Erin Santini-Bell

University transit systems face pressure to reduce costs, emissions, and public-health impacts, yet most sustainability analyses emphasize propulsion technology while treating vehicle size and service model as fixed. This study evaluates how vehicle sizing, service organization, and fuel technology jointly shape low-load campus transit sustainability, using the University of New Hampshire Wildcat Transit as an empirical application. An integrated framework combining Life Cycle Cost Analysis, Life Cycle Assessment, air pollutant externality monetization, and multi-objective optimization compares alternative fleet-service configurations across five propulsion technologies. Results show that replacing full-size buses with passenger vans reduces annualized costs by 78%, life cycle GHG emissions by over 80%, and externality costs by up to 90% per passenger-mile. Hybrid on-demand service performs well only when ridership is retained and VMT reduced. Pareto optimization identifies 14 B100 and 16 electric vans as the closest-to-ideal solution. Low-load transit decarbonization thus emerges as an integrated fleet-service design problem.

Road–stream crossings (RSCs) are vital for the sustainability of both stream ecosystems and transportation networks, yet many are aging, undersized, or failing. Limited funding and lack of stakeholder coordination hinder effective RSC management. This study develops a multi-objective optimization (MOO) framework utilizing the non-dominated sorting genetic algorithm (NSGA-II) to maximize and balance diverse stakeholder interests (i.e., environmental and transportation agencies) while minimizing management costs. MOO was used to identify optimal RSC management scenarios at a watershed scale, using the Piscataqua–Salmon Falls watershed, New Hampshire, as a testbed. It was found that MOO consistently outperformed the currently used scoring and ranking method by the environmental and transportation agencies, improving the environmental and transportation objectives by at least 19.56% and 37.68%, respectively, across all evaluated budget limits. These improvements translate to a maximum cost saving of USD 19.87 million under a USD 50 million budget limit. Structural conditions emerged as the most influential factor, with a Pearson coefficient of 0.60. This research highlights the potential benefits of a data-driven, optimization-based approach to sustainable RSC management.

The mechanical properties of joints may impact a system’s static and dynamic behavior. Since connections are usually complex, modeling their geometric details could take time and effort. Thus, joints of structures are often overlooked in model creation and calibration. Therefore, reliable analytical modeling of in-service structures requires accurate and efficient parameter estimation of the connections in their simplified models. However, joints are physically small parts of a system, and parameter estimation techniques may not be sufficiently sensitive to the variations of connections’ mechanical properties. This paper examines the finite element model updating of a laboratory steel grid focusing on the structural parameter estimation of its complex connections using modal data. The mechanical properties of the joints are parametrized by added mass and reduced rigidity. Therefore, several modified models with different combinations of heavier semi-rigid joints are developed. Each model is updated using two modal-based error functions, and the most representative updated model is selected. The results demonstrate how the grid modal outputs are influenced by updating the mass and stiffness of its connections. Moreover, mass and stiffness interactions of the grid joints in the parameter estimation procedure are illustrated. The updated models can efficiently simulate the structural behavior of the grid with increased confidence and reliability.

In any structural system, the mechanical properties of its connections impact the structure’s global performance. Particularly, the rigidity of joints plays a substantial role in the system’s vibration response. Since connections typically include several components with complex geometry, they are usually oversimplified in the modeling process. Therefore, the analytical model is typically a low-fidelity representation of the connections’ performance. Although simplifying joints for modeling purposes is a good practice for engineering applications, it may reduce the accuracy of the model predictions. Thus, it is essential to reconcile accuracy with efficiency, particularly for complex joint modeling. In addition to the rigidity, the mass of connections may be another influential structural parameter for the system’s global response. The joint mass may be underestimated in the modeling procedure because the elements of joints in the steel connection regions, including plates, stiffeners, and bolts, are not often modeled in simplified analyses. Hence, as the analytical model is updated, connections’ mass and stiffness need to be estimated and incorporated into the updated model. This paper studies the interaction of mass and stiffness estimation for bolted connections of an experimental steel grid. The structure used in this research is a well-known structure built, instrumented, and tested at the University of Central Florida. The laboratory setup simulates the behavior of deck-on-beam bridges. In its initial analytical model, all grid joints are assumed to be completely fixed, and no extra mass is considered in the joint regions. The model updating procedure is limited to estimating joints’ mass and stiffness values. The modal information of the structure is extracted from the measured frequency response functions of the grid excited in a vertical hammer impact test. Stiffness- and flexibility-based error functions are employed for the model updating. The results demonstrate how the selection of joints’ uncertain parameters can affect the procedure of finding suitable structural layouts for the semi-rigid connections of the grid. The model updating procedure may be generalized to condition assessment and structural health monitoring of bridges, especially when their connections are subject to damage and failure.

Future transportation networks and modes will embrace efficient energy usage, produce low or no emissions, use alternative construction materials and novel construction and manufacturing processes, enhance the efficiency of freight and logistics, and employ appropriate levels of automation and connectivity. Such was the consensus of participants at a recent visioning event coordinated by the Engineering Research Visioning Alliance (ERVA) to identify priorities for the engineering research community to pursue for a future with sustainable transportation networks. Four topics were considered in depth: infrastructure, vehicles and transportation modes, data; and our people and communities. Within these four topics, 12 "big ideas" were prioritized for engineering research investment to advance progress toward more sustainable, equitable transportation systems. Those priorities are discussed in the report. Given transportation’s central role in society, boldly re-envisioning how all modes can benefit current and future generations is a compelling priority. Engineering-led research will take a central role in shifting transportation from an obstacle to negotiate to a quality-enhancing experience. And it must do so in ways that are efficient, affordable, and safe. This report aims to inspire researchers and funders (public, private, and nonprofit) to support and pursue these engineering research priorities.

The paper briefly introduces an element-based multi-objective optimization (EB-MOO) methodology to support state departments of transportation with their decision-making process, asset management, and performance-based transportation planning and programming. The methodology focuses on the bridge asset class and consists of five modules: (i) data processing, (ii) improvement, (iii) element-level optimization (ELO), (iv) bridge-level optimization (BLO), and (v) network-level optimization (NLO) modules. These five modules jointly produce short- and long-term intervention strategies detailed at the bridge element level for planning and programming. The paper focuses on the BLO module, specifically: the basic framework of underlying processes and concepts, the optimization problem types and mathematical formulations, and the heuristic algorithm to solve the BLO problems. A prototyping tool is developed to implement these five modules of the EB-MOO methodology, test concepts, prove effectiveness, and demonstrate potential benefits. The paper also includes an illustrative example using the prototyping tool. The example consists of the BLO problems under different budget and/or performance scenarios. The implementation proves the module’s capability in producing a diverse set of Pareto optimal or near-optimal solutions, recommending set of element intervention actions and timings, predicting performance, and determining budget requirements for the entire program period. The BLO results associated with the recommended solutions serve as the fundamental inputs for the NLO module. Nevertheless, the BLO module can be used independently, providing a systematic process for the development of bridge improvement/preservation programs detailed at the element level.