Loneliness and social isolation are subjective measures associated with the feeling of discomfort and distress. Various factors associated with the feeling of loneliness or social isolation are: the built environment, long-term illnesses, the presence of disabilities or health problems, etc. One of the most important aspect which could impact feelings of loneliness is mobility. In this paper, we present a machine-learning based approach to classify the user loneliness levels using their indoor and outdoor mobility patterns. User mobility data has been collected based on indoor and outdoor sensors carried on by volunteers frequenting an elderly nursing house in Tampere region, Finland. The data was collected using Pozyx sensor for indoor data and Pico minifinder sensor for outdoor data. Mobility patterns such as the distance traveled indoors and outdoors, indoor and outdoor estimated speed, and frequently visited clusters were the most relevant features for classifying the user’s perceived loneliness levels.Three types of data used for classification task were indoor data, outdoor data and combined indoor-outdoor data. Indoor data consisted of indoor mobility data and statistical features from accelerometer data, outdoor data consisted of outdoor mobility data and other parameters such as speed recorded from sensors and course of a person whereas combined indoor-outdoor data had common mobility features from both indoor and outdoor data. We found that the machine-learning model based on XGBoost algorithm achieved the highest performance with accuracy between  Loneliness and social isolation are prevailing especially in the aging population. These two factors are considered as a major public health concern for the elderly people. Although loneliness is not a permanent condition, it can also be chronic in nature . If not Our previous work in  introducConnections between loneliness feelings and mobility patterns have been previously analyzed in  based onThe main goal of this study has been to find correlations (or features) relating the sensor-harnessed data to quantitative indicators or metrics which play an important role in identifying the loneliness or social isolation. For this purpose, two main loneliness metrics were identified based on our previous work in : UCLA scThe rest of the paper is organized as follows: research questions being addressed in this study are described in The objective of this study was been to explore relationships between the mobility patterns collected automatically via indoor and outdoor sensors and the metrics reflecting feeling of loneliness or social isolation of elderly. This study relies on using a machine-learning based approach on indoor and outdoor sensors data for classifying the loneliness levels of a person. This paper has also focused on identifying which type of data, indoor, outdoor or joint indoor-outdoor could better classify a person’s loneliness level. Moreover, this study has also contributed towards identifying various mobility-related features which plays an important role towards loneliness level classification.Measurements were conducted with volunteers frequenting an elderly nursing house/community center in Tampere, Finland ?The four main research questions addressed by this study are:Answers to these research questions are summarized in This sections includes the details of the data collection process. The data were collected within a project run at Tampere University, called AISOLA . The datThe surveys were conducted twice: first in the so called pre-study stage  and the second one in the so-called post-study stage . In between the pre- and post-studies, the participants were given two sensors each to carry around (one for indoor and one for outdoor measurements). The sensors were given at successive times, not simultaneously, in order to avoid confusions: first, the indoor measurements were performed with the Pozyx sensors, and afterwards, the outdoor measurements were performed with the Pico Minifinder sensors.The pre-study survey, conducted in person, was organized in order to get a baseline profile of the individual participants and their existing levels of social isolation and loneliness through the Lubben Social Network Scale-6 (LSNS-6) and the UCLA 3-Item Loneliness Scale, described in the following subsections; the post-study survey was also based on a in-person questionnaire collecting feedback regarding the devices and the participants’ physical/emotional wellbeing. In between the pre- and post-studies, data was collected via indoor and outdoor sensors by the volunteer participants.The sensors were primarily utilized to collect the location data of the individual participants, while they were indoors or outdoors. Two separate sensor devices (with their own respective principles of operation) were utilized to collect the location data, in real-time and for the entire duration of the study. The reason to use different sensors indoors and outdoors was the fact that high accuracy is achievable with Global Positioning System (GPS)-based sensors outdoors and Ultra-Wideband (UWB)-based sensors indoors.https://minifinder.com/products/pico, accessed on 27 May 2022) sensors were used for monitoring/collecting the position and movements of each individual participant outdoors (using GPS). The monitored indoor spaces were the common gathering areas of the elderly in the communal nursing facility  and it was utilizing UWB technology from Pozyx . The indoor sensors could also be utilized to document the broad emotional status of the participant, at any given point (while they are inside the monitored area). The next sub-sections give details regarding the demographic profile, various types of data collected, the nature of the collected data, and certain limitations of the sensor systems.Pico Minifinders  and they were all female. However, we were able to gather sensor data from only five of them, as one of them was hospitalized during the study. All the participants were provided both the indoor and outdoor sensors. Most participants used a lanyard to carry the Pozyx tag and a key chain to carry around the Pico Minifinder. The participants were in the range of 68–87 years of age (with an average of 80.2 years), and were all mentally and physically capable to understand the requirements of the study and were fully involved.The pre-study survey consisted of a combined questionnaire for the Lubben Social Network Scale-6 (LSNS-6) and the UCLA 3-Item Loneliness Scale.The LSNS-6 is a tool used to document and quantify the social isolation among the older adults, by determining the number of contacts and the frequency of contact with their family members and friends, while also determining the perceived amount of social support received by the older adult from these sources . The queThe UCLA 3-Item Loneliness Scale is a simplified derivative of the Revised UCLA Loneliness Scale (R-UCLA test) which is renowned for its efficacy in quantifying and documenting loneliness in individuals, across multiple demographics and sample sizes . The UCLThe post study survey collected information from the participants of the study regarding their physical and emotional state during the study, device usability and ease of maintenance, general information regarding their hobbies, as well as some general feedback (which we could use to improve our next studies and practices with the next sample size). On device usability, the participants ensured that they carried the indoor and outdoor devices with them at all times as mentioned during the information session held with them, while giving out the devices. The caregivers at the elderly home also played a role in reminding the participants to carry the devices with them. In addition, there were regularly scheduled weekly calls with the participants to follow up on device usage as this was very critical to obtaining quality results.The participants mostly interacted with the indoor device, whose button they had to always push to reflect their current mood . On the other hand, there was less interaction with the outdoor device as the participants only had to carry it around to track their locations at all times. To ensure the quality and appropriateness of the data gathered, the indoor and outdoor were collected continuously over a period of one month. The indoor data were collected at a frequency of 8 h per day and outdoor data, 24 h daily. With this, an adequate amount of indoor and outdoor data were collected to build the machine learning models for predicting loneliness in the elderly.The post study interview questionnaire can be found in The indoor data were collected by utilizing the indoor positioning systems operating based on UWB, which is a wireless radio technology having a wide spectrum of 500 MHz and offering position accuracies indoors of up to 10 cm . AdditioThe hardware aspects of the Pozyx system comprise an interactive tag , the anchors , and the gateway .https://www.pozyx.io/products/hardware/kits, accessed on 27 May 2022) are shown in The hardware components which are utilized in the existing Pozyx setup  technology and they are connected to a PoE switch, to provide the anchors power and to enable the connection of large number of anchor branches to the gateway (to minimize the cable and LAN port consumption). X, Y and Z coordinates. This could provide valuable information regarding the speed of movements which could help build the mobility profile of the participants. The position and accelerometer values of the individual tags are updated at a high rate of The interactive tags carried by the participant transmit their Cartesian coordinates along with the device status (representing the participant mood in this study). The device state can be changed easily  with the push of the only button on the tag, and the tag can have three states (which can be cycled through by pushing the button). The three moods identified by the study are ‘Neutral, Happy and Sad’. The tag has an LED indicator which provides visual feedback to the participant regarding the state of the tag: green was associated with neutral; blue was associated with happy; and red was associated with sad. The state and its corresponding LED color was labelled at the back of the tag, for the convenience of the participants as a reminder. The interactive tags also have inbuilt accelerometers which provide acceleration data across The gateway processes all of the information and has provisions to provide analytics of the collected data to visualize heatmaps, anchor-tag participation, tag tracing, etc. However, for the requirements of the study, we utilized the capability of the gateway to publish the real-time location, emotional, and acceleration data of all the participant tags to the cloud. By utilizing the MQTT (Message Queuing Telemetry Transport) protocol, which is a lightweight network protocol used in locations with resource constraints, we can access the positioning data uploaded to the cloud and the newest entries are recorded as soon as they are made. The real time data entries are all in JSON (Javascript Object Notation) format and are collected into .txt files on a day-to-day basis (data collection for a particular day begins at 09.00 h and ends at 17.00 h). There is a dedicated computer which automatically begins and terminates the data collection for the indoor data. Each individual entry is a combination of JSON objects and arrays representing the information of a single tag, which is obtained from the Pozyx cloud through an MQTT subscribe protocol. A sample JSON message is shown in The limitations for this particular positioning system are minimal; they are as follows: only indoor location data with a local coordinate system could be collected and the participant must physically carry the tag to all places within the built environment. However, the tags are very compact (50 × 42 × 15 mm) and lightweight (21 g) and do not require any charging for the battery for the duration of the study, thus they are very easy to maintain.https://go.minifinder.com/, accessed on 27 May 2022). The Pico MiniFinder is supported by a server-based