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- W592741796 abstract "Severe traffic congestion on urban arterials causes delays and extra travel time when the signal control systems are incapable of responding to high traffic fluctuations. This is especially the case during non-recurrent traffic congestion that is due to unexpected events such as incidents, construction, weather, etc. Pre-timed signal systems and actuated controllers cannot accommodate these types of traffic variation, due to a fixed signal plan. The objective of this research is the development of a responsive signal control strategy, which responds to varying traffic demand that arises from non-recurrent traffic congestion. Case-based reasoning (CBR), a technique in artificial intelligence (AI), was used to formulate a control strategy that the author called RESSICA (RESponsive SIgnal Control for Arterial), which matches the traffic pattern and assigns a corresponding timing plan from its library with minimal re-computation. RESSICA was tested in a corridor network with four signalized intersections under various levels of non-recurrent congestion scenarios. The results showed that RESSICA outperformed the existing pre-timed/actuated signal controller, in terms of reducing travel time, delay, stop delay and intersection delay in the study area." @default.
- W592741796 created "2016-06-24" @default.
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- W592741796 date "2011-01-01" @default.
- W592741796 modified "2023-09-24" @default.
- W592741796 title "Responsive Signal Control for Non-recurrent Traffic Congestion on an Arterials" @default.
- W592741796 hasPublicationYear "2011" @default.
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