A Novel Traffic Speed Prediction for Road Weaving Sections: Incorporating Traffic Flow Characteristics

Yuming ZHOU; Min ZHANG; Chi ZHANG; Zilong XIE; Bo WANG

doi:10.7307/ptt.v36i4.505

Authors

Yuming ZHOU Chang’an University, School of Highway
Min ZHANG Chang’an University, School of Transportation Engineering
Chi ZHANG Chang’an University, School of Highway
Zilong XIE Chang’an University, School of Highway
Bo WANG Chang’an University, School of Highway

DOI:

https://doi.org/10.7307/ptt.v36i4.505

Keywords:

traffic safety, weaving sections, speed prediction method, vehicle trajectory data, variable importance

Abstract

Weaving sections on roads are crucial areas with high concentrations of mandatory lane changes, which can increase the likelihood of traffic accidents. Speed is a key factor in determining traffic safety, and the development of an accurate speed prediction method is essential for improving safety in weaving sections. While current methods are effective in predicting speeds in straightforward situations, they face challenges in more complex scenarios such as weaving sections. This study presents a refined traffic speed prediction approach specifically designed for weaving sections in order to tackle the aforementioned issue. Initially, novel variables were formulated to capture the unique traffic flow attributes present in weaving sections, which distinguish them from standard road segments. Subsequently, supplementary empirical variables that are known to impact speed were incorporated. We conducted a variable importance assessment to ascertain the extent and direction of each variable’s contribution. Lastly, variables with significant positive effects were chosen as inputs for three machine learning algorithms: Random Forest (RF), Backpropagation Neural Network optimised with Genetic Algorithm (BPNN-GA) and Support Vector Regression (SVR). This method was evaluated using aerial footage from five distinct weaving sections in China, maintaining an approximately 3 km/h prediction error. In addition, the study also finds that the speed distribution in weaving sections is negatively correlated with the number of lane-changing. Vehicles experience deceleration at both on-ramp and off-ramp, with a more significant deceleration occurring at the on-ramp. Speed is significantly affected by short length, number of lanes and proportion of large vehicles. The proposed method can be embedded into intelligent traffic systems for safe speeds of autonomous vehicles in weaving sections. Reconstructing spatiotemporal patterns of traffic congestion, predicting traffic accidents and implementing active traffic management strategies in weaving sections could be investigated in the future.

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