Advancing traffic safety through the safe system approach: A systematic review.

The Safe System Approach (SSA) has emerged as a comprehensive framework for enhancing traffic safety through system-wide interventions. This systematic review, conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, analyzes 82 relevant studies categorized based on the SSA pillars: safe road users, safe vehicles, safe speeds, safe roads, and post-crash care. The review provides insights into SSA's effectiveness in reducing road traffic fatalities and injuries, exploring implementation challenges and opportunities, including policy initiatives, institutional frameworks, and stakeholder collaborations. The findings highlight the potential for SSA to create a more forgiving and resilient transportation system, offering valuable guidance for policy decisions, future research, and interventions aimed at promoting safer road environments. SSA's comprehensive strategy for Safe Road Users encompasses considerations of road system design, behavior modification, and tailored measures for vulnerable users, showcasing its versatility in addressing diverse challenges. In the realm of Safe Vehicles, SSA actively involves manufacturers in a cycle of continuous improvement, rigorous testing, and collaborative efforts to establish new safety regulations. The emphasis on managing Safe Speeds, aligning with human parameters, and involving communities reflects SSA's adaptable nature and provides insights for establishing context-specific speed limits. SSA contributes significantly to Safe Roads through its implementation of innovative countermeasures, forgiving road designs, and the integration of emerging disciplines, resulting in a notable reduction in fatalities and injuries. In the domain of Post-Crash Care, SSA's integrated perspective fosters collaboration among emergency services, medical professionals, and the justice system. It addresses challenges through standardized approaches and information sharing, ensuring a comprehensive and unified approach to road safety. This review contributes to the ongoing efforts to prioritize safety and transform the transportation landscape on a global scale.

Predicting pedestrian-involved crash severity using inception-v3 deep learning model.

This research leverages a novel deep learning model, Inception-v3, to predict pedestrian crash severity using data collected over five years (2016-2021) from Louisiana. The final dataset incorporates forty different variables related to pedestrian attributes, environmental conditions, and vehicular specifics. Crash severity was classified into three categories: fatal, injury, and no injury. The Boruta algorithm was applied to determine the importance of variables and investigate contributing factors to pedestrian crash severity, revealing several associated aspects, including pedestrian gender, pedestrian and driver impairment, posted speed limits, alcohol involvement, pedestrian age, visibility obstruction, roadway lighting conditions, and both pedestrian and driver conditions, including distraction and inattentiveness. To address data imbalance, the study employed Random Under Sampling (RUS) and the Synthetic Minority Oversampling Technique (SMOTE). The DeepInsight technique transformed numeric data into images. Subsequently, five crash severity prediction models were developed with Inception-v3, considering various scenarios, including original, under-sampled, over-sampled, a combination of under and over-sampled data, and the top twenty-five important variables. Results indicated that the model applying both over and under sampling outperforms models based on other data balancing techniques in terms of several performance metrics, including accuracy, sensitivity, precision, specificity, false negative ratio (FNR), false positive ratio (FPR), and F1-score. This model achieved prediction accuracies of 93.5%, 77.5%, and 85.9% for fatal, injury, and no injury categories, respectively. Additionally, comparative analysis based on several performance metrics and McNemar's tests demonstrated that the predictive performance of the Inception-v3 deep learning model is statistically superior compared to traditional machine learning and statistical models. The insights from this research can be effectively harnessed by safety professionals, emergency service providers, traffic management centers, and vehicle manufacturers to enhance their safety measures and applications.

Advances in CO2 activation by frustrated Lewis pairs: from stoichiometric to catalytic reactions.

The rise of CO2 concentrations in the environment due to anthropogenic activities results in global warming and threatens the future of humanity and biodiversity. To address excessive CO2 emissions and its effects on climate change, efforts towards CO2 capture and conversion into value adduct products such as methane, methanol, acetic acid, and carbonates have grown. Frustrated Lewis pairs (FLPs) can activate small molecules, including CO2 and convert it into value added products. This review covers recent progress and mechanistic insights into intra- and inter-molecular FLPs comprised of varying Lewis acids and bases (from groups 13, 14, 15 of the periodic table as well as transition metals) that activate CO2 in stoichiometric and catalytic fashion towards reduced products.

