Adjustment of key lane change parameters to develop microsimulation models for representative assessment of safety and operational impacts of adverse weather using SHRP2 naturalistic driving data.

Introduction: Adverse weather has a considerable negative impact on safety and mobility of transportation networks. Microsimulation models are one of the potential tools that could be used to evaluate the safety and operational impacts of adverse weather. The development of a realistic microsimulation model requires the adjustment of driving behavior parameters with disaggregate trajectory-level data. This study presented a novel approach to update and adjust lane change model parameters for the development of realistic microsimulation models in different weather conditions by leveraging the trajectory-level data from SHRP2 Naturalistic Driving Study (NDS).

Method: Representative key lane change parameters in various weather conditions were extracted from an automatic identification algorithm. These lane change parameters were used to develop microsimulation models in VISSIM in an attempt to assess the safety and operational impacts of adverse weather on a freeway weaving segment.

Results: The evaluation of safety impacts of adverse weather with regard to three Surrogate Measures of Safety (SMoS) namely Time-to-Collision (TTC), Post Encroachment Time (PET), and Deceleration Rate to Avoid Collision (DRAC) suggested that extreme adverse weather (including heavy rain, heavy snow, and heavy fog) produced a higher total number of simulated conflicts compared to clear weather. The operational analysis results revealed that adjusted parameters in most of the adverse weather produced lower average speeds with higher total travel times and total delays than clear weather.

Conclusions: The outcomes of safety and operational assessments for the adjusted parameters showed that the development of microsimulation models should be based on weather-specific, rather than default parameters.

Practical Applications: The methodology presented in this study could be adopted by transportation agencies to develop weather-specific microsimulation models. Moreover, the demonstrated approach could be used to evaluate different Connected Vehicle (CV) applications related to lane change in terms of safety and operations in microsimulation platforms.