software (MiniFinder GO), which monitors the GPS trackers. The captured real-time location data are then processed, stored, and presented on a map.The outdoor data were monitored and collected using the Pico MiniFinder, which is based on the Global Positioning System (GPS) . The PicThe hardware components of the outdoor monitoring system consist of an outdoor tag, which is very light (35 g) and carried around by the participants. It can be attached to keyholders or worn around your neck with a lanyard or inside a handbag or pockets, while going out. The main function of the outdoor sensor is to to monitor the location of the participants when they are doing activities outside the elder care facility. The outdoor tag consists of components such as a panic button, microphone, speaker, charging contact pins, call button etc. as shown in In addition, different alarms such as Geofence, panic alarm, fall alarm and low battery alarm can be configured on the MiniFinder GO app to become alerted when any of the situation arises. A notification for a low battery alarm is shown in Following the tracking of the outdoor devices, the real-time location data of the outdoor devices are processed and stored in the cloud. They are presented in NMEA format and downloaded as .txt files on a daily basis as the data collection runs for 24 h. The location data collected consists of the timestamp, longitude, latitude etc. The location data are downloaded from the MiniFinder web portal. $GPRMC,200637,A,6128.2257,N,02346.8965,E,0.54,126,280522,,The data are presented in as GPS Mobile Recommended Minimum Configuration (RMC) sentence ($GPRMC) sentences, shortly known also as the Recommended minimum specific GPS/Transmit data. The structure of the recorded entry, detailing the first line of the entry in A limitation of the Pico MiniFinder is the constant need to charge the batteries. Another limitation is associated with the fact that it is dependent on GSM networks, which means that location data is not transmitted when there is no GSM coverage.Machine-learning algorithms are used in various classification, prediction, clustering applications ,16,17,18x-y-z coordinates of each person, cluster or zone in which the user was present, and the emotional state as manually defined by each user . The collected outdoor sensor data consists of attributes such as time, date, speed, latitude and longitude, the course of the person which is direction of progress of a person, between two points, with respect to the surface of the earth. Data related to person’s perceived social support and feeling of loneliness as well as social isolation was also collected using the UCLA loneliness scale and Lubben’s social network scale based on the pre-study survey. Before training the model using the indoor or outdoor data or both, it is important to pre-process the data, to extract meaningful mobility patterns and to understand the characteristics and associations between these patterns. We explored research questions RQ1 and RQ2 mentioned in The machine-learning algorithms are highly dependent on the available data. The cleaner the data are, the more accurate the model and its evaluation matrix are. Prior to feeding the data into our machine-learning models, it is also important to observe the distribution, patterns, trends, or features in the data through statistical analysis or visual representations, such as scatter plots, bar plots, histograms etc. This process helps in identifying the association and main characteristics in the data. This approach of performing initial investigations is known as exploratory data analysis. Exploratory data analysis is followed by a feature-engineering process. Feature engineering includes feature extraction, feature selection, or feature elimination methods. For example, the feature extraction is required to identify the time-domain and frequency-domain features from the continuous data. Similarly, feature selection methods such as wrapper-based methods and filter-based methods can help to select the most relevant features. Based on the correlation between the variables, related or redundant features can be eliminated from the data. This feature engineered data are then given to the machine-learning algorithm to generate the model. The indoor sensor data collected for this study consists of various attributes such as timestamp, accelerometer data of person, Converting date and time information into “YYYY-MM-DD HH:MM:SS” format.Extracting relevant attributes from the sensor data. This includes eliminating the metadata from the raw sensor data such as error information, version information, message identifier (ID) etc.A step-by-step process based on To understand the behavioural context of a person, certain parameters need to be extracted from these indoor and outdoor data which will represent person’s mobility patterns. For the available data, we extracted several parameters such as estimated speed, distance travelled, frequently visited spots, time spent at those spots, etc. This subsection provides the description of the methodology for identifying the clusters.By means of visual representation, this subsection also provides an overview of the average time spent by users in clusters.Identifying clusters or frequently visited placesFor indoor data, the clusters or frequently visited places indoor were recorded by the sensors. For outdoor data, the clusters or frequently visited hotspot information were not recorded by the sensors. Outdoor sensors recorded only the information about the latitude and longitude of a person. We utilized this information to identify ourselves the clusters or frequently visited places outdoors. We used k-means clustering algorithms to identify each user’s frequently visited areas. These clusters or frequently visited places were recognized better after plotting the data on the maps. Based on the information obtained from the mobility patters, we performed exploratory data analysis to explore the behavioural context of the users. We summarized the information obtained from this analysis in the form of bar plots. The bar plot represented in Average percentage of time spent by users in indoor and outdoor clustersTo obtain the average percentage of time spent by users, we used the cluster information and the days and times the user visited those clusters. For analyzing and visual representation of the average time spent by the users, indoor data were available from five users whereas outdoor data were obtained from six users. Users IDs are from 1 to 7, with some users missing, as initially 7 participants were enrolled to the trial, but one participant was retrieved before the process started, and a second participant was retrieved  after the outdoor data was collected, but before the collection of the indoor data. So, for indoor data from user 1, 2, 3, 4, 7 were used and outdoor data from user 1, 2, 3, 4, 6, 7 were used. The average percentage of time spent by users in different places indoors and outdoors is represented by bar plot as shown in For indoor clusters, all five users—whose indoor data were being collected—visited up to four places frequently, marked by Place1, Place2, Place3, Place4 . They visited these places on certain days. The bar plot in From the outdoor data and the computed clusters, we observed that all the users have been visiting to multiple places as per their interest and so overall, there are quite many clusters identified outdoors. Some of these clusters are common between the users. The bar plot in We also compared information obtained from the indoor and outdoor mobility patterns with the Lubben score of the users. Lubben score obtained from 6-item social network scale tells about the social engagement of the user. The higher the score, the better the social engagement of the participant is. We observed that user1 and user2, who have less mobility indoors and outdoors than the other three users, also have lower Lubben scores. User3, who has better mobility patterns indoors and outdoors than user1 and user2, has good Lubben score. However, user4, who has better indoor mobility than user1 and user2, but lower outdoor mobility, has intermediate Lubben score values. We also observed that for user6 and user7, who have better indoor and outdoor mobility that user1 and user2, have very low Lubben score values. From the above information, it can be inferred that the mobility patterns can be associated to some extent with the social engagement of the person and hence the feeling of loneliness. Also, the average time spent in multiple places indoors and outdoors can also be used as one of the important characteristic to understand the mobility of users and this can help to understand the user’s behaviour such as social engagement, feeling of loneliness, etc. For a better understanding of the findings from the exploratory data analysis we have performed the classification task on the users’ mobility pattern using machine learning algorithms, the results of which are presented in Feature-extraction methods refer to the process of extracting relevant numerical values from the raw data. For large amount of continuous data collected from the sensors, it is required to sample the dataset into small window size and to extract the relevant features out of it. The feature extraction is followed by the feature selection methods. Feature-selection methods can help to improve the performance of the model by selecting the most relevant features of the model. This subsection provides the information about the extracted features and the selected features from indoor and outdoor data for the classification task.Indoor data feature extraction and selectionThe raw data collected from the indoor sensors consist of attributes containing information about the timestamp, user ID, user’s positional coordinates, three axis accelerometer data, cluster ID, cluster name, emotional state of person, additional metadata related to error information, latency, communication success rate, etc. After initial pre-processing on the various attributes, raw data containing accelerometer values, user’s positional coordinates, cluster information, and emotional state were considered further for feature extraction. User’s position coordinates were used to calculate the distance and the speed of the user. Raw accelerometer values in three axis were transformed to extract the statistical features from them.We used a windowing technique with a window size of 10 s and we applied feature-extraction methods on that window size. Various statistical features such as mean, median, standard deviation, kurtosis, skewness, number of peaks, energy of signal, signals magnitude area, and average resultant acceleration were extracted from the three axis accelerometer data. The average value for these transformed statistical features were taken over the considered window period. Features such as traveled distance and speed, calculated from the position coordinates were also averaged over the window period. For categorical variables such as cluster ID and emotional state of person, the most frequent value in that window period was taken. This transformed feature vector consisting of statistical feature, average distance, average speed, cluster information from all windows was formed by appending the different individual features. This transformed dataset (or feature vector) was given as input to the machine learning algorithms for further analysis.Outdoor data feature extraction and selectionx, y, z coordinate information were further used to calculate the distance and the speed of the user.The raw data collected from outdoor sensor consist of attributes containing information about the message header, UTC  time, status tag to indicate if the data is valid or not, speed over ground in knots, latitude and longitude information, course over ground, north/south indicator, east/west indicator. In outdoor data, the information about the frequently visited places or cluster was not automatically recorded by the sensors, unlike in the indoor measurements. As mentioned in previous subsection, we extracted the cluster