Quantifying spatial inequities in traffic injury rates through the integration of urban road network measures and social vulnerability.

Mitigating traffic injury rate plays an essential role in sustainable urban development and is closely related to public health and human well-being. The inequity of traffic injury rate undermines equitable access to transportation infrastructure and poses a significant threat to the safety of residents during their commutes. Although previous studies have examined the association between socio-demographic characteristics and regional traffic crash risk, they seldom consider the spatial heterogeneity of the traffic injury rate inequity especially for the vulnerable groups.

Can we realize seamless traffic safety at smart intersections by predicting and preventing impending crashes?

Intersections are frequently identified as crash hotspots for roadways in major cities, leading to significant human casualties. We propose crash likelihood prediction as an effective strategy to proactively prevent intersection crashes. So far, no reliable models have been developed for intersections that effectively account for the variation in crash types and the cyclical nature of Signal Phasing and Timing (SPaT) and traffic flow.

An advanced real-time crash prediction framework for combined hard shoulder running and variable speed limits system using transformer.

One of the important applications of real-time crash prediction analysis lies in the field of proactive traffic management, where instantaneous crash risk evaluation and dynamic decision-making are prerequisites. This research proposes an integrated and advanced real-time crash risk prediction framework for Variable Speed Limits (VSL) and Hard Shoulder Running (HSR) implemented freeways considering their operational periods. Statistical methods are utilized to identify the significant crash contributing factors (related to traffic, roadway geometry, and weather conditions) and explain their relationships with crashes.

Effects of helmet usage on moped riders' injury severity in moped-vehicle crashes: Insights from partially temporal constrained random parameters bivariate probit models.

Mopeds are small and move unpredictably, making them difficult for other drivers to perceive. This lack of visibility, coupled with the minimal protection that mopeds provide, can lead to serious crashes, particularly when the rider is not wearing a helmet. This paper explores the association between helmet usage and injury severity among moped riders involved in collisions with other vehicles.

Assessing the impact of express lanes on traffic safety of freeways.

The rise of Express Lanes also known as High Occupancy Toll (HOT) Lanes and Managed Lanes, signifies a major leap in traffic management and transportation funding. Despite their increased deployment to ensure reliable travel times through dynamic tolling during peak traffic periods, a comprehensive evaluation of their safety impact is notably lacking. Presently, the Crash Modification Factors Clearinghouse, a vital resource, only lists two case studies related to Express Lanes, one of which is our own research.

Exploratory analysis of injury severity under different levels of driving automation (SAE Levels 2 and 4) using multi-source data.

Vehicles equipped with automated driving capabilities have shown potential to improve safety and operations. Advanced driver assistance systems (ADAS) and automated driving systems (ADS) have been widely developed to support vehicular automation. Although the studies on the injury severity outcomes that involve automated vehicles are ongoing, there is limited research investigating the difference between injury severity outcomes for the ADAS and ADS equipped vehicles.

Analyzing speed-difference impact on freeway joint injury severities of Leading-Following vehicles using statistical and data-driven models.

Rear-end (RE) crashes are notably prevalent and pose a substantial risk on freeways. This paper explores the correlation between speed difference among the following and leading vehicles (Δν) and RE crash risk. Three joint models, comprising both uncorrelated and correlated joint random-parameters bivariate probit (RPBP) approaches (statistical methods) and a cross-stitch multilayer perceptron (CS-MLP) network (a data-driven method), were estimated and compared against three separate models: Support Vector Machines (SVM), eXtreme Gradient Boosting (XGBoost), and MLP networks (all data-driven methods).

A study on diversion behavior in weaving segments: Individualized traffic conflict prediction and causal mechanism analysis.

Lane change behavior disrupts traffic flow and increases the potential for traffic conflicts, especially on expressway weaving segments. Focusing on the diversion process, this study incorporating individual driving patterns into conflict prediction and causation analysis can help develop individualized intervention measures to avoid risky diversion behaviors. First, to minimize measurement errors, this study introduces a lane line reconstruction method.

A matched case-control analysis of autonomous vs human-driven vehicle accidents.

Despite the recent advancements that Autonomous Vehicles have shown in their potential to improve safety and operation, considering differences between Autonomous Vehicles and Human-Driven Vehicles in accidents remain unidentified due to the scarcity of real-world Autonomous Vehicles accident data. We investigated the difference in accident occurrence between Autonomous Vehicles' levels and Human-Driven Vehicles by utilizing 2100 Advanced Driving Systems and Advanced Driver Assistance Systems and 35,113 Human-Driven Vehicles accident data. A matched case-control design was conducted to investigate the differential characteristics involving Autonomous' versus Human-Driven Vehicles' accidents.

Real-time crash prediction on express managed lanes of Interstate highway with anomaly detection learning.

To facilitate efficient transportation, I-4 Express is constructed separately from general use lanes in metropolitan area to improve mobility and reduce congestion. As this new infrastructure would undoubtedly change the traffic network, there is a need for more understanding of its potential safety impact. Unfortunately, many advanced real-time crash prediction models encounter an important challenge in their applicability due to their demand for a substantial volume of data for direct modeling.

Advancing urban traffic accident forecasting through sparse spatio-temporal dynamic learning.

Traffic accidents have emerged as one of the most public health safety matters, raising concerns from both the public and urban administrators. The ability to accurately predict traffic accident not only supports the governmental decision-making in advance but also enhances public confidence in safety measures. However, the efficacy of traditional spatio-temporal prediction models are compromised by the skewed distributions and sparse labeling of accident data.

