Abstract
Adverse weather has significant impacts on road conditions and traffic dynamics. It is observed that adverse weather as a set of exogenous factors lowers the free flow speed, shifts critical density, decreases flow capacity, and makes the freeway more prone to congestion. This paper proposes a weather factor model to be plugged into a macroscopic traffic prediction model, so that under bad weather traffic variables can be more accurately and reasonably estimated and predicted for traffic control use. To be specific, weather-specific fundamental diagrams are built by introducing weather factors to free flow speed, capacity, and critical density. The weather factors are modelled by selected weather measurements. Weather factor parameters are trained by recent historical weather and traffic data and then can be put into real-time macro traffic prediction and control. The traffic prediction model in the case study is METANET model, in which fundamental diagram parameters are one source of input. The weather-specific prediction error and conventional prediction error are compared. Real data collected by loop detectors on Whitemud Drive, Edmonton, Canada, is used for parameter calibration and prediction error evaluation. The results show that the proposed weather models reasonably improved the accuracy of macro traffic state prediction model compared to conventional model.
Highlights
Over the past two decades, the focus of efforts in modelling and forecasting macroscopic traffic states has transitioned from univariate temporal correlation to multivariate temporal-spatial correlation and from linear to nonlinear forms
We assume that free flow speed (FFS), capacity, and critical density of the fundamental diagram (FD) of each day are impacted by weather
This paper demonstrated that weather conditions impact the driving environment and driver behavior so that it is necessary to build weather-specific FDs
Summary
Over the past two decades, the focus of efforts in modelling and forecasting macroscopic traffic states has transitioned from univariate temporal correlation to multivariate temporal-spatial correlation and from linear to nonlinear forms. The reason to choose METANET model is that it has three separate dynamic functions to predict traffic flow and average speed and density [21] It has a discrete space-time form and allows for convenient discretization intervals [6], so that field collected data can be implemented. What HCM (2010) provided does not consider the weather features in different countries such as Canada or in real time Those reduction percentages cannot be applied to traffic features other than capacity, such as free flow speed or critical speed.
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