Validation of the persian version of spiritual well-being questionnaires
- Biglari Abhari, Mrayam, Fisher, John, Kheiltash, Azita, Nojomi, Marzieh
- Authors: Biglari Abhari, Mrayam , Fisher, John , Kheiltash, Azita , Nojomi, Marzieh
- Date: 2018
- Type: Text , Journal article
- Relation: Iranian journal of medical sciences Vol. 43, no. 3 (2018), p. 276-285
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- Description: Spiritual well-being is an important issue in health sciences, hence the need for validated instruments to assess this aspect of health in the Iranian population. The aim of the current study was to determine the validity of the Persian versions of 2 most common measures of spiritual health (Spiritual Well-Being Questionnaire [SWBQ] or Spiritual Health and Life-Orientation Measure [SHALOM] and Spiritual Well-Being Scale [SWBS]). This was a cross-sectional study via a convenience sampling method in Iran University of Medical Sciences with 170 participants aged above 18 years comprising students, teachers, and administrative staff and managers. The study was conducted from September 7, 2014 to September 20, 2015 in Tehran. Four questionnaires, namely the SWBQ, SWBS, General Health Questionnaire (GHQ-12), and Oxford Happiness Questionnaire (OHQ), were used. Statistical analysis was done using SPSS 18 and LISREL (version 8.2). Cronbach's alpha, intra-class correlation coefficient, Pearson correlation, and confirmatory factor analysis were employed to assess the validity and reliability of the questionnaires. Cronbach's alpha for the SWBQ and the SWBS was greater than 0.85. The repeatability of both questionnaires was between 0.88 and 0.98. The Pearson correlation for the SWBQ and the SWBS ranged from 0.33 to 0.53 and all the correlations were significant. The respondents who indicated a higher spiritual well-being also reported better general health and happiness. The Persian versions of the SWBS and the SWBQ have good reliability, repeatability, and validity to assess spiritual health in the Iranian population.
- Authors: Biglari Abhari, Mrayam , Fisher, John , Kheiltash, Azita , Nojomi, Marzieh
- Date: 2018
- Type: Text , Journal article
- Relation: Iranian journal of medical sciences Vol. 43, no. 3 (2018), p. 276-285
- Full Text:
- Reviewed:
- Description: Spiritual well-being is an important issue in health sciences, hence the need for validated instruments to assess this aspect of health in the Iranian population. The aim of the current study was to determine the validity of the Persian versions of 2 most common measures of spiritual health (Spiritual Well-Being Questionnaire [SWBQ] or Spiritual Health and Life-Orientation Measure [SHALOM] and Spiritual Well-Being Scale [SWBS]). This was a cross-sectional study via a convenience sampling method in Iran University of Medical Sciences with 170 participants aged above 18 years comprising students, teachers, and administrative staff and managers. The study was conducted from September 7, 2014 to September 20, 2015 in Tehran. Four questionnaires, namely the SWBQ, SWBS, General Health Questionnaire (GHQ-12), and Oxford Happiness Questionnaire (OHQ), were used. Statistical analysis was done using SPSS 18 and LISREL (version 8.2). Cronbach's alpha, intra-class correlation coefficient, Pearson correlation, and confirmatory factor analysis were employed to assess the validity and reliability of the questionnaires. Cronbach's alpha for the SWBQ and the SWBS was greater than 0.85. The repeatability of both questionnaires was between 0.88 and 0.98. The Pearson correlation for the SWBQ and the SWBS ranged from 0.33 to 0.53 and all the correlations were significant. The respondents who indicated a higher spiritual well-being also reported better general health and happiness. The Persian versions of the SWBS and the SWBQ have good reliability, repeatability, and validity to assess spiritual health in the Iranian population.
Examining Bangladesh's responses to COVID-19 in light of Vietnam : lessons learned
- Hilda, Nazmul, Uddin, Helal, Hasan, Kamrul, Malo, James Sujit, Duong, Minh Cuong, Rahman, Muhammad Aziz
- Authors: Hilda, Nazmul , Uddin, Helal , Hasan, Kamrul , Malo, James Sujit , Duong, Minh Cuong , Rahman, Muhammad Aziz
- Date: 2021
- Type: Text , Journal article
- Relation: Global Biosecurity Vol. 3, no. 1 (2021), p.
