Submitted by Luciana Ferreira (lucgeral@gmail.com) on 2015-05-15T15:12:22Z No. of bitstreams: 2 Dissertação - Alisson Neves Harmyans Moreira - 2015.pdf: 1546433 bytes, checksum: 590490e9b894eddd80e967413ea2a2c7 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Approved for entry into archive by Luciana Ferreira (lucgeral@gmail.com) on 2015-05-15T15:19:25Z (GMT) No. of bitstreams: 2 Dissertação - Alisson Neves Harmyans Moreira - 2015.pdf: 1546433 bytes, checksum: 590490e9b894eddd80e967413ea2a2c7 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) / Made available in DSpace on 2015-05-15T15:19:26Z (GMT). No. of bitstreams: 2 Dissertação - Alisson Neves Harmyans Moreira - 2015.pdf: 1546433 bytes, checksum: 590490e9b894eddd80e967413ea2a2c7 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5) Previous issue date: 2015-02-13 / Conselho Nacional de Pesquisa e Desenvolvimento Científico e Tecnológico - CNPq / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / To ensure the grinding of cane sugar is necessary for the stages of harvest or CCT logistics (Cut, loading and transportation) are efficient, being in constant operation and interaction, as in harvest period, the plants work uninterruptedly. Any delay in delivery will result in production losses and hence increased cost. Estimates suggest that only the CCT is responsible for 30% of the cost of production and 12% are represented by the transport. The sugarcane transport involves several types of costs, including the cost of freight and variable costs (fuel consumption, maintenance and tire wear). Shortest paths are commonly chosen as the best route option, however, some variables such as the types of roads (quality) and fleet (individuals or third parties) are not considered in decision making. Routes measurements are obtained from rudimentary way or are neglected, not being employed methods that demonstrate specific information path. In this work, elaborated a Geographic Information System (GIS) for routing the sugarcane transportation. For construction of the road network database, we used an orbital OLI scene image sensor on board the Landsat 8. To this end, we generated a color composite with the bands 6, 5 and 4 (RGB654), and performed fusion and filtering to enable observation and vectorization of roads and carriers. After the generated file, the vectors were submitted to the Arc-node model, dividing them into segments. Then, the GIS was fed attributes collected in the field and data reported in the literature in order to network classification concerning slope and operational performance, being created impedances and restrictions on the roads, in order that the routing simulated of interest to reliable information. The scenarios were organized based on two criteria: operational performance and is less. It was observed that 96% of the roads have steepness less than 5%, favoring the cultivation and transportation of cane sugar. Moreover, 94% of the roads are classified as tertiary. Despite the higher index roads with low quality, the spatial arrangement of the primary and secondary roads provided that the results were not among the conflicting criteria and in most pathways, the best operating performance criterion time was shorter compared to the shortest distance. Still, it was found that the analyzed transport costs did not differ significantly and traffic on roads with better quality can reduce cost Repair and Maintenance (CRM) significantly. Thus, the study showed that there are two aspects in choosing the best route and that this choice is related to the company's variables. / Para garantir a moagem da cana-de-açúcar é necessário que as fases da colheita ou a logística CCT (Corte, carregamento e transporte) sejam eficientes, estando em constante operação e interação, pois em período de safra, as usinas trabalham ininterruptamente. Qualquer atraso na entrega ocasionará prejuízos na produção e, consequentemente, aumento do custo. Estimativas apontam que apenas o CCT é responsável por 30% do custo de produção e 12% destes são representados pelo transporte. O transporte canavieiro envolve diversos tipos de custos, entre eles, o custo de frete e os custos variáveis (consumo de combustível, manutenção e desgaste dos pneus). Caminhos mais curtos são comumente escolhidos como a melhor opção de rota, entretanto, algumas variáveis como os tipos de estradas (qualidade) e frotas (particulares ou terceiros) não são considerados na tomada de decisões. As determinações de rotas são obtidas de maneira rudimentar ou são negligenciadas, não sendo empregados métodos que demonstram informações específicas de cada trajeto. Neste trabalho, elaborou-se um Sistema de Informação Geográfica (SIG) para a roteirização do transporte canavieiro. Para construção da base de dados da malha viária, empregou-se uma cena de imagem orbital do sensor OLI, a bordo do satélite Landsat 8. Para esta finalidade foi gerada uma composição colorida com as bandas 6, 5 e 4 (RGB654), sendo realizada fusão e filtragem para possibilitar a observação e vetorização das estradas e carreadores. Após gerado o arquivo, os vetores foram submetidos ao modelo Arco-Nó, dividindo-os em segmentos. Em seguida, o SIG foi alimentado com atributos coletados em campo e dados referenciados na literatura, visando a classificação da rede quanto à declividade e ao desempenho operacional, sendo criadas impedâncias e restrições na rede viária, com o objetivo de que o roteirizador simulasse as rotas de interesse com informações fidedignas. Os cenários foram sistematizados baseados em dois critérios: desempenho operacional e a menor distância. Observou-se que 96% das estradas apresentam declividades inferiores a 5%, o que favorece o cultivo e o transporte da cana-de-açúcar. Por outro lado, 94% das estradas foram classificadas como terciárias. Apesar do maior índice de estradas com baixa qualidade, a disposição espacial das estradas primárias e secundárias proporcionou que os resultados entre os critérios não fossem discrepantes e que na maioria dos percursos, o tempo do critério de melhor desempenho operacional fosse menor comparado ao de menor distância. Ainda, verificou-se que os custos de transporte analisados não diferiram significativamente e o tráfego em estradas com melhor qualidade pode reduzir Custo de Reparo e Manutenção (CRM) de forma expressiva. Assim, o estudo apresentou que existem duas vertentes na escolha da melhor rota e que essa escolha está associada às variáveis da empresa.

