Impact of traffic citations to reduce truck crashes on challenging roadway geometry

Identifying crash-prone traffic conditions under different weather on freeways

BackgroundMotor vehicle crashes are a leading cause of injury mortality. Adverse weather and road conditions have the potential to affect the likelihood of motor vehicle fatalities through several pathways. However, there remains a dearth of assessments associating adverse weather conditions to fatal crashes in the United States. We assessed trends in motor vehicle fatalities associated with adverse weather and present spatial variation in fatality rates by state.MethodsWe analyzed the Fatality Analysis Reporting System (FARS) datasets from 1994 to 2012 produced by the National Highway Traffic Safety Administration (NHTSA) that contains reported weather information for each fatal crash. For each year, we estimated the fatal crashes that were associated with adverse weather conditions. We stratified these fatalities by months to examine seasonal patterns. We calculated state-specific rates using annual vehicle miles traveled data for all fatalities and for those related to adverse weather to examine spatial variations in fatality rates. To investigate the role of adverse weather as an independent risk factor for fatal crashes, we calculated odds ratios for known risk factors (e.g., alcohol and drug use, no restraint use, poor driving records, poor light conditions, highway driving) to be reported along with adverse weather.ResultsTotal and adverse weather-related fatalities decreased over 1994–2012. Adverse weather-related fatalities constituted about 16 % of total fatalities on average over the study period. On average, 65 % of adverse weather-related fatalities happened between November and April, with rain/wet conditions more frequently reported than snow/icy conditions. The spatial distribution of fatalities associated with adverse weather by state was different than the distribution of total fatalities. Involvement of alcohol or drugs, no restraint use, and speeding were less likely to co-occur with fatalities during adverse weather conditions.ConclusionsWhile adverse weather is reported for a large number of motor vehicle fatalities for the US, the type of adverse weather and the rate of associated fatality vary geographically. These fatalities may be addressed and potentially prevented by modifying speed limits during inclement weather, improving road surfacing, ice and snow removal, and providing transit alternatives, but the impact of potential interventions requires further research.Electronic supplementary materialThe online version of this article (doi:10.1186/s12940-016-0189-x) contains supplementary material, which is available to authorized users.

Urban green spaces are an important part of the urban ecosystem and hold significant ecological value. To effectively protect these green spaces, urban managers urgently need to identify them and monitor their changes. Common urban vegetation positioning methods use deep learning segmentation models to process street view data in urban areas, but this is usually inefficient and inaccurate. The main reason is that they are not applicable to the variable climate of urban scenarios, especially performing poorly in adverse weather conditions such as heavy fog that are common in cities. Additionally, these algorithms also have performance limitations such as inaccurate boundary area positioning. To address these challenges, we propose the UGSAM method that utilizes the high-performance multimodal large language model, the Segment Anything Model (i.e., SAM). In the UGSAM, a dual-branch defogging network WRPM is incorporated, which consists of the dense fog network FFA-Net, the light fog network LS-UNet, and the feature fusion network FIM, achieving precise identification of vegetation areas in adverse urban weather conditions. Moreover, we have designed a micro-correction network SCP-Net suitable for specific urban scenarios to further improve the accuracy of urban vegetation positioning. The UGSAM was compared with three classic deep learning algorithms and the SAM. Experimental results show that under adverse weather conditions, the UGSAM performs best in OA (0.8615), mIoU (0.8490), recall (0.9345), and precision (0.9027), surpassing the baseline model FCN (OA improvement 28.19%) and PointNet++ (OA improvement 30.02%). Compared with the SAM, the UGSAM improves the segmentation accuracy by 16.29% under adverse weather conditions and by 1.03% under good weather conditions. This method is expected to play a key role in the analysis of urban green spaces under adverse weather conditions and provide innovative insights for urban development.