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- Description: This review aimed to examine the extent of Bangladesh's COVID-19 preparedness and control measures up to 20 January 2021, and to draw some lessons for informing the current and future pandemic responses in Bangladesh in light of Vietnam's responses, which had successfully controlled the pandemic. We performed a content analysis of data to identify similarities and critical discrepancies in epidemiological features and COVID-19 responses between the two countries. Findings indicated that Vietnam reported lower COVID-19 incidence (15 cases per million) and death rate (0.4 cases per million) than Bangladesh, with 3,129 cases per million and a death rate of 46 cases per million. Vietnam reported only 35 deaths, with 22 older individuals (>60 years) compared with 7,950 deaths in Bangladesh, with the highest death rate in older people (45%). An integrated approach combined with widespread contact tracing, better health investment, vaccine development, and strong political commitment enabled Vietnam to control the disease and mitigate its impacts. In contrast, Bangladesh seemed to adopt inadequate and untimely measures in the same domains, potentially contributing to relatively high COVID-19 infections and death rates. To control COVID-19 or inform responses to future pandemics, Bangladesh and similar countries can learn eight lessons from Vietnam. Such transferable responses could prepare health systems and populations for an appropriate global response to the next potential pandemic.
- Authors: Hilda, Nazmul , Uddin, Helal , Hasan, Kamrul , Malo, James Sujit , Duong, Minh Cuong , Rahman, Muhammad Aziz
- Date: 2021
- Type: Text , Journal article
- Relation: Global Biosecurity Vol. 3, no. 1 (2021), p.
- Full Text:
- Reviewed:
- Description: This review aimed to examine the extent of Bangladesh's COVID-19 preparedness and control measures up to 20 January 2021, and to draw some lessons for informing the current and future pandemic responses in Bangladesh in light of Vietnam's responses, which had successfully controlled the pandemic. We performed a content analysis of data to identify similarities and critical discrepancies in epidemiological features and COVID-19 responses between the two countries. Findings indicated that Vietnam reported lower COVID-19 incidence (15 cases per million) and death rate (0.4 cases per million) than Bangladesh, with 3,129 cases per million and a death rate of 46 cases per million. Vietnam reported only 35 deaths, with 22 older individuals (>60 years) compared with 7,950 deaths in Bangladesh, with the highest death rate in older people (45%). An integrated approach combined with widespread contact tracing, better health investment, vaccine development, and strong political commitment enabled Vietnam to control the disease and mitigate its impacts. In contrast, Bangladesh seemed to adopt inadequate and untimely measures in the same domains, potentially contributing to relatively high COVID-19 infections and death rates. To control COVID-19 or inform responses to future pandemics, Bangladesh and similar countries can learn eight lessons from Vietnam. Such transferable responses could prepare health systems and populations for an appropriate global response to the next potential pandemic.
Evaluation of an assessment model to reduce waitlist times for occupational therapy in a rural community health setting
- Missen, Karen, Mills, Alyssa, McDonald, Georgia, Di Corleto, Erin, Telling, Laura, Davey, Alice
- Authors: Missen, Karen , Mills, Alyssa , McDonald, Georgia , Di Corleto, Erin , Telling, Laura , Davey, Alice
- Date: 2021
- Type: Text , Journal article
- Relation: Australian Journal of Rural Health Vol. 29, no. 6 (2021), p. 987-992
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- Description: Objective Community occupational therapy services have seen an increase in demand over the last three years, resulting in longer waitlist times for service provision, particularly in rural areas where it is difficult to recruit experienced occupational therapists. Utilising a demand management model, the Basic Assessment Model Pre‐Screening Tool was developed by a team of Occupational Therapists and allied health assistants to decrease client waitlist times at one rural community health service. Design An evaluation of the implementation of an assessment model with comparison of quantitative data pre and post intervention. Setting Rural Community Health Service in Victoria, Australia Participants 456 clients that were registered as community‐based clients requiring occupational therapy services. Main Outcome measure Following the implementation of the newly developed Basic Assessment Model the number of occupational therapy assessments increased and there was a decrease in the median wait time that clients were on the waitlist in comparison to pre implementation. Results There was a statistically significant decrease (p<0.001) in the median number of days spent on the waitlist for the post intervention group (80 days) compared to the pre intervention group (105 days). Conclusion The results of this study suggest that waiting lists for community occupational therapy services can be reduced by implementing this basic assessment model ultimately improving the health outcomes of clients.
