=Paper=
{{Paper
|id=Vol-2851/paper31
|storemode=property
|title=Mobile Location-Based Social Distancing Recommender System with Context Evaluation: a Project Approach
|pdfUrl=https://ceur-ws.org/Vol-2851/paper31.pdf
|volume=Vol-2851
|authors=Olga Artemenko,Nataliia Kunanets,Volodymyr Pasichnyk,Vasyl Kut,Oleksandr Lozytskyi
|dblpUrl=https://dblp.org/rec/conf/itpm/ArtemenkoKPKL21
}}
==Mobile Location-Based Social Distancing Recommender System with Context Evaluation: a Project Approach==
https://ceur-ws.org/Vol-2851/paper31.pdf
Mobile Location-Based Social Distancing Recommender System
with Context Evaluation: a Project Approach
Olga Artemenko a,b, Nataliia Kunanetsb,c, Volodymyr Pasichnykb, Vasyl Kutd and Oleksandr
Lozytskyy b
a
PHEI "Bukovinian University", Darvina Street 2-a, Chernivtsi, 58000, Ukraine
b
Lviv Polytechnic National University, Stepana Bandery Street 32-a, Lviv, 79013, Ukraine
c
Ivan Franko National University of Lviv, Universutetska Street 1, Lviv, 79000, Ukraine
d d
Uzhhorod National University, Universytetska Street 14, Uzhhorod, 88000, Ukraine
Abstract
This paper describes main modern tendencies for projects in the design and development of
urban route planning recommender systems. In this research the difficulties and unsolved
problems were analyzed for development and use of location-based mobile recommender
systems for social distancing. This paper focuses on project of development of mobile
location-based recommender system with context evaluation. The main features of decision-
making on passing and changing the route are analyzed. Technologies for determining the
danger of crowding and managing human flows have been studied. The study considers the
possibility of creating software to manage the flow of people within the city to ensure social
distancing. A prototype of a mobile application has been created, which will be used to teach
the multi-agent system the peculiarities of the space-time behavior of pedestrians during
quarantine.
Keywords 1
Mobile location-based recommender systems, urban route planning, social distancing, crowd
prevention management
1. Introduction
The key element for social distancing is the management of human crowd [1]. Everyone has a
smart gadget of one type or another and information technology for managing big data,
including personal data of smartphone users, opens up new opportunities to prevent crowds.
Route planning in the context of urban infrastructure and with the provision of social distancing is
a guarantee of safe, efficient and "smart" navigation of people within large settlements. Classically,
the task of paving a route is usually solved for cases of tourist travel, long-distance travel with a large
number of points to visit, or travel to unfamiliar places.
Crowd management, organization of human flows during mass events, during rush hours at traffic
interchanges, in crowded places was still considered a task relevant to megacities, festive and festival
events, places of religious pilgrimage, etc [2].
This was not a problem for non-urban areas, the daily life of provincial cities, and even low-
density countries. The 2020 pandemic has changed the situation radically.
2. The human crowd prevention management
Proceedings of the 2nd International Workshop IT Project Management (ITPM 2021), February 16-18, 2021, Slavsko, Lviv region, Ukraine
EMAIL: olga.hapon@gmail.com (A. 1); nek.lviv@gmail.com (A. 2); vpasichnyk@gmail.com (A. 3); kut.vasilij81@gmail.com (A. 4);
Oleksandr.A.Lozytskyy@lpnu.ua (A. 5)
ORCID: 0000-0002-4057-1217 (A. 1); 0000-0003-3007-2462 (A. 2); 0000-0001-9434-563X (A. 3); 0000-0001-5267-331X (A. 4); 0000-
0001-8395-8385 (A. 5);
©️ 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
The current level of development of mobile devices allows them to implement different types of
sensors (eg, GPS, accelerometer, camera, digital compass, magnetometer, barometer, gyroscope) [3].
