=Paper=
{{Paper
|id=Vol-2333/paper6
|storemode=property
|title=Walkability Assessment for the Elderly Through Simulations: The LONGEVICITY Project
|pdfUrl=https://ceur-ws.org/Vol-2333/paper6.pdf
|volume=Vol-2333
|authors=Stefania Bandini,Luca Crociani,Andrea Gorrini,Katsuhiro Nishinari,Giuseppe Vizzari
|dblpUrl=https://dblp.org/rec/conf/aiia/BandiniCGNV18
}}
==Walkability Assessment for the Elderly Through Simulations: The LONGEVICITY Project==
https://ceur-ws.org/Vol-2333/paper6.pdf
Walkability Assessment for the Elderly Through
Simulations: The LONGEVICITY Project
Stefania Bandini1,2 , Luca Crociani1 , Andrea Gorrini1 , Katsuhiro Nishinari2 ,
and Giuseppe Vizzari1
1
Complex Systems and Artificial Intelligence research center,
Department of Computer Science, University of Milan-Bicocca, Milan (ITALY)
name.surname@unimib.it
2
Research Center for Advanced Science and Technology,
The University of Tokyo, Tokyo (JAPAN)
tknishi@mail.ecc.u-tokyo.ac.jp
Abstract. In the context of global progressive urbanization and age-
ing of the population, the LONGEVICITY Project has the objective to
study advanced solutions to assess and enhance the walkability of urban
environments. This is aimed at sustaining the social inclusion of the el-
derly citizens. The paper introduces one of the research activities of the
project, focused on the design of innovative agent-based model for the
simulation of pedestrian circulation dynamics and street crossing behav-
ior for the assessment of the level of walkability of the City of Milan
(Italy). This is based on the possibility to model age-driven pedestrian
dynamics while crossing as characterized by heterogeneous behaviors in
terms of speeds and crossing decision. This is aimed at providing applica-
tive design solutions to enhance the level of comfort and safety of the
elderly while walking through the city and to sustain their active ageing.
Keywords: Modeling · Simulation · Ageing · Walkability
1 Introduction
In the context of progressive urbanization [14] and population ageing [17] global
trends, the project “LONGEVICITY - Social Inclusion for the Elderly through
Walkability” has the objective to study advanced solutions to sustain the social
inclusion of the elderly in urban contexts by enhancing their pedestrian mo-
bility. According to the Age-friendly City framework [15], the project aims at
developing innovative strategies to sustain the active ageing of the citizens by
enhancing the walkability [16] of the City of Milan (Italy), with reference to
the level of usefulness, comfort, safety, attractiveness and accessibility of urban
environments.
The LONGEVICITY project is based on a strongly cross-disciplinary re-
search approach, integrating skills, methodologies and tools ranging from Social
Sciences, Design of Services, Artificial Intelligence and Complex Systems Sci-
ence. The international team that will carry out the project is composed by four
main partners: University of Milano-Bicocca, Politecnico di Milano, Research
Center for Advanced Science and Technology, AUSER Volontariato Lombardia.
The research plan of the project is composed of several work packages, which
aims at: (i) assessing the level of walkability of the City of Milan (Italy) through
advanced GIS analyses; (ii) setting up of the living labs where to execute a se-
ries of outdoor and indoor activities participated by a large sample of elderly
citizens (e.g., walking groups, participatory design activities); (iii) empirically
investigating age-driven pedestrian mobility through questionnaires, field obser-
vations, controlled experiments, mobile app for smart-phones and simulations.
In particular, the paper introduces one of the research activities of the project,
focused on the design of innovative agent-based model for the simulation of
pedestrian circulation dynamics for the assessment of the level of walkability of
the City of Milan (Italy). This is based on the possibility to model age-driven
pedestrian dynamics as characterized by heterogeneous behaviors in terms of
speeds and crossing behaviour. Recent empirical contributions already presented
by the authors [13, 12] have highlighted the heterogeneity of both walking and
crossing behavior of adult and elderly pedestrians, due to the decline of motor
and perceptual capabilities linked to ageing. In particular, aged pedestrians are
characterized by lower speed while walking and a higher exposure to risks while
crossing.
To this end, the project is based on methodological and computational tools
aimed at achieving solutions considering the needs and perceptions of senior
citizens with respect to infrastructures and mobility services in the City of Milan.
This is aimed at providing applicative design solutions to enhance the level of
comfort and safety of the elderly while walking through the city and to sustain
their active ageing.
2 Related Works on Walkability Assessment
The walkability assessment of urban areas embraces different types of knowledge
and skills, within a multi-disciplinary approach [3] (e.g., urban studies, architec-
ture, environmental psychology, computer science). Nowadays, the contributions
present in literature about this topic offer a robust theoretical/methodological
framework for the evaluation of the pedestrian friendliness of urban areas, in
terms of methods for the analysis of data to evaluate a series of walkability
criteria.