information from the latitude and longitude attributes using k-means clustering algorithm. To extract the features such as distance and the estimated speed, latitude and longitude information was converted into Cartesian coordinate system. The For outdoor data, a smaller window size was taken to obtain the transformed dataset since the time difference between the two consecutive values was 10 min. We used a similar methodology as used for indoor data in order to obtain the complete feature vector. Here, we used the window size of 40 min. However, due to the difference of 10 min. between consecutive values a particular window consisted of 4 values. We calculated the average distance, average estimated speed, speed (converted from knots to meter/second) and average course over a window period. For a categorical attribute such as the cluster ID, we used the most frequent value in the window. The values from multiple windows were appended and the transformed outdoor dataset (or complete feature vector) was further given as input to the machine-learning algorithm.Indoor and outdoor feature fusionFeature fusion combines the features obtained from the different sources into a single feature set. To obtain the loneliness prediction accuracy for the combined indoor and outdoor mobility patterns, we applied a feature level fusion. The main challenge for indoor and outdoor feature level fusion was to take the features from the same user on same days. Only the features common to both the indoor and outdoor data were selected. The selected features were: the estimated speed, distance, and indoor cluster ID from the indoor data and the estimated speed, speed over ground, distance, and the outdoor cluster ID from the outdoor data. For selecting the relevant features, we calculated the correlation matrix.It was observed from the correlation matrix of all users that the indoor distance and indoor estimated speed were highly correlated. Estimated speed and speed over grounds from outdoor data were also correlated. Similar behaviour is shown from the example correlation matrix for two users, in The combined dataset from the indoor and outdoor data was given as input to the machine-learning analysis and we found that the best performance was achieved with the indoor distance used as a feature extracted from indoors and with the speed over ground used as a feature extracted from outdoors.In here, we focus on using machine learning algorithms for the classification of loneliness risk of elderly using the indoor and outdoor sensor data. This subsection describe in detail about the machine learning methods being used. In the context of this study we performed three types of classification tasks.Classification of users (user1 to user4 and user6 to user7) based on their mobility patterns and other extracted features.Classification of users into three labeled classes, having high risk of loneliness, medium risk of loneliness, and low risk of loneliness with labels assigned based on the UCLA score of the user as mentioned in Classification of users into three labeled classes, having high risk of loneliness, medium risk of loneliness, and low risk of loneliness with labels assigned based on the Lubben score of the user as mentioned in These three types of classification were performed on indoor data, outdoor data, and an indoor/outdoor combined feature set. In order to identify the user’s risk of loneliness, different types of data were used. The data obtained from indoor and outdoor sensors were continuous in nature, having a floating point data type. Additionally, the variables representing the clusters and user’s emotional state were categorical in nature. Input data consisted of features from indoor, outdoor and combined indoor-outdoor data. Features used for loneliness classification using indoor data were statistical features such as mean, median, standard deviation, kurtosis, skewness, number of peaks, energy of signal, signals magnitude area, and average resultant acceleration from accelerometer data, mobility features such as traveled distance and speed, cluster ID and emotional state of person refer Random forest is a type of ensemble algorithm using bagging technique with base estimator as decision trees. It has certain parameters such as nestimators, criterion, max features, max depth, min sample split, min samples leaf, max leaf nodes etc. For all the three classification task we have kept the default values for the hyperparameters except nestimators, which tells about the number of trees. Similarly XGBoost is also a type of ensemble algorithm which uses gradient boosting technique of combining weak learners to form strong learners. Moreover, it also works well for unbalanced datasets and in this study the classes were unbalanced for indoor data. Hyperparameters in case of XGBoost were set to default values. Support vector machines on the other hand perform the classification by dividing the classes through hyperplane. One of the important parameters for support vector machines is the kernel which helps in determining the shape of the hyperplane and decision boundary. In this study we have used the rbf kernel. The input data mentioned above were given to these algorithms to perform classification into three levels of loneliness.How well the algorithm is able to identify the users based on the selected features from sensor data?How well the algorithm is able to classify the users into three defined loneliness levels (using the UCLA and Lubben loneliness metrics)?For the classification problems, the most commonly used evaluation criteria as stated by  are accuHence, to achieve a wider perspective on the relationship of loneliness levels to the mobility patterns, the extracted mobility features were given to tree-based ensemble algorithms such as XGBoost, random forest and to a support vector machine classifier and the algorithm performance was evaluated using an accuracy and confusion matrix. The results of the classification are presented in the next section.In the previous section, we identified different features that can be used to classify the loneliness levels of a person. We also explored the correlation of a user’s mobility pattern with their behaviour. In this section, we provide a precise description of the result of applying machine-learning algorithm for the classification of user’s loneliness levels based on their indoor, outdoor and combined indoor and outdoor data. This section is further subdivided into the three subsection, the first subsection describes the result of classification using indoor data features, the second subsection describes the result of classification using outdoor data features, and the last subsection describes the result of classification using combined features from indoor and outdoor data. A comparative analysis on the results of classification using different data is also presented in this section. Results of different machine-learning algorithms for the classification of loneliness levels using indoor and outdoor data are presented in The raw indoor data collected from the sensors were given to the feature extraction engine and for classifying the loneliness levels. We assigned three labels based on the score of user’s UCLA and Lubben scores. The labels symbolizes the output data. Details of input data and algorithms used are presented in The raw outdoor data collected from the sensors were given to feature extraction engine to extract the mobility related features such as outdoor estimated speed, distance travelled, frequently clusters visited, etc. We used two features obtained from sensors, they are speed and course over ground. From the other features available from sensors such as latitude and longitude we converted them into cartesian coordinates and derived features such as distance travelled. Other attributes such as datetime, north-south indicator, east-west indictaor, valid or invalid entry tag were mostly metadata. The outdoor data were available for six users. For classifying the user, the user ID was used as labels. For classifying the loneliness levels, we assigned three labels based on the score of user’s UCLA and Lubben score. The results summarized in To identify the correlation between indoor and outdoor data, we first tried to classify user and their loneliness levels by combining the indoor and outdoor mobility features. We combined features such as indoor and outdoor cluster information, indoor and outdoor distance travelled and estimated speed, outdoor speed over ground. Both indoor and outdoor data were present for five users. Based on the feature relevance information obtained from the indoor and outdoor data based classification, we selected indoor and outdoor cluster ID, distance travelled indoor and outdoor, outdoor speed over ground as features for the classification. The result of classification is summarized in We also tried predicting the indoor and outdoor mobility patterns by training the machine learning models on either indoor or outdoor data and vice versa. To explore this possibility we trained XGBoost regressor with the indoor distance travelled, indoor estimated speed of one user. We tried predicting outdoor distance travelled and outdoor speed of the user. We found that for all the user’s data, the root mean square error values and the mean absolute error value were good, but the r-squared value which tells about the model fit was negative. This showed that the model was leading to worst fit than horizontal line. We also explored whether we can identify the user by training the model using either indoor or outdoor behaviour. We trained the model using indoor distance travelled, indoor estimated speed of all the users and tried predicting the user using outdoor distance travelled and outdoor speed. The accuracy of classification was In this paper, we examined various machine learning algorithms to identify the user’s level of loneliness using a Pozyx sensor for indoor data and Pico Minifinder for outdoor data. We identified different indoor and outdoor mobility patterns that can be used as features to identify the loneliness levels and user’s characteristics. This study also explored the association between the user’s mobility patterns and their UCLA, Lubben scores. We also studied, if it is possible to predict either outdoor or indoor mobility patterns by training the model using either of indoor and outdoor data.Exploratory data analysis on the indoor and outdoor data revealed that average time spent indoors and outdoors can be considered as an important characteristic for understanding a user’s mobility and these characteristics have some positive correlation with the Lubben score. We also found that to identify user’s characteristics or loneliness levels, indoor and outdoor distance travelled, indoor and outdoor estimated speed, frequently visited indoor and outdoor places, statistical features from accelerometers are some the mobility patterns which can be used as features for machine learning models. Machine-learning algorithms such as support vector machines, random forest and XGBoost were used for classification, and a evaluation criteria such as accuracy and confusion matrix was employed. Results from the machine learning study revealed that XGBoost performed better among all the other algorithms for classifying the user and their loneliness levels. XGBoost performed well with indoor, outdoor and combined indoor-outdoor mobility patterns with its accuracy range between This study showed that it is possible to predict user classes based on metrics related to loneliness and social isolation with significant accuracy using indoor, outdoor, or combined indoor-outdoor sensor data and machine-learning algorithms. However, there is an as-yet unexplored-yet possibility of increasing the classification accuracy if data from more users were available. This could also help with developing more generalized models. Another open research issue is to further explore the mobility-specific information with additional sensors  to be able to establish a better association between the indoor and outdoor data belonging to the same user.


