability in inner areas within an ageing society. members living together and multigenerational house-
holds, has created a strong intergenerational exchange
and family support system for older adults requiring
2. Walkability and Inner Areas care in rural areas [33]. This makes these areas highly
promising for the development of new walkability in-
The term rural is commonly understood to refer to areas
dices aimed at creating socially sustainable communities.
with a small population, few settlements, and remote-
Our research activity is indeed being focused on studying
ness [30]. However, there is no consensus among aca-
how AI-based solution can aid in developing sustainable
demics on whether all of these characteristics need to be
walkability indexes in Italian inner areas.
present together in order to define a settlement as rural,
or if it is enough for just one of these elements to be
present [30]. A significant portion of the Italian territory 3. AI-based solutions to improve
is organised spatially according to “minor centres", which
are commonly of small size and frequently provide resi- walkability in Italian inner
dents with limited access to essential services [31]. The areas: the intended value of
characteristics of this territory can be summarised using
the term “inner areas" [31] which overlap with the iden-
using agent-based simulations
tification of rural regions that face a notable deficiency Scholarly literature provides several examples of using
in the provision of essential services [32, 33]. However, Artificial Intelligence (AI) techniques to assess walkabil-
inner areas exhibit three primary attributes [34]: socio- ity. For instance, scholars employed machine learning
1 (ML) techniques to evaluate the state of sidewalks, as a
This publication was produced with the co-funding of Euro-
pean Union – Next Generation EU, in the context of the Na- substantial predictor of a pedestrian-friendly neighbour-
tional Recovery and Resilience Plan, PE8 Conseguenze e sfide hood, through the analysis of pedestrians’ physiological
dell’invecchiamento", Project Age-It (AGE - IT - A Novel Public- responses as gathered by wearable accelerometers [35].
private Alliance to Generate Socioeconomic, Biomedical and Tech- Several studies have examined the integration of deep
nological Solutions for an Inclusive Italian Ageing Society- Age-
learning (DL) with environmental sensors to evaluate
ing Well in an Ageing Society) - AGE-IT- PE00000015 CUP:
H43C22000840006 the walkability of urban streets [40], have explored the
utilisation of Streetview Image and semantic segmenta- models into Digital Twins environments with GIS-based
tion to create a walkability evaluation index [41] or have analysis, maybe allowing for a deeper understanding of
estimated walkability measures at a street level [42]. Fur- older adults personal mobility also in inner areas. In
thermore, a few studies have suggested a network-based this regard, Liu et al [49] recently examined the benefits
measure of walkability that incorporates road network of employing agent-based models (ABM) as a quantita-
structure and user opinions using machine learning tech- tive tool for assessing walkability due to their inclusion
niques with the aim to get predictive models that yield of subjective aspects, integration of many parameters,
comprehensive walkability scores at a spatial level [43]. and capacity to distinguish between various populations.
Other studies have suggested novel methods for utilis- Similarly, Bandini et al [50] conducted a study with the
ing pre-trained models to create adaptable models that purpose to evaluate the walkability of the city of Milan
can predict walkability scores for cities that were not by simulating the age-driven pedestrian dynamics, which
included in the original training process [44]. involve various behaviours such as different speeds and
The scientific publications on walkability studies have crossing behaviour. Badland et al. [51], instead, devised
significantly expanded over the past twenty years [45]. an agent-based modelling tool that integrated the advan-
However, our current study is revealing that scholarly tages of Service Area Approach mapping to examine the
research on the use of AI algorithms to evaluate walkabil- correlation between amenity access and neighbourhood
ity in rural areas, and especially in the Italian inner areas, walkability while also enabling the testing of various
is under-investigated. Nonetheless, AI has significant planning scenarios. Therefore, given that agent-based
potential for evaluating the pedestrian-friendliness of simulation models simulate the actions and interactions
inner areas. Geographical information, socio-economic of individual agents from various demographic groups
data, and pedestrian behavioural patterns can be utilised in a specific environment, the incorporation of AI tech-
through the application of ML algorithms to provide niques may have the potential to accurately depict the
walkability scores that are specifically customised for pedestrians’ mobility patterns or the barriers for their
the distinct attributes of inner areas and ageing pop- personal mobility at each age categories, contributing to
ulation. Some existing studies in urban context have attain our goal to define a sustainable walkability concept
highlighted the need of using Geographical Information in Italian inner areas for older population.
