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:
- 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%.
- 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%.
Coordination in adaptive organisations : Extending shared plans with knowledge cultivation
- Keogh, Kathleen, Sonenberg, Liz, Smith, Wally
- Authors: Keogh, Kathleen , Sonenberg, Liz , Smith, Wally
- Date: 2009
- Type: Text , Conference paper
- Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Estoril : 13th May 2008 Vol. 5368 LNAI, p. 90-107
- Full Text:
- Description: Agent-based simulation can be used to investigate behavioural requirements, capabilities and strategies that might be helpful in complex, dynamic and adaptive situations, and can be used in training scenarios. In this paper, we study the requirements of coordination in complex unfolding scenarios in which agents may come and go and where there is a dynamic organisational structure. This is a step on the way to developing a simulation framework that can be part of a training system in the domain of emergency management. We argue the need for an extension to the SharedPlans formalism required to support the sharing of knowledge about a dynamically unfolding situation, specifically: who is in the team? and who holds relevant knowledge? Our rationale for such an extension is presented based on a prior case study of a railway accident and a further analysis of the coordination and communication activities amongst the disaster management team during its recovery. We conclude that in addition to the obligations imposed by the standard SharedPlans framework, agents in complex unfolding scenarios also need knowledge cultivation processes to reason about the dynamic organisational structure and the changing world state. We briefly express the requirements of knowledge cultivation as obligations that could be imposed on agents. We argue that in order to facilitate appropriate knowledge cultivation, agents need access to explicit models of organisational knowledge. This knowledge encapsulates the relational structure of the team, along with shared beliefs, goals and plans. We briefly present a formal representation of this model in order to clearly identify the rich information needed in an adaptive organisation. © 2009 Springer Berlin Heidelberg.
- Authors: Keogh, Kathleen , Sonenberg, Liz , Smith, Wally
- Date: 2009
- Type: Text , Conference paper
- Relation: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Estoril : 13th May 2008 Vol. 5368 LNAI, p. 90-107
- Full Text:
- Description: Agent-based simulation can be used to investigate behavioural requirements, capabilities and strategies that might be helpful in complex, dynamic and adaptive situations, and can be used in training scenarios. In this paper, we study the requirements of coordination in complex unfolding scenarios in which agents may come and go and where there is a dynamic organisational structure. This is a step on the way to developing a simulation framework that can be part of a training system in the domain of emergency management. We argue the need for an extension to the SharedPlans formalism required to support the sharing of knowledge about a dynamically unfolding situation, specifically: who is in the team? and who holds relevant knowledge? Our rationale for such an extension is presented based on a prior case study of a railway accident and a further analysis of the coordination and communication activities amongst the disaster management team during its recovery. We conclude that in addition to the obligations imposed by the standard SharedPlans framework, agents in complex unfolding scenarios also need knowledge cultivation processes to reason about the dynamic organisational structure and the changing world state. We briefly express the requirements of knowledge cultivation as obligations that could be imposed on agents. We argue that in order to facilitate appropriate knowledge cultivation, agents need access to explicit models of organisational knowledge. This knowledge encapsulates the relational structure of the team, along with shared beliefs, goals and plans. We briefly present a formal representation of this model in order to clearly identify the rich information needed in an adaptive organisation. © 2009 Springer Berlin Heidelberg.
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