Transportation engineering

novel approach to extend the decision-making capabilities of unmanned surface vehicles
(USVs) is presented in this work. A multi-objective framework is described where separate
controllers command different behaviors according to a desired trajectory. Three behaviors
are examined – transiting, station-keeping and reversing. Given the desired trajectory, the
vehicle is able to autonomously recognize which behavior best suits a portion of the
trajectory. The USV uses a combination of a supervisory switching control structure and a
reinforcement learning algorithm to create a hybrid deliberative and reactive approach to
switch between controllers and actions. Reinforcement learning provides a deliberative
method to create a controller switching policy, while supervisory switching control acts
reactively to instantaneous changes in the environment. Each action is restricted to one
controller. Due to the nonlinear effects in these behaviors, two underactuated backstepping
controllers and a fully-actuated backstepping controller are proposed for each transiting, reversing and station-keeping behavior, respectively, restricted to three degrees of freedom.
Field experiments are presented to validate this system on the water with a physical USV
platform under Sea State 1 conditions. Main outcomes of this work are that the proposed
system provides better performance than a comparable gain-scheduled nonlinear controller
in terms of an Integral of Absolute Error metric. Additionally, the deliberative component
allows the system to identify dynamically infeasible trajectories and properly
accommodate them.

Due to the presence of deep deposits of organic soils at shallow depths, roadways in western Palm Beach County, FL show premature distress and failure requiring frequent rehabilitation. In an effort to develop effective mitigation strategies, 24 test sections, containing various types of pavement reinforcing materials, were constructed during Fall of 2008. This study presents a forensic investigation and dynamic characterization of reinforced asphalt cores obtained from SR15/US98 for evaluating the uniformity of test sections, qualitative comparisons, and developing empirical models for predicting permanent deformation and material damage with stress cycles simulating traffic loading.

Nowadays it is very hard to find available spots in public parking lots and even harder at facilities such as universities and sports venues. A system that provides drivers with parking availability and parking lot occupancy will allow users find a parking space much easier and faster. This thesis presents a system for automatic parking lot occupancy computation using motion tracking. The use of computer vision techniques and low cost video sensors makes it possible to have an accurate system that allows drivers to find a parking spot. Video bitrate and quality reduction and its impact on performance were studied. It was concluded that high quality video is not necessary for the proposed algorithm to obtain accurate results. The results show that relatively inexpensive and low bandwidth networks can be used to develop large scale parking occupancy applications.

In recent years, there has been an exponential increase in container volume shipment within intermodal transportation systems. Container terminals as part of the global port system represent important hubs within this intermodal transportation system. Thus, the need to improve the operational efficiency is the most important issue for container terminals from an economic standpoint. Moreover, intermodal transportation systems, ports and inland transport facilities should all be integrated into one coordinated plan. More specifically, a method to schedule different types of handling equipment in an integrated way within a container terminal is a popular topic for researchers. However, not many researchers have addresses this topic in relationship to the simulation aspect which will test feasible solutions under real container terminal environment parameters. In order to increase the efficiency of operations, the development of mathematical models and algorithms is critical in finding the best feasible solution. The objective of this study is to evaluate the feasible solution to find the proper number of Yard Trailers (YTs) with the minimal cost for the container terminals. This study uses the Dynamic YTs operation's method as a background for modeling. A mathematical model with various constraints related to the integrated operations among the different types of handling equipment is formulated. This model takes into consideration both serving time of quay cranes and yard cranes, and cost reduction strategies by decreasing use of YTs with the specific objective of minimum total cost including utilization of YTs and vessel berthing. In addition, a heuristic algorithm combined with Monte Carlo Method and Brute-Force Search are employed. The early Stage Technique of Monte Carlo method is proposed to generate vast random numbers to replicate simulation for real cases.