Thus, "smart" devices (such as smartphones, Google glasses, Apple Watch and Mi Band) can be used
to collect a variety of spatial information about the user, as well as from the environment. Mobile
devices can now be used to determine noise levels, speeds, temperatures, and more. Meanwhile, these
devices can upload sensor-sensitive data to the data collector's server over wireless access networks,
such as cellular or Wi-Fi, at a convenient time and location.
Thus, it becomes technologically possible to develop mobile crowd sensing (MCS) [4]. MCS is a
paradigm that encompasses a large number of people with sensor and computing devices, in which
people can collect probing data and receive certain information. Depending on the level of user
involvement, MCS can be classified into two categories: opportunistic sounding and participatory
sounding. The first is a passive process where probing data is collected automatically without the
user's knowledge of the gadget. The latter is an active process when users need to directly initiate
probing actions, such as the choice of sensory tasks and the solution of inquiring efforts [5].
Due to the advantages of sensing functions in smartphones and user mobility, MCS provides
unique functions for processing big data arrays [4]:
• Scalability: High scalability is one of the key features of MCS. In a fixed network of
sensors, increasing the sensing area will lead to the deployment of sensors in an extended
area, which is time consuming and expensive. With MCS technology, it is easy to achieve
the collection of probing information over a large area, motivating more mobile users in
the extended area to participate in probing tasks. Thus, MCS is an effective paradigm with
high scalability and is very suitable for collecting sounding data over a large area, such as
the territory of settlements. Meanwhile, high scalability also makes MCS more reliable
compared to a traditional touch network for creating software to manage the flow of
people within the city to ensure social distancing.
• Flexibility: Another unique feature of MCS is its great flexibility. In a fixed sensor
network, changing the sensing area will require changing the settings and relocating the
sensors. However, in the MCS system, the system must recruit some mobile users in the
target area for probing, which is easy to achieve due to the large number of mobile users
among citizens and the fact, that smartphone is always with the user. Therefore, MCS is a
flexible paradigm for collecting sounding data.
• Self-Determination of User Behavior: Self-determination of gadget user behavior is a
major feature of participatory probing, which is a major category of MCS. When probing
participation, mobile users can make their own decisions instead of being guided by a
central controller. Mobile device users are heterogeneous. They may be in different places
with different interests and opportunities. Their mobile devices can have different
functions, memory capacity, power, etc [6]. Thus, each mobile user can decide whether to
participate in MCS or not, and take on different tasks based on their own situations.
Meanwhile, the usefulness of each mobile user may be influenced by the decisions of
other mobile users, which make each mobile user's self-determination more difficult.
• Different data quality: due to the heterogeneity of mobile users, different functionality of
their smart devices, different data quality is obtained - another feature of MCS. In fixed
sensor networks, sensors are usually pre-deployed based on a single model or format.
Therefore, the number of sensors and the type of data they can produce are known a priori.
This makes it easier to control the quality of the data. However, with MCS, data quality
may change over time due to mobile user mobility, mobile user preferences, and different
probing contexts. To maintain the confidentiality of participants, some programs remove
identification information from the sensor data. In this way, anonymous users can send
low-quality or even fake data to the platform. In addition, data provided by different
participants may be redundant, duplicate or inconsistent. Therefore, how to collect high
quality data is a critical issue in MCS due to the variety of data quality.
• Weak power limitation: In fixed sensor networks, power limitation is a major problem due
to the inconvenience of replacing the power supply of the sensor. Many studies have
focused on how to extend the life of a network. However, with MCS, the sensors are built
into users' mobile devices. Users can be aware of the remaining charge at any time and
conveniently charge their devices when the battery is below a certain level. Therefore,
power limitation is not a major issue for MCS [4].
3. A project of Mobile Crowd Sensing software for Multi-criteria route
planning with risk evaluation recommender system
In order to reduce the severity of the problem and establish procedures for effective
communication with the masses, to avoid manipulation of the public consciousness, there is a need to
implement a project that involves the creation of Mobile Crowd Sensing software [7] for Multi-
criteria route planning with risk evaluation recommender system [8].