- Authors: Missen, Karen , Mills, Alyssa , McDonald, Georgia , Di Corleto, Erin , Telling, Laura , Davey, Alice
- Date: 2021
- Type: Text , Journal article
- Relation: Australian Journal of Rural Health Vol. 29, no. 6 (2021), p. 987-992
- Full Text:
- Reviewed:
- Description: Objective Community occupational therapy services have seen an increase in demand over the last three years, resulting in longer waitlist times for service provision, particularly in rural areas where it is difficult to recruit experienced occupational therapists. Utilising a demand management model, the Basic Assessment Model Pre‐Screening Tool was developed by a team of Occupational Therapists and allied health assistants to decrease client waitlist times at one rural community health service. Design An evaluation of the implementation of an assessment model with comparison of quantitative data pre and post intervention. Setting Rural Community Health Service in Victoria, Australia Participants 456 clients that were registered as community‐based clients requiring occupational therapy services. Main Outcome measure Following the implementation of the newly developed Basic Assessment Model the number of occupational therapy assessments increased and there was a decrease in the median wait time that clients were on the waitlist in comparison to pre implementation. Results There was a statistically significant decrease (p<0.001) in the median number of days spent on the waitlist for the post intervention group (80 days) compared to the pre intervention group (105 days). Conclusion The results of this study suggest that waiting lists for community occupational therapy services can be reduced by implementing this basic assessment model ultimately improving the health outcomes of clients.
Participation and dropout of Hockey New South Wales participants in 2017 and 2018: a longitudinal study
- Owen, Katherine, Foley, Bridget, Eime, Rochelle, Rose, Catriona, Reece, Lindsey
- Authors: Owen, Katherine , Foley, Bridget , Eime, Rochelle , Rose, Catriona , Reece, Lindsey
- Date: 2022
- Type: Text , Journal article
- Relation: BMC Sports Science Medicine and Rehabilitation Vol. 14, no. 1 (2022), p. 103-103
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- Description: Sports have a focus on increasing participation, which contributes to increasing population levels of physical activity, social cohesion and longevity of the sport. The primary aim of this study was to examine reasons for drop-out of a popular team sport in Australia, Field Hockey and identify opportunities to increase participation. This longitudinal study obtained routinely collected registered player data from Hockey New South Wales over two consecutive years, and survey data from registered players who dropped out. Logistic regression models identified demographic subgroups who were more likely to drop out of sport, and the reasons for dropping out. In 2018, 8463 (31%) of hockey players did not return to play hockey after the previous season and 805 (10%) of these completed a survey. Specific groups who were more likely to stop playing included 5-6 years (OR: 2.1, 95% CI 1.8-2.6 reference: 12-17 years), females (OR: 1.1, 95% CI 1.0-1.2 reference: males), Indigenous (OR: 1.2, 95% CI 1.1-1.4 reference: non-Indigenous), most disadvantaged (OR: 1.1, 95% CI 1.0-1.2 reference: least disadvantaged) or regional and remote (1.1, 95% CI 1.0-1.2 reference: major cities). Top reasons for drop out were medical/age (17%), change in circumstances (16%) and high cost (13%), lack of time (13%) and lack of enjoyment (7%). Although Hockey successfully reaches a large proportion of underrepresented groups in sport, these groups are more likely to drop out. Sports should consult these groups to develop enjoyable, flexible, and modifiable versions of the game that are appropriate to their needs.
