Abstract
Emergency response vehicles (ERVs) need to move fast, which can lead to potential accidents. A system is proposed to utilize connected vehicle technology for facilitating the movement of multiple ERVs in a two-way roadway. A biobjective model was developed to minimize the ERVs’ travel/response times while maximizing safety. The proposed system can help non-ERVs stop in assigned locations and wait for the ERVs to pass. According to mission priority, different types of ERVs (ambulance, fire truck, and police car) can use a travel lane from the opposite direction (contraflow) to move faster. A hybrid nondominated sorting genetic algorithm II-particle swarm optimization (NSGAII-PSO) approach was used to solve the model faster than CPLEX. To evaluate the system’s performance, different experiments, including various road types, numbers and types of ERVs, direction priorities, and congestion levels, have been conducted. Results demonstrate that the proposed system can improve the travel time of ERVs that have the priority to use the contraflow lane in cases where the other side of the roadway is less congested and has a shoulder (when opposing ERVs use a two-way roadway simultaneously). The proposed system can eliminate weaving and passing among non-ERVs in all cases compared to two current strategies (going to the right edge and vacating the ERV lane without receiving instructions).
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon a reasonable request.
Acknowledgments
This manuscript was supported by the National Science Foundation (NSF) through CPS: Synergy: Collaborative Research: Semi-Automated Emergency Response System under Grant 1812524. However, the authors are solely responsible for the content.
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Published In
Journal of Transportation Engineering, Part A: Systems
Volume 150 • Issue 5 • May 2024
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© 2024 American Society of Civil Engineers.
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Received: Mar 7, 2023
Accepted: Dec 5, 2023
Published online: Mar 15, 2024
Published in print: May 1, 2024
Discussion open until: Aug 15, 2024
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