Highway collisions are a major source of societal losses, both social and economic. Among the promising approaches to improve highway safety performance is the concept of highway design consistency. Several research efforts have concentrated on translating this concept into quantitative guidelines. However, a number of concerns and challenges in applying the concept and the guidelines still persist and may limit their applicability. Such challenges were identified with the objective of recommending the optimum way to overcome them and establish priorities for future research. The challenges focus on the optimum criteria and parameters to be used in consistency evaluation, models to estimate these parameters, and the relationship between the criteria and safety performance.

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Existing sight distance models are applicable only to two-dimensional (2-D) separate horizontal and vertical alignments or simple elements of these separate alignments (vertical curve, horizontal curve). A new model is presented for determining the available sight distance on 3-D combined horizontal and vertical alignments. The model is based on the curved parametric elements that have been used in the finite element method. The elements presented are rectangular (4-node, 6-node, and 8-node elements) and triangular. These elements are used to represent various features of the highway surface and sight obstructions, including tangents (grades), horizontal curves, vertical curves, traveled lanes, shoulders, side slopes, cross slopes, superelevation, lateral obstructions, and overpasses. The available sight distance is found analytically by examining the intersection between the sight line and the elements representing the highway surface and the sight obstructions. Application of the new model is illustrated using numerical examples, and the results show that existing 2-D models may underestimate or overestimate the available sight distance. The proposed model should be valuable in establishing design standards and guidelines for 3-D highway alignments and determining the effect of various highway features on sight distance.

Nation's economy, safety, and quality of life are influenced by a well-behaved transportation system. Yet, demands in transportation are ever increasing due to trends in population growth, emerging technologies, and the increased globalization of the economy which has kept pushing the system to its limits. The rate of increasing the number of vehicles is at points even more than the overall population increase rate, which leads to more congested and dangerous roadways. This problem is not going to be addressed by just adding to the number of roads anymore. The construction cost is very high and the time to return the result is too lengthy to catch up with the vehicle increase rate.One way to improve upon the fleet management is by viewing the road as an information highway as opposed to highway for vehicles. The scale of ingested data in the transportation system and even the interaction of various components of the system that generates the data have become a bottleneck for the traditional data analytics solutions. On the other hand, machine learning is a form of Artificial Intelligence (AI) and a data-driven solution that can cope with the new system requirements. Machine learning learns the latent patterns of historical data to model the behavior of a system and to respond accordingly in order to automate the analytical model building.The availability of increased computational power and collection of the massive amount of data have redefined the value of the machine learning-based approaches for addressing the emerging demands and needs in transportation systems.Machine learning solutions have already begun their promising marks in the transportation industry, where it is proved to even have a higher return on investment compared to the conventional solutions. However, the transportation problems are still rich in applying and leveraging machine learning techniques and need more consideration. The underlying goals for these solutions are to reduce congestion, improve safety and diminish human errors, mitigate unfavorable environmental impacts, optimize energy performance, and improve the productivity and efficiency of surface transportation.In this special issue, we present original research work aimed at reporting on new models, algorithms, and case studies related to the use of machine learning in the field of transportation and further analysis of the reliability and robustness of the whole transportation system. In particular, the special issue focuses on prediction methods in transportation, transport network traffic flows and signals, public transportation including air fleet, driving styles, electric cars, and car sharing.In recent years, machine learning techniques have become an integral part of realizing smart transportation. In this context, using an improved deep learning model, the complex interactions among roadways, transportation traffic, environmental elements, and traffic crashes have been explored. The proposed model includes two modules, an unsupervised feature learning module to identify ...

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