Abstract
Traffic crashes and other emergencies have impacts on traffic operations in transportation networks, often resulting in non-recurring congestion. Congestion, in turn, may impede the ability of Emergency Medical Services (EMS) to provide timely response to those in need of medical attention. The work in this paper investigated the impact of incidents of varying severity and duration on transportation network performance in the Birmingham (AL, USA) area. The intensity and extent of the impact over space and time were assessed on the basis of average speeds. The analysis of incident scenarios was performed using the Visual Interactive System for Transport Algorithms (VISTA) platform. Moreover, first responders’ travel times to the scene of the incident were collected to identify best units for responding, in an effort to improve current dispatching practices. Finally, a secondary incident on the EMS to the hospital was considered to further demonstrate the superiority of Dynamic Traffic Assignment (DTA) over traditional static assignment methods in capturing dynamically changing traffic conditions. The study findings are expected to benefit local transportation planners, traffic engineers, emergency responders, and policy makers by allowing them to assess various response strategies to major incidents and emergencies and select the ones that minimize their potential impacts.
Highlights
Traffic incidents and other emergencies are highly likely to impact transportation network performance when they occur
In support of incident management, there is a need for models which can capture the fast changing dynamic traffic conditions taking into consideration traffic management measures implemented to meet the management objectives stated above and potential infrastructure failures
Successful traffic incident management programs depend on strong interagency involvement and commitment
Summary
Traffic incidents and other emergencies are highly likely to impact transportation network performance when they occur. Reduction in traveling speeds and formation of queues are commonly observed upstream of incident sites. Reduction in supply associated with lane closures and/or increase in demand as a result of traffic bottlenecks may impede the ability of first responders to reach the scene of the incident and transport victims to emergency rooms in a rapid and efficient manner. Lessons learned from past experiences confirm that in case of incidents or emergencies, effective realtime traffic management is essential to avoid deterioration of traffic conditions [1]. In support of incident management, there is a need for models which can capture the fast changing dynamic traffic conditions taking into consideration traffic management measures implemented to meet the management objectives stated above and potential infrastructure failures.
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