<div class="section abstract"><div class="htmlview paragraph">Studies of automatic emergency braking (AEB) find that AEB-equipped vehicles are around half as likely to crash. This study examines whether driver characteristics and road and weather conditions modify this preventive effect of AEB.</div><div class="htmlview paragraph">Toyota production data were merged with police reported crash files from eight U.S. states for crash years 2015 up to 2019 by 17-digit vehicle identification number (VIN). Using a case-control design, this study investigated the relationship of AEB presence with being a case vehicle in a system-relevant crash (the striking vehicle in front-to-rear crash; n=30,056) versus an AEB non-relevant control vehicle (the struck vehicle in a front-to-rear crash; n=62,820). The analysis was stratified by driver characteristics and by weather and road conditions. Logistic regression modeled the relationship, controlling for exposure (vehicle-days) and possible confounding factors. The resulting odds ratios for AEB equipment from the separate models were compared to determine if the effect of AEB presence was modified by the characteristic or condition of interest.</div><div class="htmlview paragraph">Overall, AEB-equipped vehicles were 43% (p<0.001) less likely to be the striking (case) vehicle compared to non-equipped vehicles. However, the preventive effect of AEB was significantly lower among older drivers (over 65 years) compared to younger drivers; 29% less likely to be a striking vehicle (OR=0.71) versus 46% (OR=0.54), respectively. The effect of AEB was also lower in adverse weather conditions (rain, fog, snow) (OR=0.66) and on wet or snowy roads (OR=0.65), though these differences were not significant compared to clear weather and dry roads. The AEB effect was also lower among risk-taking drivers (alcohol-involved, speeding, or unrestrained) compared to non-risk-taking (OR=0.72 versus OR=0.59, respectively).</div><div class="htmlview paragraph">AEB prevents crashes, regardless of driver characteristics and environmental conditions. This study suggests, however, that the size of the effect is smaller among older and risk-taking drivers, and in adverse weather and road conditions.</div></div>

Objectives: The major purpose of this study was to identify risk factors affecting truck crashes on freeways and propose recommendations for safer truck traffic operations. Methods: Truck crashes were analyzed to identify how truck traffic safety is related to prevailing traffic and weather conditions. Prevailing traffic conditions were characterized by central tendencies and the spatiotemporal variation of traffic parameters collected from freeway traffic surveillance systems. A total of 377 truck crashes occurring on Korean freeways in a recent 3-year period, 2008–2010, were analyzed together with corresponding prevailing traffic conditions and weather conditions. Several statistical tests were conducted to understand the characteristics of prevailing traffic conditions before crash occurrence based on different weather conditions. In addition, a binary logistic regression technique was applied to identify causal factors affecting truck crash severity under normal and adverse weather conditions. Results: Major findings from the analyses are discussed with truck operations strategies including speed enforcement, variable speed limit, and truck lane restriction from the safety enhancement point of view. Speed-related variables representing prevailing traffic conditions before crash occurrences were found to be the most statistically significant factors affecting truck crash severity, compared to volume-related variables such as the volume-to-capacity ratio (v/c). It is inferred that speed management is an effective tool for safer truck traffic operations on freeways. The major findings can be further discussed to derive valuable insights into truck traffic operations based on different weather conditions, such as normal and adverse. Conclusions: Some recommendations for safer truck traffic operations were presented based on the results obtained. The outcomes of this study could be effectively utilized to support the development of various traffic operations strategies and policies for truck traffic safety. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental file.

Autonomous driving is challenging in adverse road and weather conditions in which there might not be lane lines, the road might be covered in snow and the visibility might be poor. We extend the previous work on end-to-end learning for autonomous steering to operate in these adverse real-life conditions with multimodal data. We collected 28 hours of driving data in several road and weather conditions and trained convolutional neural networks to predict the car steering wheel angle from front-facing color camera images and lidar range and reflectance data. We compared the CNN model performances based on the different modalities and our results show that the lidar modality improves the performances of different multimodal sensor-fusion models. We also performed on-road tests with different models and they support this observation.

Autonomous driving is challenging in adverse road and weather conditions in which there might not be lane lines, the road might be covered in snow and the visibility might be poor. We extend the previous work on end-to-end learning for autonomous steering to operate in these adverse real-life conditions with multimodal data. We collected 28 hours of driving data in several road and weather conditions and trained convolutional neural networks to predict the car steering wheel angle from front-facing color camera images and lidar range and reflectance data. We compared the CNN model performances based on the different modalities and our results show that the lidar modality improves the performances of different multimodal sensor-fusion models. We also performed on-road tests with different models and they support this observation.

The impact of weather on summer and winter exercise behaviors

Study shows that road accidents cause nearly 6,000 people to die and more than 400,000 people injured in the United States every year. Adverse weather and road conditions are some of the major reasons that contribute to 22% of accidents. People get injured and sometimes even lose their life due to road accidents that cause physical and mental instability. Regardless of the expertise of a good driver, at some point, an intelligent transportation system is necessary for the vehicle to make an immediate decision to avoid accidents. Road and weather-related mishap are those which occur due to adverse conditions like fog, winds, snow, rain, slick pavement, sleet, etc. Such accidents, though completely unavoidable, but can be reduced to some extent if proper measures are taken. Vehicle velocity, vehicle size, vehicle weight, momentum are a few of the reasons for a vehicle to go out of control. An intelligent accident avoidance system can predict the safe speed of a vehicle according to its size, weight, and momentum in different weather and road conditions. It can reduce the likelihood of accidents related to weather and road conditions. In this paper, we propose an Internet of Things (IoT) based intelligent accident avoidance system for adverse weather and road conditions. The proposed system comprises of an IoT system that perceives the environment for different weather and road conditions and a machine learning-based intelligent system that learns the adverse conditions that influence an accident to predict and suggest safe speed to the driver. The proposed system is experimented with real-time datasets and simulated using the Blynk application.

