Abstract: Traffic flow uncertainty quantification is important for making reliable decisions in transportation operations. Compared with well-studied level prediction or point prediction models, the study of uncertainty quantification ...

Abstract: Because of the growing awareness of the importance of the traffic condition uncertainty-related studies, traffic condition uncertainty modeling is gaining increasing attention from the transportation research community. ...

Abstract: Civil engineers have introduced sophisticated sensing and control systems to better operate infrastructure systems, resulting in what are termed

Abstract: The ability to measure or estimate accurate speed data are of great importance to a large number of transportation system operations applications. Estimating speeds from the widely used single inductive loop sensor has ...

Abstract: In nature, expansive soil is generally in a state of swelling during rainfall, and its soil–water characteristic curve (SWCC) exhibits a three-dimensional space shape under the action of an increasing void ratio. In this ...

Abstract: Heteroscedasticity modeling in transportation engineering is primarily conducted in short-term traffic condition forecasting to generate time varying prediction intervals around the point forecasts through quantitatively ...

Abstract: The ability to predict freeway accident likelihood promises significant benefits to freeway operations. However, the development of such prediction models has proven to be very challenging because of the random nature of ...

Abstract: Uncertainty quantification is important for making reliable decisions in transportation planning and operations. In the field of short-term traffic condition forecasting, uncertainty quantification methods include primarily ...

Abstract: Advances in information technology are making the close integration of vehicles and the infrastructure in the surface-transportation system feasible. In particular, one approach to this integration receiving significant ...