A traffic signal control algorithm for emergency vehicles

Asaduzzaman, Md (2017) A traffic signal control algorithm for emergency vehicles. Masters thesis, Memorial University of Newfoundland.

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Signal preemption disrupts normal traffic signal to allow emergency vehicles to pass through the intersection more safely and quickly. In medical emergency situations, EVP (Emergency Vehicle Preemption) offers a faster response to the sufferer which improves the chance of survival. Despite this lifesaving advantage, conventional preemption has some problems which need more attention. Two important issues are increased delay of overall traffic due to preemption and absence of prioritization of conflicting preemption requests. This thesis presents a traffic signal control algorithm that addresses the above. We have used TSP (Transit Signal Priority) techniques to improve the EVP system. TSP is a proven strategy to provide a better quality public transit operation in urban areas. Our proposed algorithm adjusts signal phases using TSP techniques to serve an emergency vehicle. We consider both single and multiple simultaneous emergency vehicle requests. TSP techniques help us to alleviate the impact on general traffic. For multiple emergency vehicle requests, a branch and bound algorithm is developed that prioritizes among conflicting requests. Experiments have been conducted using the VISSIM microscopic traffic simulator. Results show that the proposed traffic control algorithm reduces overall traffic delay by up to 8% compared to conventional EVP system.

Item Type: Thesis (Masters)
URI: http://research.library.mun.ca/id/eprint/12705
Item ID: 12705
Additional Information: Includes bibliographical references (pages 88-96).
Keywords: Emergency Vehicle Preemption, Intelligent Transportation Systems, Traffic Signal Control, Transit Signal Priority
Department(s): Science, Faculty of > Computer Science
Date: March 2017
Date Type: Submission
Library of Congress Subject Heading: Traffic signal preemption; Emergency transportation; Intelligent control systems

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