Application of adaptive weights to intelligent information systems: An intelligent transportation system as a case study

Abstract

Optimization of information feedback technologies is very important for many socioeconomic systems such as stock markets and traffic systems aiming to make full use of resources. In this paper, we propose an adaptive weight method, which has potential value for a variety of information processing contexts. We apply this adaptive weight method to an intelligent transportation system (ITS) as a case study. A feedback strategy named Improved Congestion Coefficient Feedback Strategy (ICCFS) is introduced based on a two-route scenario in which dynamic information can be generated and displayed on the roadside in order to enable drivers to make an informed route decision. Our model incorporates the effects of adaptability into the cellular automaton models of traffic flow. Simulations demonstrate that adopting this optimal information feedback strategy provides a high efficiency in controlling spatial distribution of traffic patterns when compared with the three other information feedback strategies, i.e., Travel Time Feedback Strategy (TTFS), Mean Velocity Feedback Strategy (MVFS) and Congestion Coefficient Feedback Strategy (CCFS).