Systems (GIS) to define an index that measures the walk-
ability of older people and supports the creation of ad-
vanced simulation models based on AI [46]. In a similar 4. Conclusions
vein, another study assessed the emotional experience of
Although social sustainability is an expected outcome
pedestrians, specifically focusing on older adults, propos-
of the walkability concept, the literature has focused on
ing an “affective walkability” indicator [47]. The aim was
urban social sustainability and the development of so-
to investigate how safe, comfortable, and walkable an
cially sustainable urban communities, while attention
environment was, within the context of promoting social
to pedestrian-friendly rural areas is lacking. Moreover,
and active inclusion of older adults in urban areas. Based
there exists a dearth of scholarly publications both in
on this, our research activity is being focused on studying
walkability metrics targeted to rural communities and
villages in Italian inner areas, including Premeno (VB)
in the use of AI-based techniques to promote pedestrian
and Petrella Tifernina (CB), with a high aged population
friendliness in rural areas. The article reviews some of
density. Our goal is to understand the factors that af-
the currently available contributions on the topic and
fect walkability in inner areas and its accessibility, and
highlights the potential of agent-based simulation to de-
how the integration of AI techniques can promote so-
velop indicators that facilitate service accessibility and
cial inclusion and equity of access to healthcare services,
inclusivity, i.e., sustainable walkability in rural areas with
ie a sustainable walkability in these areas. Indeed, one
a significant older population density. Further research
potential benefit of integrating AI-based tools, such as
will be carried out using multiple case studies to derive a
remote sensing or GIS technologies, is the provision of
generalised research methodology for the specific context
comprehensive mapping and geographical analysis. This
of Italian inner areas.
information can be utilised to guide targeted measures
that improve the walkability of inner areas by revealing
crucial details regarding pedestrian access, connectivity, References
and safety. In addition, according to a recent systematic
review of the literature [48] the combination of wear- [1] A. Baobeid, M. Koç, S. G. Al-Ghamdi, Walkability
able sensors and GIS-based approach shows potential and its relationships with health, sustainability, and
as a method for accurately capturing older people’s im- livability: elements of physical environment and
mediate physical reactions to environmental stressors. evaluation frameworks, Frontiers in Built Environ-
This could lead to the integration of simulation-based ment 7 (2021) 721218.
[2] S. H. Rogers, K. H. Gardner, C. H. Carlson, Social Health 62 (2008) e9–e9.
capital and walkability as social aspects of sustain- [16] S. Cinderby, H. Cambridge, K. Attuyer, M. Bevan,
ability, Sustainability 5 (2013) 3473–3483. K. Croucher, R. Gilroy, D. Swallow, Co-designing
[3] S. Bahadure, R. Kotharkar, Social sustainability and urban living solutions to improve older people’s
mixed land use, in: Proceedings of International mobility and well-being, Journal of urban health
Conference on Advances in Architecture and Civil 95 (2018) 409–422.
Engineering (AARCV 2012), volume 21, 2012, p. 725. [17] B. Aguiar, R. Macário, The need for an elderly
[4] Y. Gul, The effectiveness of walkability index for centred mobility policy, Transportation research
social sustainability in developing countries using procedia 25 (2017) 4355–4369.
geographical information system, Journal of Design [18] R. Bayar, M. Yılmaz, Measuring age-friendliness
Sciences and Applied Arts 2 (2021) 141–154. based on the walkability indices of older people
[5] N. Dempsey, G. Bramley, S. Power, C. Brown, The to urban facilities, Urban Design International 28
social dimension of sustainable development: Defin- (2023) 35–51.
ing urban social sustainability, Sustainable devel- [19] L. Cheng, M. Yang, J. De Vos, F. Witlox, Examining
opment 19 (2011) 289–300. geographical accessibility to multi-tier hospital care
[6] W. Sabir Abdullah, Optimizing social sustainabil- services for the elderly: A focus on spatial equity,
ity in walkable university campus: A comparison Journal of Transport & Health 19 (2020) 100926.
between the old and new campuses of sulaimani [20] J.-F. Levesque, M. F. Harris, G. Russell, Patient-
university, Amazonia Investiga 9 (2020) 44–56. centred access to health care: conceptualising ac-
[7] K. M. Leyden, Social capital and the built environ- cess at the interface of health systems and popula-
ment: the importance of walkable neighborhoods, tions, International journal for equity in health 12
American journal of public health 93 (2003) 1546– (2013) 1–9.
