Hesham Ahmed Rakha

Coupled evaluation of communication system loading and ATIS/ATMS efficiency

Significant efforts are being made at present to define, evaluate, and ultimately deploy, various forms of intelligent transportation systems (ITS). These systems permit the implementation of advanced traffic control strategies through the application of advanced surveillance, control and communication systems. Traditionally, the evaluation of the performance of these communication systems and traffic networks has been performed predominantly independent of each other. This paper describes the development and application of an extension to an ITS benefits evaluation model to permit a partially coupled evaluation of the communication system loading and the ATMS/ATIS functions. This extended model is capable of estimating dynamic local and network wide communication loadings that depend on the spatial and temporal traffic demands, the network topology, the characteristics of the communication hardware and the communication system operating rules. The model is applied in this paper to a simple hypothetical network to demonstrate the potential benefits of carrying out a partially coupled evaluation of communication system loading and traffic network efficiency. A sensitivity analysis was carried out to determine the impact of the fraction of ITS equipped vehicles and the level of congestion on the level of communication loadings. It was demonstrated that, as a result of congestion and traffic diversion, antenna communication loads did not uniformly increase in direct proportion to the average number of equipped vehicles entering the network.

Travel Time Modeling using Spatiotemporal Speed Variation and a Mixture of Linear Regressions.

Real-time, accurate travel time prediction algorithms are needed for individual travelers, business sectors, and government agencies. They help commuters make better travel decisions, avert traffic congestion, help the environment by reducing carbon emissions, and improve traffic efficiency. Travel time prediction has begun to attract more attention with the rapid development of intelligent transportation systems (ITSs), and is considered one of the more important elements required for successful ITS subsystems deployment. However, the stochastic nature of travel time makes accurate prediction a difficult task. This paper proposes travel time modeling using a mixture of linear regressions. The proposed model consists of two normal components. The first component models the congested regime while the other models the free-flow regime. The means of the two components are modeled by two linear regression equations. The predictors used in the linear regression equation are selected out of the spatiotemporal speed matrix using a random forest machine-learning algorithm. The proposed model is tested using archived data from a 74.4-mile freeway stretch of I-66 eastbound connecting I-81 and Washington, D.C. The experimental results show the ability of the model to capture the stochastic nature of travel time and to predict travel time accurately.