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Cabrera-Arnau, Carmen, i Steven R. Bishop. "Urban population size and road traffic collisions in Europe". PLOS ONE 16, nr 8 (27.08.2021): e0256485. http://dx.doi.org/10.1371/journal.pone.0256485.
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Millions of road traffic collisions take place every year, leading to significant knock-on effects. Many of these traffic collisions take place in urban areas, where traffic levels can be elevated. Yet, little is known about the extent to which urban population size impacts road traffic collision rates. Here, we use urban scaling models to analyse geographic and road traffic collision data from over 300 European urban areas in order to study this issue. Our results show that there is no significant change in the number of road traffic collisions per person for urban areas of different sizes. However, we find individual urban locations with traffic collision rates which are remarkably high. These findings have the potential to inform policies for the allocation of resources to prevent road traffic collisions across the different cities.
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Tian, Zhun. "Investigating Impact of Speed on Traffic Safety using Collision Prediction Model". Advanced Materials Research 779-780 (wrzesień 2013): 482–85. http://dx.doi.org/10.4028/www.scientific.net/amr.779-780.482.
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Motor vehicle speed is a risk factor contributing to many road accidents which result in significant social and economic costs. Although a review of previous research shows that the literature is extensive on the impact of speed on traffic safety, the majority of previous researches mainly focused on rural roads while rarely on urban roads. It is crucial that the relationship between speed and road collisions should be investigated in urban areas because of the fact that a high ratio of collisions is occurred on urban roads. The objective of this study is to examine the influence of motor vehicle speed on road collisions on urban road sections. This objective is achieved by developing collision prediction models which quantitatively correlate collision frequency to speed characteristics. It is found that both mean speed and speed standard deviation are positively related to collision frequency. Both of them are risk factors in traffic safety.
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Babaoglu, Liza, i Ceni Babaoglu. "Prediction of Fatalities in Vehicle Collisions in Canada". Promet - Traffic&Transportation 33, nr 5 (8.10.2021): 661–69. http://dx.doi.org/10.7307/ptt.v33i5.3782.
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Traffic collisions affect millions around the world and are the leading cause of death for children and young adults. Thus, Canada’s road safety plan is to reduce collision injuries and fatalities with a vision of making the safest roads in the world. We aim to predict fatalities of collisions on Canadian roads, and to discover causation of fatalities through exploratory data analysis and machine learning techniques. We analyse the vehicle collisions from Canada’s National Collision Database (1999–2017.) Through data mining methodologies, we investigate association rules and key contributing factors that lead to fatalities. Then, we propose two supervised learning classification models, Lasso Regression and XGBoost, to predict fatalities. Our analysis shows the deadliness of head-on collisions, especially in non-intersection areas with lacking traffic control systems. We also reveal that most collision fatalities occur in non-extreme weather and road conditions. Our prediction models show that the best classifier of fatalities is XGBoost with 83% accuracy. Its most important features are “collision configuration” and “used safety devices” elements, outnumbering attributes such as vehicle year, collision time, age, or sex of the individual. Our exploratory and predictive analysis reveal the importance of road design and traffic safety education.
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Wang, Ningcheng, Yufan Liu, Jinzi Wang, Xingjian Qian, Xizhi Zhao, Jianping Wu, Bin Wu, Shenjun Yao i Lei Fang. "Investigating the Potential of Using POI and Nighttime Light Data to Map Urban Road Safety at the Micro-Level: A Case in Shanghai, China". Sustainability 11, nr 17 (30.08.2019): 4739. http://dx.doi.org/10.3390/su11174739.
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The way in which the occurrence of urban traffic collisions can be conveniently and precisely predicted plays an important role in traffic safety management, which can help ensure urban sustainability. Point of interest (POI) and nighttime light (NTL) data have always been used for characterizing human activities and built environments. By using a district of Shanghai as the study area, this research employed the two types of urban sensing data to map vehicle–pedestrian and vehicle–vehicle collision risks at the micro-level by road type with random forest regression (RFR) models. First, the Network Kernel Density Estimation (NKDE) algorithm was used to generate the traffic collision density surface. Next, by establishing a set of RFR models, the observed density surface was modeled with POI and NTL variables, based on different road types and periods of the day. Finally, the accuracy of the models and the predicted outcomes were analyzed. The results show that the two datasets have great potential for mapping vehicle–pedestrian and vehicle–vehicle collision risks, but they should be carefully utilized for different types of roads and collision types. First, POI and NTL data are not applicable to the modeling of traffic collisions that happen on expressways. Second, the two types of sensing data are quite suitable for estimating the occurrence of traffic collisions on arterial and secondary trunk roads. Third, while the two datasets are capable of predicting vehicle–pedestrian collision risks on branch roads, their ability to predict vehicle safety on branch roads is limited.
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Find’o, S., M. Skuban, M. Kajba, J. Chalmers i M. Kalaš. "Identifying attributes associated with brown bear (Ursus arctos) road-crossing and roadkill sites". Canadian Journal of Zoology 97, nr 2 (luty 2019): 156–64. http://dx.doi.org/10.1139/cjz-2018-0088.
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Habitat fragmentation caused by transportation infrastructure is an issue of growing concern worldwide. We show how secondary roads may affect landscape permeability for brown bears (Ursus arctos Linnaeus, 1758). We focused on identifying environmental variables that govern the selection of road-crossing zones by bears (crossing model). We also investigated whether variables that characterize road-crossing zones differ from those that are typical for bear–vehicle collision sites (collision model). The study area was located in north-central Slovakia. To identify road-crossing sites, we used the GPS fixes of 27 bears and identified 35 bear–vehicle collision sites from a different data set. We used mixed-effects logistic regression to model resource selection at road-crossing sites and to compare bear-crossing sites with bear-kill sites. The crossing model showed that the traffic volume with distance to forest and grassland were the most influential factors in bear selection of road-crossing sites. Results of the collision model indicated that successful road crossings by bears were located at different road sections from vehicle collisions, which differed by a traffic volume of 5000 vehicles/24 h. The outcomes of this study can facilitate improved mitigation measures on secondary roads.