1551. [21] H. Wang, P. C. Coyte, W. Shi, X. Zong, R. Zhong,
[8] P. Morales-Flores, C. Marmolejo-Duarte, Can we Social governance and sustainable development in
build walkable environments to support social cap- elderly services: Innovative models, strategies, and
ital? towards a spatial understanding of social capi- stakeholder perspectives, Sustainability 15 (2023)
tal; a scoping review, Sustainability 13 (2021) 13259. 15414.
[9] S. Rogers, S. Aytur, K. Gardner, C. Carlson, Mea- [22] C. Tacoli, The links between urban and rural devel-
suring community sustainability: Exploring the in- opment, 2003.
tersection of the built environment & social capital [23] R. C. Brownson, C. M. Hoehner, K. Day, A. Forsyth,
with a participatory case study, Journal of Environ- J. F. Sallis, Measuring the built environment for
mental Studies and Sciences 2 (2012) 143–153. physical activity: state of the science, American
[10] A. N. Mohamed, A. A. E. Elmokadem, S. M. Ali, journal of preventive medicine 36 (2009) S99–S123.
N. Badawey, Improve urban form to achieve high [24] S. S. Frost, R. T. Goins, R. H. Hunter, S. P. Hooker,
social sustainability in a residential neighborhood L. L. Bryant, J. Kruger, D. Pluto, Effects of the
salam new city as a case study, Buildings 12 (2022) built environment on physical activity of adults
1935. living in rural settings, American journal of health
[11] A. Sonta, X. Jiang, Rethinking walkability: Ex- promotion 24 (2010) 267–283.
ploring the relationship between urban form and [25] C. I. Nykiforuk, K. Coupland, L. M. Nieuwendyk,
neighborhood social cohesion, Sustainable Cities J. Ann McGetrick, Universal design for the rural
and Society 99 (2023) 104903. walks of life: operationalizing walkability in bon-
[12] K. Komp-Leukkunen, J. Sarasma, Social sustainabil- nyville, alberta, canada, Critical Public Health 28
ity in aging populations: A systematic literature (2018) 213–224.
review, The Gerontologist (2023) gnad097. [26] L. G. Hart, E. H. Larson, D. M. Lishner, Rural def-
[13] F. Alves, S. Cruz, A. Ribeiro, A. Bastos Silva, J. Mar- initions for health policy and research, American
tins, I. Cunha, Walkability index for elderly health: journal of public health 95 (2005) 1149–1155.
A proposal, Sustainability 12 (2020) 7360. [27] F. Milella, D. D. Russo, S. Bandini, How artificial
[14] N. Shellito, N. V. Roldan, Walking away from lone- intelligence can support informal caregivers in their
liness: The mediating role of social isolation, Inno- caring duties to elderly? a systematic review of
vation in Aging 3 (2019) S836. the literature, in: AIxIA 2023 Italian Workshop
[15] T. Sugiyama, E. Leslie, B. Giles-Corti, N. Owen, As- on Artificial Intelligence for an Ageing Society, co-
sociations of neighbourhood greenness with physi- located with the 22nd International Conference of
cal and mental health: do walking, social coherence the Italian Association for Artificial Intelligence
and local social interaction explain the relation- (AIxIA 2023), 2023, pp. 6–9.
ships?, Journal of Epidemiology & Community [28] B. Mullachery, S. Alismail, A smart healthcare
framework: Opportunities for integrating emerging Journal of the Korean Cartographic Association 22
technologies (5g, iot, ai, and gis), in: Proceedings (2022) 53–68.
of the Future Technologies Conference, Springer, [42] M. M. Hasan, V. Kwigizile, J.-S. Oh, Automated mea-
2022, pp. 325–340. surement of walkability applying artificial intelli-
[29] F. Milella, D. D. Russo, S. Bandini, Ai-powered gence (ai) and spatial analysis techniques, Journal
solutions to support informal caregivers in their of Transport & Health 22 (2021) 10.
decision-making: A systematic review of the litera- [43] A. M. S. Alfosool, Y. Chen, D. Fuller, Alf–score—a
ture 1, OBM Geriatrics 7 (2023) 1–11. novel approach to build a predictive network–based
[30] N. Morelli, S. Barello, M. Mayan, G. Graffigna, Sup- walkability scoring system, Plos one 17 (2022)
porting family caregiver engagement in the care e0270098.
