Development of a three dimensional lane change model

We will not hesitate to point out congestion and accidents as the two most notorious problems in urban transportation systems. However, constructing more highways and facilitating them to solve the problems cannot be a good solution any more, because of high construction costs and limited spaces in urban areas. In the transportation engineering field, attention is being focused on a new technology called Intelligent Transportation Systems (ITS) which is believed to incline mobility, decrease congestion, and enhance safety in a cost-effective manner. There also are a number of applications of ITS, such as Automated Highway Systems (AHS), Adaptive/Automatic Cruise Control (ACC), and Automated Platooning (AP), and so on.;This research deals with a microscopic traffic flow behavior which is known as the "Lane Change Model (LCM)" and can be applied to those applications in ITS. Although there has been much research on LCMs, they have some problems arising from their two-dimensional concept, such as inaccurate calculations of acceleration or deceleration of objective vehicles and no consideration of highway design factors. Those problems can be regarded as key obstacles that prevent such research from being implemented as applications of ITS.;To overcome those problems and finally enable LCMs to match up with actual road conditions, a new LCM was developed in this research. It is based on three-dimensional and real time (assumed) positional data transmitted from each vehicle within an objective section. In addition, a more realistic kinematic structure for vehicle movement was built with the consideration of highway design factors (grade and super-elevation) in the modeling process. Development of a driver's decision-making framework for the LC maneuver resulted from a review of transportation system components and the identification of influencing factors. The new three-dimensional LCM was expressed as a number of stochastic models that are used as a tool for estimating probability distributions of potential outcomes by allowing for random variations in one or more inputs over time, such as the lane utility estimation model, the urgency checking model, the safety checking model, and the gap acceptance model.;This research selected two sections of expressway for data collection. One section came from JookJeon and the other from ShinGal in South Korea. And traffic images captured by a video recorder from the two sites were processed using a photogrammetric image-processing method to retrieve positional data. Using the collected and processed data, statistical methods such as the "Maximum Likelihood Estimate" method, "paired samples t-test," and "Pearson chi-square test" had been utilized for estimation of the model parameters and validation of the models.;With the results from this research it can be said that it advances the state of the art in modeling drivers' lane changing behavior and the kinematic structure of vehicle movement within it. It also enhances the existing models by adding more realistic urgency checking and safety checking models. Another major contribution of this research is the empirical work, i.e., estimating models using statistically rigorous methods and microscopic data collected from actual traffic movement.