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Xu, Zhi Qiang. "Research of Mechanics Based on Accident Collision". Advanced Materials Research 1021 (sierpień 2014): 214–17. http://dx.doi.org/10.4028/www.scientific.net/amr.1021.214.
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With the rapid development of the economy, the automotive industry and road transport have also been rapidly developed. Road traffic accidents occur frequently, which not only brings a lot of inconvenience to road traffic management but also threatens people's lives and property. In many road accidents, vehicle collisions are the most serious and dangerous also the largest. So having a comprehensive, systematic study of the car's collision is becoming the world's most pressing subject. The whole process of the collision of moving vehicle is inseparable from the role of the force. So in order to better analyze vehicle collisions and reproduce this process, a lot of mechanical knowledge should be required. This paper studies the vehicle accident reconstruction mechanical problems, laying the foundation of further research.
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Zhong, Weifan, i Lijing Du. "Predicting Traffic Casualties Using Support Vector Machines with Heuristic Algorithms: A Study Based on Collision Data of Urban Roads". Sustainability 15, nr 4 (6.02.2023): 2944. http://dx.doi.org/10.3390/su15042944.
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Traffic accidents on urban roads are a major cause of death despite the development of traffic safety measures. However, the prediction of casualties in urban road traffic accidents has not been deeply explored in previous research. Effective forecasting methods for the casualties of traffic accidents can improve the manner of traffic accident warnings, further avoiding unnecessary loss. This paper provides a practicable model for traffic forecast problems, in which ten variables, including time characteristics, weather factors, accident types, collision characteristics, and road environment conditions, were selected as independent factors. A mixed-support vector machine (SVM) with a genetic algorithm (GA), sparrow search algorithm (SSA), grey wolf optimizer algorithm (GWO) and particle swarm optimization algorithm (PSO) separately are proposed to predict the casualties of collisions. Grounded on 4285 valid urban road traffic collisions, the computing results show that the SSA-SVM performs effectively in the casualties forecast compared with the GWO-SVM, GA-SVM and PSO-SVM.
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Lovegrove, Gordon R., i Tarek Sayed. "Macro-level collision prediction models for evaluating neighbourhood traffic safety". Canadian Journal of Civil Engineering 33, nr 5 (1.05.2006): 609–21. http://dx.doi.org/10.1139/l06-013.
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This study describes the development of macro-level (i.e., neighbourhood or traffic zone level) collision prediction models using data from 577 neighbourhoods across the Greater Vancouver Regional District. The objective is to provide a safety planning decision-support tool that facilitates a proactive approach to community planning which addresses road safety before problems emerge. The models are developed using the generalized linear regression modelling (GLM) technique assuming a negative binomial error structure. The resulting models relate traffic collisions to neighbourhood characteristics such as traffic volume, demographics, network shape, and transportation demand management. Several models are presented for total or severe collisions in rural or urban zones using measured and (or) modelled data. It is hoped that quantifying a predictive traffic safety – neighbourhood planning relationship will facilitate improved decisions by community planners and engineers and, ultimately, facilitate improved neighbourhood traffic safety for residents and other road users.Key words: neighbourhood safety, macro-level collision prediction models, road safety, safety planning, transportation demand management, sociodemographic, generalized linear regression modelling.
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Keisham, Supriya, Pabitramala Nandeibam, Kh Pradipkumar Singh, George Vanlalchhuanga i H. Nabachandra. "Retrospective study on fatal road traffic accident in Aizawl, Mizoram during 2014-2018". IP International Journal of Forensic Medicine and Toxicological Sciences 6, nr 3 (15.09.2021): 91–94. http://dx.doi.org/10.18231/j.ijfmts.2021.020.
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A traffic collision, also called a motor vehicle collision, car accident, occurs when a vehicle collides with another vehicle, pedestrian, animal, road debris, or any stationary obstruction, such as a tree, pole or building. Traffic collisions often result in injury, disability, death and damage to property as well as financial cost to both the society & individuals involved.
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Niemi, Milla, Sari C. Cunningham, Robert Serrouya, Veli-Matti Väänänen i Sakari Mykrä-Pohja. "Traffic mortality of wild forest reindeer Rangifer tarandus fennicus in Finland". Nature Conservation 57 (16.12.2024): 89–102. https://doi.org/10.3897/natureconservation.57.107332.
