Online optimization with look-ahead for freeway emergency vehicle dispatching considering availability
•Online dispatching minimizing a sequence of emergency responses.•Network dependency with probability distribution of secondary incidents.•The lookahead online algorithm is compared with other dispatching policies.•Server availability is considered by estimating service times of each request.•Dispat...
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| Published in: | Transportation research. Part C, Emerging technologies Vol. 109; no. C; pp. 95 - 116 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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United Kingdom
Elsevier Ltd
01.12.2019
Elsevier |
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| ISSN: | 0968-090X, 1879-2359 |
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| Abstract | •Online dispatching minimizing a sequence of emergency responses.•Network dependency with probability distribution of secondary incidents.•The lookahead online algorithm is compared with other dispatching policies.•Server availability is considered by estimating service times of each request.•Dispatching strategies visualized different scenarios.
Traditional emergency management studies have made resource allocation decisions to serve the current emergency without knowing which future emergency will be occurring. Different ordered combinations of emergencies result in different performance outcomes. Even though future events can be anticipated, previous studies follow an assumption that events over a time interval are independent. This study follows an assumption that events are interdependent, because speed reduction and rubbernecking due to an initial incident provoke secondary incidents on freeways and the resource availability depends on service times of each request. The misconception that secondary incidents are not common has resulted in overlooking a look-ahead concept. This study is the pioneer in relaxing the structural assumptions of independency during the assignment of servers and approaching the challenge from an operational perspective, online optimization. The main objective is to minimize the time needed to respond to a sequence of requests. We introduce online dispatching strategies with visualization applied in different network sizes, number of requests, and service times to provide insights on model behavior and solution quality. The experimental evidence indicates that the algorithm works well in practice. We envision a new era in which an optimal resource allocation adapts to external events effectively and anticipates the future learning from the past to produce effective solutions. |
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| AbstractList | •Online dispatching minimizing a sequence of emergency responses.•Network dependency with probability distribution of secondary incidents.•The lookahead online algorithm is compared with other dispatching policies.•Server availability is considered by estimating service times of each request.•Dispatching strategies visualized different scenarios.
Traditional emergency management studies have made resource allocation decisions to serve the current emergency without knowing which future emergency will be occurring. Different ordered combinations of emergencies result in different performance outcomes. Even though future events can be anticipated, previous studies follow an assumption that events over a time interval are independent. This study follows an assumption that events are interdependent, because speed reduction and rubbernecking due to an initial incident provoke secondary incidents on freeways and the resource availability depends on service times of each request. The misconception that secondary incidents are not common has resulted in overlooking a look-ahead concept. This study is the pioneer in relaxing the structural assumptions of independency during the assignment of servers and approaching the challenge from an operational perspective, online optimization. The main objective is to minimize the time needed to respond to a sequence of requests. We introduce online dispatching strategies with visualization applied in different network sizes, number of requests, and service times to provide insights on model behavior and solution quality. The experimental evidence indicates that the algorithm works well in practice. We envision a new era in which an optimal resource allocation adapts to external events effectively and anticipates the future learning from the past to produce effective solutions. |
| Author | Waddell, Deion Haghani, Ali Park, Hyoshin |
| Author_xml | – sequence: 1 givenname: Hyoshin surname: Park fullname: Park, Hyoshin email: hpark1@ncat.edu organization: Department of Computational Science & Engineering, North Carolina A&T State University, United States – sequence: 2 givenname: Deion surname: Waddell fullname: Waddell, Deion organization: Department of Computational Science & Engineering, North Carolina A&T State University, United States – sequence: 3 givenname: Ali surname: Haghani fullname: Haghani, Ali organization: Department of Civil & Environmental Engineering, University of Maryland College Park, United States |
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| Keywords | Work function algorithm Online optimization Service time Secondary incidents Emergency response Nearest neighbor algorithm |
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