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Global temperature is increasing at an alarming rate, which increases the number of heatwaves. Heatwaves have significant impacts, both directly and indirectly, on human and natural systems and can create considerable risk to public health. Predicting the occurrence of a heatwave can save lives, increase the production of crops, improve water quality, and reduce transportation restrictions. Because of its geographical location, Bangladesh is particularly vulnerable to cyclones, droughts, earthquakes, floods, and heatwaves. The Bangladesh Meteorological Department collects temperature data at multiple weather stations, and we use data from 10 weather stations in this research. Data show that most heatwaves occur in the summer months, namely, April, May, and June. In this research, we develop Classification and Regression Tree (CART) models that use daily temperature data for the months of March, April, May, and June to predict the likelihood of a heatwave within the next 7 days, the next 28 days, and on any particular day based on daily high temperatures from the previous 14 days. We also use different model parameters to evaluate the accuracy of the models. Finally, we develop treed Stepwise Logistic Regression models to predict the probability of heatwaves occurring. Even though this research uses data from Bangladesh Meteorological Department, the developed modeling approach can be used in other geographic regions. [ABSTRACT FROM AUTHOR]

Purpose: To incorporate four‐dimensional computed tomography (4DCT)‐based ventilation imaging into intensity‐modulated radiation therapy (IMRT) treatment planning for pulmonary functional avoidance. Methods and Materials: Nineteen locally advanced lung cancer patients are retrospectively studied. 4DCT images are employed to create ventilation maps for each patient via a density‐change‐based algorithm with mass correction. The regional ventilation is directly incorporated into the mathematical formulation of a direct aperture optimization model in IMRT treatment planning to achieve functional avoidance and a voxel‐based treatment plan. The proposed functional avoidance planning and voxel‐based planning are compared to the conventional treatment planning approach purely based on the anatomy of patients. Paired sample t‐tests are conducted to see whether dosimetric differences among the three approaches are significant. Results: Similar planning target volume (PTV) coverage is achieved by anatomical, functional avoidance, and voxel‐based approaches. The voxel‐based treatment planning performs better than both functional avoidance and anatomical planning to the lung. For a total lung, the average volume reductions in a functional avoidance plan from an anatomical plan, a voxel‐based plan from an anatomical plan, and a voxel‐based plan from a functional avoidance plan are 7.0%, 16.8%, and 10.6%, respectively for V40; and 0.4%, 6.4%, and 6.0%, respectively for mean Lung Dose (MLD). For a functional lung, the reductions are 8.8%, 17.2%, and 9.2%, respectively, for fV40; and 1.1%, 6.2%, and 5.2%, respectively, for functional mean lung dose (fMLD). These reductions are obtained without significantly increasing doses to other organs‐at‐risk. All the pairwise treatment planning comparisons for both total lung and functional lung are statistically significant (p‐value <α=0.05$< \alpha =0.05$) except for the functional avoidance plan with the anatomical plan pair in which the p‐value >α=0.05$> \alpha =0.05$. From these results, we can conclude that voxel‐based treatment planning outperforms both anatomical and functional‐avoidance planning. Conclusions: We propose a treatment planning framework that directly utilizes functional images and compares voxel‐based treatment planning with functional avoidance and anatomical treatment planning. [ABSTRACT FROM AUTHOR]

Cities around the world are piloting projects to evaluate the feasibility and benefits of shared autonomous vehicles (SAVs), as their large-scale implementation and integration into public transit systems have the potential to improve individuals' accessibility and transportation equity. To understand the full potential of SAVs and their likely adoption, it is important to identify how the services can be utilized most effectively and what determines the composition of the ridership. This research aims to explore the usage and adoption of SAVs, focusing on a project called RAPID (Rideshare, Automation, and Payment Integration Demonstration) that was launched in Arlington, Texas. We used real-time trip-level ridership data from the SAV platform, conducted a survey of SAV riders, and developed a study based on ordered logistic regression to estimate the determinants of ridership frequency. Data analysis of real-time ridership data revealed that spatial distribution of activities and service accessibility have crucial roles in forming the current users' travel patterns. The findings from the logistic regression demonstrated that those with higher household incomes are less likely to be frequent riders of RAPID, while those who usually walk, bike, or utilize on-demand ridesharing services are likely to use SAVs often. Users with higher levels of safety perception are also more likely to be frequent users of the service. The findings of this study will provide planners with a better understanding of SAV ridership patterns and will guide decision-makers nationwide in establishing and adopting policies that will be appropriate for future SAV implementation projects. [ABSTRACT FROM AUTHOR]