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Vehicle collisions kill at least one million ungulates annually in Europe. The number of traffic-killed individuals is usually relatively low for managed species, compared to the annual harvest quota. Therefore, traffic mortality in common ungulate species has historically been seen as a management and traffic safety problem, rather than a conservation issue. However, rare ungulate species, such as European wild forest reindeer (WFR) Rangifer tarandus fennicus, challenge this paradigm. The global population of WFR is approximately 5 300 individuals, divided into three main subpopulations: Suomenselkä and Kainuu in Finland, and N-W Russia. WFR females generally produce only one calf per year, which makes this species particularly vulnerable to any additional source of mortality. Here, we investigate traffic mortality of WFR in Finland. For both Finnish WFR subpopulations we estimated a kill rate (the proportion of individuals killed/struck) and, in relation to their winter population sizes, the collision and traffic mortality rates. Our collision data was collected during 2017–2022 by volunteer hunters and consisted of 390 road traffic collisions (407 WFR individuals), with supplementary data on railway collisions. In total, 259 individuals were killed directly in road traffic collisions or euthanized later after tracking (kill rate 64%). An age class (adult/juvenile) was determined for 265 animals (65%), and the results indicated that noticeably more adults than juveniles were killed. In relation to wintering subpopulation sizes, there were higher collision and traffic mortality rates in Suomenselkä (3.0% and 2.0% of the winter population, respectively) than in Kainuu (1.8% and 1.3%). WFR-train collisions occurred in both subpopulations. In Suomenselkä, a railway mortality rate of 0.2% was recorded, while in Kainuu it was 0.7%. We found collision and traffic mortality rates that were relatively low and comparable with those of other ungulate species. However, the relatively high proportion of adults observed among road-killed individuals lends support for further studies to develop species-specific mitigation measures for WFR.
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Darus, Nur Shaeza, Muhamad Nazri Borhan, Siti Zaharah Ishak, Rozmi Ismail, Siti Fatin Mohd. Razali, Nor Aznirahani Mhd Yunin i Rizati Hamidun. "The Effect of Physical Environment Risk Factors on Vehicle Collisions Severity Involving Child-Pedestrians in Malaysia". SAGE Open 12, nr 1 (styczeń 2022): 215824402110684. http://dx.doi.org/10.1177/21582440211068494.
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This study is aimed at investigating the association between child-pedestrian severity levels of collisions and physical environmental variables. The outcome of this study could be applied to road safety intervention for improving engineering modifications related to children pedestrians. The retrospective analysis was carried out using 6-year data from Royal Malaysia Police records from the years 2009 to 2014. Multinomial logit modeling (MNL) was applied. The results demonstrated that the injury severity of the collisions is related to road geometry, road surface material, road surface condition, traffic system, road marking, traffic control type, lighting condition, speed limit, time of collision, type of location, and land use characteristics. Specifically, fatal injury collisions are significantly increased by t/y intersection; concrete and earth-road surfaces; two-way traffic and dual carriageways; posted speed limits of 70 to 90 km/h; time of collision: 0 to 0659 hours (early morning) and 0700 to 0959 hours (morning); lighting conditions, including dark without street light, and dark with street light; and control type involving police. Meanwhile, cross intersections’ posted speed limits of 80 km/h to 90 km/h and the time of collision from 0 to 0659 hours (early morning) and 1900 to 2459 hours (night), significantly increased serious injury collisions. Notably, the findings revealed the importance of more in-depth studies on physical environmental features that relate to child-pedestrians’ severity level of collisions. This is essential for improvements to physical environmental designs by policymakers. Thus, policymakers and stakeholders can utilize the findings to further improve the physical environment through structure and design.
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Liu, Yan, Siqin Wang, Xuanming Fu i Bin Xie. "A network-constrained spatial identification of high-risk roads for hit-parked-vehicle collisions in Brisbane, Australia". Environment and Planning A: Economy and Space 51, nr 2 (30.10.2018): 279–82. http://dx.doi.org/10.1177/0308518x18810531.
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The severe loss of human life and material damage caused by traffic accidents is a growing concern faced by many countries across the world. In Australia, despite a decline in the total number of traffic collisions since 2001, the number of hit-parked-vehicle (HPV) collisions as a special type of road accident has increased over time. Utilizing the road collisions and roadway network data in Brisbane, Australia over a 10-year period from 2001 to 2010, we generated graphics illustrating the spatial patterning of high-risk road segments for HPV crashes identified using the local indicator of network-constrained clusters (LINCS) approach. These spatial patterns vary by days of the week and times of the day. Roads with high risk for HPV collision tend to occur in high-density road networks and cluster around road intersections. The methodology applied in this work is applicable to other network-constrained point-pattern analysis.
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Metar, Manas. "Software Design for Adaptive Laser Headlights". International Journal for Research in Applied Science and Engineering Technology 9, nr 11 (30.11.2021): 581–88. http://dx.doi.org/10.22214/ijraset.2021.38847.
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Abstract: The road traffic collisions and injuries, is still a major concern in automotive field. A disproportionate number of fatal accidents happen at nighttime. The major part of the collision is contributed by the human factors. Yet the technology in automobile is continuously assisting drivers while driving. Driving in the dark is not always easy as roads aren’t always illuminated, in such cases a powerful headlight is needed to illuminate most of the road. But such headlights cause glare to the oncoming traffic and again the chances of collision increase, with risking lives of passengers. Therefore, a need of smart headlamps which can illuminate the road far ahead without glaring the oncoming traffic is generated. This research aims to build a Laser based adaptive headlight system which can fulfil the need. The headlight design is proposed using Tinkercad software in which Arduino circuit has been used and software design is presented. The system works well with responding to the steering angle and controls the intensity of light preventing oncoming traffic from getting glared. Keywords: Adaptive headlight system, laser headlights, Arduino, Arduino software design, design of headlight system, cornering lights, effects of laser headlights, Tinkercad
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Yena, Maksym. "Uav urban mobility control: swarm intelligence and collision avoidance". INNOVATIVE TECHNOLOGIES AND SCIENTIFIC SOLUTIONS FOR INDUSTRIES, nr 4(30) (11.12.2024): 59–66. https://doi.org/10.30837/2522-9818.2024.4.059.
