Abstract: Operating speed profiles represent drivers’ responses to roadway geometry and are widely used to evaluate safety performance of roadway design. To predict operating speed profile, the majority of early research followed a two-step modeling procedure: (1) estimate speeds at start, middle, and end points of road segments, and (2) fill the profile between the points with assumed driver behavior. This sparse-spot-based modeling strategy has been shown to be inadequate for capturing the complex speed changes resulting from the overlapping horizontal and vertical curves on mountainous roads. This paper proposes a high-resolution modeling approach for operating speeds measured in a dense series of equidistant spots along a road. This type of model is more conducive to analysis of mountainous freeway alignments as operating speeds are predicted along the entire roadway. The high-resolution data were obtained, using the Tongji University Driving Simulator, from a simulated section of mountainous freeway. The estimated linear mixed model includes geometric variables representing the road upstream and downstream of each data collection spot. To determine the suitable lengths of the upstream and downstream segments, the data were extracted from several alternative segment lengths, including fixed lengths and varying downstream length accordingly to sight distances. The model with a spherical structure of error covariance, using geometric data extracted from 300-meter upstream and downstream segments, performed the best. An out-of-sample evaluation of the model has the mean absolute error of 3.2 km/h and the root mean square error of 4.2 km/h, which indicates a promising prediction ability of the proposed model.
Xuesong Wang, Qiming Guo, Andrew Tarko. Modeling Speed Profiles on Mountainous Freeways using High Resolution Speed Data. Transportation Research Part C: Emerging Technologies, 2020, 117: 102679.