The Master’s thesis focuses on logistics and inventory management, which is integral part of every production oriented economic activity. It deals with methods used to lower expenditures blocked in inventory, storage instruments and transportation for company.

<p>  </p> <p>Motor vehicle crashes at represent a major cause of fatalities and injuries. As such, road transportation agencies continue to seek proactive intersection traffic monitoring initiatives to reduce crashes. Proper monitoring and assessment of crash risk can not only help identify and mitigate safety hazards in real time but also enhance the design of safer facilities and promote safe road-user behavior. It has been suggested in recent literature that unmanned aerial vehicles (UAVs), given their wide visual field and movement flexibility, can potentially help monitor road traffic operations when deployed on the field. In addition, it is recognized that it is still challenging to realize a large-scale ground-based vehicle-to-everything (V2X) network at the current time and in the very near future. In this regards, UAVs can play a critical connectivity role by serving as a hub to facilitate communications among roadway entities (vehicles, infrastructure, and pedestrians). This thesis first presented a methodology that integrates UAVs and V2X connectivity to track the trajectory of intersection users and to monitor potential collisions at intersections. The proposed methodology includes deep-learning-based tracking algorithms and time-to-collision assessments. The methodology was applied using a case study, and the results demonstrated the efficacy of the tracking methodology. Next, the thesis addressed the issue of image quality. During inclement weather, traffic monitoring can be challenging because the video quality is often corrupted by streaks of falling rain on the video image. This may hinder the reliability of characterizing the road environment and road-user behavior during such events. To fully exploit the benefits of video captured by UAVs in traffic monitoring, crash risk assessment, and other safety-related domains, it is critical to ensure high video quality. Therefore, this thesis proposed a two-stage self-supervised learning method to remove rain streaks in traffic videos, where the first and second stages address intra- and inter-frame noise, respectively. The results suggest that the proposed method provides satisfactory performance.</p> <p>  </p>

No description available.