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Subject matter: Intelligent management of traffic flows in urban environments using swarm intelligence principles and collision avoidance algorithms to ensure safe and efficient urban mobility. Special attention is given to the management of unmanned vehicles and drones. Goal: To develop and analyze an approach to managing urban mobility that combines swarm intelligence principles and collision avoidance algorithms to optimize traffic flows, improve traffic safety, and reduce the number of accidents. Tasks: Investigate the safety and efficiency problems of urban transportation in the context of growing urbanization; develop a model that integrates swarm intelligence and collision avoidance algorithms for managing the movement of unmanned vehicles; conduct a series of experiments to test the effectiveness of the proposed approach; analyze the results of the experiments and determine the potential for improving urban mobility and ensuring road safety. Methods: Mathematical modeling of traffic flows using the swarm intelligence algorithm to coordinate the movement of unmanned vehicles and avoid collisions. Results: The proposed urban mobility management algorithm has demonstrated the ability to improve traffic flows, reduce the risk of collisions, and increase overall road safety. The results of the experiments confirmed the effectiveness of using swarm intelligence for coordination vehicles and collision avoidance algorithms to prevent accidents.
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Butsenko, Elena. "How Data Mining Can Improve Road Safety in Cities". Social Sciences 11, nr 3 (16.03.2022): 130. http://dx.doi.org/10.3390/socsci11030130.
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Traffic collisions pose a serious problem for cities due to the annually increasing number of vehicles. Information about incidents that occur on roads is important for the corresponding monitoring bodies, authorities, and emergency services. To ensure traffic safety, the data have to be visible, clean, and transparently displayed. This research was, therefore, aimed at developing a methodology for monitoring motor vehicle collision data and applying visualization techniques to evidence from New York City. The method showed that the largest number of motor vehicle traffic crashes occurred in Lower Manhattan due to its high population and traffic density. With these data, the road agencies of the city can put potentially dangerous road sections under control and make them safer for both drivers and pedestrians. Further development of the system may be associated with data analytics and visualization, resulting in new layers of heatmaps that not only provide details on car collision hotspots, which serve as the main target indicator for traffic safety authorities, but also break them down into social facilities, such as schools. This feature will enable assessment of how safe it is around a school and the evaluation of the impact of an underpass or a traffic enforcement camera on the number of collisions. The motor vehicle traffic crash (MVTC) monitoring system will help in comparing city districts and regions in terms of safety, seeing trends, realizing what exactly is happening at interchanges, and understanding the reasons behind. The methodology, in addition, can be supplemented with an analysis of risk factors for MVTCs, the efficiency of adopted measures and road renovations that are carried out, and many other functions.
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Yun, Jeongin, Aya Selmoune, Junseong Chae, Myoungkook Seo i Jinwoo Lee. "A Before-and-After Study of a Collision Risk Detecting and Warning System on Local Roads". Journal of Advanced Transportation 2023 (28.02.2023): 1–13. http://dx.doi.org/10.1155/2023/6894065.
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Local roads have numerous blind spots caused by complex geometry, obstacles, and narrow width. Thus, conventional proactive countermeasures, such as passive traffic signs and convex mirrors, have not always been effective in preventing local road collisions. In this paper, we present a novel proactive two-step approach for traffic safety on local roads, comprised of detection of pedestrian-to-vehicle and vehicle-to-vehicle collision risks and warning systems. First, using video surveillance and radars to eliminate blind spots, the system detects road objects, predicts their trajectories and reachable areas, and identifies a potential risk situation. Second, it provides road users such as vehicles and pedestrians with warnings through LED variable message signs, which allows them to react effectively in risky situations. We have applied the system to two local road sites in South Korea, including a university campus in Seoul City and an apartment complex in Daejeon City. The detecting system has been validated using a confusion matrix. We have assessed the warning effect through a before-and-after study and found that the proposed system contributed to the improvement of traffic safety at the case study site in that traffic conflicts decreased by 55–62%.
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Popescu, Bianca, i Tarek Sayed. "Developing Macrolevel Collision Prediction Models to Evaluate Bicycle Safety in Vancouver, British Columbia, Canada". Transportation Research Record: Journal of the Transportation Research Board 2659, nr 1 (styczeń 2017): 25–32. http://dx.doi.org/10.3141/2659-03.
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To encourage greener cities while reducing the impacts of the transportation system—such as impacts on climate change, traffic congestion, and road safety—governments have been investing in sustainable modes of transportation, such as cycling. A safe and comfortable cycling environment is critical to encourage bicycle trips because cyclists are usually subject to greater safety risks. Engineering approaches to road safety management have traditionally addressed road safety by reacting to existing collision records. For bicycle collisions, which are rare events, a proactive approach is more appropriate. This study described the use of bicycle-related macrolevel (i.e., neighborhood or zonal-level) collision prediction models as empirical tools in road safety diagnosis and planning. These models incorporated an actual bicycle exposure indicator (the number of bicycle kilometers traveled). The macrolevel bicycle–vehicle collisions models were applied at the zonal level to a case study of Vancouver, British Columbia, Canada. Collision-prone zones in Vancouver were identified, and the highest-ranked zones were diagnosed to identify bicycle safety issues and to recommend potential safety countermeasures. The findings from this study suggest that the safety issues may be a result of high density and commercial land use type, coupled with a high traffic volume, particularly on arterial routes, and high bicycle volumes on routes with mixed vehicle and bicycle traffic. The case study demonstrated the use of the models to enhance bicycle safety proactively.
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Brow, Gerald R. "Traffic conflicts for road user safety studies". Canadian Journal of Civil Engineering 21, nr 1 (1.02.1994): 1–15. http://dx.doi.org/10.1139/l94-001.
