Crashes among young and inexperienced drives are a major safety problem in the United States, especially in an area with large rural road networks, such as West Texas. Rural roads present many unique safety concerns that are not fully explored. This study presents a complete machine leaning pipeline to find the patterns of crashes involved with teen drivers no older than 20 on rural roads in West Texas, identify factors that affect injury levels, and build four machine learning predictive models on crash severity. The analysis indicates that the major causes of teen driver crashes in West Texas are teen drivers who failed to control speed or travel at an unsafe speed when they merged from rural roads to highways or approached intersections. They also failed to yield on the undivided roads with four or more lanes, leading to serious injuries. Road class, speed limit, and the first harmful event are the top three factors affecting crash severity. The predictive machine learning model, based on Label Encoder and XGBoost, seems the best option when considering both accuracy and computational cost. The results of this work should be useful to improve rural teen driver traffic safety in West Texas and other rural areas with similar issues.

Trajectory tracking and crossing intention prediction of pedestrians at intersections are important to intersection safety. Recently, on‐board video sensors have been developed for detection of pedestrians. However, both the detection range and operating environment of video‐based systems seem to be constrained by the advancement of image‐processing technologies. Additionally, on‐board systems cannot alarm pedestrians to take evasive actions when at risk, a feature which is critical to saving lives. This paper summarises the authors' practice on using roadside LiDAR sensors to monitor and predict pedestrians' crossing intention, as part of an ongoing effort to develop a pioneering LiDAR‐based system to systematically reduce pedestrian and vehicle collisions at intersections. The LiDAR sensors were installed at intersections to collect pedestrian data such as presence, location, velocity, and direction. A new method based on deep autoencoder – artificial neural network (DA‐ANN) was used to process data and predict pedestrian crossing intention. The case study shows the proposed model is about 95% prediction accuracy and computational efficiency for real‐time systems. The roadside LiDAR system has great potential to significantly reduce vehicle‐to‐pedestrian crashes both at intersections and non‐intersection areas, either used as a stand‐alone system or in conjunction with the connected V2I and I2V technologies.

Compared to major structural repair or even replacement, preventative preservation of in-service pavements has been more popular in engineering practices, but recently, pavement preventative maintenance (PPM) has become more complex in China as the competition for pavement preservation funds has grown and the need to justify decisions has increased. Therefore, the life cycle cost analysis (LCCA) has increasingly attracted attention from transportation agencies. However, most of previous studies were conducted deterministically or only focused on a single factor, while PPM is apparently affected by many potential sources of uncertainty. The risk-based analysis to investigate potential risks and combined effects of multiple factors is a necessary component of PPM. This paper aims at presenting a risk-based PPM with the probabilistic LCCA for a Chinese highway case. Major analysis variables of different range are examined to probe risks of different scenarios, investigate combined effects of multiple variables and identify an optimal preservation strategy.

The transportation sector is a complex system. Collecting transportation activity and the associated emissions data is extremely expensive and time-consuming. Grey Relational Analysis provides a viable alternative to overcome data insufficiency and gives insights for decision makers into such a complex system. In this paper, we achieved three major goals: (i) we explored the inter-relationships among transportation development, energy consumption and CO₂ emissions for 30 provincial units in China; (ii) we identified the transportation development mode for each individual province; and (iii) we revealed policy implications regarding the sustainable transportation development at the provincial level. We can classify the 30 provinces into eight development modes according to the calculated Grey Relational Grades. Results also indicated that energy consumption has the largest influence on CO₂ emission changes. Lastly, sustainable transportation policies were discussed at the province level according to the level of economy, urbanization and transportation energy structure.

This paper presents a methodology to detect rear-end conflicts at signalized intersections with the help of roadside LiDAR sensors. Raw data collected in the point cloud format from the sensors was processed using a series of data processing algorithms to obtain vehicle trajectories. Time-based (MTTC), deceleration-based (SDI), and severity-based (CSI) surrogate safety indices were calculated from the vehicle trajectories to identify the conflict threats at every frame of the dataset, which were further aggregated together to evaluate the risk exposure and risk severity at different temporal segments of the leader/follower car-following period to obtain a rear-end conflict index. The identified conflicts were compared with the historical crash records using negative binomial models. The results indicate correlation between the identified conflicts and the crashes, and further provide new information about the rear-end crash risks at the intersection which could support the proactive approach of traffic safety analysis.