The necessity for transportation research has been prompted by the exponential rise in vehicle use, research in the field has expanded and diversified accordingly. Despite substantial work having been carried out in Macroscopic modelling, the theory is far from complete [1]. The paper presents a hybrid model which combines the strengths of macroscopic models; the continuum method with forecasti...

In this research a model of forecasting the travel times of links will be addressed. Forecasting is one of the main topics of an integrated traffic management system and a necessity of dynamic route planning systems. To be able to forecast properly we use both historic data and current data from monitoring devices as input for our dynamic model. Thus, combining the best of both worlds, we are a...

We consider the problem of vehicle routing in the fully general setting of a transportation network with stochastic, time-dependent travel times. To forecast future system state, we adapt a traffic flow model to provide probabilistic predictions. This becomes a generative model for the Markov decision process formalizing the planning problem. We propose an efficient planning algorithm to take a...

This paper presents a method of adaptive traffic signal control using time series forecasting and real time signal phase adjustment. The situation in which a green light turns red before passing an intersection is a familiar and frustrating experience to drivers. The proposed forecast based traffic signal adjustment attempts to predict and alleviate this situation by extending green lights in r...

In this study, statistical models were developed to forecast truck VMT growth of four facility categories at the county and statewide levels. These models incorporate both socioeconomic and transportation system supply variables. Different model specifications were tested and evaluated in terms of statistical and forecasting validity. A selected set of models was used to forecast truck VMT for ...