Adverse weather conditions are a critical external factor that threaten traffic safety on freeways. Variable speed limit (VSL) operations can be used as a traffic management tool to harmonize vehicle speed and reduce the likelihood of crashes during adverse weather conditions. This paper proposes a novel VSL operations strategy based on the prediction of weather and traffic conditions, referred to as a proactive VSL operations strategy. The k–nearest neighbors ( k-NN) statistic was used to predict weather and traffic conditions. VSL operations logic, which used visibility distances and safe stopping distances obtained from road weather information systems and average speeds obtained by vehicle detection systems, was developed. The proposed methodology is focused on the restricted visibility distance resulting from foggy weather conditions on freeways. A microscopic traffic simulator, VISSIM, was used to simulate a freeway traffic stream and collect vehicle-maneuvering data. An external application program interface, the VISSIM COM interface, was used to implement the proposed VSL operations strategy. A surrogate safety assessment model was used to derive indirect safety measures to evaluate the effectiveness of the proposed methodology. Total conflicts in the proposed VSL algorithm were reduced in comparison with a system lacking VSL by 19.10% and 27.27% under moderately and severely foggy weather conditions, respectively. These outcomes are expected to be of great use in the development of more effective VSL systems to enhance freeway safety during adverse weather conditions.

A Comprehensive Analysis of the Association of Highway Traffic with Winter Weather Conditions

The negative effects of weather (e.g., strong winds, snow, and icy rain) on the safety of vehicles have been recognized and reported upon for some time worldwide. As an important category of vehicle accidents, the single-vehicle noncollision accident has not been studied sufficiently under adverse environmental and topographic conditions. In the United States, strong wind, together with other adverse weather and topographic conditions, has been blamed for many single-vehicle accidents every year, especially those involving trucks. Vehicle safety not only threatens people's lives during normal operations, but may even put many people in miserable situations when an emergency evacuation is interrupted by frequent accidents on key routes. As a result, the safety of many people who are delayed in congestion on evacuation routes may be jeopardized. The reasons that cause single-vehicle accidents can be very complicated: from a single primary reason such as strong wind gusts to the combination of several reasons such as weather conditions, vehicle conditions, road surface conditions, driver operational errors, etc. This study seeks to investigate the safety of vehicles during normal operations as well as emergencies through replicating the natural environments. It starts with a brief overview of single-vehicle accidents caused by environmental and topographic conditions around the United States in normal operations, followed by discussions about the current challenges existing in the evacuation practices. An attempt is then made to model the complicated weather, road surface, and driver operational process, such as rain, snow, camber, grade, and acceleration/deceleration as well as steering processes. With the proposed accident assessment framework, the accident-related response is studied and accident risks are assessed for vehicles. The study may also provide a useful basis for traffic designs on highways with complicated topographic and weather conditions and optimization of evacuation routes and strategy with minimized single-vehicle accident risks.

Analysis of Driver Behaviour and Crash Characteristics during Adverse Weather Conditions

Changing Regulations, Changing Care?

The study of vehicular movement modes in adverse weather conditions allowed defining the dependence of traffic modes at certain road segments on weather conditions. It is found that the average vehicle speed changes within a year over a wide range depending on road conditions even on its straight horizontal sections. There is a considerable difference in the maximum speed of traffic movement. Complex road sections significantly influence the traffic mode during any period of the year, especially in adverse weather conditions. The road surface condition exerts the greatest impact on the traffic speed. If the adhesion coefficient equals 0.2-0.3 the average traffic speed is 20-25% lower than if that adhesion coefficient equals 0.5-0.6, and in ice conditions the average traffic speed decreases by 40-50%. The proposed calculation formula allows defining the influence of weather conditions on the traffic mode of free-moving cars or cars in a joint traffic flow. It provides for the feasibility study regarding measures aimed to increase the traffic speed in adverse weather conditions of the year. Based on the observations and calculations the correlation dependences of average and maximum traffic speeds on the adhesion coefficient within a 7.0 m-wide road without the hard shoulder were obtained.