For what concerns the criteria, Jeff Speck [16] has recently proposed a Gen-
eral Theory of Walkabaility, which includes the following set of indicators: (i )
presence of services within a walkable distance; (ii ) level of comfort and safety
experienced by people while walking; (iii ) attractiveness of the urban areas in
terms of architectural design and social context. The data enabling the assess-
ment of walkability of a urban area include:
– structured data, related to its topographical, cadastral, infrastructural and
architectural elements (e.g., presence of public services, quality of road in-
frastructures, census indicators of the socio-demographical characteristics of
the inhabitants);
– behavioral data, related to how the spatial features of the area influences
the actual behaviors of pedestrians (e.g., problematic level of services due to
high density, pedestrian exposure to road accidents);
– subjective data, focused on the bottom up evaluations of the citizens about
the level of walkability of the area (e.g., perceived level of comfort and safety).
The methodologies and techniques developed and applied to empirically mea-
sure the level of walkability of urban environments include: field observations [13,
12], audit tools [9], GIS-analysis [18, 11], web-based applications [8], social media
data mining [4], computer-based simulations [1, 6, 10]. In particular, the usage
of validated microscopic agent-based simulation models allows to represent age-
driven pedestrian dynamics as characterized by heterogeneous behaviors in terms
of speeds and other choices related to movement behaviors. This is aimed at pro-
viding applicative and optimized architectural and design solutions to enhance
the level of comfort and safety of urban indoor/outdoor infrastructures.
3 Empirical Results
A video-recorded observation was performed in 2015 at an urban unsignalized
intersection in the city of Milan (ITALY). The chosen scenario was characterized
by a significant presence of elderly inhabitants and an high number of pedes-
trian/car accidents involving elderlies pedestrians in the past years.
A HD ultra wide lens camera was mounted on a light stand tripod overhung
from the balcony of a private flat in correspondence of the zebra crossing. Data
analysis was based on the use of the open source software Tracker Video Analysis
and Modelling Tool, which allowed to correct the distortion of the video images,
and then to semi-automatically track a sample of 50 pedestrians and 79 vehicles
while interacting at the zebra crossing, considering one frame every ten (every
0.4 sec). Data analysis was aimed at comparing the speeds of pedestrians while
crossing and the safety gap accepted by pedestrians to cross, comparing data
among adults and elderlies.
A sample of 50 pedestrians and 79 vehicles was considered for data analy-
sis. The sample was selected avoiding situations such as: platooning of vehicles
on the roadway inhibiting a crossing episode, the joining of pedestrians already
crossing, and in general situations influencing the direct interaction between the
pedestrians and the drivers. Part of the selected crossing episodes was char-
acterised by the multiple interaction between the crossing pedestrian and two
vehicles oncoming from the near and the far lane. Considering the effects of
several interfering variables on results, the sample has been designed as follows:
– Age: 27 adult and 23 elderly crossing pedestrians;
– Direction: 27 pedestrians toward point B; 23 pedestrians toward point A;
– Gender: 22 males, 28 females;
– Lane: 50 vehicles from the near lanes, 29 vehicles from the far lane;
Fig. 1. An exemplification of the trend analysis performed on the time series of speeds.
– Vehicle typologies: 77 cars, 1 motorbike, 1 bus.
Pedestrian speeds have been analyzed among the time series of video frames
(trend analysis), as characterized by: (i) a stable trend on side-walks, (ii) a sig-
nificant deceleration in proximity of the cross-walk (decision making) and (iii) an
acceleration on the zebra crossing. The trend of speeds was analyzed by calcu-
lating the difference between: the moving average (MA, time period length: 0.8
s, three frames), and the cumulative average (CA) of the entire frames series.
This allowed to smooth out short-term fluctuations of data (intrinsically due
to pedestrian gait, but also caused by the frame discretization) and to highlight
longer-term trends (deceleration/acceleration). According to results, crossing be-
haviour is defined as composed of three distinctive phases (see Figure 1):
1. Approaching: the pedestrian travels on the side-walk with a stable speed
(Speed MA - CA ' 0);
2. Appraising: the pedestrian approaching the cross-walk decelerates to evalu-
ate the distance and speed of oncoming vehicles (safety gap). We decided to
consider that this phase starts with the first value of a long-term deceleration
trend (Speed MA - CA < 0);
3. Crossing: the pedestrian decides to cross and speed up. The crossing phase
starts from the frame after the one with the lowest value of speed before a
long-term acceleration trend (Speed min).