Real-time snowy weather detection based on machine vision and vehicle kinematics: A non-parametric data fusion analysis protocol.

INTRODUCTION: This study introduces a new analysis protocol for detecting real-time snowy weather conditions on freeways by utilizing trajectory-level data extracted from the Second Strategic Highway Research Program (SHRP2) Naturalistic Driving Study (NDS) dataset. The data include parameters reduced from a real-time image feature extraction technique, time series data collected from external sensors, and CANbus data collected by the NDS ego-vehicles. To provide flexibility in winter maintenance, two segmentation types of one-minute and one-mile segments were used to sample snowy trips and their matched clear weather trips. METHOD: In this study, four non-parametric models were developed using six data assemblies to detect snowy weather on freeways. The data assemblies are arranged based on three data sources, including image database extracted from an in-vehicle video camera, sensors, and CANbus data, to examine the effectiveness of snow detection models for different data types considering real-time availability of data. RESULTS: Overall, the developed models successfully detected snowy weather on freeways with an accuracy ranging between 76% to 89%. Results indicated that high accuracy of estimating snowy weather can be accomplished using the data fusion between external sensors data and texture parameters of images, without accessing to CANbus data. PRACTICAL APPLICATIONS: Practical applications can be driven with respect to the time or distance coordinates, using different data fusion assemblies, and data availability. The study proves the importance of employing vehicles as weather sensors in the Connected Vehicles (CV) applications and Variable Speed Limit (VSL) to improve traffic safety on freeways.

Global lessons learned from naturalistic driving studies to advance traffic safety and operation research: A systematic review.

The state of practice of investigating traffic safety and operation is primarily based on traditional data sources, such as spot sensors, loop detectors, and historical crash data. Recently, researchers have utilized transportation data from instrumented vehicles, driving simulators, and microsimulation modeling. However, these data sources might not represent the actual driving environment at a trajectory level and might introduce bias due to their experimental control. The shortcomings of these data sources can be overcome via Naturalistic Driving Studies (NDSs) considering the fact that NDS provides detailed real-time driving data that would help investigate the safety and operational impacts of human behavior along with other factors related to weather, traffic, and roadway geometry in a naturalistic setting. With the enormous potential of the NDS data, this study leveraged the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) approach to shortlist the most relevant naturalistic studies out of 2304 initial studies around the world with a focus on traffic safety and operation over the past fifteen years (2005-2020). A total of 117 studies were systematically reviewed, which were grouped into seven relevant topics, including driver behavior and performance, crash/near-crash causation, driver distraction, pedestrian/bicycle safety, intersection/traffic signal related studies, detection and prediction using NDSs data, based on their frequency of appearance in the keywords of these studies. The proper deployment of Connected and Autonomous Vehicles (CAV) require an appropriate level of human behavior integration, especially at the intimal stages where both CAV and human-driven vehicles will interact and share the same roadways in a mixed traffic environment. In order to integrate the heterogeneous nature of human behavior through behavior cloning approach, real-time trajectory-level NDS data is essential. The insights from this study revealed that NDSs could be effectively leveraged to perfect the behavior cloning to facilitate rapid and safe implementation of CAV.

Chiral Triptycenes: Concepts, Progress and Prospects.

Triptycenes have been established as unique scaffolds because of their backbone π-structure with a propeller-like shape and saddle-like cavities. They are some of the key organic molecules that have been extensively studied in polymer chemistry, in supramolecular chemistry and in material science. Triptycenes become chiral molecules when substituents are unsymmetrically attached in at least two of their different aromatic rings. This Minireview highlights the chirality of triptycenes from basics to an advanced stage for the development of functional molecules.

Recent Applications of Azo Dyes: A Paradigm Shift from Medicinal Chemistry to Biomedical Sciences.

