Several recently published studies have demonstrated that employing planning strategies oriented on improving the internal characteristics of transit service, including frequency, coverage, intermodal integration, and seamless connectivity between all important trip attractors, positively influences ridership and productivity of multimodal bus and light rail transit systems. However, the research has not assessed overall economic outcomes of implementing these strategies, including social benefits and capital costs. Another emerging body of scholarship pointed to transit service contracting and consolidated regional governance as another possible strategy for improving transit feasibility. Again, not all economic aspects of these decisions have been evaluated thus far, and the available assessments of contracting and transit governance models do not consider long-term effects of specific organizational decisions. This study intends to fill these research gaps by investigating the influence of several internal and external transit performance factors on the amount of net benefits generated by 13 U.S. bus and light rail transit systems, observed annually during the 2001 - 2011 period. The evaluation starts with an estimation of net benefits (agency revenues plus non-direct social benefits minus operating and capital costs). Next, a panel regression model is employed to examine the statistical relationship between specific performance factors and the average net benefits generated by the case systems. The results of this study indicate that higher frequency, higher service density, higher ratio of contracted service and the presence of strong regional transit governance positively influence net benefits. The role of network decentralization (volume of service headed outside of the central business district) appears to be insignificant. These results bring additional evidence indicating the positive outcomes of certain internal transit planning strategies, which corresponds with the findings offered by previous research studies. / A Dissertation submitted to the Department of Urban and Regional Planning in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / Fall Semester, 2014. / September 9, 2014. / benefit-cost analysis, light rail, public transportation, regional governance, transportation economics, transportation planning / Includes bibliographical references. / Jeffrey R. Brown, Professor Directing Dissertation; Keith Ihlanfeldt, University Representative; Andrew Aurand, Committee Member; Michael Duncan, Committee Member; Gregory L. Thompson, Committee Member.

The minibus taxi industry has grown from a modest provider of public transportation to the largest supplier to the urban public. Attempts have been made by government to regulate, integrate, and upgrade this sector but such efforts have been met with varying levels of success. Taxi drivers face immense pressure from passengers and the taxi industry to increase their performance which leads to hostile driving behaviour and often fatal accidents on the road. Transit priority measures, which are techniques used to reduce delays for buses or other forms of public transport on congested roads, have been used to advance the quality of service of buses and BRT vehicles but have not been extended to include the paratransit industry. The purpose of the study is to quantify the economic impact that these forms of infrastructure would have on minibus taxi operators, passengers, and other road users. The various forms of infrastructure were modelled to represent conditions in various parts of the city where frequent stops to load and offload passengers take place. Four alternative service options to the traditional curb-side stop were identified which included a queue-jumping lane, a queue-bypass lane, a single lane pre-signal strategy, and a dedicated minibus taxi lane. Five analytical models were developed, based on macroscopic traffic flow theory, using Excel, to gain a strategic understanding of how the benefits and costs of the infrastructure vary with different traffic conditions. It was observed that all the infrastructure alternatives result in a decrease in travel time, user cost, operating cost, and the total cost per trip for the minibus taxis. Pertaining to the car drivers, a decrease in travel time and total cost was observed because of the reduced delay due to taxi stops no longer impeding traffic. Environmentally, a reduction in harmful gas emissions was noted, particularly in the case of the minibus taxis. The single lane pre-signal strategy and the queue-jumping lane fared the best out of the five options with the lowest travel times and overall cost per hour, resulting in a decrease in total hourly cost of 56%, which consists of construction cost, user cost, and operating cost. A low-cost, commercially available drone was used to monitor the traffic behaviour of minibus taxis on a selected road segment in Pretoria in order to determine the applicability and suitability of the various infrastructure forms. It was observed that the drivers often try to cut corners and skip traffic to save time during peak traffic scenarios. In two cases driving patterns like the case modeled for the queue-jumping lane were displayed cutting time off the drivers’ trip. It was also observed that there is a shortage of infrastructure for minibus taxi operators to pick up and drop off passengers often resulting in them making informal stops that cause congestion. The time passengers save on their often-long travel distances would go a long way to redress the transportation injustices of the past. The monthly savings of over R32 000,00 per taxi driver in operating cost would serve as a subsidy to a public transportation industry currently operating unaided. It was concluded that implementing such significant changes in the public transport industry in South Africa would be equivalent to providing minibus taxi operators with much needed financial support. / Dissertation (MEng)--University of Pretoria, 2019. / Centre for Transport Development / Civil Engineering / MEng / Unrestricted

The transportation program in Madison County and the problems which stem from it are unique for the state of Florida. Madison County is one of the few counties in the state which handle school transportation on a complete contractual basis. At present in Florida, Duval and Madison Counties operate school buses on a complete contractual basis. Liberty County owns one bus and operates seven others on a contractual basis. In Union County all the buses are jointly owned. / Typescript. / "June, 1951." / "Submitted to the Graduate Council of Florida State University in partial fulfillment of the requirements for the degree of Master of Science." / Advisor: H. W. Dean, Professor Directing Study. / Includes bibliographical references (leaves 52-55).