Doctor of Philosophy / Department of Grain Science & Industry / Dirk E. Maier / The feasibility of utilizing cellulosic biomass as an energy feedstock is dominated by factors such as facility location, feedstock availability, and transportation cost. The main goal of this research was to develop a GIS-based method that will generate more accurate biomass residue availability data as input data to biomass supply chain logistics models. This research was carried out in four objectives to ensure that, as improvement parameters were implemented, the methodology remained valid and became more accurate. The first objective compared an existing method to a proposed method to quantify feedstock availability given a facility’s location using a geographical information system. The proposed method proved to be more robust (by a factor of 1.45) than the existing method because it calculates the distance from the facility to farm fields using a real road network, and the acreage of crop-specific fields in a given service area based on crop season specific satellite images. The second objective implemented two improvement parameters to the previously proposed constant removal rate (CRR) method. It examined the effect of field-level yield variance and variable removal rates (VRR) on quantification of the feedstock availability supply for a biorefinery. The new VRR method predicted on average 113,384 ± 38,770 dry tons (DT) of additional residue per service area compared to the CRR method. The third objective further improved the VRR method by utilizing multiple crops as biomass sources and estimating VRR based on crop rotation. On average a 3,793 ± 5,733 DT per service area difference resulted when increasing the number of crop-specific VRR rates used to estimate feedstock quantification. The supplementary use of crop-specific VRR rates affected residue availability given a crop’s residue removal rate is influenced by crop yield, crop rotation, soil characteristics, as well as field location and management. The fourth objective assessed the suitability of potential feedstock storage locations (FSL) to store multi-crop biomass remotely based on a spatial and location-allocation analysis. The sensitivity analysis showed that scenario 2 (16-km; 10-mile service area) appeared to be the more cost-effective option given fewer FSLs (35) were needed and more demand points could be serviced (98.1%) compared to scenario 1 (8-km; 5-mile service area; 62.1% demand points; 50 FSLs), despite presumably higher transportation costs.

<p>  </p> <p>The trucking industry seems to be resistant to electrification, even though truck electrification can lead to large societal as well as user benefits. This dissertation develops a framework to inform policy making and enhance electric vehicle (EV) preparedness in the trucking industry through the study of two interrelated elements: (a) the adoption of electric trucks and (b) the appropriate implementation of electric truck technology. These two elements cover the user perspective, which is not adequately studied, and the agency perspective, which is pivotal in the decision-making process. Specifically, this study addressed the following research questions: (i) which factors affect the purchase decisions of truck fleet managers or owners for electric trucks? (ii) what is the ranking of and interrelationships between the barriers to the adoption of electric trucks? (iii) which location criteria should be considered for the strategic implementation of dynamic wireless charging (DWC) in a freight transportation network and where should this technology be located based on these criteria, and (iv) what is the impact of electric truck adoption on highway revenue and potential of alternative funding mechanisms to recover the revenue loss?</p> <p>For the adoption of electric trucks, a stated preference survey was designed and distributed online to truck fleet managers/owners in the U.S., gathering 200 completed responses. Statistical and multi-criteria decision-making approaches were employed to identify the factors that affect the purchase intentions of truck fleet managers and explore the barriers to electric truck adoption. The results showed that the purchase intentions of truck fleet managers are affected by trucking firm and truck fleet characteristics, behavioral factors/opinions regarding electric trucks, and awareness of innovative charging technologies. Furthermore, electric truck adoption would be accelerated if stakeholders focused on the barriers related to the business model, product availability, and charging time. Additionally, electric truck adopters and non-adopters may not be viewed as one homogenous group, since differences were found in the ranking and interrelationships of barriers to electric truck adoption between these two groups. </p> <p>The implementation of electric truck technology was examined based on the truck fleet managers’ survey, secondary data sources and the case of Indiana, U.S. A multi-criteria decision-making spatial approach was proposed to identify the candidate locations for the deployment of DWC. It was concluded that the most suitable locations for DWC lanes were on interstates, near airports and ports and away from EV charging stations. A data-driven framework was also developed to quantify the impact of electric truck adoption and estimate the optimal fee for each truck to recover the revenue loss. Using the market penetration levels estimated based on the survey data collected, the average annual fuel tax revenue loss for Indiana was approximately $349M. To maintain the same tax revenue per vehicle, annual fees ranging from $969 (in 2021) to $1,243 (in 2035) for single-unit trucks and $6,192 to $7,321 for combination trucks would be needed. To address public relations problems of EV fee implementation, this study also discussed alternative mitigation measures: a vehicle-miles-traveled fee and a pay-as-you-charge fee.</p> <p>In summary, this dissertation contributes to the body of literature by providing significant insights regarding the perspectives of truck fleet managers for electric trucks as well as a comprehensive list of all the location criteria for DWC. The proposed frameworks and study findings can be used by policymakers and other major stakeholders of the EV ecosystem to frame certain strategies to accelerate electric truck adoption, identify the most suitable locations for charging infrastructure, better understand the impact of electric trucks on the highway revenue, and provide the groundwork for developing EV roadmaps.</p>