Freight and freightage

The project starts from a literature review of the topics from these aspects: the studies of emission models, the eco-driving applications for heavy-duty vehicles (Trucks), Eco-driving and signal control, the benefits from CAVs and Simulation using MOVES and VISSIM. The research on Multiclass/heterogeneous traffic modeling is also reviewed. To define the problem, the research starts with the analysis of the influence the truck percentage has on the individual signalized intersection and on a coordinated signal corridor. The simulation results show the high percentage of heavy-duty vehicles in traffic may significantly degrade the signal control based on the concept of delay optimization mainly considering passenger cars. To solve the problem, an eco-driving strategy for freight mobility control at signalized intersections is introduced. It is by optimizing the travel time while maintaining optimal fuel consumptions and emissions. A two-level dynamic optimization is formulated. An emission weighted optimization is used to simulate vehicles passing the intersection with balanced travel time and emissions savings and compared to a baseline simulation without eco-driving consideration. A jerk penalty is added to ensure safety and comfort. Heavy-Duty Vehicles (HDVs) are the focus of this modeling effort. The emission term in the optimization used an instantaneous speed-acceleration based microscopic fuel consumption models and the results were validated by EPA's MOtor Vehicle Emission Simulator (MOVES) model. The results from this study showed that the weighting factor of the emission term in the objective function reaches an optimal at 0.5. Generally, the proposed method provided dynamic trajectories with slightly longer travel time than the baseline but reduce the emission at about 4% for Nitrogen oxide (NOx) and 7% for carbon dioxide (CO2) for different initial conditions (different distance approaching intersection). Based on the results, an optimal weighting factor of emission term and the range of distances to apply the eco-driving strategy are recommended. A case study is performed to simulate the recommended model, with varying HDV percentages. The test results showed an overall emission reduction of 6% for NOx and 6% for CO2 according to MOVES. To show the relationship between truck percentage and discharge rate, a multiply linear regression is conducted, and the results are shown in the appendix. The data in MOVES and the emission models used are also presented in the appendix.

Over the last few years, a rapid explosion of new technologies has created opportunities to address critical freight transportation challenges in urban, suburban, and rural areas. These innovations include truck platooning, smart parking systems, collaborative and shared logistics techniques, and connected autonomous vehicles. These new technologies are influencing consumer behavior and reshaping freight supply chain management at the urban, regional, and international level. In order to understand how these innovations are changing the field of freight transportation, it is essential to understand how organizations choose to adopt innovations. Adoption methods available from consumer behavior research are mostly based on individuals, and there is limited material on the behavior of organizations in regards to innovation adoption. The general adoption methods cannot be directly used in modeling adoption of innovations by organizations without further study and modifications.
Approaches to innovation adoption can be broken into two sections: theoretical and methodological approaches. The theoretical approaches attempt to identify the forces that cause an organization to accept or reject an innovation. Once the forces have been identified, the theoretical approaches explain how the forces interact and influence the adoption process. Methodological approaches are composed of modeling techniques which can be applied to innovations in order to generate predictions of adoption patterns. By identifying the benefits and drawbacks of each approach, it is possible to select the most appropriate theoretical and methodological approach for organizational adoption.

Efficient freight mobility plays a major role in the economy, and its performance is closely related to the quality of the transportation system. Requirements for funding transportation infrastructure projects often do not specify the analytical tools planners should use to request funding. Critical Urban and Rural Freight Corridors are sections of the National Highway Freight Network providing critical connectivity of goods and must have improved system performance. This research study offers a method for identifying these corridors considering temporal and spatial inputs. For this end, a multi-criteria spatial decision support system (MC-SDSS) was developed. This framework attributes a score to highway corridors (links) based on policy eligibility and prioritization. We apply the Analytic Hierarchy Process (AHP) to structure the problem and consider different stakeholder preferences and available data. The product of this study is a tool for decisionmakers to optimize the selection of critical freight corridors and analyze alternatives. It also offers flexibility to manipulate the framework to meet various agency goals, using the State of Florida as a case study.

The Freight Mobility Research Institute’s (FMRI) contribution focuses on promoting smart cities, improving multimodal connections, system integrations and security, data modeling, and analytical tools. The ultimate goal of the FMRI is to optimize freight movements for improving the overall freight transportation efficiency. The FMRI's mission is to address critical issues affecting planning, design, operation, and safety of the nation’s intermodal freight transportation systems, in order to strengthen nation’s
economic competitiveness.

The Freight Mobility Research Institute’s (FMRI) contribution focuses on promoting smart cities, improving multimodal connections, system integrations and security, data modeling, and analytical tools. The ultimate goal of the FMRI is to optimize freight movements for improving the overall freight transportation efficiency. The FMRI's mission is to address critical issues affecting planning, design, operation, and safety of the nation’s intermodal freight transportation systems, in order to strengthen nation’s
economic competitiveness.

The Freight Mobility Research Institute’s (FMRI) contribution focuses on promoting smart cities, improving multimodal connections, system integrations and security, data modeling, and analytical tools. The ultimate goal of the FMRI is to optimize freight movements for improving the overall freight transportation efficiency. The FMRI's mission is to address critical issues affecting planning, design, operation, and safety of the nation’s intermodal freight transportation systems, in order to strengthen nation’s
economic competitiveness.

The Freight Mobility Research Institute’s (FMRI) contribution focuses on promoting smart cities, improving multimodal connections, system integrations and security, data modeling, and analytical tools. The ultimate goal of the FMRI is to optimize freight movements for improving the overall freight transportation efficiency. The FMRI's mission is to address critical issues affecting planning, design, operation, and safety of the nation’s intermodal freight transportation systems, in order to strengthen nation’s
economic competitiveness.

The Freight Mobility Research Institute’s (FMRI) contribution focuses on promoting smart cities, improving multimodal connections, system integrations and security, data modeling, and analytical tools. The ultimate goal of the FMRI is to optimize freight movements for improving the overall freight transportation efficiency. The FMRI's mission is to address critical issues affecting planning, design, operation, and safety of the nation’s intermodal freight transportation systems, in order to strengthen nation’s
economic competitiveness.

The Freight Mobility Research Institute’s (FMRI) contribution focuses on promoting smart cities, improving multimodal connections, system integrations and security, data modeling, and analytical tools. The ultimate goal of the FMRI is to optimize freight movements for improving the overall freight transportation efficiency. The FMRI's mission is to address critical issues affecting planning, design, operation, and safety of the nation’s intermodal freight transportation systems, in order to strengthen nation’s
economic competitiveness.

The Freight Mobility Research Institute’s (FMRI) contribution focuses on promoting smart cities, improving multimodal connections, system integrations and security, data modeling, and analytical tools. The ultimate goal of the FMRI is to optimize freight movements for improving the overall freight transportation efficiency. The FMRI's mission is to address critical issues affecting planning, design, operation, and safety of the nation’s intermodal freight transportation systems, in order to strengthen nation’s
economic competitiveness.