"From pioneer days until shortly after the close of the war between the States, transportation facilities were limited. The child who lived more than a walking distance from school, journeyed to and from school by whatever means his family could provide. Usually this meant a long and tedious ride in a rough wagon which discouraged regular attendance. In many instances the child rode a horse or rowed a boat. Pupil transportation during this era was entirely on a private basis"--Introduction. / "May, 1951." / Typescript. / "Submitted to the Graduate Council of Florida State University in partial fulfillment of the requirements for the degree of Master of Arts under Plan II." / Advisor: H. W. Dean, Professor Directing Paper. / Includes bibliographical references (leaves 50-52).

Accurate vehicle classification data is fundamental to pavement design and road safety analysis. In addition, vehicle classification data is important for Florida Department of Transportation’s Transportation Statistics Office’s clients including consultants, researchers, designers, and planners who use the data to perform various analyses. In the mid-1980s, the Federal Highway Administration (FHWA) developed a standardized vehicle classification system which was designed to meet the needs of many traffic data users. This resulted in the FHWA 13-category classification rule set presently used for most Federal reporting requirements. Furthermore, this serves as the foundation for most State vehicle classification reporting efforts.The Florida Department of Transportation (FDOT) uses the FHWA F-Scheme to classify vehicles throughout the state highway system. This scheme relies mainly on the number of axles and the axle spacing, but on some Weigh-in-Motion (WIM) sites, vehicle weights are also used to improve classification. This thesis evaluates the performance of the Florida vehicle classification table of non-WIM sites using video data as the ground truth.This thesis has two main parts. Part I compares the performance of different data recorders that use FDOT vehicle classification table for WIM and non-WIM sites in classifying vehicles and evaluate the misclassification rates for each recorder. Part II evaluates the accuracy of the Florida vehicle classification table, determines the sources of misclassification, describes the changes recommended in the classification table to improve the classification accuracy, proposes and validates the improved vehicle classification table. This report will be of interest to Florida Department of Transportation and consultants, researchers, engineers, designers, and planners who require accurate vehicle classification information for planning, designing and maintenance of transportation infrastructures. / A Thesis submitted to the Department of Civil Engineering in partial fulfillment of the requirements for the degree of Master of Science. / Summer Semester 2016. / July 6, 2016. / classifier, decision tree, misclassification, vehicle classification / Includes bibliographical references. / Ren Moses, Professor Directing Thesis; Eren Erman Ozguven, Committee Member; John Sobanjo, Committee Member.

No description available.

This thesis identifies the potential net economic gains from substituting less costly transit for relatively costly parts inventory through pipeline reduction. The analysis considers long run steady state economic implications of adjusting the current mix of inventory investments and transportation expenses. The scope of the analysis is focused on a case study of Department of Defense (DoD) parts inventories within the US. The inventory scope is limited to high cost low priority, non-mission critical, replenishment parts (engines, electronics, assemblies and components, etc.). The study uses baseline data collected over 9 months from over 200 inventory sites on thousands of parts. Pipeline reductions are achieved by replacing the current 6 day transit time with a conservative 3 day commercial carrier (e.g., UPS, DHL, Emory Air, etc.). Warehouses in the case study automatically lower inventory levels in response to lowered transit times to prevent inventory buildup. These lower inventory levels will generate reductions in inventory investment and associated holding costs. The reductions in these inventory costs are compared to the increases in transportation expenses to generate benefit to cost ratios. / Master of Arts

An important challenge for human-robot interaction is for both the human and the robot to agree on a model of motion. However, most applications require machine learning and heavy communication for the robot to be an active member in executing a joint task and adapt to human behavior. In this work, we develop a decentralized approach to control and track a catenary cable for object transportation in a human robot interaction scope. Our system is composed of a linked chain that is attached to a quadrotor on one end, and the human on the other. The chain is defined as a catenary curve with five degrees of freedom, and Motion Capture technology is used to track the components of our system. Given the human’s position, we use shape estimation of the curve to determine the drone’s position and control the trajectory of the chain and thus the load attached to it. We then proceed to implement a swing load controller that minimizes the oscillations of the load created by the chain’s movement.