Implementation of an IT project to create mobile location-based social distancing recommender
system with context evaluation involves the passage of several classical stages.
Stage 1 Stage before project preparation. At this stage it is substantiated that in the implementation
of external public mass communications, caused by the desire to implement such communications,
there is a desire to expand the scope of operation and development of information exchange, and later
the objects of material production. In the implementation of social communications, it is important not
only what is used, but who participates in the interaction, how information and connections are used,
to whom the information is addressed and where the communication links are directed. The
effectiveness of information exchange and the whole process of communication with the masses
largely depends on the recognizability of the subjects of communication of the subject of discussion
(information exchange) and the communicative competence of those who try to control the
consciousness of the masses.
The effectiveness of information exchange, which causes certain actions, depends on the level of
communicative competence. Moreover, the key mechanism of influencing the masses of people is
manifested at all levels of socio-cultural organization of society, which generates the social
significance of the project. This approach contributed to the formulation of the goals and objectives of
the project, the development of the project concept.
Stage 2. Communicative. This stage is to analyze the main components of the problem based on a
wide range of sources and search for possible solutions; study of the project environment, selection of
the optimal variant of the project task; tools for the project task.
Stage 3. Technological Execution of tasks by each project participant in accordance with the
developed plan and division of responsibilities. Scheduling the project to determine the start and end
dates of all project operations.
Stage 4. Final Presentation of the results of the project, presentation of the main functional
characteristics of the information system.
One of the requirements of social distancing is to keep the distance between people in society and
to avoid crowds of large numbers of people. That is, ideally, it is necessary to organize the movement
of people so that their routes intersect as little as possible both in time and space [9]. It is desirable
that people not only were not in one place at the same time (space), but also did not visit the same
place in large number (time). To ensure these conditions when laying the route for the user of the
recommended application, you must do the following [10]:
• Obtain data on the stay and movement trajectories of other city residents
• Analyze the density of people on different routes, streets
• Identify places of potential crowd formation
• Identify potential people who may encounter on the route and identify the risks of such a
collision [11]
• Laying the trajectory in such a way as to obtain the least number of intersections with other
people[12].
Analysis of the applications of tourist routes shows that the task of route optimization is usually to
make it the shortest and cover the most popular destinations [13]. Unlike maritime navigation
research [14], where safety criteria are preferred and traffic congestion is avoided. That is why the
combination of models to take into account the risk of too close intersection with other road users
with the functionality of spatially oriented tourist recommendation systems [15] will allow to change
the algorithm of routing for the user so that the priority is not saving time and finding the most
popular route [16].
The risk of forming a crowd of people according to the trajectories of the closest participants can
be described as follows:
Qi , places
СRi = H i , place
H i ,context wi , weathercond
Qi ,context (1)
CRi
where is the crowd creation risk for area i, the subscript i is the area identification number,
H i , place
is the hazard index of the area popularity history (depends on how many people visit area in
H i ,context Qi , places
short time) , is the hazard index of context factors influence, is a coefficient of
Qi ,context
quarantine restrictions influence, is a coefficient of quarantine necessity (got from level of
wi , weathercond
morbidity in the region) and describes the influence of the weather conditions.
Figure 1: Risk evaluation analysis for avoiding human interaction on the urban area route.
To adequately assess the risk of crowding in a group of people walking through the city streets [17],
when choosing a safe route, the following types of risk should be identified:
• The level of safety on different sections of the route and, in particular, pay attention to the peak
risk indicator for the route [18].
• Optimize the total length of the route. The walking route cannot be too long. However, the
shortest route can be more risky to cross with other people and form crowds [19]. In addition,
during quarantine restrictions, people can use hiking for shopping or business as a kind of fitness
exercise or a walk in the fresh air.
