The platform of a microscopic traffic simulation provides an opportunity to study the driving behavior of vehicles on a roadway system. Compared to traditional conventional cars with human drivers, the car-following behaviors of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) would be quite different and hence require additional modeling efforts. This paper presents a thorough review of the literature on the car-following models used in prevalent micro-simulation tools for vehicles with both human and robot drivers. Specifically, the car-following logics such as the Wiedemann model and adaptive cruise control technology were reviewed based on the vehicle’s dynamic behavior and driving environments. In addition, some of the more recent “AV-ready (autonomous vehicles ready) tools” in micro-simulation platforms are also discussed in this paper.

The scientific advancements in the vehicle and infrastructure automation industry are progressively improving nowadays to provide benefits for the end-users in terms of traffic congestion reduction, safety enhancements, stress-free travels, fuel cost savings, and smart parking, etc. The advances in connected, autonomous, and connected autonomous vehicles (CV, AV, and CAV) depend on the continuous technology developments in the advanced driving assistance systems (ADAS). A clear view of the technology developments related to the AVs will give the users insights on the evolution of the technology and predict future research needs. In this paper, firstly, a review is performed on the available ADAS technologies, their functions, and the expected benefits in the context of CVs, AVs, and CAVs such as the sensors deployed on the partial or fully automated vehicles (Radar, LiDAR, etc.), the communication systems for vehicle-to-vehicle and vehicle-to-infrastructure networking, and the adaptive and cooperative adaptive cruise control technology (ACC/CACC). Secondly, for any technologies to be applied in practical AVs related applications, this study also includes a detailed review in the state/federal guidance, legislation, and regulations toward AVs related applications. Last but not least, the impacts of CVs, AVs, and CAVs on traffic are also reviewed to evaluate the potential benefits as the AV related technologies penetrating in the market. Based on the extensive reviews in this paper, the future related research gaps in technology development and impact analysis are also discussed.

This article focused on quantifying environmental impact of asphalt pavement preservation at construction and use stage using life-cycle assessment (LCA) approach. Preservation treatments considered in the analysis include thin overlay, chip seal, and crack seal that are typically used by highway agencies. At construction stage, there are significant differences in emissions caused by various preservation treatments mainly due to different raw material components and manufacturing processes. At use stage, fuel consumptions vary significantly depending on tire rolling resistance that is affected by pavement surface characteristics. The Carbon Dioxide (CO₂) emissions at the use stage were predicted using the Motor Vehicle Emission Simulator (MOVES) and pavement roughness models obtained from Long-Term Pavement Performance (LTPP) program. The results show that pavement preservation brings significant environmental benefit in reduction of CO₂ emission due to the improved pavement surface condition despite the emission generated at construction stage. Thin overlay produces the highest life-cycle reduction in CO₂ emission due to the large International Roughness Index (IRI) jump after treatment; while crack seal has the lowest reduction of CO₂ emission. The optimum application timing of preservation treatment was determined for chip seal and thin overlay assuming one-time treatment applied before reaching the IRI threshold.

Highway–rail grade crossings (HRGCs) are critical spatial locations that are of utmost importance for transportation safety because traffic crashes at these locations are often catastrophic. Compared with traditional regression models, the decision tree is more advanced in its ability to handle large data sets, deal with missing values, and not require predefined underlying relationships between target variables and predictors. Thus the decision tree approach is explored in this study, which evaluates HRGC crashes. Because crashes at HRGCs are rare, the majority of data will have a zero-crash classification. A traditional decision tree method will have a bias toward the majority classification, which will result in a good prediction for the majority class but a relatively poor prediction for rare events. To improve model accuracy with the decision tree model, especially for forecasting rare events, previous probability and decision profit values are adjusted. Historical crash data of North Dakota State from 1996 to 2014 are analyzed, and factors relevant to HRGC crashes are investigated. Results reveal that 23 variables are considered as contributors to crashes at highway–rail crossings. Results indicate that railway traffic, highway traffic, and train speed are main influential factors and have a positive impact on crash likelihood. Presence of train detecting and advance warning systems is helpful in reducing crash risk. Predicting accuracies of the model are 84.1% and 77.2% for event class and nonevent class, respectively.