Reduce Vehicle Crashes Research Articles

A novel context-aware system to improve driver’s field of view in urban traffic networks

Principal objectives of the Intelligent Transportation Systems (ITS) are to improve traffic safety, facilitate informed traffic decision making, and enhance quality of life and services in a smart traffic environment. Vehicle crashes at urban traffic intersections are among the rudimentary sources of injuries and fatalities in the cities. According to the report of the World Health Organization (WHO), in every 25 seconds, one vulnerable road-user is being killed by a vehicle crash. Therefore, it is necessary to take a novel and smart approach for improving the safety and reducing vehicle crashes. This leads to a contextual perception and spatial awareness of driver to increase security and safety for the driver, vehicle, and road users. Autonomous vehicles collects the information from the environment through equipped sensors on the vehicle such as camera, laser, radar, and Global Navigation Satellite Systems (GNSS). The main challenge arises when the person or objects are located beyond the driver’s Field of View (FOV) and cannot be detected by embedded sensors on the vehicles. This paper proposes an Advanced Driver Assistance System (ADAS) to increase the safety on road intersections by taking advantage of existing infrastructures (e.g road camera) being used for traffic control. The aim of this research is improving the driver’s FOV using a computer vision approach (e.g background subtraction algorithm) and Location Based Service (LBS). The case study results at Tehran metropolitan demonstrate the reduction in traffic collision risk and improvement of pedestrian safety using the proposed system.

Autonomous Vehicle Accident Data Analysis: California OL 316 Reports: 2015–2020

Abstract Six years (2015–2020) of autonomous vehicle (AV Level 3) crash data from California's (CA) OL 316 collision reports of AV crashes while in the autonomous mode (AM) or disengaged from AM just before the collision, divided by the associated CA AV make and mileage driven in the AM, are compared with the University of Michigan Transportation Research Institute (UMTRI) corrected human driver crash frequency. AV test drivers in CA mandatorily self-report every crash, whereas average drivers underreport minor accidents, so the UMTRI reporting correction factor permits comparison. CA's AV AM mileage is only a few million miles over the last few years, with virtually no police-reported crash data yet available. OL 316 crash consequence data (e.g., damage, injuries, etc.) is anecdotal and inconsistently self-reported. The CA collision report data indicate the CA AV test fleet exhibits multiples of the human crash frequency. Invariably, the AV accidents are the human driver's fault, with a majority being rear collisions. The human drivers appear less able to anticipate the AV's more conservative driving. CA's AV experience predicts more widespread deployment of existing AV technologies is not likely to reduce vehicle crash frequency, at least in the short term, and might well increase it.

Do everyday proactive policing activities reduce vehicle crashes? Examining a commonly held law enforcement belief using a novel method

Traffic accidents present a significant challenge to public safety and are a top priority for many law enforcement agencies in the United States. Empirical studies suggest that police agencies commonly engage in traffic enforcement activities to be proactive, spending much of their downtime patrolling areas with heightened risks for vehicle crashes. Despite the ongoing focus on traffic enforcement in daily police work, the impact of these efforts on traffic accidents remains unclear. The current study uses a Generalized Method of Moments (GMM) framework to examine everyday police proactive work and its impact on vehicle crashes at “hot spots” of collisions in a large suburban jurisdiction. Findings indicate that routine patrol levels and activities produce short-term reductions in crashes at the most serious crash hot spots but have less impact elsewhere. To generate a more sustained and optimal effect on traffic safety, everyday police proactivity needs to be more carefully calibrated in terms of its targeting, dosage, and types of activities.

A closer look at urban development under the emergence of autonomous vehicles: Traffic, land use and air quality impacts

Autonomous vehicles (AVs) hold great promise for increasing the capacity of existing roadways and intersections, providing more mobility to a wider range of people, and are likely to reduce vehicle crashes. However, AVs are also likely to increase travel demand which could diminish the potential for AVs to reduce congestion and cause emissions of greenhouse gases (GHG) and other air pollutants to increase. Therefore, understanding how AVs will affect travel demand is critical to understanding their potential benefits and impacts. We evaluate how adoption of AVs affects travel demand, congestion and vehicle emissions over several decades using an integrated travel demand, land-use and air quality modeling framework for the Albuquerque, New Mexico metropolitan area. We find that AVs are likely to increase demand and GHG emissions as development patterns shift to the region's periphery and trips become longer. Congestion declines along most roadways as expanded capacity from more efficient AV operation outpaces increasing demand. Most of the population can also expect a reduction in exposure to toxic vehicle emissions. Some locations will experience an increase in air pollution exposure and traffic congestion from changes in land-use and traffic patterns caused by the adoption of AVs.