A two-factors analysis of variance3 (two-way ANOVA) showed a significant
difference among the speeds of pedestrians while approaching, appraising and
crossing [F(2,144) = 61.944, p = .000], and a significant effect of pedestrian’ age
on results [F(1,144) = 63.751, p = .000] (see Tab. 1). A series of post hoc Tukey
test showed a non significant difference between the speeds of pedestrians while
approaching and crossing, considering both adults and elderlies (p > .05). The
difference between the speed of adults and elderlies was significant among all the
three crossing phases (p = .000).
3
All statistics have been conducted at the p < .01 level.
Table 1. The speed of adult and elderly pedestrians among the crossing phases.
Speed and Crossing Phases Adults Elderly
Approaching speed 1.28 m/s ± .18 sd 1.03 m/s ± .18 sd
Appraising speed .94 m/s ± .21 sd .69 m/s ± .23 sd
Crossing speed 1.35 m/s ± .18 sd 1.09 m/s ± .17 sd
In conclusion, results demonstrated that pedestrians’ crossing decision is
based on a significant deceleration in proximity of the curb (appraising) to eval-
uate the distance and speed of oncoming vehicles. Elderly walked in average 22%
slower than adults among the three crossing phases, decelerating 6% more than
adults while appraising. This demonstrated the negative impact of ageing on
crossing behaviour in terms of locomotion skills decline.
4 Model Description
We here briefly describe an agent-based model developed by the authors, and
thoroughly discussed in [2, 7]. For reasons of space, we will omit the discussion
of this baseline and we will only explain the general characteristics of the model,
fundamental for the understanding of the proposed method for managing speed
heterogeneity.
The model is an extension of the classic floor field model [5] and it employs the
same space discretization by means of a rectangular grid of 0.4 × 0.4 m2 cells.
Positions of obstacles and the configuration of the environment is allowed by
means of spatial markers, defining: (i ) areas where pedestrians will be generated;
(ii ) obstacles; (iii ) final destinations; (iv ) intermediate destinations, used to
divide the environment in smaller components and to allow the computation of
higher-level paths for pedestrians to their final destination; (v ) labels describing
the name and typology of environment the cell belongs to (e.g. staircase, ramp,
flat floor, etc.). This is aimed at modeling age-driven pedestrian dynamics while
walking as characterized by heterogeneous behaviors in terms of speeds and other
behavioral decisions that can be related to route choice (e.g. possibly avoiding
stairs).
Space annotation allows the definition of additional grids to the one repre-
senting the environment, as containers of information for pedestrians and their
movement. This describes the well-known floor field approach [5]. These discrete
potentials are used to support pedestrians in the navigation of the environment,
representing their interactions with static objects or with other pedestrians.
Three kinds of floor fields are defined in our model:
– path field (static), which indicates distances from one destination;
– obstacles field (static), which indicates distances from neighbor obstacles or
walls;
– proxemic field (dynamic), which provides information to identify crowded
areas at a given time-step.
The walking behavior of simulated pedestrians is defined with probabilistic
mechanisms. According to their desired speed and to the assumed duration of
the time-step of the model, pedestrians are activated for the movement at each
turn, and they can move in the Moore neighborhood of their position. The choice
of movement is modeled in a probabilistic fashion by means of a utility function
U (c):
κg G(c) + κob Ob(c) + κs S(c) + κc C(c) + κd D(c) + κov Ov(c)
U (c) = (1)
d
P (c) = N · eU (c) (2)
Parameters κ are the calibration weights allowing to configure a pedestrian-
like behaviour and N of Eq. 2 is a normalization factor. Individual functions of
Eq. 1 model respectively: (i) attraction towards the current target; (ii) obstacle
repulsion; (iii) keeping distance from other pedestrians; (iv) cohesion with other
group members; (v) direction inertia; (vi) moving in a cell occupied by another
pedestrian (overlapping) to avoid gridlock in counter-flow situations.
4.1 Modeling Heterogeneous Speeds and Crossing Phases
In the literature related to the simulation of pedestrian dynamics, classic discrete
models assume only one speed profile for all the population. Efforts towards the
modeling of different speed profiles consider two main approaches: (i) increasing
agents movement capabilities (i.e. they can move more than 1 cell per time step),
according to their desired speed ; in this way, given k the side of cells and n the
maximum number of movements per step, it is possible to obtain n different
speed profiles, less or equal to n · k m/step; (ii) modifying the current time scale,
making it possible to cover the same distance in less time and achieving thus
a higher maximum speed profile but at the same time allowing each pedestrian
to yield their turn in a stochastic way according to an individual parameter,
achieving thus a potentially lower speed profile.