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In this paper we advocate an approach to road safety studies in which traffic conflicts are used as indicators of roadway hazard. The critical traffic process event used to define conflict severity is the time to collision, modified by an observer's evaluation of the risk of collision. Field studies were done to assess the validity, reliability, and practicality of traffic conflicts. Experiments were conducted to test the threshold value of time to collision, and to examine a visual perception theory of crash avoidance behaviour. The experiments demonstrate that a time to collision threshold value of 1.5 s is a reasonable time-based index of hazard and that a visual perception model of breaking is a promising concept. The wider potential for traffic conflicts as a measure of driver risk, and as a basis for safety models, is discussed in the paper. The technique has been used for some 45 intersection safety studies. From the application to date it appears that the systematic observation of crash avoidance behaviour of drivers in unsafe conditions provides a useful tool for traffic engineering analysis of intersection traffic control and design problems. Key words: road safety, traffic conflicts, time to collision, driver risk, safety models.
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Abbas, Alaa K., Ashraf F. Hefny i Fikri M. Abu-Zidan. "Seatbelts and road traffic collision injuries". World Journal of Emergency Surgery 6, nr 1 (2011): 18. http://dx.doi.org/10.1186/1749-7922-6-18.
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Wang, Michael I. C., Charles H. P. Wen i H. Jonathan Chao. "Roadrunner+: An Autonomous Intersection Management Cooperating with Connected Autonomous Vehicles and Pedestrians with Spillback Considered". ACM Transactions on Cyber-Physical Systems 6, nr 1 (31.01.2022): 1–29. http://dx.doi.org/10.1145/3488246.
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The recent emergence of Connected Autonomous Vehicles (CAVs) enables the Autonomous Intersection Management (AIM) system, replacing traffic signals and human driving operations for improved safety and road efficiency. When CAVs approach an intersection, AIM schedules their intersection usage in a collision-free manner while minimizing their waiting times. In practice, however, there are pedestrian road-crossing requests and spillback problems, a blockage caused by the congestion of the downstream intersection when the traffic load exceeds the road capacity. As a result, collisions occur when CAVs ignore pedestrians or are forced to the congested road. In this article, we present a cooperative AIM system, named Roadrunner+ , which simultaneously considers CAVs, pedestrians, and upstream/downstream intersections for spillback handling, collision avoidance, and efficient CAV controls. The performance of Roadrunner+ is evaluated with the SUMO microscopic simulator. Our experimental results show that Roadrunner+ has 15.16% higher throughput than other AIM systems and 102.53% higher throughput than traditional traffic signals. Roadrunner+ also reduces 75.62% traveling delay compared to other AIM systems. Moreover, the results show that CAVs in Roadrunner+ save up to 7.64% in fuel consumption, and all the collisions caused by spillback are prevented in Roadrunner+.
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Cunningham, Calum X., Glen E. Liston, Adele K. Reinking, Natalie T. Boelman, Todd J. Brinkman, Kyle Joly, Mark Hebblewhite i in. "Human and animal movements combine with snow to increase moose-vehicle collisions in winter". Environmental Research Letters 17, nr 12 (1.12.2022): 125007. http://dx.doi.org/10.1088/1748-9326/aca8bf.
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Abstract Wildlife-vehicle collisions imperil humans, wildlife, and property. Collisions with moose (Alces alces) are especially consequential and there are indications they may increase during severe winters. We tested hypotheses regarding the influence of moose movements and vehicular traffic patterns on collision risk. We first modeled daily snow depth and accumulation across 5.6 million km2 of the North American Arctic-Boreal region. Next, we analyzed the movements and road use of 113 GPS-collared moose in response to snow depth. Finally, we examined the influence of these snow properties on vehicular traffic and 7680 moose-vehicle collisions. As winter progressed and the snowpack deepened in each study area, GPS-collared moose migrated to lower elevations, leading them into areas with shallower snow but higher road densities. This elevational migration corresponded with a higher probability of road-use by moose (by up to ten-fold) in winter than in summer. Corresponding to these patterns, moose-vehicle collisions were 2.4–5.7 times more frequent from December through February (compared to early summer). Collision risk was highest when and where snow depth was less than 120 cm, indicating that migration into areas with shallower snow increased collision risk in those areas. Most (82%) moose-vehicle collisions occurred after dark. This pattern was strongest during winter, when nighttime traffic volumes were eight times higher than summer due to longer nights. Overall, our findings suggest that concurrent seasonal changes in human and wildlife behavior increase the frequency of moose-vehicle collisions during winter. Snow depth influences collisions primarily through its impacts on moose movement, while strong seasonal changes in daylight hours cause an increase in nighttime traffic that further contributes to risk. This information may help predict times and places where risk of moose-vehicle collisions are highest and to develop seasonally dynamic mitigation strategies.
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Davies, Christopher, Wendy Wright, Fiona Hogan i Casey Visintin. "Predicting deer–vehicle collision risk across Victoria, Australia". Australian Mammalogy 42, nr 3 (2020): 293. http://dx.doi.org/10.1071/am19042.
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The risk of deer–vehicle collisions (DVCs) is increasing in south-east Australia as populations of introduced deer expand rapidly. There are no investigations of the spatial and temporal patterns of DVC or predictions of where such collisions are most likely to occur. Here, we use an analytical framework to model deer distribution and vehicle movements in order to predict DVC risk across the State of Victoria. We modelled the occurrence of deer using existing occurrence records and geographic climatic variables. We estimated patterns of vehicular movements from records of average annual daily traffic and speeds. Given the low number of DVCs reported in Victoria, we used a generalised linear regression model fitted to DVCs in California, USA. The fitted model coefficients suggested high collision risk on road segments with high predicted deer occurrence, moderate traffic volume and high traffic speed. We used the California deer model to predict collision risk on Victorian roads and validated the predictions with two independent datasets of DVC records from Victoria. The California deer model performed well when comparing predictions of collision risk to the independent DVC datasets and generated plausible DVC risk predictions across the State of Victoria.