This study aims to identify young adults' travel behavior using ridesharing services. We analyze data from an online survey of university students, regarding three free ridesharing services, including fixed-route, on-demand, and shared autonomous vehicles (SAVs). Ordinal regression and structural equation model (SEM) are employed to explore the frequency of service usage. Results indicate that most students had never taken a ride by available ridesharing services due to their preferences for using private vehicles and lack of service information. Regression results reveal that individuals' usual mode of transportation and residential location significantly influence ridesharing behavior. Our results also show significant associations between travel attitudes and students' travel behavior. We also found that shared on-demand and autonomous vehicle services could complement fixed-route services. Further research is needed on the link between young people's adoption of integrated ridesharing transportation services. [ABSTRACT FROM AUTHOR]

Aircraft deicing/anti-icing fluids (ADFs) are applied to remove and prevent icing on aircraft during taxi and takeoff. The Dallas-Fort Worth (DFW) International Airport uses deicing pads for deicing activities that collect and contain the spent deicing fluids for proper treatment or disposal. Local waterways receive ADF as "drip and shear" during the aircraft taxi on the runway and then takeoff. The glycol-based ADF serves as a nutrient for bacteria that grow exponentially, deplete dissolved oxygen (DO) from receiving waterways, and subsequently kill aquatic life. In this paper, we present a data-driven discrete-event simulation modeling process developed in collaboration with DFW Airport to assess aircraft assignment strategies to deicing pad locations by monitoring impact on DO. Our process consists of the following phases: (1) Data Collection, (2) Probability Distribution Modeling, and (3) State Transition Modeling. Both Phases (2) and (3) utilized data mining approaches, including treed regression and variable selection via false discovery rate. Detailed implementation of these phases is described for the DFW Airport case study, and the DFW Airport deicing activities simulation tool framework is presented. The actual data and simulation results were compared in terms of the DO levels in airport receiving waterways to verify the model validity after implementing the proposed model for DFW. Thus, the proposed model can be implemented by airports to control and minimize the adverse environmental effects resulting from deicing activities by optimizing the aircraft assignment to the pad locations. [ABSTRACT FROM AUTHOR]

On-demand ridesharing services are suggested to provide several benefits, such as improving accessibility and mobility, reducing drive-alone trips and greenhouse gas emissions. However, the impacts of these services on traffic crashes are not completely clear. This paper investigates the availability of Via- an on-demand ridesharing service in Arlington, TX, to identify the effects of this service on traffic crashes. We hypothesize that the launch of Via would result in more shared rides, fewer drive-alone trips and fewer traffic crashes. We implement an Interrupted Time Series Analysis (ITSA) approach to study the impact of Via service availability on traffic crashes using weekly counts of all traffic crashes, the number of injuries, and serious injuries that occurred in Arlington from 2014 to 2021. The results show a statistically significant reduction in the weekly number of total crashes and total injuries but do not show any significant impact on the number of serious injuries. Shared Autonomous Vehicles have the potential to reduce traffic crashes caused by driver's fault. This study reveals the potential impacts ridesharing services can have on traffic crashes and injuries in a mid-sized city. The results of this study can help decision and policymakers to understand the full potential of ridesharing services that can contribute to making relevant decisions toward creating sustainable and safer transportation systems in cities. [ABSTRACT FROM AUTHOR]