This Topical Collection of European Transport Research Review includes a selection of papers presented at the 48th European Transport Conference (ETC), organized on-line by the Association for European Transport (AET), from September 7th to September 9th 2020. The ETC is a major annual event where European transport practitioners and researchers come together to keep abreast of policy issues, research findings and best practices across a broad spectrum of transport topics: from advanced modelling for passenger and freight transport to appraisal methods; from sustainable planning to public transport and rail case studies. Uniquely in Europe, the Conference provides a forum for those engaged in research, policy and business in transport, bridging the gap that often arises between theory and practice.This Topical Collection on Inclusive and Collaborative Transport brings together a number of contributions to reflect on how to promote an effective and sustainable transition toward new forms of mobility, including advanced level of automation and digitalization. In fact, thanks to the digital transformation, new transport modes and mobility services are now available  or will be available in the future , paving the way to a wider range of options to improve accessibility to places nowadays accessible only by private transport. Moreover, this creates the opportunities to supply safer and more suitable transport services also for users with limited access to transport .However, as the complexity of the transport system increases on both the demand and the supply side, new challenges arise for transport researchers, planners, and developers. For each innovative solution that succeeds, many concepts fail at different development stages or even after deployment, because they were not technically feasible, economically viable, environmentally sustainable, or simply because they were ahead or behind of their time , 42. MorThis Topical Collection includes 11 papers that can advance the research on collaborative approaches and inclusive solutions for transport systems, exploring new fields and proposing up-to-date policy recommendations to govern the transition towards future sustainable mobility scenarios from a strongly car-dependent society.According to the Eurostat , the modThese figures show that the comfort and flexibility of private cars still make them very attractive for those who can afford ownership costs , despiteTaking the example of shopping, two papers included in this TC established a direct connection between their findings and car dependence. Rokseth et al.  noted thBönisch et al.  also ackAlthough this Topical Collection does not include any paper that addresses the impacts of the COVID-19 pandemic, as the research on this topic was still at its early stages at the time of the submissions to the 48th ETC, it is now becoming evident that such disruptive event changed the way we live, work and commute, with effects that may last for a long time . When thThe lack of infrastructure  has always been identified as one of the major causes of car dependency, and therefore of social exclusion and inequitable access to opportunities . However, also in areas with good endowment of metro and tram lines, it has been observed a persistent predominance of car use due to the increasing complexity of travellers’ trip chains that requires flexible transport services and seamless door-to-door travel solutions for multiple origin and destination journeys.In this Topical Collection the accessibility problems have been studied at very different scales of analysis. Mocanu et al.  developeThe analysis of travel behaviour can also be useful to infer accessibility gaps. In this sense, Ek et al.  conducteIt has been observed  that infUndeniably, digital services and technology innovations have the potential to attract new users and make life easier for those who, for some economic, environmental or physical reason, face some sort of transport-related exclusion . HoweverIn this Topical Collection, there are three papers that approach the preferences of the users and the new risks introduced by emerging forms of transportation. Stam et al.  analysedIn turn, the research of Soares et al.  and StröStrömberg et al.  developePrivate cars remain attractive for a large share of the population who can afford their costs, mainly due to comfort and flexibility benefits and/or poor access to alternative modes. In fact, car trips represent more than 80% of all land transport (measured in passenger-km) in the EU. This value has remained stable over the last 20 years, despite the proliferation of new transport options and digital services with potential to avoid many trips.The COVID-19 pandemic brought a new reality that increased the gap between the high and the low ends of social stratification, also in relation to the transport mode choice. In fact, it has been observed that the reduction of car trips has been entirely recovered in only 1 year, whereas public transport trips have not. Passenger volumes by car  have started to raise again, and, as the pandemic is still evolving, the long-term impacts on the transport sector are uncertain.Accessibility gaps are ubiquitous at all scales. The availability of public transport infrastructure to cope with the increasing complexity of people’s daily activities is still deficient, especially in rural and suburban areas. Emerging on-demand riding services and the availability of shared vehicles, such as cars, bikes, and micro-mobility ones, have a potential to bridge up the gap with car-based services, provided that these would be kept affordable for all, and potentially capturing users from more sustainable modes .Emerging big data storage and processing tools have been consolidating an integrated analysis of different transport modes, with a potential to improve tactical and strategic transport planning actions oriented to shift behaviours toward more sustainable choices. Younger generations may be supportive of new technologies but are not necessarily giving up on driving. There are still major safety concerns towards AVs regarding their interactions with the human user and their capabilities to deal with complex traffic situations, especially for intermediate levels of automation. Once the safety problems are solved, the integration of AVs in the transport ecosystem will be quite challenging over fears of increased congestion, pollution and competition with more sustainable modes. The potentially high costs of technology and the difficulties of users in understanding/using it also raise concerns of social exclusion.


Safety barriers are widely accepted in various industries as effective risk management tools to prevent hazardous events and mitigate the consequences caused by these events. Studies on safety barriers have been increasing in recent decades; therefore, the general idea of this article is to present a systematic review of the field. The purpose of this article is threefold: (1) to map various networks for the barrier-related articles collected from WoS; (2) to summarize the advances of the safety barrier at both the individual level and barrier management level on the basis of six issues, and (3) to propose the research perspectives associated with safety barriers considering the latest theories and methodologies in the field of safety management. Based on the findings and insights obtained from the literature collected by a bibliometric and systematic review, studies on barrier management within the complex socio-technical system are analyzed, and the framework of “risk-barrier capacity” is proposed for future development, in which the challenges stemming from industrial intelligence may be solved through resilience theory. Meanwhile, intelligent technologies are also able to serve as health status monitoring devices for various barrier elements. Over the past decades, a growing interest in risk analysis and system reliability has been observed in various industries. The risks are not accepted to some extent within the framework of safety engineering, in which hazards are strictly mitigated. As a result, inherently safe design is popular to mitigate the potential risks involved in operational actions , especiaThe wide application of safety barriers in various industries can be found in the existing literature. As early as the 1960s ,12, the This paper is mainly aimed at addressing the issues associated with safety barriers in the areas of definition, classification, research topics, advances, and future perspective studies. For this purpose, CiteSpace is applied in this study to implement bibliometric analysis and research mapping. There have already been several review articles on the topic of safety barriers; for example, Sklet  performe(1) This study is the first to apply bibliometrics to map academic networks for the topic of safety barriers and barrier management in terms of authors, affiliations, citations, co-occurrences and co-citations.(2) The issues of barrier management involved in industrial practice are identified on the basis of the proposed safety barrier management system.(3) The research perspectives in this paper bridge the gaps between academic research and industry application by the proposed safety management framework of “risk-barrier capacity”.The remainder of this paper is organized as follows: bibliometrics and research mapping for safety barriers will be discussed in Web of Science is selected as the source of the dataset collection because most high-quality and important scientific productions are indexed in this database defences”Ringdahl . AccordiRingdahl , safety Based on the definitions in The function of safety barriers is closely related to barrier performance; therefore, the issues associated with the assessment of safety barrier performance are widely considered in the fields of industry and academia. In the implementation of the aforementioned ARAMIS project , safety According to the existing literature, many methodologies have been proposed to evaluate safety barrier performance by a limited number of parameters or indicators, as shown in (1) Effectiveness is widely accepted as a fundamental indicator to assess safety barrier function ,64,76. KThe methodologies determining the effectiveness of safety barriers are mainly based on the performance data of the system comprising the objective barriers, and in most cases, operational management, system statement and maintenance are also involved . Accordifound in ,81, who (2) Availability is another widely accepted parameter to describe safety barrier performance, especially for barriers that are active, as discussed by . AccordiPFD) is widely utilized to describe the possibility that the system comprised of barriers is unavailable when expressing the safety function is required PFD) is wrding to , φj,i isproaches , and in proaches , while shes [PFD . It shouhes [PFD  argued tGenerally, the definition of reliability or robustness is closely related to availability, and in most cases, the availability of safety barriers under fluctuating conditions or assumptions changes when referring to the reliability of objective safety barriers ,73. A sa(1)Modeling safety barrier performance evaluationAccording to the existing literature, the studies associated with safety barrier performance assessment at the individual level are mainly concentrated in the field of SIS systems, domino effect events and Natech scenarios. Even though