Thesis (M.S.)--California Polytechnic State University, 2009. / Mode of access: Internet. Title from PDF title page; viewed on Jan. 7, 2010. Major professor: Helen Yu. "Presented to the faculty of California Polytechnic State University, San Luis Obispo." "In partial fulfillment of the requirements for the degree [of] Master of Science in Electrical Engineering." "2009." Includes bibliographical references (p. 82-83).

Thesis (Ph. D.)--University of Hong Kong, 2002. / Includes bibliographical references (leaves 179-187).

In the continuum modeling of traffic networks, a macroscopic cost-flow function (MCF) and macroscopic fundamental diagram (MFD) can be used to represent the fundamental relationships between traffic quantities such as speed, flow, and density. The MCF governs the steady-state cost-flow relationship, whereas the MFD represents the instantaneous inter-relationship between speed, flow, and density of traffic streams. This thesis explores the influence of network topologies on the MCF and MFD. The Hong Kong road system is divided into unit-sized road networks with various physical characteristics for which the network structure and signal timings are reserved. By universally scaling the origin-destination (OD) matrices of the morning peak, traffic conditions ranging from free-flow to congestion are created for microscopic simulation. From the simulation results, an MCF that relates the average journey time and the number of vehicles traveling through the network in one hour and an MFD that relates space to the mean speed and average density aggregated across 300s intervals are derived. The MCF and MFD are calibrated with mathematical models for each network. The density of roads, junctions, and signal junctions all influence the value of the macroscopic parameters in the MCF and MFD, and predictive equations are constructed that relate the macroscopic parameters to the network topological characteristics. Based on the fitting performance of the mathematical models, recommendations are made for selecting MCF and MFD models for continuum modeling. / published_or_final_version / Civil Engineering / Master / Master of Philosophy

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published_or_final_version / Civil Engineering / Master / Master of Philosophy

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In contemporary traffic flow theory, the distinction between user classes is rarely made. However, the accuracy and the descriptive power of the macroscopic traffic flow models can be improved significantly by separating user – classes and their specific flow characteristics. Additionally, the availability of a realistic multiple user – class traffic flow model enables the automated generation of user – dedicated traffic control policies by means of mathematical optimal control theory. A macroscopic multiple user – class model is derived from mesoscopic principles. In this paper there are analysed such a multiple user – class traffic flow model. Starting point for the analysis of the macroscopic flow model is the user – class specific phase – space density, which can be considered as a generalization of the traditional density. The gas – kinetic equations describing the dynamic of the multiclass Phase – Space Density (MUC - PSD) are governed by various interactions processes, such as accelerations towards a class – specific desired velocity, deceleration caused by vehicle interactions and the influence of lane changing. The gas – kinetic equations serve as the foundation of the proposed macroscopic traffic flow models, describing the dynamic of the class – dependent spatial density, velocity and velocity variance. These relations show competing processes: on the one hand, drivers accelerate towards their class – dependent desired velocity, while on the other hand, they need... [to full text]

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We want to be able to use information about the traffic flows at road junctions and covariates describing those junctions to predict the number of accidents occurring there. We develop here a Bayesian predictive approach. Initially we considered three simpler but related problems to assess the efficiency of some approximation techniques, namely: (I) Given a treatment with an effect that can be described mathematically as of a multiplicative form, we record Poisson countings before and after the treatment is applied. Then, given a new individual with a known counting before the treatment is used, we want to predict the outcome on that individual after the treatment is applied. (II) After observing the value on an individual before any treatment is applied, we decide, based on that value, which of two treatments to apply, and then register the post- treatment outcome. Given a new individual, with an observed value before he receives any treatment, we aim to derive the predictive distribution for the outcome after one of the treatments is used. (This problem is also considered when several possible treatments are available). (III) We compare the effects of two treatments, through a two-period crossover design. We assume that both the treatment effect and the period effect are of multiplicative forms. Estimative and approximation methods are developed for each of these problems. We use the Gibbs sampling approach, normal asymptotic approximations for the posterior distributions and the Laplace approximations. Examples are presented to compare the efficiency and performance of the different methods. We find that the Laplace method performs well, and has computational advantages over the other methods. Using the knowledge obtained solving these simpler problems we develop solutions for the traffic accidents problem and analyse a real data set. Stepwise procedures for the incorporation of the covariates through the use of Kullback-Leibler measure of divergence are developed. We also consider the three simpler problems assuming that the observations are exponentially and binomially distributed.

The dissertation examines the relationship between the number of lane changes, the speed of the ramp lane, and the location upstream of the ramp split. Analyses indicate the number of lane changes exhibits a parabolic relationship with respect to the ramp lane speed, and the number of lane changes exhibits gamma-distributed relationship with respect to the distance upstream of the ramp. The macroscopic lane changing model presented is best characterized as the development of generalized lane-changing relationships, and provides a starting point from which more complex corridor-level models can be developed. This study also identifies an unusual car-following behavior exhibited by certain lane-changing drivers. When the target lane is moving slowly, some lane-changing drivers will slow down, causing a disruption in their initial lane. Regression analysis is used to estimate the speed upstream of the initial lane to indicate the disruption is responsible for the lateral propagation of congestion. The lane choice of exiting vehicles is also studied. Lane choice appears to be a function of origin/destination, and freeway speed. As speeds in the general purpose lanes decrease, exiting vehicles are more likely to wait longer to move into the exit ramp lanes, resulting in an increased lane changing density. Results from this study are expected to have the greatest impact on microscopic lane-change model validation. Additionally, results have implications for design and safety issues associated with freeway ramps. As data collection technologies improve and data becomes increasingly available, this research provides the basis for the further development of more elaborate lane-changing models.