Modeling Drivers ' Acceleration and Lane Changing Behavior

Abstract

This thesis contributes to the development of microscopic traffic performance models which includes the acceleration and lane changing models. It enhancees the existing models and develops new ones. Another major contribution of this thesis is the empirical work, i.e., estimating the models using statistically rigorous methods and microscopic data collected from real traffic. The acceleration model defines two regimes of traffic flow: the car-following regime and the free-flow regime. In the car-following regime, a driver is assumed to follow his/her leader, while in the free-flow regime, a driver is assumed to try to attain his/her desired speed. A probabilistic model, that is based on a time headway threshold, is used to determine the regime the driver belongs to. Heterogeneity across drivers is captured through the headway threshold and reaction time distributions. The parameters of the car-following and free-flow acceleration models along with the headway threshold and reaction time distributions are jointly estimated using the maximum likelihood estimation method. The lane changing decision process is modeled as a sequence of three steps: decision to consider a lange change, choice of a target lane, ans gap acceptance. Since acceptable gaps are hard to find in a heavily congested traffic, a forced mergin model that captures forces lane changing behavior and courtesy yielding is developed. A discrete choice model framework is used to model the impact of the surrounding traffic environment and lane configuration on drivers' lane changing decision process. The models are estimated using actual traffic data collected from Interstate 93 at the Central Artery, located in downtown Boston, MA, USA. In addition to assessing the model parameters from statistical and behavioral standpoints, the models are validated using a microscopic traffic simulator. Overall, the empirical results are encouraging, and demonstrate the effectiveness of the modeling framework.