different application scenarios are used, the aspects or indicators used for the assessment are mainly limited to the aforementioned effectiveness and availability. Many methodologies are applied to evaluate the safety barrier performance according to the existing literature, for instance, the well-developed framework of LOPA is aimed at assessing barrier performance by analyzing the independent protection layers ; howeverMore recently, the availability-effectiveness methodology was proposed by Landucci et al.  for the η, while In (2)Modeling safety barrier degradationHarsh or adverse conditions or events may deteriorate the performance of safety barriers or critical safety elements, regardless of whether they are active or passive ; as a reBased on the framework illustrated in The design of safety barriers theoretically prevents hazardous events and mitigates the consequences of accidents; however, the roles of various barriers are not correspondingly systematic and stringent in practice. As a result, accidents still occur even though safety barriers exist . An examFrom the engineering perspective, in most cases, the safety of a system or infrastructure is successfully maintained with the comprehensive application of various barriers, as discussed by Kjellen . The des(1)Barrier elementsThe safety barrier system, as illustrated in To date, the definition, function and classification of barriers or barrier elements have been studied extensively, as discussed in (2)Barrier management systemAccording to PSA, barrier management has been regarded as the main priority because accident investigation clearly indicates that the failure and weakening of barrier elements are the principal contributing factors to accidents . There iThe practical activities associated with safety management have proven that all the barrier elements are related to human factors , and in (3)Barrier management evaluationAccording to the perspective of safety management, the design and implementation of safety barrier management systems should be an integrated part of safety management . As illuAfter the establishment of a barrier system, dynamic barrier management should be developed and implemented because the performance and function of barrier elements involved in this system may be degraded or influenced by external environmental conditions and the internal factors within the barrier elements themselves. Essentially, dynamic barrier management is aimed at preventing the degradation of barrier elements and repairing degraded barriers . For thiThe popularity and acceptance of safety barriers and barrier management in both industry and academia are mainly due to their applicability in risk reduction and accident prevention. Barrier management can certainly be regarded as one of the advances in the field of safety management, and barrier-based diagrams have proven to be a useful tool in documenting safety measures adapted to prevent accidents . As the The potential of safety barriers to manage risk allocation before and after accidents is developed and put into industrial practice in the form of safety-oriented projects. In this paper, these projects are reviewed and analyzed hereinafter.(1)Accidental risk assessment methodology for industry projects.The ARAMIS project was co-funded by the European Commission with the objective of satisfying the requirement of the SEVESO II directive. This three-year project was launched in January 2001 and ended in 2004. One year later, the methodology proposed in the project was applied in the industry. Within the ARAMIS project, there are mainly six steps involved in implementing the risk assessment in the decision-making process .The first step is to identify all the major hazardous events involved in the process industry, during which the Bow-tie diagram is developed with the integration of fault tree analysis and event tree analysis. In most cases, the identification of critical events for specific scenarios is emphasized in this step, and many probabilistic-based methodologies can be utilized here.The second step focuses on the identification of safety barriers. In this stage, the safety barriers are defined by their function, performance, classification, and level of confidence. Notably, the performance monitoring and assessment of safety barriers are considered important and need to be studied.The third step is to evaluate the safety management efficiency to barrier reliability. Within the ARAMIS project, the existing safety management system and safety culture are assumed to influence the reliability of safety barriers; therefore, a process-oriented audit protocol is embedded in the ARAMIS procedures to review the activities relating to safety barriers.In the fourth step, the reference accident scenario (RAS) is defined and identified. Usually, the RAS refers to the initiating events that cause critical events; in some cases, the terms trigger events are also used to describe the RAS. The specific severity index for RAS can be quantified with reference to .The fifth step is to map the risk severity of reference scenarios based on the results of risk severity assessment. Risk severity is represented geographically by a combination of the frequency level and intensity effects. Finally, risk severity can be mathematically calculated by multiplying the frequency and severity index obtained in the fourth step.The last step in ARAMIS is to evaluate and map the vulnerability of the environment independently of hazardous events, which is beneficial for local authorities to take measures to reduce the global risk level, perhaps neglected by the operator on site. Global vulnerability is actually a linear combination of each target vulnerability, which is determined by the concerns of all stakeholders.(2)The barrier management project launched by DNV GL.barrier management in operation for the rig industry—good practices”, which aims to increase the understanding of barrier management at the management level and operational phase for both onshore and offshore [Almost at the same time as initiating the ARAMIS in 2001, the DNV GL collaborated closely with Statoil to implement a program named the technical condition of safety barriers (TTS), which is mainly aimed at monitoring the identified key safety barriers . In the offshore . In this(3)Standards (generic and industry) and guidelines associated with barrier management.The development and application of safety barriers are taking place continuously at a rapid pace, and the objective of barrier management is to harmonize the various safety barriers in an orderly way; as a result, the anticipating functions of barrier systems can be successfully maintained. For this purpose, many nonprofit organizations and authorities, including but not limited to standardization organizations, industrial associations, industrial committees and industrial authorities have offered standards and guidelines, both generic and industrial, in recent decades. These standards and guidelines are beneficial for practitioners in terms of barrier application and management. In this paper, some of the standards and guidelines associated with barrier management are listed in Guidance for barrier management in the petroleum industry” was published by Hauge and Øien [Human Factors in Barrier Management” guideline. The highlights in this publication are represented by (1) the proposed principle to determine whether a safeguard is a safety barrier element, (2) the performance standards for human-related barriers, and (3) the management procedures designed for human-related barriers.It can be seen from and Øien  from SINand Øien . In the and Øien ,83,138 tAlthough there are many documents associated with safety barriers and barrier management, such as standards, guidelines, reports, and research papers, many challenges still exist during the implementation of safety barrier frameworks in practice. Across various scenarios, different barrier-related aspects can hardly maintain consistency; for instance, barrier strategies, performance requirements, and operational procedures, in many cases, cannot harmonize comprehensive integration according to the audit results reported by PSA . Therefo(1)Issue 1: Lack of clear clarification for the boundary of safety barriersAccording to the existing literature or technical reports, nearly thousands of safety barriers or barrier elements have been proposed and defined . However(2)Issue 2: Role of human or organizational barrier elements in barrier managementThis issue is discussed under the assumption that human-related barriers are important components in the barrier management system, which is widely accepted at present. First, the distinction between human-related barriers and the PIFs for barriers needs to be clarified further. For instance, safety culture is considered a kind of barrier in some studies; however, is there any interaction between safety culture and other technical barriers? In addition, practitioners on site are frequently confused by the relationship between human-related or organizational safety barriers and standard operating procedures (SOPs). More importantly, in the case of the introduction of human-related barriers into safety management, an important issue that emerges is “how to cope with the relationship between barrier management and human reliability analysis (HRA)?” In this case, some concepts have to be recognized to obtain a better understanding of human-related barriers. For example, human errors or human-related mistakes in most cases are regarded as the causes or trigger events leading to incidents or accidents, as described in many accident models under the HRA framework; however, from the barrier management perspective, human errors are considered the consequence of human-related barrier failure, that is, human errors are results rather than causes. Unfortunately, to date, few studies have investigated the functioning of human-related barriers with reference to the HRA framework.The ambiguous understanding of human-related barriers is essentially determined by the difficulties of describing these barriers in the aspects of function, performance and the monitoring approach applicable for specific scenarios, especially in the case of the unavailability of required reliable data. Although CIEHF  tentativ(3)Issue 3: Integrating various safety barriers into existing safety managementIn the early application of barrier management, barrier elements were generally identified as technical barriers that could be described and evaluated quantitatively. However, the occurrence of accidents is a reminder that accident prevention measures should be comprehensive, especially after the Deepwater Horizon accidents. The Chemical Safety Board argued that with the necessary actions taken, serious consequences may be avoided , which eAt present, the safety management system is running well in most industrial companies, and the safety audit approaches are also standardized. Therefore, a common concern among industrial practitioners is how to map barrier management into the existing safety management system. In practice, it can be reasonably predicted that the introduction of barrier management would increase the complexity of the safety management system. Furthermore, most of the quantitative analysis approaches (QRAs) frequently used in traditional safety management are not applicable for barrier management because most of the nonphysical barrier elements