Weather-Based Lane-Change Microsimulation Parameters for Safety and Operational Performance Evaluation of Weaving and Basic Freeway Segments

Abstract

It is well recognized that adverse weather conditions have significant negative impacts on safety and mobility of transportation systems. Microsimulation modeling has emerged as a cost-effective tool to quantify the safety and operational effects arising from adverse weather. Developing a realistic microsimulation model necessitates adjusting driving behavior models through trajectory-level data. This study contributes a methodology to update lane-change parameters to develop weather-specific microsimulation models based on different freeway facilities, and ultimately evaluate the safety and operational performance of the roadways. Representative parameters in various weather and facility types were extracted using an automated process. As part of the comprehensive assessment of the adjusted parameters, a weaving section and a basic freeway segment on Interstate 80 in Wyoming were identified as potential candidates. The safety and operational analyses were conducted using VISSIM. Various simulation scenarios were designed based on the field traffic flow data. The safety analysis using three surrogate measures of safety including time-to-collision, deceleration rate to avoid collision, and post encroachment time revealed that adverse weather generated a higher number of conflicts than did clear weather for both facilities. The operational analysis suggested that adverse weather produced lower average speed and higher total travel time and delay than clear weather. The demonstrated methodology could be used in assessing various connected vehicle applications associated with lane change in microsimulation from safety and operational perspectives and could be adopted by transportation agencies to develop weather-based microsimulation models.

Deep Learning Approach for Detecting Lane Change Maneuvers Using SHRP2 Naturalistic Driving Data

“…The developed detection models could be highly beneficial to gather fundamental information on lane change behavior and how the behavior changes according to driver demographics, geometric attributes, weather events, and traffic conditions. This information could be used to develop representative scenarios in microsimulation platforms to assess and validate the effectiveness of the safety and operational treatments aimed at reducing roadway disturbances, such as turbulences associated with lane changes (47)(48)(49)(50).…”