Title:
Emergency Vehicle Preemption Strategies using Machine Learning to Optimize Traffic Operations
Emergency Vehicle Preemption Strategies using Machine Learning to Optimize Traffic Operations
Author(s)
Roy, Somdut
Advisor(s)
Guin, Angshuman
Hunter, Michael P.
Hunter, Michael P.
Editor(s)
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Abstract
Emergency-Response-Vehicles (ERVs) operate with the purpose of saving lives and mitigating property damage. Emergency-response Vehicle Preemption (EVP) is implemented to provide the right-of-way to ERVs by displaying the green indications along the ERV route. Two EVP strategies were developed as part of this effort. First, a strategy was developed, defined as “Dynamic-Preemption” (DP), that utilizes Connected-Vehicle (CV) technology to detect, in real time, the need for preemption prior to the ERV reaching the vicinity of an intersection. The DP strategy is based on several generalized traffic demand and simplified traffic flow assumptions. Second, a machine learning approach was utilized to develop an EVP call strategy that sought to (1) preemptively clear queues at intersections prior to ERV arrival, (2) create a "delay-free" path for the ERV, and (3) minimize excess delay to the conflicting traffic in the event of an EVP call. The ML approach utilizes currently available vehicle detection data streams and is trained based on simulated EVP scenarios. Existing field strategies and the developed strategies were tested under varying scenarios, on a simulated signalized corridor testbed. It was observed that the proposed methodologies showed tangible improvement over the existing baseline algorithms for EVP, both in terms of ERV travel time and delay to the conflicting movements. In summary, this research is expected to lay the foundation for use of novel computational approaches in solving the EVP problem in traffic ecosystems with limited CV penetration, with the aid of microsimulation.
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Date Issued
2023-04-30
Extent
Resource Type
Text
Resource Subtype
Dissertation