Because of the population increasing so high, and traffic density remaining same, prediction has become a great challenge today. Creating higher degree communication in automobiles results time wastage, fuel environmental damage, even death caused by citizens being trapped middle traffic. Only few researchers work congestion control systems, but it may provide less accuracy. So, this paper prop...

In this paper we propose a “prediction-correction” framework to model traveler’s perception evolution under network disruption. Distinctive from existing models, the proposed framework assumes drivers make their trip choices according to their predictions on future traffic, since previous daily experiences become invalid when network is disrupted. Drivers predict travel costs after network disr...

Predictive reasoning, or the problem of estimating future observations given some historical information, is an important inference task for obtaining insight on cities and supporting efficient urban planning. This paper, focusing on transportation, presents how severity of road traffic congestion can be predicted using semantic Web technologies. In particular we present a system which integrat...

In this paper, we present our work on clustering and prediction of temporal dynamics of global congestion configurations in large-scale road networks. Instead of looking into temporal traffic state variation of individual links, or of small areas, we focus on spatial congestion configurations of the whole network. In our work, we aim at describing the typical temporal dynamic patterns of this n...

Title of Document: UNCERTAINTY ASSOCIATED WITH TRAVEL TIME PREDICTION: ADVANCED VOLATILITY APPROACHES AND ENSEMBLE METHODS YANRU ZHANG, PH.D, 2015 Directed By: ALI HAGHANI,PROFESSOR DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING Travel time effectively measures freeway traffic conditions. Easy access to this information provides the potential to alleviate traffic congestion and to increase t...

Accurate prediction of network traffic is very important in allocating resources. With the rapid development technology, becomes more complex and diverse. The traditional model cannot accurately predict current within effective time. This paper proposes a Network Traffic Prediction Model----NTAM-LSTM, which based on Attention Mechanism with Long Short Time Memory. Firstly, preprocesses historic...