This dissertation research is concerned with the study of two important traffic phenomena; merging and lane-specific traffic behavior. First, this research investigates merging traffic behavior through empirical analysis and evaluation of freeway merge ratios. Merges are important components of freeways and traffic behavior around them have a significant impact in the evolution and stability of congested traffic. At merges, drivers from conflicting traffic branches take turns to merge into a single stream at a rate referred to as the “merge ratio”. In this research, data from several freeway merges was used to evaluate existing macroscopic merge models and theoretical principles of merging behavior. Findings suggest that current merge ratio estimation methods can be insufficient to represent site-specific merge ratios, due to observed within-site variations and unaccounted effects of downstream merge geometry. To overcome these limitations, merge ratios were formulated based on their site-specific lane flow distribution (LFD), the proportion of flow in each freeway lane, for two types of merge geometries. Results demonstrate that the proposed methods are able to improve merge ratio estimates, reproduce within-site variations of merge ratio, and represent more effectively disproportionate redistribution of merging flow for merges where vehicles compete directly to merge due a downstream lane reduction.

Second, this research investigates lane-specific traffic behavior through empirical analysis and statistical modeling of lane flow distribution. Lane-specific traffic behavior is also an important component in evaluating freeway performance and has a significant impact in the mechanism of queue evolution, particularly around merges, and bottleneck discharge rate. In this research, site-specific linear LFD trends of three-lane congested freeways were investigated and modeled. A large-scale data collection process was implemented to systematically characterize the effects of several traffic and geometric features of freeways in the occurrence of between-site LFD variations. Also, an innovative three-stage modeling framework was used to model LFD behavior using multiple logistic regression to describe between-site LFD variations and Dirichlet regression to model recurrent combinations of linear LFD trends. This novel approach is able to represent both between and within site variations of LFD trends better, while accounting for the unit-sum constraint and distribution assumptions inherent of proportions data. Results revealed that proximity to freeway merges, a site’s level of congestion, and the presence of HOV lanes are significant factors that influence site-specific recurrent LFD behavior.

Findings from this work significantly improve the state-of-the-art knowledge on merging and lane-specific traffic behavior, which can help to improve traffic operations and reduce traffic congestion in freeways.