The impact of mandating a driving lesson for elderly drivers in Japan using count data models: Case study of Toyota City

As one crucial mobility problem for elderly drivers, vehicle crashes due to elderly drivers account for an increased ratio of total vehicle crashes in recent years in Japan. Mandating a driving lesson for elderly drivers was implemented and revised continuously to reduce the number of vehicle crashes. To perform a practical driving lesson for elderly drivers, it is essential to evaluate whether it can reduce vehicle crashes significantly or not. Most previous studies only investigated its effects on fatal or severe rates using rather simple methods, without consideration for the increasing number of elderly drivers and the variability of vehicle crashes in different months. To bridge these research gaps, this study examines the impact of mandating a driving lesson for elderly drivers by some advanced statistical methods based on a monthly level. Vehicle crash records from April 2005 to December 2019 collected in Toyota City, Japan are used for empirical analysis. Three types of count data models, i.e., the Poisson regression model, the Negative Binomial regression model, and the Poisson Integer-Valued Autoregressive (INAR) (1) model are proposed in this study. A comparison of three proposed models was implemented to indicate the similarity and distinction of estimation results. The significant findings of this study suggest that: 1) three proposed models have the same prediction accuracy referring to indexes of Root Mean Squared Error, Mean Absolute Error, and Mean Squared Error; 2) all of them indicate that the revision of license renewal legislation for elderly drivers in March 2017 is a significant factor negatively affecting the number of vehicle crashes at a 10 % significance level; 3) all of them indicate that the number of drivers aged 65 years or older and month-variability are significant factors affecting the number of vehicle crashes at least a 10 % significance level; 4) the time-series nature of vehicle crashes due to elderly drivers was not existing indicated by the result of the Poisson INAR (1) model. Statistical methods proposed in this study can be referred by researchers and engineers to evaluate the effects of traffic safety measures in the research field of traffic and transportation engineering.

Evaluating the safety benefits of adaptive headlamps for reducing vehicle crashes with pedestrians at night

Vehicle crashes with pedestrians and bicyclists at night pose a significant safety problem. National Highway Traffic Safety Administration (NHTSA) statistics show that despite overall fatalities as...

Examining the Environmental, Vehicle, and Driver Factors Associated with Crossing Crashes of Elderly Drivers Using Association Rules Mining

In the aging society, reducing vehicle crashes caused by elderly drivers has become a crucial issue. To find effective methods to reduce these vehicle crashes, it is necessary to give some insights into the characteristics of vehicle crashes and those of traffic violations caused by elderly drivers. However, multiple significant factors associated with crossing crashes due to elderly drivers were not extensively observed in previous studies. To fill this research gap, this study identifies the crash pattern and examines the environmental, vehicle, and driver factors associated with crossing crashes due to elderly drivers. The 5-year crash data in Toyota City, Japan, are used for empirical analysis. The emerging data mining method called association rules mining is applied to discover various factors associated with crossing crashes of elderly and nonelderly drivers, respectively. The significant findings indicate that (1) elderly drivers are more likely to lead to crossing or right-turn crashes, compared with nonelderly drivers; (2) there are more factors including crash location (intersection without signal), lighting (daylight), road condition (dry and other), weather condition (clear and raining), vehicle type (light motor truck), and traffic violation (fail to confirm safety) associated with the large proportion of crossing crashes due to elderly drivers. The findings of this study can be used by traffic safety professionals to implement some countermeasures to reduce the crossing crashes due to elderly drivers.

