Thesis (Ph. D.)--University of Texas at Austin, 2001. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Supervisor: Carl. T. Haas. Vita. Includes bibliographical references.

Different innovative concepts are aiming to improve last-mile urban logistics and reduce traffic congestion. Congested metropolitan cities are implementing last-mile delivery robots to make the delivery cheaper and faster. A key factor for the success of Automated Delivery Robots (ADRs) in the last-mile is its ability to meet the fluctuating demand for robots at each micro-hub. Delivery companies rent robots from micro-hubs scattered around the city, use them for deliveries, and return them at micro-hubs. This paper studies the dynamic assignment of the robots to satisfy their demands between the micro-hubs. A Mixed-Integer Linear Programming (MILP) model is developed, which minimizes the total transportation costs by determining the optimum required fleet size. The result determines the number of robots required for each planning period to meet all the demands. It provides algorithms to operate and schedule the robot-sharing system in the last leg of the delivery in dense urban areas. / Includes bibliography. / Thesis (MS)--Florida Atlantic University, 2021. / FAU Electronic Theses and Dissertations Collection

Freight transportation is a significant component of the nation’s economy. However, the augmented volume of the freight movements contributed to continuously increasing congestion on the urban road networks, that affects the timeliness and reliability of freight transportation. In addition, congestion has a negative impact on the transit operations as well. Various studies conducted on multi-modal corridors recognized the importance of the simultaneous performance of freight and transit operations. Thus, Intelligent Transportation System (ITS) components, such as Freight Signal Priority (FSP) and Transit Signal Priority (TSP), present traffic operations strategies "shaped" to give priority, reduce delay and travel time, and overall improve the performance of freight and transit movements, respectively. The primary objective of the thesis refers to evaluate possible improvements in freight mobility, while sustaining good transit services and minimizing congestion on the multi-modal corridor, through simultaneous implementation of the FSP and the TSP. The effectiveness of the newly established criteria was evaluated through real-world case study on a micro-simulation platform. The results showed significant improvements on all the vehicle movements. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2019. / FAU Electronic Theses and Dissertations Collection

Thesis (MScIng)--University of Stellenbosch, 2007. / Bibliography. Also available via the Internet.

Thesis (Ph. D.)--University of Louisville, 2008. / Department of Civil and Environmental Engineering. Vita. "December 2008." Includes bibliographical references (leaves 193-197).

Dissertation (PhD)--University of Stellenbosch, 2006. / Bibliography. Also available via the Internet.

The traffic safety and efficiency applications made possible by vehicular communications have the potential to improve the lives of millions of people who, every day, use automobiles as their primary means of transportation. To be well connected and fully functional, these networks of cars require a minimum number of active nodes, which often may not happen due to a lack of radio-equipped vehicles on the road. These same networks can also be overwhelmed with traffic and signaling in the presence of too many cars, requiring careful coordination between all nodes to ensure proper operation. One way to overcome both these problems is to supplement vehicle-to-vehicle (V2V) communications with vehicle-to-infrastructure (V2I) systems by deploying Roadside Units (RSUs) along the road to support the network of moving cars. RSUs are infrastructure nodes that can supplement sparse networks in low-density scenarios, and help coordinate and move data in denser networks. RSUs have an associated cost, however, and so their numbers need to be minimized while still maintaining a significant improvement to the vehicular network. he work presented in this thesis quantities the benefits of Roadside Unit deployments and proposes innovative approaches that can reduce and even eliminate the need for RSUs altogether.he first part of the thesis focuses on highway networks: first, an analytical model is developed to analyze communication delay in scenarios with sparse bi-directional traffic, considering both disconnected and connected RSUs.hen, a study on connectivity and message dissemination in these networks reveals how significant benefits of RSUs are only achieved when the deployed RSUs are interconnected. Extensive simulation work paired with sets of experimental measurements validate both model and study. Supplementing the work on sparse highway networks, an infrastructure-less approach is then proposed, consisting of two methods to improve communication delays in these scenarios: decelerate disconnected vehicles as they receive safety messages, and boost the same vehicles’ radio transmit power, to shorten the time to restore connectivity. Both techniques are modeled analytically, and data from a simulation study validate the models and show significant improvements in the connectivity of sparse highway networks with this infrastructure-less approach. he second part of the thesis sets its sights on urban vehicular networks. High costs associated with RSUs prevent their deployment at scale, and therefore finding alternative solutions to this longstanding problem is very important. A novel, low-cost self-organizing network approach to leveraging parked cars as RSUs in urban areas is proposed here, enabling parked cars to create coverage maps based on received signal strength and to decide whether to become RSUs from that knowledge. Initial simulation work reveals significant benefits to emergency message broadcasting delay in sparse scenarios and shows the ability of the self-organizing approach in providing robust and widespread coverage to dense urban areas, using only a small fraction of the cars parked in a city. he parking behaviors of individual drivers are then studied, by analyzing and gathering statistics on travel survey data from various metropolitan areas. Daily and hourly analytical models of parking events are provided, along with important derivations.he statistical data show that parking events can be classified into two major groups based on the time a car spends parked, and that these patterns vary substantially throughout the day while being markedly similar across different cities. he last part of the thesis focuses on self-organization for parked car RSUs. Novel mechanisms for self-organization are introduced that are innovative in their ability to keep the network of parked cars under continuous optimization, in their multicriteria decision process, and in their control of each car’s battery usage, rotating roadside unit roles between vehicles as required.he first comprehensive study of the performance of such approaches is presented, via realistic modeling of mobility, parking, and communication, thorough simulations, and an experimental verification of concepts that are key to self-organization. his analysis leads to strong evidence that parked cars can serve as an alternative to fixed roadside units, and organize to form networks to support smarter transportation and mobility.

