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Abstract
The Motorist delay-Awareness System (MODAS), which comprises Artificial Neural Network (ANN) delay prediction module and traveler information/driver diversion module, is used to improve overall traffic system efficiency by disseminating delay information to motorists and diverting traffic to alternative routes if necessary. The microscopic traffic simulation software Paramics was employed to simulate the MODAS applied to two independent and distinct test cases: the Niagara border crossing area and downtown Toronto Waterfront network. Simulation results show that the application of MODAS is beneficial to the whole test transportation networks, with increased average speed, as well as reduction in both mean travel time and mean stopped time. The performance of ANN in short-term traffic prediction has also proved to be superior to other conventional traffic prediction methods such as the road segment method.