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Yang, Bo, Yao Wu, Weihua Zhang i Jie Bao. "Modeling Collision Probability on Freeway: Accounting for Different Types and Severities in Various LOS". Sustainability 12, nr 18 (9.09.2020): 7386. http://dx.doi.org/10.3390/su12187386.
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In this study, collision-related data were collected on the I-880 freeway of California in the United States from 2006 to 2011. Our objective was to study the collision probability of different collision types and severities in different traffic states. The traffic states were divided by the traditional level of service (LOS) method. Various Bayesian conditional logit models have been established to analyze the relationship between the collision probability of different collision patterns and LOSs. The results showed that LOS A had the best safety performance associated with all of the collision types and severities, LOS C had the worst safety performance associated with hit object collisions, LOS D had the worst safety performance associated with sideswipe collisions and rear end collisions, and LOS F had the worst safety performance associated with injury collisions. The five-stage Bayesian random parameter sequential logit model was established to quantify the effects of different variables on the collision probability of various collision types and severities. In addition to LOS, the visibility, road surface, weather, ramp, and number of lanes had significant effects on different collision types and severities.
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Olmez, Sedar, Liam Douglas-Mann, Ed Manley, Keiran Suchak, Alison Heppenstall, Dan Birks i Annabel Whipp. "Exploring the Impact of Driver Adherence to Speed Limits and the Interdependence of Roadside Collisions in an Urban Environment: An Agent-Based Modelling Approach". Applied Sciences 11, nr 12 (8.06.2021): 5336. http://dx.doi.org/10.3390/app11125336.
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Roadside collisions are a significant problem faced by all countries. Urbanisation has led to an increase in traffic congestion and roadside vehicle collisions. According to the UK Government’s Department for Transport, most vehicle collisions occur on urban roads, with empirical evidence showing drivers are more likely to break local and fixed speed limits in urban environments. Analysis conducted by the Department for Transport found that the UK’s accident prevention measure’s cost is estimated to be £33bn per year. Therefore, there is a strong motivation to investigate the causes of roadside collisions in urban environments to better prepare traffic management, support local council policies, and ultimately reduce collision rates. This study utilises agent-based modelling as a tool to plan, experiment and investigate the relationship between speeding and vehicle density with collisions. The study found that higher traffic density results in more vehicles travelling at a slower speed, regardless of the degree to which drivers comply with speed restrictions. Secondly, collisions increase linearly as speed compliance is reduced for all densities. Collisions are lowest when all vehicles comply with speed limits for all densities. Lastly, higher global traffic densities result in higher local traffic densities near-collision sites across all adherence levels, increasing the likelihood of congestion around these sites. This work, when extended to real-world applications using empirical data, can support effective road safety policies.
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Kučas, Andrius, i Linas Balčiauskas. "Impact of Road Fencing on Ungulate–Vehicle Collisions and Hotspot Patterns". Land 10, nr 4 (25.03.2021): 338. http://dx.doi.org/10.3390/land10040338.
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The number of road traffic accidents decreased in Lithuania from 2002 to 2017, while the ungulate–vehicle collision (UVC) number increased and accounted for approximately 69% of all wildlife–vehicle collisions (WVC) in the country. Understanding the relationship between UVCs, traffic intensity, and implemented mitigation measures is important for the assessment of UVC mitigation measure efficiency. We assessed the effect of annual average daily traffic (AADT) and wildlife fencing on UVCs using regression analysis of changes in annual UVCs and UVC hotspots on different categories of roads. At the highest rates, annual UVC numbers and UVC hotspots increased on lower category (national and regional) roads, forming a denser network. Lower rates of UVC increase occurred on higher category (main) roads, forming sparser road networks and characterized by the highest AADT. Before 2011, both UVC occurrence and fenced road sections were most common on higher-category roads. However, as of 2011, the majority of UVCs occurred on lower-category roads where AADT and fencing had no impact on UVCs. We conclude that wildlife fencing on roads characterized by higher speed and traffic intensity may decrease UVC numbers and at the same time shifting UVC occurrence towards roads characterized by lower speed and traffic intensity. Wildlife fencing re-allocates wildlife movement pathways toward roads with insufficient or no mitigation measures.
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Apongnwu W Awamachi, Mbu D Tambi i Nkeng G Elambo. "Effectiveness of speed cameras on the prevention of road traffic collisions and casualties in cameroon". Journal of Management and Science 14, nr 2 (30.06.2024): 78–93. http://dx.doi.org/10.26524/jms.14.21.
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Issues related to transport collision and casualties cannot be undermined as it has direct effect on human health and death, hence relevant policies are required to reduce road traffic accidents. To do this, our study has as objectives: to discuss the drivers of the used of speed cameras by drivers in Cameroon., investigate the effects of speed cameras on the prevention of road traffic collisions and casualties in Cameroon and to determine the actual problems faced by drivers in the use of speed Cameras in Cameroon. Methodologically, the study applied the probit technique to estimate the result via primary data. The sample size consisted of 200 drivers collected through a well-structured questionnaire and collect among the drivers plying the Yaoundé-Doula, Douala –Bafoussam and Bafousam-Yaoundé major highway. The key findings revealed that the factors influencing the drivers used of speed cameras are: main occupation, attainment of higher education, married, age, victim of traffic collision and casualties. The result also shows that Speed camera is highly corroborating with the prevention of road traffic collisions and casualties in Cameroon. Finally, the result shows that the lack of awareness, student speed change, inadequate signage, limited visibility, technical limitations and privacy concerns are the actual physical challenges of the use of speed camera in Cameroon. The study suggests that decision makers in the transport sector should multiply the provision of speed cameras. This is a wise step in the prevention of road traffic collisions and casualties in Cameroon and better labour market.