- Authors: Owen, Katherine , Foley, Bridget , Eime, Rochelle , Rose, Catriona , Reece, Lindsey
- Date: 2022
- Type: Text , Journal article
- Relation: BMC Sports Science Medicine and Rehabilitation Vol. 14, no. 1 (2022), p. 103-103
- Full Text:
- Reviewed:
- Description: Sports have a focus on increasing participation, which contributes to increasing population levels of physical activity, social cohesion and longevity of the sport. The primary aim of this study was to examine reasons for drop-out of a popular team sport in Australia, Field Hockey and identify opportunities to increase participation. This longitudinal study obtained routinely collected registered player data from Hockey New South Wales over two consecutive years, and survey data from registered players who dropped out. Logistic regression models identified demographic subgroups who were more likely to drop out of sport, and the reasons for dropping out. In 2018, 8463 (31%) of hockey players did not return to play hockey after the previous season and 805 (10%) of these completed a survey. Specific groups who were more likely to stop playing included 5-6 years (OR: 2.1, 95% CI 1.8-2.6 reference: 12-17 years), females (OR: 1.1, 95% CI 1.0-1.2 reference: males), Indigenous (OR: 1.2, 95% CI 1.1-1.4 reference: non-Indigenous), most disadvantaged (OR: 1.1, 95% CI 1.0-1.2 reference: least disadvantaged) or regional and remote (1.1, 95% CI 1.0-1.2 reference: major cities). Top reasons for drop out were medical/age (17%), change in circumstances (16%) and high cost (13%), lack of time (13%) and lack of enjoyment (7%). Although Hockey successfully reaches a large proportion of underrepresented groups in sport, these groups are more likely to drop out. Sports should consult these groups to develop enjoyable, flexible, and modifiable versions of the game that are appropriate to their needs.
Subgraph adaptive structure-aware graph contrastive learning
- Chen, Zhikui, Peng, Yin, Yu, Shuo, Cao, Chen, Xia, Feng
- Authors: Chen, Zhikui , Peng, Yin , Yu, Shuo , Cao, Chen , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics (Basel) Vol. 10, no. 17 (2022), p. 3047
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- Description: Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
- Authors: Chen, Zhikui , Peng, Yin , Yu, Shuo , Cao, Chen , Xia, Feng
- Date: 2022
- Type: Text , Journal article
- Relation: Mathematics (Basel) Vol. 10, no. 17 (2022), p. 3047
- Full Text:
- Reviewed:
- Description: Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised methods in some tasks, most of them depend on node-level comparison, while ignoring the rich semantic information contained in graph topology, especially for social networks. However, a higher-level comparison requires subgraph construction and encoding, which remain unsolved. To address this problem, we propose a subgraph adaptive structure-aware graph contrastive learning method (PASCAL) in this work, which is a subgraph-level GCL method. In PASCAL, we construct subgraphs by merging all motifs that contain the target node. Then we encode them on the basis of motif number distribution to capture the rich information hidden in subgraphs. By incorporating motif information, PASCAL can capture richer semantic information hidden in local structures compared with other GCL methods. Extensive experiments on six benchmark datasets show that PASCAL outperforms state-of-art graph contrastive learning and supervised methods in most cases.
Automated segmentation of mouse OCT volumes (ASiMOV): Validation & clinical study of a light damage model
- Antony, Bhavna, Kim, Byung-Jin, Lang, Andrew, Carass, Aaron, Prince, Jerry, Zack, Donald
- Authors: Antony, Bhavna , Kim, Byung-Jin , Lang, Andrew , Carass, Aaron , Prince, Jerry , Zack, Donald
- Date: 2017
- Type: Text , Journal article
- Relation: PLoS One Vol. 12, no. 8 (2017), p. e0181059-e0181059
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- Description: The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study.
- Authors: Antony, Bhavna , Kim, Byung-Jin , Lang, Andrew , Carass, Aaron , Prince, Jerry , Zack, Donald
- Date: 2017
- Type: Text , Journal article
- Relation: PLoS One Vol. 12, no. 8 (2017), p. e0181059-e0181059
- Full Text:
- Reviewed:
- Description: The use of spectral-domain optical coherence tomography (SD-OCT) is becoming commonplace for the in vivo longitudinal study of murine models of ophthalmic disease. Longitudinal studies, however, generate large quantities of data, the manual analysis of which is very challenging due to the time-consuming nature of generating delineations. Thus, it is of importance that automated algorithms be developed to facilitate accurate and timely analysis of these large datasets. Furthermore, as the models target a variety of diseases, the associated structural changes can also be extremely disparate. For instance, in the light damage (LD) model, which is frequently used to study photoreceptor degeneration, the outer retina appears dramatically different from the normal retina. To address these concerns, we have developed a flexible graph-based algorithm for the automated segmentation of mouse OCT volumes (ASiMOV). This approach incorporates a machine-learning component that can be easily trained for different disease models. To validate ASiMOV, the automated results were compared to manual delineations obtained from three raters on healthy and BALB/cJ mice post LD. It was also used to study a longitudinal LD model, where five control and five LD mice were imaged at four timepoints post LD. The total retinal thickness and the outer retina (comprising the outer nuclear layer, and inner and outer segments of the photoreceptors) were unchanged the day after the LD, but subsequently thinned significantly (p < 0.01). The retinal nerve fiber-ganglion cell complex and the inner plexiform layers, however, remained unchanged for the duration of the study.
