Incorporating Behavioral Adaptation of Human Drivers in Predicting Traffic Efficiency of Mixed Traffic: A Case Study of Priority T-Intersections - Reddy, Nagarjun; Raju, Narayana; Farah, Haneen; Hoogendoorn, Serge (European Journal of Transport and Infrastructure Research, 2025)
As automated vehicles (AVs) are increasingly being deployed on our road network, it becomes important to understand how human drivers interact with them in mixed traffic. This research investigates how mixed traffic factors affect human-driven vehicles (HDV) gap acceptance behavior at a priority T-intersection. Using a driving simulator, four scenarios were tested by varying AV driving style (less defensive, more defensive, and HDV-like) and AV recognizability (distinguishable or not from HDVs). Gap acceptance models were estimated based on the collected trajectory data. These models were then implemented in SUMO, a microscopic traffic simulation platform, where a T-intersection network was set up. Simulation runs varied based on AV driving style, recognizability, penetration rate of AVs (0-75% in 25% increments), and whether HDV behavioral adaptation was considered. The minor road had only traffic composed of HDVs, while the major road had both AVs and HDVs (i.e., mixed traffic). Vehicle delay and queue length on the minor road were used as performance indicators to evaluate the effect of mixed traffic, on the travel efficiency of minor road vehicles. The results indicate that minor road vehicle delays increase with higher AV penetration rates in traffic on the major road. Recognizable and less defensive AVs causes more delays for minor road vehicles compared to other scenarios. Ignoring behavioral adaptation led to a delay underestimation of up to 75% for minor road vehicles. In conclusion, there is a behavioral adaptation in gap acceptance of HDVs in mixed traffic environments. This is affected by AVs recognizability, the driving style of AVs, and their penetration rate. Taking into account the behavioral adaptation is essential for accurately assessing traffic efficiency in mixed traffic conditions. This will help to direct policy decisions related to AVs recognizability and driving style and about infrastructural interventions.