• Convenience of route adjustment while moving. A route in which it is difficult or impossible to
make changes quickly (choose a detour, turn in another direction) is assessed as more risky. To
obtain a rating in this case, the total number of route points at which it is possible to transform it
is important. Such points are, for example, intersections and open areas, where a person can
freely change direction and avoid contact with other people [20].
• The ability of the user to overcome the route: is assessed as the average risk gradient, which
takes into account the impact on the assessment of the route of weather conditions, the physical
condition of the pedestrian, time of day, etc.
4. Using smartphone big data and functionality in the project
For developing efficient tool for social distancing urban route planning various types of contextual
information becomes important [21] and plays an increasing role in the calculation of complete,
qualitative and correct recommendations from the user point of view .
Server part (data management) Sensor
module
data of
evaluation
other
Risk
Context people in
the area
Recommender system (knowledge management)
Recommendations
(selection
Priorities
criteria)
Client part (user interface)
on
ati
nd e
co t
mm
t
Re ues
or s
pp tion
req
u
s da
e
ut en
R o mm
co
re
The User submits places to
create and navigate the
route
User
Figure 2: Using context analysis for social distancing location-based recommender system
The main challenges of the research include the development of a data collection mechanism for
the training sample, to ensure the same quality and eliminate data redundancy, to take into account the
requirements for confidentiality and security of personal data when using mobile applications, etc.
• Development of a data collection mechanism for the training sample: The purpose of the
study is to collect sufficiently high-quality data from probing the routes of pedestrians on
the streets of the city. However, conducting sensory tasks involves either implicit efforts
(eg, monetary costs) or explicit efforts (eg, provides some feedback or evaluation) [22].
Meanwhile, mobile devices equipped with sensors are owned and controlled by individual
users. Therefore, they may be reluctant to provide their sounding capabilities if they are
unable to obtain adequate compensation for consumption caused by the performance of
sounding tasks. To achieve the goal of collecting sufficient data on the navigation of
people in the city, an effective incentive mechanism is important for the success of MCS.
Incentives are usually divided into three categories: interest and entertainment, socio-
ethical, financial rewards.
• Optimizing activities for mobile users: Given the sufficient number of MCS participants,
their activities, such as the selection of sounding tasks and reporting of sounding data, are
also important for both users and the system. Given the heterogeneity of mobile users, on
the one hand, heterogeneous mobile users compete with each other; on the other hand,
they can work together to reduce costs or perform some complex tasks. How to model and
analyze the competition and cooperation of mobile users is one of the main challenges for
achieving group or individual optimization of mobile users.
In addition, data redundancy and data diversity should be taken into account when optimizing the
spatial, temporal and spatio- temporal behavior of mobile users. For example, users of nearby mobile
devices may send similar data to the noise level collection, resulting in data redundancy. In addition,
devices in the same location may have different sensing capabilities due to a different brand of mobile
communication or a different configuration [23]. Even such devices can receive different probing data
when they are in different conditions (for example, a device in a pocket or out of pocket). Therefore,
from a system point of view, how to optimize the behavior of mobile users is also an important
question (Fig. 3).
Figure 3: The process of human density (crowd) estimation procedure via mobile location-based
social distancing recommender system [23]
• Privacy and Security: In mobile applications, privacy or security concerns may discourage
users from sharing data from the sensors. For example, disclosing the identity of users or
certain confidential attributes, such as location (such as home address or workplace), daily
routes, and personal activities or conditions. On the other hand, some tasks cannot be
performed without sensitive sensor data. For example, in a noise measurement program,
you need to read the GPS sensor and the microphone sensor at the same time. Thus,
exploring approaches that can both ensure the number of participants and maintain the
confidentiality of participants is a necessary and challenging task. A popular technique is
anonymization [24], which removes identification information from sensor data before
disclosing it to a third party. However, the disadvantage is that an anonymous user can
provide the system with incorrect or even false data - and this is currently one of the
biggest problems in collecting sensor data from mobile devices (Fig. 4).