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Drapalyuk, Mikhail, Vladimir Zelikov, Gennadiy Denisov, N. Zlobina i Natal'ya Zelikova. "INVESTIGATION AND ANALYSIS OF EMERGENCY SITUATIONS ON THE PERMANENT HIGHWAYS". Forestry Engineering Journal 11, nr 2 (1.07.2021): 108–20. http://dx.doi.org/10.34220/issn.2222-7962/2021.2/11.
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The statistics of road traffic accidents in general on federal and forest roads of the Russian Federation, as well as with the participation of large wild animals, have been considered. An analysis of the number of incidents reported in various official sources of information has been made. Significant underestimation of the main accident rates was noted, especially with the participation of wild animals on forest roads. The features of the investigation and examination of accidents involving pedestrians and wild animals, taking into account the requirements of the RF Traffic Rules, are described. Deficiencies in the requirements of the RF Traffic Rules have been identified. Possible variants of hazardous traffic situations on forest road sections have been considered. A deterministic analysis of possible variants of a vehicle collision with a wild animal has been carried out. The speed of movement of a vehicle on an emergency-dangerous section of the road has been determined, which allows the driver to timely apply emergency braking to prevent a collision with an animal. Additions to the Traffic Rules of the Russian Federation have been proposed, aimed at ensuring safety on emergency-hazardous sections of highways and forest roads
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Raynham, P., J. Unwin, M. Khazova i S. Tolia. "The role of lighting in road traffic collisions". Lighting Research & Technology 52, nr 4 (26.08.2019): 485–94. http://dx.doi.org/10.1177/1477153519870857.
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The paper reports a study that examines how to determine if a road traffic collision took place in daylight or in the dark. An innovative method was developed, based on solar altitude, to establish cut-off points of daylight and darkness determined from a study of daylight availability in England, Scotland and Wales. This approach provides a rigorous method to differentiate daytime and night-time collisions. The criteria were used in a study of the collisions reported in the STATS19 data set for the weeks either side of the clock changes that are necessary between Greenwich Mean Time and British Summer Time. By comparing periods with the same clock time either side of the time change, using the aforementioned method, it was possible to isolate collisions within the same time period that during one week occurred in darkness and in the other week in daylight. The initial finding was that there are 19.3% more collisions in the dark periods and there is an even greater increase (31.7%) in pedestrian injuries.
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Xing, Jian Ping, Ling Guo Meng, Can Sun i Jian Wen Li. "Simplified Monte Carlo Model of Real-Time Traffic Flow Prediction". Applied Mechanics and Materials 97-98 (wrzesień 2011): 867–71. http://dx.doi.org/10.4028/www.scientific.net/amm.97-98.867.
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Simplified Monte Carlo collision model of real-time traffic flow prediction is proposed. In this model, two different road cells, roads and crosses, are configured. Vehicle distribution is generated by real-time traffic flow randomly. Based on two-dimensional topology, Monte Carlo collision between road and vehicles promote time evolution of the system. Monte Carlo collision is the core of the model and traffic flow is study target. The solution of relationship equations of road and vehicles is very simple in this model to speed up the computing. In addition, parameters can be corrected and configured at any time in the process of time evolution. Experimental results show that the model has the advantages of real-time, visual interface, easy configuration, and can be corrected by real-time feedback. The model can not only simulate and predict macroscopic data, such as flow, velocity distribution, but also follow the track of each vehicle in detail. So, the model can be used in researching both macroscopic and microscopic characteristics of vehicle movement.
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Anil Kumar, Karmakonda, i Qurat Ul Ain Shadan Taj. "A study of pattern of injuries and factors affecting the injury pattern among road traffic accident victims". International Journal Of Community Medicine And Public Health 7, nr 5 (24.04.2020): 1926. http://dx.doi.org/10.18203/2394-6040.ijcmph20201567.
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Background: Road traffic injuries remain a critical public health concern. The most affected are the young population. Road accidents are one of the top three reasons for deaths among the population from the age group of 5 to 44 years, globally. Road traffic injuries can be prevented by a national plan or various strategies.Methods: It is a hospital based study done on all road traffic accident cases admitted in Owaisi Hospital between April 2013 to July 2014 using a pretested, pre-designed questionnaire and collecting Medico legal case records. Data is expressed in percentages. Association between factors and severity of injuries was calculated using Chi-square test.Results: Majority of the victims had suffered grievous injury (95.29%). Majority (62.48%) had injuries involving upper limb, 52% had injuries involving multiple body regions. Majority of the times it was a sideways collision 275 (48.00%), followed by head on collision 198 (34.55%). Factors such as use of the seat belt, alcohol consumption, lighting on the road are found to be statistically significant with the severity of the injuries.Conclusions: Majority of the victims had suffered from multiple injuries (52%). The road traffic accidents can be prevented mainly putting emphasis on the use of seat belt, avoidance of consumption of alcohol by the drivers, adequate lighting on roads which can reduce the burden of occurrence of the road traffic accidents.
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Mofolasayo, Adekunle. "Towards ‘Vision-Zero’ in Road Traffic Fatalities: The Need for Reasonable Degrees of Automation to Complement Human Efforts in Driving Operation". Systems 12, nr 2 (25.01.2024): 40. http://dx.doi.org/10.3390/systems12020040.
