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Road traffic accident prediction is to provide traffic safety change trend for road safety administrator institution.Based on the analysis of road traffic accident,a set of RBF neural network model which are applied to forecast traffic accident are presented.The RBF neural network model used to predict and extrapolate the number of death and economy lose of 2000~2006.The result shows the forecasting...
Effective traffic incident management requires a full understanding of the various corresponding properties to accurately estimate incident durations and to benefit decision makings of reducing the impact of non-recurring incident relevant congestions. This paper incorporates discrete choice theory in incident duration prediction. Ordered Probit Model is employed to forecast the incident duration...
This paper addresses the use of social behavior models for the prediction of a pedestrian's future motion. Recently, such models have been shown to outperform simple constant velocity models in cases where data association becomes ambiguous, e.g. in case of occlusion, bad image quality, or low frame rates. However, to account for the multiple alternatives a pedestrian can choose from, one has to go...
Agent-based simulations show their potential in many context of transport management in presence of unusual demand, such as airport passenger terminals, railway stations, urban pedestrian areas, public buildings, street events or open space exhibitions, where management or control by related authorities and public safety are strongly affected by spatial geometry and crowd behavior. We illustrate these...
The paper presents three kinds of grey neural network combined model for short-term prediction of urban traffic parameters, which are parallel grey neural network, series grey neural network, and inlaid grey neural network. They are employed to forecast a real vehicle speed in Barbosa road of Macao with satisfied precision. The experiment shows that the above three kinds of mode are feasible and effective...
Prediction of traffic volume is the key technology in intelligent transportation systems. BP neural network is universally used in prediction of traffic volume. This research aimed at advancing BP neural networkpsilas precision in prediction of traffic flow. The method of prediction of traffic volume was based on the subsection learning of double-layers BP neural network. The improved method was used...
This paper proposes a framework of the dynamic data driven multi-agent simulation system (Gary M. Pereira, 2007) in maritime traffic domain. It presents a survey on the current developments of maritime traffic simulation. Multi-agent templates have been devised to capture the knowledge of how the individual agents interaction in pursuit of the shared goals to solve the complex problems of port maritime...
In the paper, SVM (support vector machines) with SRM is aided to forecast readiness and sustainable capability, which can be improved by machine learning. The status parameters of armored vehicle engine are used as a case to analyses, establishes a model to forecast, which can be optimized in model indexes. Finally, the conclusion comes to the validity of method.
Accurate, reliable, and timely traffic information is critical for deployment and operation of intelligent transportation systems (ITSs). Traffic forecasting for travelers and traffic operators should become at least as useful and convenient as weather reports. In the US, the Federal Highway Administration (FHWA) has envisioned a real-time traffic estimation and prediction system (TrEPS) as an ITS...
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