cannot be described quantitatively. Another important concern about this issue lies in the compatibility of barrier management and planned maintenance . As ment(4)Issue 4: Dynamic assessment of barrier elements based on a system perspectiveAccording to the definition proposed by PSA , the purIt is essential to set a reasonable benchmark for dynamic barrier assessment; however, the determination of the benchmark for various barrier elements is not easy, especially for nonphysical barrier elements. In the guidance provided by PSA  and DNV According to the review of the collected papers, the studies associated with hardware barriers or technical barriers have far exceeded those associated with operational and organizational barriers. There is no denying that technical barriers alone are not able to respond to the expected functions of barrier management by industrial companies. Therefore, the development and improvement of barrier management must be implemented within the framework of a complex socio-technical system in which the role of human-related barriers, such as operational barriers and organizational barriers are given much attention to integrating other kinds of barrier elements. In such a complex system, the human-related components should be reviewed and analyzed thoroughly to determine which act as the safeguards and which can be attributed to barrier elements, based on which the definitions, functions, expectations, performance requirements and assessment approaches of human-related barrier elements need to be described. In addition, at the system level, traditional QRA methodologies may be inapplicable for complex socio-technical systems, and advanced technologies should be considered for barrier management system assessments, such as artificial neural networks, complex networks and data mining techniques. In the process of modeling barrier system assessment, the various barrier elements, including technical, operational or organizational elements, can be regarded as “agents” that facilitate the quantitative or logical description of potential interactions by means of developing interactive rules and algorithms. Meanwhile, the paravirtualization of barrier elements as “agents” is also able to trigger the initiation of positive human-related barrier elements by adjusting the barrier elements themselves to adapt to new or hazardous situations; this may be a potential research perspective of great importance in the near future.According to the discussion presented in Industry 4.0, also known as the fourth industrial revolution, mainly refers to the convergence of manufacturing with the digital revolution, artificial intelligence, the internet of things and smart devices . In this(1) The first aspect lies in the application of intelligent techniques to barrier management. The most feasible application currently may be the status monitoring of physical barriers by intelligent sensors, which is still in development . Intelli(2) The second aspect emphasizes the application of big data to characterize the barriers. The acquisition of real-time data associated with barriers is critical for barrier management ; however(3) The last aspect of the research perspective would focus on the application of barrier management defending the risks presented in the era of Industry 4.0. The risks within Industry 4.0 are characterized by complexity and uncertainty, which requires the comprehensive integration of various barrier elements, and the interaction between different barrier elements needs to be emphasized, as discussed in In a broader sense, the failure or performance degradation of components in the complex socio-technical system can be essentially regarded as a kind of abnormal status of the system, which is similar to the viewpoint of safety II ,156. TheAnother important aspect associated with barrier management is the health status monitoring of barriers. Then, early warnings for performance-degraded barrier elements could be given. As an important proposal, data processing algorithms should be embedded in the designed barrier management system. The data processing methodology for barrier element health status monitoring or assessment is expected to be developed based on advanced technologies, such as machine learning, artificial neural networks and data-driven Bayesian networks.Safety barrier management is one of few safety management frameworks with an industrial application, including the development of industrial guidelines, standards, and application software. In this article, a review of barriers and barrier management from the perspective of accident prevention was implemented by two different methods, namely, bibliometrics and a systematic literature review that were integrated to investigate the basic principles, advances and research perspectives in the fields of barriers and barrier management.The main body of this study is represented by three modules. First, the maps associated with barriers and barrier management are illustrated to analyze the various scientific networks, obtaining insights for networking and collaborations for the study of barriers by means of bibliometrics. Then, the advances in safety barriers are discussed on the basis of barrier research topics at the individual level and barrier management level. In this section, six aspects of safety barriers are reviewed in detail. Finally, five research perspectives for safety barriers are proposed. The general idea emphasizes the importance of nonphysical barrier elements, such as operational and organizational barrier elements, which should be studied from the perspective of complex socio-technical systems. As a result, the proposed “risk-barrier capacity” framework in this paper can be developed. In addition, the authors argue that the development of barrier management will benefit from the application of intelligent techniques and the framework of system resilience.


An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things–intelligent transportation system (IIoT-ITS). IIoT sensing technologies play a significant role in acquiring raw data. The application continuously performs the complex task of managing traffic flows effectively based on several parameters, including the number of vehicles in the system, their location, and time. Traffic density estimation (TDE) is another important derived parameter desirable to keep track of the dynamic state of traffic volume. The expanding number of vehicles based on wireless connectivity provides new potential to predict traffic density more accurately and in real time as previously used methodologies. We explore the topic of assessing traffic density by using only a few simple metrics, such as the number of surrounding vehicles and disseminating beacons to roadside units and vice versa. This research paper investigates TDE techniques and presents a novel Markov model-based TDE technique for ITS. Finally, an OMNET++-based approach with an implementation of a significant modification of a traffic model combined with mathematical modeling of the Markov model is presented. It is intended for the study of real-world traffic traces, the identification of model parameters, and the development of simulated traffic. Transportation plays a vital role in the daily lives of people. According to a prediction , by 2030Traffic flow is the intersection between roadside units, travelers, and vehicles. This information will impact the intelligent transportation system. Road networks become intelligent when they can predict traffic flow over time, and this resonates with how we live our daily lives. This information attracts researchers from a wide range of domains, including science, statistics, engineering, and machine learning.Due to its inclusion in microscopic models, traffic density is a measurement that is frequently utilized in ITS. To improve the effectiveness of intelligent transportation systems, the computation of traffic density is a crucial component of predictions used for warnings of systems, roadside networks, and people. The capacity of the driver to choose routes is enhanced by this knowledge. Consequently, a variety of strategies are employed in TDE, as shown in Traffic volume is a way by which the volume of traffic or number of cars traveling on a road section at a specific period of time can be found. There are numerous volumes studied in traffic engineering, including daily volume, hourly volume, and peak hour volume. Additionally, the volume of a day or hour might change significantly based on the day of the week or the hour of the day.What are the dimensions used for identifying traffic density estimations?What evaluation tools are available for analyzing suggested methods?How do TDE algorithms analyze various traffic behaviors to process the road network?How to choose a suitable approach to investigate an efficient traffic analysis technique?The following is how the research questions for the studies are put together.There are several ways by which to estimate traffic; however the techniques employed nowadays are more useful and frequently involve ML-recognized algorithms, which are typically used for real-time estimations. The Background section of We present the concept of a Markov approach for estimating traffic density in the road model and mathematical explanation, which illustrates the behavior of a single vehicle and depicts the traffic on the entire road network. We use the microscopic traffic simulator SUMO to build a traffic network with stochastic traffic demand and real values for traffic density in order to assess the effectiveness of the Markov traffic model. The SUMO-generated traffic demand is provided to the Markov traffic model, and the link state will change to the mode with the highest transition probability on each iteration. Because traffic conditions might fluctuate from day to day, it is highly convenient to estimate future traffic conditions based on initial traffic conditions. Due to the simplicity of this model, once it has been expanded, the public can use it to investigate traffic estimation on their local road network. The counts of each type of vehicle accessing a road segment are also shown as nodes 0, 1, 2, 3, and 4. The study’s main goal was to create an effective traffic analysis model that could be used to simulate traffic in a system of roads. To depict the traffic system, mathematical traffic flow models are also typically utilized. They are mathematical descriptions of the complicated traffic systems used to characterize and predict traffic behavior in the future. This research aims to develop a Markov-based mathematical method for estimating density in mixed traffic conditions.Vehicles increased traffic in cities enormously. These produce traffic bottlenecks, which cause air pollution, personal health issues, lost productive labor time, and economic losses. Because of this, and considering the inconvenience that traffic causes, managing traffic with intelligent systems and technology is necessary. As traffic density on a route is an excellent indicator, quick and reliable real-time estimation of traffic density is critical for traffic control. This research is being carried out by employing intelligent technologies to estimate traffic density. By utilizing the current traffic state as well as mathematical modeling based on the Markov process to predict the future traffic state while taking into consideration a predetermined road structure with a minimum of 