Figure 4: The process of collected from sensor data analysis and application in location-based
recommender systems for social distancing urban route planning
Factors that play a role in the choice of individual’s route around the city area may be devided into
four categories: the available routes (including due to health conditions of the user, time of the day,
weather conditions etc. [25]); the character (physical condition, age, mood and other personalized
characteristics) of the traveller; the trip that is to be made (purpose of the trip, duration, importance
for the user, etc.); other circumstances (context factors). The individual traveller chooses his path on
the basis of route characteristics; these items form his choice factors. The other three groups of
characteristics are of influence only on the relative importance and perception attached to those route
characteristics [26–32]. These characteristics derive from directly measurable route attributes. The
process of building a safe walking route with support for social distancing is shown in Figure 5.
Figure 5: Flowchart of the multi-criteria context evaluation for social distancing urban route planning
process.
The process is based on visualizing risk contours on a map as a framework for route planning, and
essential data are input according to the circumstances. The procedure is followed by defining the
navigational direction[33–36], projecting the persons route according to the gradient, analyzing a
combination of reference points, deriving feasible route options, analyzing and assessing the risk
criteria and social distancing options, and finally suggesting a safe route.
5. Construction of the training sample
The decision-making process for laying and promptly changing the route should be provided by a
multi-agent system. To successfully configure it, you must first collect as large an array of data as
possible that describes the temporal, spatial, and spatiotemporal behavior of different categories of
pedestrians. In addition, the actual territory matters. Characteristics of streets, relief, features of city
infrastructure. Therefore, the streets of the city for which the software is created must be used to
collect data. In our case, it is the city of Chernivtsi (Ukraine). Given the research challenges described
in Section 4, the simplest way to collect big data describing pedestrian behavior was chosen: a mobile
application was created, the users of which joined the research group using digital authorization. They
know that data from their mobile sensors will be collected in the cloud storage of the group
administrator.
Figure 6: The process of collecting big data describing the typical behavior of a pedestrian in
Chernivtsi.
The data collected in this way will be further used to train a multi-agent system. This will build a
knowledge base, descriptive rules, to control the walking route of the smartphone user in order to
organize social distancing. It is assumed to use the model shown in Figure 7.
Figure 7: The proposed model for safe route planning, using agent-based decision making [3]
In an agent-based crowd model, crowd control solu-tions/strategies can be implemented as a set of
scenario-specificparameters and rules to affect agents’ movement. Due to thelarge number of
parameter/rule combinations, it is necessary to automate the process of searching for the optimal
combination togenerate the desired effect on crowd dynamics.
6. Conclusions
The study provides an analysis of the ways and resources for creating context-oriented mobile
location-based recommender systems for social distancing route planning in urban area. The approach
to building location-based recommender systems is proposed. It allows considering the context data in
calculation of the risks for crowd appearance process. That provides better quality of
recommendations and gives the opportunity to create recommendations for safe trip in the city in real-
time. The prototype of the system is designed to provide a quality interactive trip support for the user
during his moving. Preventing unexpected and unplanned crowds is currently a pressing issue for the
health of urban residents. The use of data on the movement of city residents, taken from their
smartphones and gadgets, as well as data from cellular stations will be useful to optimize the walking
route with the provision of social distance. The previously listed data sets belong to the category of
big data. Therefore, it is information technology for big data analysis and smartphone functionality
should be the basis for the development of information support projects for social distancing. The
main features of decision-making on passing and changing the route are analyzed. Technologies for
determining the danger of crowding and managing human flows have been studied. The study
considers the possibility of creating software to manage the flow of people within the city to ensure
social distancing. A prototype of a mobile application has been created, which will be used to teach
the multi-agent system the peculiarities of the space-time behavior of pedestrians during quarantine.
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