MODELING ACCELERATION BEHAVIOR IN A CONNECTED ENVIRONMENT

Connected vehicles technology will provide drivers with information on the presence and behavior of other drivers in their vicinity. This information is intended to help drivers make safe and reliable decisions. It will also affect drivers’ strategic and operational decisions, with the most impact on the operational decisions, including acceleration choice. From the modeling standpoint, however, capturing the effects of this additional information on drivers’ decisions is a challenging task and requires a more thorough understanding of humans’ decision-making processes. Acceleration behavior has been studied extensively in the literature, and several models with varying levels of complexity have been introduced to capture the underlying processes. Unfortunately, most of these models are designed to capture driving behavior in the absence of communications. Their modeling capabilities are even more limited in a mixed environment where only a portion of the vehicles are equipped with the essential communication tools. This additional information motivates different behaviors in this mixed environment. The addition of autonomous vehicles could further contribute to the complexity in this environment. This paper is intended to introduce an acceleration framework to capture the impacts of this additional information on driving behavior. Accordingly, different acceleration models with different assumptions are used for regular, connected, and autonomous vehicles.