No description available.

Maritime transportation has been continuously playing an undeniable role for the global trade and economy of many countries. Based on the fast growth of the maritime trade, the marine container terminal (MCT) operators should focus on improving the operations planning at the MCTs. Seaside operations have substantial impacts on the general throughput of the MCTs. The daily berth planning as a seaside operation is the point of focus herein. The daily berth planning is modeled as a berth scheduling problem (BSP) in this dissertation. The BSP (as a decision problem) aims to assign the arriving vessels to the available berthing positions and can be reduced to the unrelated machine scheduling problem, which has NP-hard complexity. The large-size instances of decision problems with NP-hard complexity cannot be solved using exact optimization algorithms, while metaheuristic algorithms can effectively solve large-size problem instances and return good-quality solutions. Evolutionary Algorithms (EAs) are among the most popular metaheuristic algorithms deployed to solve the real-size BSPs. There are some algorithmic parameters in EAs (e.g., crossover probability, mutation probability, population size, etc.), which should be assigned the appropriate values to have the best possible performance of the algorithm for a given BSP. The process of determination of algorithmic parameters values is called the parameter selection. Several methodologies have been introduced in the EA literature for parameter selection, which can be classified as follows: (1) parameter tuning; and (2) parameter control. In parameter tuning, the algorithmic parameter values remain constant throughout the algorithmic evolution, while the parameter control strategy updates the algorithmic parameters considering different approaches. In this dissertation, an EA with a self-adaptive parameter control strategy is proposed to solve the developed BSP. Based on a self-adaptive parameter control strategy, the crossover and mutation probabilities are encoded in the solutions and evolve with the EA. The problem is formulated as a mixed-integer linear programming model, minimizing the total weighted vessel turnaround time and the total weighted vessel late departures. Comprehensive numerical experiments are conducted to assess performance of the proposed self-adaptive EA against the alternative EAs, which rely on the different parameter selection strategies. Results demonstrate that all the considered solution algorithms show a promising performance in terms of the objective function values at termination. However, application of the self-adaptive parameter control strategy substantially enhances the objective function values at convergence without a significant impact on the computational time. Furthermore, an EA with an augmented self-adaptive parameter control strategy is presented in this dissertation as another solution algorithm for the BSPs. Based on an augmented self-adaptive parameter control strategy, not only the crossover and mutation probabilities are encoded in the solutions but they are also updated based on the feedback from the search. A mixed-integer linear programming mathematical model is developed for the BSP, aiming to minimize the total costs for serving vessels at the MCT. The designed algorithm is evaluated against nine alternative state-of-the-art metaheuristic algorithms, which have been widely utilized in the BSP literature. The results show that all the developed algorithms have a high level of stability and return high-quality solutions at termination. The computational experiments also prove the superiority of the designed augmented self-adaptive EA over the alternative algorithms considering different performance indicators. Another innovative solution methodology is developed in this dissertation, which relies on the island-based concept. Specifically, a universal island-based metaheuristic algorithm is designed for the BSP, where four different population-based metaheuristics are executed simultaneously in order to effectively search for solutions. A mixed-integer linear mathematical model is developed for the BSP, minimizing the total cost to serve the arriving vessels at the MCT. Comprehensive numerical experiments are conducted to evaluate performance of the island-based algorithm against seven commonly used metaheuristics in the BSP literature. The stability and the capability of the adopted algorithms in providing high-quality solutions at convergence are proven. The results demonstrate that the island-based algorithm outperforms other adopted algorithms considering different performance indicators. To summarize, this dissertation proposes three different solution methodologies for various BSP mathematical formulations. The algorithms have been evaluated based on extensive numerical experiments against the alternative algorithms, which have been widely used in the MCT and freight terminal operations literature. Findings confirm effectiveness of the proposed solution methodologies. Therefore, the developed solution methodologies can serve as promising decision support tools and assist MCT operators with the development of berth schedules. The latter will also assist with serving the growing demand for containerized trade and ensure that the vessel service will be completed in a timely manner. / A Dissertation submitted to the Department of Civil and Environmental Engineering in partial fulfillment of the requirements for the degree of Doctor of Philosophy. / 2019 / October 29, 2019. / Berth Scheduling Problem, Marine Container Terminal, Metaheuristic, Optimization, Supply Chain / Includes bibliographical references. / Maxim A. Dulebenets, Professor Directing Dissertation; O. Arda Vanli, University Representative; Ren Moses, Committee Member; Eren Ozguven, Committee Member; Hui Wang, Committee Member.