0–4 vehicles and a length of 1 km that is connected to a distinctive roadside unit. In contrast to statistical analysis, these systems use artificial intelligence to determine traffic density. These are equipped with traffic data and used to estimate the traffic in real time. In this research, different types of conventional methods are compared, and a novel framework is proposed to examine the usefulness, appropriateness, and robustness of the proposed framework, which is analyzed through simulations.The structure of this essay is as follows: The backdrop of the research findings is presented in Numerous academics have made good progress in the past in increasing traffic control and analysis observations for traffic density analysis. For estimating traffic density, magnet loop detectors, and monitoring cameras are used. These traditional methods include wireless sensors, speed cannons for vehicles, roadside radars, and infrared counters. Magnetic loop detectors require a lot of hardwiring technology-based physical work. In addition, its setup and maintenance are highly expensive. Therefore, these technologies are inappropriate for use in large-scale and real-time applications. There are several approaches that can be employed to use the data gathered by cameras or sensors. Vision-based traffic monitoring mechanisms choose the images and videos to collate data. Studies of data extraction focused on traffic state estimation and modelling make use of stationary sources. To increase the functionality of traffic density estimation systems, many studies are discussed. For estimating traffic densities, three basic categories are typically utilized. By detecting each vehicle, such as a bus, bicycle, car, and truck, the authors of  proposedThere are shortcomings in the sensor-based estimation methods because of the sensor’s range limits. By increasing the range of these sensors, some of these shortcomings are removed on a small scale .Another method used for traffic estimation is based on vehicle trajectory detection . HoweverImage- and video-based detection methods are also used for detecting vehicles to achieve traffic density estimation. However, there are also limitations in the form of blur images and noises in the background. Many authors proposed using methods like receiver operating characteristic (ROC) analysis to overcome these limitations . ResearcThese magnetic loop detectors allow the detection of vehicles in heterogeneous, less-lane traffic. In , the autThe Kalman filtering (KF) and kernel density estimation methodology is likely the most extensively used technique for traffic density estimation. The authors of ,27,28,32The estimation of traffic parameters for a number of different methodological approaches is applied for the calculation of traffic density. In addition, research was conducted into the urban and highway-based estimating systems, as well as the relevant design strategies for TDE. The authors of ,29,30,31Traffic models are microsimulation models that are based on the representation of the behavior of each driver regarding many features’ car following, gap acceptance, and lane choice rules.Traffic density is somewhat correlated with neural network techniques, and artificial neural networks (ANNs) are a subset of artificial intelligence that may address complex problems in dynamic environments by providing clear and concise recommendations. In recent years, artificial intelligence (AI) has gained prominence as a viable solution to resource-constrained sensor networks’ complexity, scalability, and decision accuracy constraints. AI has been applied in a variety of applications, including MIMO networks, time series prediction, image processing, and WSNs. Authors of  proposedThe development of the IIoT and the use of ITS necessitate a series of procedures for architectural communication. This model depicts the V2X architecture in its entirety. Each vehicle has an onboard unit (OBU) equipped with wireless communication capabilities. The OBU, in particular, provides the interfaces required to estimate vehicle density by using data obtained via node to infrastructure communications. Infrastructure equipment, such as RSUs, is necessary to implement the V2I component of our system. Vehicles will benefit from RSUs in particular by receiving updated services. Additionally, the location of RSUs enables the cars to communicate with one another. A block diagram of the proposed model is shown in Markov is a mathematical model for understanding the process of creating evaluation state sequences at random. Currently, Markov acts as a very robust and heuristic for matching state sequences that are consistent with the randomness, uncertainty, and complexity of actual transportation systems. This is owing to the increasing expansion of big data and machine learning. The Markov is an illustration of a discrete-time stochastic process. In such processes, the probability of every future event depends only on the current state and is independent of the states that came before. Markov chains are used in a wide range of scenarios because they can be built to model various real-world processes. For their useful properties such as states, i.e., state transitions. It consists of a set of states and the conditional probabilities of their transitions from one state to another state. Markov provides a long-run probability for traffic estimation. In a Markov model, the system’s future behavior is based only on the present. Therefore, it is highly appropriate for us to predict the traffic condition with Markov. Many authors worked on Markov for the future prediction as ,48,49,50i to the state j is the transition probability from the state i at the time n to state j at the time i and j is represented as. A hidden Markov model based method also used to address the density estimation on a multilane road segment in ,50. In aHere, the Markov states with respect to time can be stated as follows: Equation  represenEquation .(3)TDTDavThe mutual exclusion of a few vehicle types is also done in this modeling; we have excluded bikes from the traffic density calculation from the range of 1 km as shown in the Equation :(7)TDavgThe probability of TD with discrete time Markov chain with interval indexes is Equation : (8)P(TDThe multistep transition probability of the Markov chain model is shown in Equation : (9)∫P(XEquation : (10)π=πFinally, calculating the traffic density using a Markov chain model can be calculated by using Equation .(11)TDaEquations –3) and  and 3) ahttps://omnetpp.org, accessed on 30 October 2022) is used in a V2I environment in conjunction with the simulation of urban mobility (SUMO), available at https://www.eclipse.org/sumo, accessed on 30 October 2022) to establish communication. The map is selected initially, and is followed by the number of vehicles as well as the route. The network is imported from the OpenStreetMap (OSM) database. This map covers an approximate area. This dataset is exported to SUMO and then imported into Omnet++ to the Cologne map. The display and storage of map data is supported by a simple and powerful idea. We used the road traffic simulator SUMO to generate vehicular traces, and then we examine the connectedness of the vehicular network. The network is then populated with random traffic. We assume the conception of a “node”, by considering any vehicle as the general vehicle in which communication passes from RSU to node, node to RSU, and from one node to another node. However, associating vehicle detection with a sequence of consecutive frames is achievable. Vehicles start moving immediately and can stop by any traffic light triggered at a particular position from where the RSU transmits a message to vehicles regarding the traffic jam.OMNET  is a collaborative initiative that allows anyone to contribute to the creation of a free map of the world. The data is made available under the terms of the Open Data Commons Open Database License (ODbL) . A simple yet powerful concept underpins the display and storage of map data. We started with the OSM map from Porto , and created a graph which used the same region of interest as the dataset filtered. The nodes in the graph are specific nodes from the OSM file. Only certain sorts of way nodes could be collected after we ran a filter on the OSM dataset, because only needed nodes that could be reached by vehicle.Tracking of vehicles is obtained by considering the speed, position, and direction in a highway scenario. SUMO is used to implement and track vehicles. We have considered some parameters for tracking, i.e., vehicle position, and speed fluctuations of each vehicle; it also simulates the acceleration of each vehicle. At the same time, the number of beacons is sent to the RSU from each node when traveled within the RSU range with a fixed time interval. The parameters associated with the characteristics are the primary concerns of the performance analysis. In this scenario experiment, these parameters are used, and they are displayed in We investigate the tracking of five vehicles on a real road network, as shown in In this evaluation, number of tracked vehicles calculated from the number of messages transferred between vehicles to the road side units. All the five vehicles are routed through the same route. In order to achieve the highest tracking accuracy, we selected the maximum continuous tracking time as a measure. The tracker first builds a traffic lane for beacons that shows throughout the scan. Then, by following intervals, it allocates beacons to the established routes and may initiate new tracks. The high peak tracking duration percentages are utilized as a tracking accuracy metric. The tracking system automatically builds a set of tracks for beacons that appear throughout the scan. Then, in following time steps, it distributes beacons to the established tracks and initiate new tracks. Vehicles are classed into a variety of groups based on their speed vectors. As a result of the tracking, it simulates the acceleration, vehicle position, and speed fluctuations of each vehicle. Because we consider monitoring and tracking on each vehicle, the tracked vehicle maintains a steady speed on the highway. The vehicle’s speed is the primary parameter that governs its dynamics.The graph in https://veins.car2x.org/download/, accessed on 30 October 2022) that is linked and evaluated for the model’s functionality. Moreover, it enables the implementation of dynamic broadcast propagation techniques, thereby enhancing the communication capacities of vehicle networks. The proposed architecture makes it possible to use effective ways to reduce traffic congestion because it makes easy for cars to act quickly. Moreover, this makes it possible to use model-based broadcast propagation techniques, which improves the ability of vehicle networks to communicate with cellular. The number of cars in this implementation moving in a range of one roadside units’ network that is represented in In this work, we investigate the modeling of traffic density by using the Markov model approach. Moreover, we successfully implemented our model in the SUMO simulation environment by using Omnet++ with the vein’s framework (available at This paper presented a comprehensive investigation for traffic density estimation techniques in the context of sustainable characteristics of intelligent transportation systems. The new approach is also introduced by constructing a Markov-based efficient model on OMNET++. The proposed and implemented model results are discussed in