An evidence theoretic approach for traffic signal intrusion detection
- Chowdhury, Abdullahi, Karmakar, Gour, Kamruzzaman, Joarder, Das, Rajkumar, Newaz, Shah
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Das, Rajkumar , Newaz, Shah
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 10 (2023), p. 4646
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- Reviewed:
- Description: The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster-Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.
- Authors: Chowdhury, Abdullahi , Karmakar, Gour , Kamruzzaman, Joarder , Das, Rajkumar , Newaz, Shah
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 10 (2023), p. 4646
- Full Text:
- Reviewed:
- Description: The increasing attacks on traffic signals worldwide indicate the importance of intrusion detection. The existing traffic signal Intrusion Detection Systems (IDSs) that rely on inputs from connected vehicles and image analysis techniques can only detect intrusions created by spoofed vehicles. However, these approaches fail to detect intrusion from attacks on in-road sensors, traffic controllers, and signals. In this paper, we proposed an IDS based on detecting anomalies associated with flow rate, phase time, and vehicle speed, which is a significant extension of our previous work using additional traffic parameters and statistical tools. We theoretically modelled our system using the Dempster-Shafer decision theory, considering the instantaneous observations of traffic parameters and their relevant historical normal traffic data. We also used Shannon's entropy to determine the uncertainty associated with the observations. To validate our work, we developed a simulation model based on the traffic simulator called SUMO using many real scenarios and the data recorded by the Victorian Transportation Authority, Australia. The scenarios for abnormal traffic conditions were generated considering attacks such as jamming, Sybil, and false data injection attacks. The results show that the overall detection accuracy of our proposed system is 79.3% with fewer false alarms.
IoT-based emergency vehicle services in intelligent transportation system
- Chowdhury, Abdullahi, Kaisar, Shahriar, Khoda, Mahbub, Naha, Ranesh, Khoshkholghi, Mohammad, Aiash, Mahdi
- Authors: Chowdhury, Abdullahi , Kaisar, Shahriar , Khoda, Mahbub , Naha, Ranesh , Khoshkholghi, Mohammad , Aiash, Mahdi
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 11 (2023), p. 5324
- Full Text:
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- Description: Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs' travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%.
- Authors: Chowdhury, Abdullahi , Kaisar, Shahriar , Khoda, Mahbub , Naha, Ranesh , Khoshkholghi, Mohammad , Aiash, Mahdi
- Date: 2023
- Type: Text , Journal article
- Relation: Sensors Vol. 23, no. 11 (2023), p. 5324
- Full Text:
- Reviewed:
- Description: Emergency Management System (EMS) is an important component of Intelligent transportation systems, and its primary objective is to send Emergency Vehicles (EVs) to the location of a reported incident. However, the increasing traffic in urban areas, especially during peak hours, results in the delayed arrival of EVs in many cases, which ultimately leads to higher fatality rates, increased property damage, and higher road congestion. Existing literature addressed this issue by giving higher priority to EVs while traveling to an incident place by changing traffic signals (e.g., making the signals green) on their travel path. A few works have also attempted to find the best route for an EV using traffic information (e.g., number of vehicles, flow rate, and clearance time) at the beginning of the journey. However, these works did not consider congestion or disruption faced by other non-emergency vehicles adjacent to the EV travel path. The selected travel paths are also static and do not consider changing traffic parameters while EVs are en route. To address these issues, this article proposes an Unmanned Aerial Vehicle (UAV) guided priority-based incident management system to assist EVs in obtaining a better clearance time in intersections and thus achieve a lower response time. The proposed model also considers disruption faced by other surrounding non-emergency vehicles adjacent to the EVs' travel path and selects an optimal solution by controlling the traffic signal phase time to ensure that EVs can reach the incident place on time while causing minimal disruption to other on-road vehicles. Simulation results indicate that the proposed model achieves an 8% lower response time for EVs while the clearance time surrounding the incident place is improved by 12%.
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