Highway–rail grade crossings (HRGCs) are one of the most dangerous segments of the transportation network. Every year numerous accidents are recorded at HRGCs between highway users and trains, between highway users and traffic control devices, and solely between highway users. These accidents cause fatalities, severe injuries, property damage, and release of hazardous materials. Researchers and state Departments of Transportation (DOTs) have addressed safety concerns at HRGCs in the USA by investigating the factors that may cause accidents at HRGCs and developed certain accident and hazard prediction models to forecast the occurrence of accidents and crossing vulnerability. The accident and hazard prediction models are used to identify the most hazardous HRGCs that require safety improvements. This study provides an extensive review of the state-of-the-practice to identify the existing accident and hazard prediction formulae that have been used over the years by different state DOTs. Furthermore, this study analyzes the common factors that have been considered in the existing accident and hazard prediction formulae. The reported performance and implementation challenges of the identified accident and hazard prediction formulae are discussed in this study as well. Based on the review results, the US DOT Accident Prediction Formula was found to be the most commonly used formula due to its accuracy in predicting the number of accidents at HRGCs. However, certain states still prefer customized models due to some practical considerations. Data availability and data accuracy were identified as some of the key model implementation challenges in many states across the country.

Many local governments have started using smartphone applications to more effectively inform and communicate with citizens. This trend is of interest, as cities can only be smart if they are responsive to their citizens. In this paper, the intention to use such a mobile application among adult residents (n = 420) of a mid-sized city in the southeastern United States was examined using hierarchical linear regression analysis. The regression model that was tested indicated significant predictors of the intention to use the app in order to report municipal problems, such as power outages, and to request services for one’s home or community, including: Performance expectancy (e.g., citizens’ beliefs that the app would be efficient, helpful, convenient), effort expectancy (citizens’ beliefs about difficulty of using the app), social influence, perceived cost (e.g., privacy loss, storage space, unwanted notifications), and prior use of city apps. Consistent with current research on technology adoption, performance expectancy had the strongest influence on app-use intentions. Additionally, citizens’ trust in their city government’s ability to effectively manage an app was a weak, positive predictor of app-use intentions; general trust in the city government did not predict app-use intentions. Implications for city governments and city app developers are discussed.

Knowing vegetation type in an area is crucial for several applications, including ecology, land use management, and infrastructure risk assessment. In combination with recent advancements in image processing, remote sensing technology has been used to perform fast vegetation type estimation and reduce the need for intensive and time-consuming field-based surveys. This paper proposes a weakly supervised method based on deep learning to estimate tree species relying on multi-spectral high-resolution satellite images. We tested the approach against noisy labels, which often occur in real-world datasets. We validate our approach for a study area in Norway and in Italy using images taken in different periods of the year. Our method significantly enhances the quality of the available forestry inventory dataset.

A new method was proposed to predict the compressive bearing capacity of driven piles based on the number of hammer strikes in the last one meter of pile penetration (known here as Flap number). To collect the data, a literature review was done on technical publications and pile driving record reports that were accessible to the authors at the time of publication. The data of a hundred driven piles including Flap number, basic properties of the surrounding soil, pile geometry, and pile-soil friction angle was collected. These data were initially used in the artificial neural network to establish a relation for predicting pile capacity. Subsequently, by using genetic programing and linear regression, equations for determining pile bearing capacity with respect to the Flap number, soil parameters, and pile geometries were proposed. Finally, the performance of all applied methods in predicting the pile bearing capacity were compared. The utmost importance was given to the comparison of the accuracy of the three models as well as the error estimation.