Title page for ETD etd-05092000-17360045

Type of Document Master's Thesis
Author Casturi, Ramakrishna
Author's Email Address rcasturi@vt.edu
URN etd-05092000-17360045
Title A Macroscopic Model for Evaluating the Impact of Emergency Vehicle Signla Preemption on Traffic
Degree Master of Science
Department Civil Engineering
Advisory Committee
Advisor Name Title
Lin, Wei-hua Committee Chair
Collura, John Committee Member
Trani, Antoino A. Committee Member
  • Signal priority
  • Signal preemption
  • Emergency vehicles
  • Macroscopic model
  • Cell transmission model
Date of Defense 2000-05-04
Availability unrestricted
In the past, the study of Emergency Vehicle (EV) signal preemption has been mostly done using field studies. None of the simulation models that are currently commercially available have the capability to model the presence of EVs and simulate the traffic dynamics of the vehicles surrounding them. This study presents a macroscopic traffic model for examining the effect of signal preemption for EVs on traffic control measures, roadway capacity, and delays incurred to the vehicles on the side streets. The model is based on the cell transmission model, which is consistent with the hydrodynamic theory of traffic flow. A special component, in the form of a moving bottleneck that handles the traffic dynamics associated with the presence of EVs, was developed in the model. Several test scenarios were constructed to demonstrate the capabilities of the model for studying the impact of signal preemption on an arterial with multiple intersections under various traffic demand levels and varying frequencies of the arrival of EVs. Performance measures, such as average vehicle delay, maximum delay, and standard deviation of delay to traffic on all approaches, were obtained. An additional advantage of the model, apart from the capability to model EVs, is that the state-space equations used in the model can be easily incorporated into a mathematical programming problem. By coupling with a desired objective function, the model can be solved analytically. Optimal solutions can be generated to obtain insights into the development of traffic control strategies in the presence of EVs.
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