Determining vehicle operating speed and lateral position along horizontal curves using linear mixed-effects models

Area of Science:

• Traffic safety engineering
• Transportation research
• Road safety analysis

Background:

• Highway safety has improved, yet crashes on curves persist as a major concern.
• Understanding factors contributing to curve crashes is crucial for developing effective safety interventions.

Purpose of the Study:

• To present and demonstrate a methodology for collecting and analyzing vehicle trajectory and speed data on rural and urban curves.
• To develop predictive models for vehicle speed and lateral position along curves.
• To identify variables influencing driver behavior on curves.

Main Methods:

• Utilized Z-configured road tubes for data collection on vehicle trajectory and speed.
• Applied linear mixed-effects models to predict vehicle speed and lateral position.
• Analyzed data from horizontal curves in Dexter and Ames, Iowa, considering operational, vehicle, and environmental variables.
• Visually represented the behavior of identified "risky" drivers.

Main Results:

• Linear mixed-effect regression models successfully predicted vehicle speed and lateral position, accounting for repeated observations.
• Models demonstrated the influence of entry speed and lateral position on trajectory and speed profiles.
• Significant variables influencing speed and lateral position on rural curves included time of day, direction of travel, and vehicle type.

Conclusions:

• Vehicle entry speed and lateral position are critical determinants of trajectory and speed profiles on curves.
• Time of day, direction of travel, and vehicle type significantly impact vehicle speed and lateral position on rural horizontal curves.
• The developed methodology provides insights into causal factors of curve crashes, aiding future safety improvements.