of old persons living in hard to reach communi- [44] A. M. Alfosool, Y. Chen, D. Fuller, Alf-score++,
ties: A scoping review, Health & social care in the a novel approach to transfer knowledge and pre-
community 27 (2019) 1363–1374. dict network-based walkability scores across cities,
[31] UVAL, Strategia Nazionale per le aree interne: Scientific Reports 12 (2022).
definizione, obiettivi, strumenti e governance, [45] X. Li, D. Xie, Z. Zhou, X. Zhang, R. Li, J. Li, Z. Chen,
https://www.agenziacoesione.gov.it/wp-content/ J. Zhang, Visualization analysis of research progress
uploads/2020/07/MUVAL_31_Aree_interne.pdf, for walkability, Scientific Reports 14 (2024) 1989.
2014. [46] A. Gorrini, S. Bandini, Elderly walkability index
[32] P. Bertolini, F. Pagliacci, Quality of life and territo- through gis: Towards advanced ai-based simulation
rial imbalances. a focus on italian inner and rural models., in: AI* AAL@ AI* IA, 2018, pp. 67–82.
areas., Bio-Based & Applied Economics 6 (2017). [47] S. Bandini, F. Gasparini, Social and active inclusion
[33] M. Arlotti, Older people and care networks in rural of the elderly in the city through affective walka-
areas: An exploratory study in italy, Sociologia bility, The Review of Socionetwork Strategies 15
Ruralis (2024). (2021) 557–573.
[34] G. Basile, A. Cavallo, Rural identity, authenticity, [48] F. Milella, M. Oltolini, S. Bandini, Can wearable
and sustainability in italian inner areas, Sustain- technologies contribute to an age-friendly walka-
ability 12 (2020) 1272. bility environment? First insights from a systematic
[35] H. R. Ng, I. Sossa, Y. Nam, J.-H. Youn, Machine review of the literature. In Conferenza PErvasive
learning approach for automated detection of irreg- Technologies Related to Assistive Environments
ular walking surfaces for walkability assessment (PETRA) (PETRA ’24), 26- 28 giugno 2024, Creta ,
with wearable sensor, Sensors 23 (2022) 193. Grecia. ACM, New York, NY, USA, 7 pages. https:
[36] B. W. Koo, S. Guhathakurta, N. Botchwey, How are //doi.org/10.1145/3652037.3652046, 2024. In press.
neighborhood and street-level walkability factors [49] Y. Liu, D. Song, Z. Wang, X. Yu, R. Wang, Walka-
associated with walking behaviors? a big data ap- bility assessment using agent-based model: Why it
proach using street view images, Environment and becomes an advantageous way, in: World Congress
Behavior 54 (2022) 211–241. of Architects, Springer, 2023, pp. 367–374.
[37] G. Dematteis, Montagna e aree interne nelle [50] S. Bandini, L. Crociani, A. Gorrini, K. Nishinari,
politiche di coesione territoriale italiane ed europee, G. Vizzari, et al., Walkability assessment for the el-
Montagna e aree interne nelle politiche di coesione derly through simulations: The longevicity project.,
territoriale italiane ed europee (2013) 7–15. in: AI* AAL@ AI* IA, 2018, pp. 83–91.
[38] P. R. Baker, D. P. Francis, J. Soares, A. L. Weight- [51] H. Badland, M. White, G. MacAulay, S. Eagleson,
man, C. Foster, Community wide interventions for S. Mavoa, C. Pettit, B. Giles-Corti, Using agent-
increasing physical activity, Cochrane database of based modelling to inform neighbourhood walkabil-
systematic reviews (2015). ity, Spatial Data Access and Integration to support
[39] A. Klann, L. Vu, M. Ewing, M. Fenton, R. Pojed- liveability (2013) 23.
nic, Translating urban walkability initiatives for
older adults in rural and under-resourced commu-
nities, International journal of environmental re-
search and public health 16 (2019) 3041.
[40] Y. Li, N. Yabuki, T. Fukuda, J. Zhang, A big data
evaluation of urban street walkability using deep
learning and environmental sensors (????).
[41] J. of the Korean Cartographic Association, et al.,
Development of walkability evaluation index us-
ing streetview image and semantic segmentation,