Recent economic and public health crises have posed important challenges to family businesses – particularly those in the hospitality sector. While sustaining a business, performance becomes critical; there is insufficient knowledge on the use of entrepreneurial behaviors in mitigating the impact of a crisis by family businesses. To help fill this gap, this study explores the configurations of entrepreneurial behaviors that lead to improved performance in small firms under crisis market conditions – particularly, risk-taking, innovativeness, proactiveness, flexibility, and digitalization. This study employs fuzzy-set qualitative comparative analysis (fsQCA). The sample consists of 117 one- and two-star Polish hotels that are comprised of both family and non-family businesses. The data was collected in November and December 2021. The results confirm the core role of risk-taking, proactiveness, and flexibility in increasing the performance of these small firms. However, performance outcomes depend on the configurations of the firms; differences between family and non-family businesses stood out. In family hotels, risk-taking is accompanied by flexibility as a core factor, and digitalization does not play an important role in achieving higher performance. Overall, these results contribute to the literature on organizational entrepreneurship  as well as family business crisis management in the tourism sector. These findings offer implications for managers by indicating combinations of entrepreneurial behaviors that can help foster business performance. Recent times have been particularly challenging for family businesses that operate in the hospitality sector. While the pandemic crisis disrupted supply chains, business models, etc. across numerous industries  to identify the configurations of their entrepreneurial behaviors.This paper makes several contributions to the literature. First, it further explores the positive impact of entrepreneurial behaviors on the business performance that has been reported in hospitality firms .In general, entrepreneurship is understood as a pursuit of opportunities  on a firm’s performance Arz,  as well This study joins the stream of research that focuses on the EO–firm performance relationship and sheds new light on the nature of this relationship. Following suggestion of Zellweger and Sieger , and basTaking the above under consideration, in our study we decided to concentrate on traditional EO dimensions, as well as on flexibility and digitalization as factors that contribute to organizational entrepreneurship. The two latter factors can enhance innovation capabilities of a company ; however, different practices need to be developed in small firms as compared to bigger ones , payments (new forms and methods), and marketing processes (including strengthening the ties among companies and their clients).Proposition P5: A high level of digitalization can lead to an increase in firm performance.In the hospitality industry, numerous digital technologies have been implemented ; the results of this analysis are presented in Table In terms of the impact of the Covid-19 pandemic crisis, the differences among the family and non-family hotels confirmed the need for a separate analysis of each of their entrepreneurial behaviors. Consequently, this study examined family and non-family businesses separately, which is in line with a call for more studies providing data on crisis/extreme events management in family firms. Due to the employed methodology, this study does not implement a type of the business  as an antecedent condition, but analyze both groups separately.This study examined six variables; namely, risk-taking (R), innovativeness (IN), proactiveness (PR), flexibility (FLEX), digitalization (DIG), and performance (PERF) – in this examination, performance mainly reflected sale outcomes. Each variable was a coefficient and consisted of several items; they are presented in Appendix 1. The coefficients related to EO and performance are based on previous entrepreneurship studies  method. This method belongs to a group of methods of configuration analysis that was proposed and developed by American sociologist Charles Ragin . Later, The advantages of this method  are its asymmetric relationships, equifinality, and complexity of causes Ragin, , 2013. IThe analysis was conducted in several stages as proposed by Pappas and Woodside ; namely,Calibration , which makes them difficult to analyze Ragin, . RegardiThe purpose of the necessary condition analysis is to identify those conditions whose occurrences are necessary for achieving the considered result. Table According to Schneider and Wagemann , a condiTable 2n (in which n is the number of conditions). In our analysis, this table had 5 columns and 32 rows. For each possible combination of factors that occurred in the work table, two parameters were determined; i.e., the number of cases that belonged to a given combination, and consistency. These factors determined which combinations were to be taken into account in the final analysis. Regarding the selection of the threshold values for the number of cases and consistency, this examination followed Pappas and Woodside  are presented in Tables Using fsQCA, this study identified several combinations of factors that can lead to the high level of a hotel’s performance; these are presented in the Table The combinations that are presented in Table Based on the results that are presented in Table In terms of their responses to the Covid-19 crisis, the observed differences between the family and non-family hotels were in line with the previous findings that family and non-family hotels were affected by the Covid-19 pandemic crisis to different degrees. This confirms the role of external factors in shaping the entrepreneurial behaviors of a company – depending on the perceived external condition, a company uses different combinations of entrepreneurial behaviors.The results of our study are in line with the majority of the propositions that we formulated on the basis of our literature review. However, there were some peculiarities (which we will discuss below). The study results can be interpreted in a two-fold manner: first – on the level of single dimensions of entrepreneurial behavior; and second – as related to combinations of the examined dimensions. This study confirms the results of numerous studies that have indicated the importance of entrepreneurial behaviors in pursuing opportunities. Our study is in line with several findings of other authors. Regarding risk-taking, our findings are consistent with other studies that highlight this behavior’s role in increasing firm performance . Furthermore, the absence of innovativeness contributed to increases in firm performance in three combinations ; this finding corresponds with previous observations that have suggested that innovativeness has a negative impact on firm performance  can lead to increases in performance. The P2 proposition regarding the role of innovativeness was partly confirmed, as innovativeness was only present as a supporting factor. The results have also shown that there are noticeable differences between family and non-family firms.This study unveils the configuration of factors that can lead to performance in small one- and two-star hotels that operate in Poland. As we demonstrated above, it deepens the understanding of the role of organizational entrepreneurship and entrepreneurial orientation in shaping firm performance during a crisis. Moreover, we offer a broader and more complex understanding of EO by presenting it in a wider context. Thus, we decided to go above traditional EO dimensions, and add two contextual factors: digitalization and flexibility to our analyses. Our empirical study allowed us to validate this new model.Our study further explores the positive impact of entrepreneurial behaviors on the business performance that has been reported in hospitality firms . In our paper, we also provide a comparison between family and non-family firm entrepreneurial behavior that deepens the understanding of the differentiated and multidimensional role of EO.Finally, the study also contributes to the hospitality industry studies by showing the behaviors of one- and two-star hotels during the Covid-19 pandemic. Such entities have rarely been studied by scholars, even though they constitute a considerable part of the market.As far as practical implications are concerned, this study offers meaningful implications for entrepreneurs (and managers) of small hotels. In particular, the findings indicate several effective combinations of entrepreneurial behaviors, thus showing that good practices that can be implemented in disruptive situations (like the one connected with the pandemic). Moreover, the results show which factors  support or reduce the roles of other factors in increasing firm performance. This can serve not only as a source of inspiration but also as a valuable insight that can prevent decision-makers from getting caught in potential traps.Like every empirical study, this work also has several limitations. First, the sample represents one country (Poland) and one type of enterprise (one- and two-star hotels). The presented combinations of factors occur in this type of enterprise; in other groups, these combinations can be ineffective, while other combinations can successfully lead to performance. Thus, replications of this research in different countries and industry contexts may lead to interesting outcomes and allow for comparisons and further generalizations.Second, the data was collected during the Covid-19 crisis; crisis market conditions can affect the behaviors of entrepreneurs (which was reported in our family hotels). Moreover, the crisis was caused by the Covid-19 pandemic ; during crises that are sourced in other factors , other entrepreneurial behaviors can be triggered. This is both a limitation and a strength of our study, as we were able to address the research gap on entrepreneurial behaviors during severe crisis conditions on the one hand, yet were in need of more studies during less turbulent times on the other, in order to fully understand the phenomenon. Additionally, entrepreneurs’ responses reflected their perception of the crisis which could be affected by their objectives and priorities (which can vary in family and non-family businesses). The study focused on perception of a crisis in family and non-family business (and possible differences between these two group) seems interesting direction of future research.Third, the employed method was a source of limitations. FsQCA did not allow us to assess the strengths of the relationships among the variables; thus, the implementation of other methods is necessary in order to measure the impact of entrepreneurial behaviors on a firm’s performance. However, this fsQCA-based study indicates those behaviors that should be examined in further research.Finally, this study only tests the roles of five factors . In our study, on the basis of the literature review and a pilot study, we added flexibility and digitalization to show EO in a wider context. Previous studies have shown that other factors can be relevant as well; in other contexts, the examined factors can be irrelevant. Thus, a continuation of this stream of research  is recommended in order to further explore the determinants of firm performance.