The method supporting movements of more than a single cell can be effective,
but it leads to complications and increased computational costs for the manag-
ing of micro-interactions and conflicts: in addition to already existing possible
conflicts on the destination of two (or more) pedestrian movements, even poten-
tially illegal crossing paths must be considered, effectively requiring the modeling
of sub-turns. In addition, the expressiveness of this method is still limited: the
maximum number of movements allowed per time step determines the number
of speed profiles reproducible with simulations (e.g., with vmax = 4 cell per step
and a turn duration of 1 second, simulations can be configured with 0.4, 0.8, 1.2
and 1.6 m/sec).
For these reasons, we decided to retain a maximum velocity of one cell per
turn, allowing the model to reproduce lower speed profiles by introducing a
stochastic yielding mechanism. Each agent has a parameter Speedd in its State,
Algorithm 1 Life-cycle update with heterogeneous speed
if Random() ≤ α/β then
if updateP osition() == true then
α←α−1
else
β ←β+1
end if
end if
β ←β−1
if β == 0 then
(α, β) = F rac(ρ)
end if
describing its desired speed. For the overall scenario, a parameter Speedm is
introduced for indicating the maximum speed allowed during the simulation
(described by the assumed time scale). In order to obtain the desired speed
of each pedestrian during the simulation, the agent life-cycle is then activated
Speedd
according to the probability to move at a given step ρ = Speed m
.
By using this method, the speed profile of each pedestrian is modeled in a
stochastic way and, given a sufficiently high number of step, their effective speed
will be equal to the wanted one. But it must be noted that in several cases speed
has to be rendered in a relatively small time and space window (think about
speed decreasing on a relatively short section of stairs).
In order to overcome this issue, we decided to consider ρ as an indicator
to be used to decide if an agent can move according to an extraction without
replacement principle. For instance, given Speedd = 1.0m/s of an arbitrary
agent and Speedm = 1.6m/s, ρ is associated to the fraction 5/8, that can be
interpreted as an urn model with 5 move and 3 do not move events. At each
step, the agent extracts once event from its urn and, depending on the result, it
moves or stands still. The extraction is initialized anew when all the events are
extracted. The mechanism can be formalized as follows:
– Let F rac(r) : R → N2 be a function which returns the minimal pair (i, j) :
i
j = r.
– Let Random be a pseudo-random number generator in [0, 1].
– Given ρ the probability to activate the life-cycle of an arbitrary agent, ac-
cording to its own desired speed and the maximum speed configured for the
simulation scenario. Given (α, β) be the result of F rac(ρ), the update pro-
cedure for each agent is described by the pseudo-code of Alg. 1. The method
updateP osition() describes the attempt of movement by the agent: in case
of failure (because of a conflict), the urn is not updated.
This basic mechanism allows synchronization between the effective speed of
an agent and its desired one every τ steps, which in the worst case (informally
Speedd
when Speed m
cannot be reduced) is equal to Speedm · 10ι step, where ι is as-
sociated to the maximum number of decimal positions considering Speedd and
Speedm . For instance, if the desired speed is fixed at 1.3m/s and the maximum
one at 2.0m/s, the resulting F rac(ρ) = 1320 , therefore the agent average velocity
will match its desired speed every 20 steps.
In conclusion, the model is now suitable to reproduce the different crossing
phases empirically observed and characterized. Pedestrians do not only have
different individual desired walking speeds comparing adults and elderly, but
also change their speed in accordance with crossing phases. The change of states
is triggered by the possible perception of an oncoming vehicle, regarding its
position and velocity.
5 Conclusions
The LONGEVICITY project is characterized by a methodological interplay be-
tween tools for social research, combined with approaches for the development
of innovative technologies. The cities of the future will be characterized, in fact,
by the growing presence of long-lived/active citizens, and it will then be neces-
sary to design technologically advanced infrastructures and services to provide
support to them.
The current paper described an ongoing activity based on the design of in-
novative agent-based model for the simulation of pedestrian crossing dynamics
for the assessment of the level of walkability of urban areas. This is based on
the possibility to model age-driven pedestrian dynamics as characterized by het-
erogeneous behaviors in terms of speeds and crossing behaviors. This work is
aimed at providing applicative design solutions to enhance the level of comfort
and safety of the elderly while walking through the city and to sustain their
active ageing. In particular, this kind of research is potentially relevant to test
the effectiveness of traffic management solutions (e.g., age-friendly traffic light
systems) and to complement studies on outdoor ambient assisted environments
in order to evaluate future transportation scenarios in Smart Cities.
Ackowledgement
The LONGEVICITY project is funded by Fondazione Cariplo, within the call
Scientific Research 2017 Ageing and social research: people, places and relations
along the period between April 2018 and December 2020 (Grant No. 2017-0938).
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