Exogenous application of nano-silicon, potassium sulfate, or proline enhances physiological parameters, antioxidant enzyme activities, and agronomic traits of diverse rice genotypes under water deficit conditions.

Water deficit is a critical obstacle that devastatingly impacts rice production, particularly in arid regions under current climatic fluctuations. Accordingly, it is decisive to reinforce the drought tolerance of rice by employing sustainable approaches to enhance global food security. The present study aimed at exploring the effect of exogenous application using different biostimulants on physiological, morphological, and yield attributes of diverse rice genotypes under water deficit and well-watered conditions in 2-year field trial.

Short-term Safety Performance Functions by Random Parameters Negative Binomial-Lindley model for Part-time Shoulder Use.

Part-time Shoulder Use (PTSU) is a traffic management and operation strategy that allows the use of the left or right shoulder as a travel lane, typically during the peak hours of the day. Though PTSU is an effective strategy for increasing roadway capacity in congested traffic conditions, there is very limited quantitative information about PTSU design elements and operational strategy in the existing literature, which could impact the occurrence of crashes on freeways. This study contributes to the safety literature by analyzing various potential crash contributing factors related to PTSU operation and design elements through the development of short-term Safety Performance Functions (SPFs).

Exploring the influence of drivers' visual surroundings on speeding behavior.

Despite awareness campaigns and legal consequences, speeding is a significant cause of road accidents and fatalities globally. To combat this issue, understanding the impact of a driver's visual surroundings is crucial in designing roadways that discourage speeding. This study investigates the influence of visual surroundings on drivers in 15 US cities using 3,407,253 driver view images from Lytx, covering 4,264 miles of roadways.

Insights into vehicle conflicts based on traffic flow dynamics.

The utilization of traffic conflict indicators is crucial for assessing traffic safety, especially when the crash data is unavailable. To identify traffic conflicts based on traffic flow characteristics across various traffic states, we propose a framework that utilizes unsupervised learning to automatically establish surrogate safety measures (SSM) thresholds. Different traffic states and corresponding transitions are identified with the three-phase traffic theory using high-resolution trajectory data.

Dynamic short-term crash analysis and prediction at toll plazas for proactive safety management.

Toll plazas are commonly recognized as bottlenecks on toll roads, where vehicles are prone to crashes. However, there has been a lack of research analyzing and predicting dynamic short-term crash risk specifically at toll plazas. This study utilizes traffic, geometric, and weather data to analyze and predict dynamic short-term collision occurrence probability at mainline toll plazas.

Curcumin Mitigates Malathion-Induced Renal Injury: Suppression of Apoptosis and Modulation of NF-κβ/TNF-α and Nrf2, and HO-1 Signaling.

Malathion is one of the most used organophosphorus pesticides that is used for many reasons such as agriculture and industry. Human exposure to malathion may occur through various means, such as eating food that has been treated with it. Malathion not only increases oxidative stress but also decreases the antioxidant capacity.

Extracorporeal blood purification with Oxiris filter in critically ill patients with COVID-19 requiring continuous renal replacement therapy.

Introduction: Severe COVID-19 is associated with a dysregulated immune response that usually leads to cytokine release syndrome. This study aimed to compare the use of extracorporeal blood purification therapy (Oxiris) versus standard continuous renal replacement therapy (CRRT) in critically-ill patients with severe COVID-19.

Methods: This was a national, multicenter, retrospective study of patients with COVID-19 admitted to the intensive care unit (ICU) between March and October 2020 who required CRRT.

Improving model robustness of traffic crash risk evaluation via adversarial mix-up under traffic flow fundamental diagram.

Recent state-of-art crash risk evaluation studies have exploited deep learning (DL) techniques to improve performance in identifying high-risk traffic operation statuses. However, it is doubtful if such DL-based models would remain robust to real-world traffic dynamics (e.g.

The impact of target speed on pedestrian, bike, and speeding crash frequencies.

This research aims to investigate the influence of adopting the target speed concept on different types of crashes including pedestrian, bike, and speeding-related crashes. The Target speed is the highest speed that vehicles should operate on a roadway segment in a specific context. Based on the reviewed literature, this is the first study to investigate the relationship between target speed and crash frequency.

A hybrid approach of random forest and random parameters logit model of injury severity modeling of vulnerable road users involved crashes.

Vulnerable road users (VRUs) involved crashes are a major road safety concern due to the high likelihood of fatal and severe injury. The use of data-driven methods and heterogeneity models separately have limitations in crash data analysis. This study develops a hybrid approach of Random Forest based SHAP algorithm (RF-SHAP) and random parameters logit modeling framework to explore significant factors and identify the underlying interaction effects on injury severity of VRUs-involved crashes in Shenyang (China) from 2015 to 2017.

Investigating surrogate safety measures at midblock pedestrian crossings using multivariate models with roadside camera data.

This study aims to evaluate and compare Surrogate Safety Measures (SSMs) at five midblock Rectangular Rapid Flashing Beacons (RRFB) and two midblock Pedestrian Hybrid Beacons (PHB) sites in Florida using extensive video data collected over the study period of July to November 2021. Computer vision and data processing resulted in four pedestrian SSMs, namely spatial gap, temporal gap, relative time to collision (RTTC) and Post Encroachment Time (PET). An initial investigation of the SSMs using Mann-Whitney-Wilcoxon tests revealed significant differences in the SSM values across different treatment types and hours of the day.