Multiple Lanes Identification for Advanced Driver Assistance System (ADAS)

Now a days, a multi-lane recognition technique that uses the ridge features and the inverse perspective mapping (IPM) is generally used to distinguish lanes since it can evacuate the perspective distortion on lines that lie in parallel in reality. The lane detection is one of the approach to design the ADAS, if the vehicles follows the lane then there is less chance to get an accident. The detected information of lane path is used for controlling the vehicles and giving alerts to drivers. Therefore most of the researchers are attracted towards this field. But, due to the varying road conditions, it is very difficult to detect the lane. The computer vision and machine learning approaches are presents in most of the articles. In this paper, a survey of different method is presents for the road picture segmentation for the multi-lane detection. The Lane Departure Warning (LDW) system can help to reduce vehicle crashes that are caused by careless or drowsy driving. There has been much research on vision based lane detection for the LDW system. In these lane detection methods, color or edge information is utilized as a feature of the lane. The feature-based methods are usually applied to localize the lanes in the road images by extracting low-level features. On the other hand, the model-based methods use several geometrical elements to describe the lanes, including parabolic curves, hyperbola and straight lines. Feature-based methods require a dataset containing several thousand images of the roads with well-painted and prominent lane markings that are subsequently converted to features. Moreover, these methods may suffer from noise.

Double-Robust Estimation in Difference-in-Differences with an Application to Traffic Safety Evaluation

Difference-in-differences (DID) is a widely used approach for drawing causal inference from observational panel data. Two common estimation strategies for DID are outcome regression and propensity score weighting. In this paper, motivated by a real application in traffic safety research, we propose a new double-robust DID estimator that hybridizes regression and propensity score weighting. We particularly focus on the case of discrete outcomes. We show that the proposed double-robust estimator possesses the desirable large-sample robustness property. We conduct a simulation study to examine its finite-sample performance and compare with alternative methods. Our empirical results from a Pennsylvania Department of Transportation data suggest that rumble strips are marginally effective in reducing vehicle crashes.

Analysis on safety impact of red light cameras using the Empirical Bayesian approach

Traffic signal violations at urban intersections have become a major safety concern in urban areas since, compared to other types of crashes, they may cause a higher percentage of injuries and fatal crashes. Therefore, the deployment of red light cameras has been concentrated on urban intersections, both to prevent traffic signal violations and to reduce vehicle crashes. Other methodologies for establishing the safety impact of red light cameras are also being researched, but accurate results are difficult to ascertain. In this study, the safety impact of red light cameras on urban intersections in Daejeon Metropolitan City, South Korea was assessed using the Empirical Bayesian (EB) approach. According to the EB analysis in this study, red light camera has increased fatal crashes in all crash types by around 2%. However, it has also increased injury crashes in all crash types by about 53%. In specific types of crashes at urban intersections, such as angle, sideswipe & rear-end, and head-on, red light cameras have reduced fatal crashes by around 23%. However, injury crashes for typical crash types at urban intersections have actually increased by around 37%. Furthermore, the EB analysis results of total crashes are similar to those of injury crashes. This may be the result of a higher than average travel speed on arterial link sections in Daejeon compared to that of other metropolitan cities like Seoul. Therefore, the higher average speed on arterial roads may be the cause of the increase of injuries resulting from sideswipe & rear-end crashes.

Development of Basalt Filament Highway Guardrail

In order to reduce vehicle crash on highway, basalt fiber reinforced guardrail was developed in this study. The wave shaped framework was coated with thermoplastic acrylic resin, using injection molding machine. Tensile strengths of the samples were tested by a 3380Instron and the corresponding property of the composite was calculated on the basis of the composite mechanics. The results indicated that the basalt filament reinforced highway guardrail have the advantages of high strength, excellent corrosion resistance, relative low cost and so on.

Real-Time Detection of Drowsiness Related Lane Departures Using Steering Wheel Angle

Drowsy driving is a significant factor in many motor vehicle crashes in the United States and across the world. Efforts to reduce these crashes have developed numerous algorithms to detect both acute and chronic drowsiness. These algorithms employ behavioral and physiological data, and have used different machine learning techniques. This work proposes a new approach for detecting drowsiness related lane departures, which uses unfiltered steering wheel angle data and a random forest algorithm. Using a data set from the National Advanced Driving Simulator the algorithm was compared with a commonly used algorithm, PERCLOS and a simpler algorithm constructed from distribution parameters. The random forest algorithm had higher accuracy and Area Under the receiver operating characteristic Curve (AUC) than PERCLOS and had comparable positive predictive value. The results show that steering-angle can be used to predict drowsiness related lane-departures six seconds before they occur, and suggest that the random forest algorithm, when paired with an alert system, could significantly reduce vehicle crashes.