Azo molecules possess the characteristic azo bond (-N=N-) and are considered fascinating motifs in organic chemistry. Since the last century, these brightly colored compounds have been widely employed as dyes across several industries in applications for printing, food, paper, cosmetics, lasers, electronics, optics, material sciences, etc. The discovery of Prontosil, an antibacterial drug, propelled azo compounds into the limelight in the field of medicinal chemistry. Subsequent discoveries including Phenazopyridine, Basalazide, and Sulfasalazine enabled azo compounds to occupy a significant role in the drug market. Furthermore, azo compounds have been employed as antibacterial, antimalarial, antifungal, antioxidant, as well as antiviral agents. The metabolic degradation of many azo dyes can induce liver problems if ingested, posing a safety concern and limiting their application as azo dyes in medicinal chemistry. However, azo dyes remain particularly significant for applications in cancer chemotherapy. Recently, a paradigm shift has been observed in the use of azo dyes: from medicinal chemistry to biomedical sciences. The latter benefits from azo dye application are related to imaging, drug delivery, photo-pharmacology and photo switching. Herein, we have compiled and discussed recent works on azo dye compounds obtained so far, focusing on their medicinal importance and future prospects.

Detecting lane change maneuvers using SHRP2 naturalistic driving data: A comparative study machine learning techniques.

Lane change has been recognized as a challenging driving maneuver and a significant component of traffic safety research. Developing a real-time continuous lane change detection system can assist drivers to perform and deal with complex driving tasks or provide assistance when it is needed the most. This study proposed trajectory-level lane change detection models based on features from vehicle kinematics, machine vision, roadway characteristics, and driver demographics under different weather conditions. To develop the models, the SHRP2 Naturalistic Driving Study (NDS) and Roadway Information Database (RID) datasets were utilized. Initially, descriptive statistics were utilized to investigate the lane change behavior, which revealed significant differences among different weather conditions for most of the parameters. Six data fusion categories were introduced for the first time, considering different data availability. In order to select relevant features in each category, Boruta, a wrapper-based algorithm was employed. The lane change detection models were trained, validated, and comparatively evaluated using four Machine Learning algorithms including Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), and eXtrem Gradient Boosting (XGBoost). The results revealed that the highest overall detection accuracy was found to be 95.9 % using the XGBoost model when all the features were included in the model. Moreover, the highest overall detection accuracy of 81.9 % using the RF model was achieved considering only vehicle kinematics-based features, indicating that the proposed model could be utilized when other data are not available. Furthermore, the analysis of the impact of weather conditions on lane change detection suggested that incorporating weather could improve the accuracy of lane change detection. In addition, the analysis of early lane change detection indicated that the proposed algorithm could predict the lane changes within 5 s before the vehicles cross the lane line. The developed detection models could be used to monitor and control driver behavior in a Cooperative Automated Vehicle environment.

Trajectory-level fog detection based on in-vehicle video camera with TensorFlow deep learning utilizing SHRP2 naturalistic driving data.

Providing drivers with real-time weather information and driving assistance during adverse weather, including fog, is crucial for safe driving. The primary focus of this study was to develop an affordable in-vehicle fog detection method, which will provide accurate trajectory-level weather information in real-time. The study used the SHRP2 Naturalistic Driving Study (NDS) video data and utilized several promising Deep Learning techniques, including Deep Neural Network (DNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Convolutional Neural Network (CNN). Python programming on the TensorFlow Machine Learning library has been used for training the Deep Learning models. The analysis was done on a dataset consisted of three weather conditions, including clear, distant fog and near fog. During the training process, two optimizers, including Adam and Gradient Descent, have been used. While the overall prediction accuracy of the DNN, RNN, LSTM, and CNN using the Gradient Descent optimizer were found to be around 85 %, 77 %, 84 %, and 97 %, respectively; much improved overall prediction accuracy of 88 %, 91 %, 93 %, and 98 % for the DNN, RNN, LSTM, and CNN, respectively, were observed considering the Adam optimizer. The proposed fog detection method requires only a single video camera to detect weather conditions, and therefore, can be an inexpensive option to be fitted in maintenance vehicles to collect trajectory-level weather information in real-time for expanding as well as updating weather-based Variable Speed Limit (VSL) systems and Advanced Traveler Information Systems (ATIS).