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In 2018, the International Maritime Organization adopted a plan to reduce greenhouse gas emissions from ships. As a result, ocean carriers and cruise lines are exploring alternative fuels, such as ammonia, which offers zero CO₂emissions. Understanding ammonia-based fuel’s impact on range, speed, and fuel logistics can help companies assess its benefits and limitations. To address this, a mixed-integer non-linear programming model is developed to determine the optimal ships’ routes with the objective of minimizing the total travel time while considering factors such as ship speeds, refueling time, and the non-linear fuel consumption rates. A unique aspect of this study is the consideration of a group of ships with different origins and destinations. To solve the non-linear and NP-hard model, a hybrid genetic algorithm–particle swarm optimization algorithm is developed. The proposed model and meta-heuristics are demonstrated using an actual network consisting of ports around the world. Numerical results from a full factorial design with three factors (number of ships, number of origins, and number of destinations) comparing the travel time differences between using ammonia and conventional fuel indicate that NH₃-fueled ships generally experience longer travel times than jet-propulsion fuel 8-fueled ships because of NH₃’s lower energy density and more frequent refueling requirements. On average, the increase in total travel time is less than 20%. This study serves as a foundation for decision-makers who must also consider additional factors such as economic feasibility, infrastructure costs, environmental impact, and regulatory requirements when assessing ammonia’s viability as an alternative fuel for fleet-wide adoption. 

The recent development in transportation, such as energy-efficient and autonomous vehicles, defines a condition for the students in transportation engineering. Students in the field of transportation engineering should be ready upon their graduation with new knowledge and skills that are compatible with the need of the industry and sustainable engineering practices. During summers of2018 and 2019, we developed and implemented an eight-week program to increase the knowledge and skills of students coming from multidisciplinary fields related to autonomous vehicles. Problem of “How much will platooning reduce fuel consumption and emissions per vehicle mile traveled?” was instrumentalized in subsequent activities to introduce the comprehensive knowledge structure of autonomous vehicles. The engineering concept of reducing the cost and sustainability was embedded in the leading research question that helped us to develop and implement activities on an overall knowledge structure in autonomous vehicles. The goal of using problem-based learning activities was not to encourage the students to focus on reaching the solution merely. We aimed to introduce the multidisciplinary knowledge and critical skills aspects of learning about disruptive technologies. In this paper, we will discuss how a multidisciplinary research approach was incorporated into a problem-based learning activity. The students were introduced the subjects related to math, physics, computer science, and biology as the integration of the knowledge structure of autonomous vehicles. We will also present the results on students’ use of critical skills such as machine learning and computer programming. 

Many studies have reported associations between respiratory symptoms and resident proximity to traffic. However, only a few have documented information about the relationship between traffic volume and air quality in local areas. This study investigates the impact of traffic volume on air quality at different geographical locations in the state of South Carolina using multilevel linear mixed models and Grey Systems. Historical traffic volume and air quality data between 2006 and 2016 are obtained from the South Carolina Department of Transportation (SCDOT) and the United States Environmental Protection Agency (EPA) monitoring stations. The data are used to develop prediction models that relate Air Quality Index (AQI) to traffic volume for selected counties and schools. For the counties, two models are developed, one with Ozone (O3) and one with PM2:5 as the dependent variable. For the schools, only one model is developed, with O3 as the dependent variable. The number of counties and schools studied are limited by the availability of air monitoring stations dedicated to measuring O3 and PM2:5. Several types of models were investigated. They include linear regression model (LM), linear mixed-effect regression model (LMER), Grey Systems (GM), error corrected GM (EGM), Grey Verhulst (GV), error corrected GV (EGV), and LMER + EGM. The LM model produced the least accurate estimate while the LMER + EGM model produced the most accurate estimate (average RMSE is less than 5%). The models’ estimates suggest that air quality in South Carolina will continue to get worse in the coming years due to increasing AADT. An interesting finding of this study is that some counties and schools will have higher levels of O3 or PM2:5 when AADT decreases. This finding suggests that there are other factors, other than AADT, that influence the air quality in these counties and schools.