Traffic congestion and over saturation is a common worldwide problem. This problem is more severe in urban areas due to rapid increase in number of vehicles. City traffic often results in traffic blockage at peak office hours. The same situation can also be observed on freeways, especially at entrance/exit areas of a city. In most cases the drivers are totally unaware about road traffic situations in advance. Intelligent transportation systems offer various applications to improve road traffic.  In this manner the timely and reliable traffic information can be greatly beneficial for travelers. Real time travel time information can be adopted by vehicles as an excellent tool which can result to reduce the impact of continuous traffic increase on urban roads. Real time travel time estimation is a complex task as it involves various factors. This travel time estimation is even more difficult for urban road networks. In our dissertation, we have investigated an estimated time of arrival approach in order to inform the drivers in advance about travel time. We have introduced Estimated Time of Arrival (ETA) in a clever and intelligent fashion by developing a Case-Based Reasoning engine. We believe that by adopting this simple and intelligent approach, it can greatly help improving traffic congestion overall. We also believe that this strategy will also help in reducing the emission of environment unfriendly gases, thus helping mankind.

This research is designed to study the effects of highly developed information technologies and logistic strategies on freight transportation. A simulation model called TTMNet is formulated as a multi-level product supply chain system that integrates the financial, informational, logistic, and physical aspects of transportation networks, to address freight transportation problems within a much broader decisionmaking and policy sensitive environment. It simulates freight movement in a regional supply chain, given real-time information, to help understand the mechanisms or principles followed by system operation, freight flow patterns, and evolution and interaction of prices and costs across the networks over time. TTMNet is implemented using micro-simulation techniques and GIS tools, and several simulators are involved, including a dynamic freight traffic simulator, a supply chain decision making simulator, and a real-time information simulator. The construction of a prototype dynamic freight traffic simulation model called DyFTS is the focus of this research. DyFTS is designed as a discrete event simulation system, highly adaptable to more comprehensive transportation simulation models. Various decision-making processes are formulated within DyFTS, such as goods-to-vehicle assignment, departure time choice and pre-trip routing, and en-route vehicle redirection. Descriptive real-time traffic information is simulated to study its influence on freight traffic operations. A knowledge-based learning process is established to refine the perceptions of decision-makers to the transportation network based on past experience. The inclusion of the simulation of regional ITS system makes the DyFTS a powerful tool to evaluate the information. A preliminary study is conducted herein to construct an integrated logistic and transportation system, a simplified version of TTMNet. It simulates enterprise-wide freight movements in a comprehensive product supply chain system that integrates the logistic, informational, and physical aspects of transportation networks. The resulting simulation model developed in this dissertation can be applied widely in freight transportation industry, such as, the study of commercial vehicle operations with and without real-time information, freight transportation of different delivery time requirements and different fleet configurations, and freight traffic patterns by product demand. It may also help direct or justify the development of real-time surface transportation information systems.