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Human factors play a huge role in road traffic safety. Research has found that a huge proportion of traffic crashes occur due to some form of human error. Improving road user behavior has been the major strategy that has been emphasized for improving road traffic safety. Meanwhile, despite the training efforts, and testing for drivers, the global status of road traffic safety is alarming. This research highlights the seriousness of human factors on road traffic safety and provides actionable strategies to greatly reduce the negative impact of human factors on road traffic safety. Motor vehicle safety data that were made available online by the U.S. Bureau of Transportation Statistics were reviewed to evaluate the severity of traffic collisions. To evaluate the extent of human factors in motor vehicle traffic fatalities, data for Canadian motor vehicle traffic collision statistics were reviewed. The study confirms that human factors (such as driver distraction, fatigue, driving under the influence of drugs and alcohol etc.) play a huge role in road traffic fatalities. The need for a reasonable degree of automation to help reduce the impacts of human factors on road safety and recommendations aimed at providing widespread support for a reasonable degree of automation systems in driving tasks are presented. Actionable strategies that can be implemented by policymakers to reduce global road traffic fatalities are also presented.
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Selmoune, Aya, Jeongin Yun, Myoungkook Seo, Hyeokhyeon Kwon, Changhee Lee i Jinwoo Lee. "Development of a Residential Road Collision Warning Service Based on Risk Assessment". Journal of Advanced Transportation 2023 (16.03.2023): 1–16. http://dx.doi.org/10.1155/2023/7496377.
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Pedestrians are more likely to be seriously injured in vehicle collisions. In fact, multiple collisions between vehicles and pedestrians occur on residential roads that lack street-to-sidewalk dividers and have numerous blind spots. Traditional traffic safety features and equipment, such as speed bumps and traffic signs, are not always sufficient to prevent pedestrian accidents on such residential roads. Therefore, we suggest a collision risk warning service for residential roads as a solution to this issue. We use CCTVs with computer vision techniques and radar to accurately detect objects in real-time and to trace their trajectories. In addition, we employ a time-to-collision-based method to identify dangerous situations. The service warns drivers and pedestrians about hazardous situations using a light-emitting diode sign board. We applied our service to three different roads on a university campus in Seoul, Korea, and then conducted a user survey to evaluate the service. In summary, more than 90% of respondents stated that the service was necessary for these specific locations, and 76.9% noted that the service significantly contributed to traffic safety on the campus. This implies that the proposed service improved traffic safety and can be applied to various locations on residential roads.
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Hamid, Shaista, i Nitin Arora. "Research paper on improvement of road infrastructure to implement road safety at an intersection of military hospital road." IOP Conference Series: Earth and Environmental Science 889, nr 1 (1.11.2021): 012027. http://dx.doi.org/10.1088/1755-1315/889/1/012027.
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Abstract Geometrical plan lacks on existing streets would prompt an expected mishap, for example, a mishap occurs at the sharp bends, layered asphalt conditions, and dangerous asphalt surface. Street auto collision has been expanding in the Southern Region of Tigray, of which this region was appeared to have disturbing rates. As indicated by the Southern Tigray Regional Police, the more rate of street car crashes were recorded in Garhi, Brimah pull and jekhani regions. This examination study zeroed in on the investigation of auto collisions identified with mathematical plan boundaries of the current black top street. While the information for the investigation covered course information, auto collision report from the police headquarters containing a few wounds, crashes, and destroyed the properties, just as meetings and poll studies to individuals who are straightforwardly engaged with street voyages, are thought of. The essential information has basically covered the math of the street which was estimated during the site study, street security review utilizing the agenda, meeting, and poll study. Then again, the auxiliary information gathered from the traffic the executive’s office in the area workplaces. On this, the outcomes introduced as line diagrams, pie outlines, figures for street car crash and sketch for the proposed improvement in the street plan issue. In view of the aftereffects of the investigation in the year 2015 to year 2019, it discovered that there were 1866 Road Traffic Accidents have been happening nearby the overview street areas. It uncovered that the essential driver of street car crashes in the investigation region exuded from the street plan components because of some mathematical lacks at the auto collision inclined regions uncovered that the essential driver of street car crashes in the examination region radiated from the street plan components because of some mathematical insufficiencies at the car crash inclined regions. Hence, this investigation reasoned that the recurrence of event of street auto collisions and the figure of setbacks is altogetherexpanding. This street mishap would endure if the concerned offices don’t satisfactorily address the disease.
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Nurdin, Arliyanti, Bernadus Seno Aji, Yupit Sudianto i Mardhiyyah Rafrin. "YOLOV4 Deepsort ANN for Traffic Collision Detection". Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI) 12, nr 3 (31.12.2023): 338–51. http://dx.doi.org/10.23887/janapati.v12i3.62923.
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Every collision must be handled right away to prevent further harm, damage, and traffic bottlenecks. Hence, the implementation of a systematic approach for accident detection becomes imperative to expedite response mechanisms. Our proposed accident detection system operates in three stages, encompassing vehicle object detection, multiple object tracking, and vehicle interaction analysis. YOLOv4 is employed for object detection, while DeepSort is utilized to the tracking of multiple vehicle objects. Subsequently, the positional and interactional data of each object within the video frame undergo thorough analysis to identify collisions, utilizing an Artificial Neural Network (ANN). Notably, collisions involving a single vehicle and not affecting other road users are excluded from the scope of this study. The evaluation of our approach reveals that the ANN model achieves a commendable F-Measure of 0.97 for detecting objects without collisions and 0.88 for objects involved in collisions, based on the conducted tests.
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Prentkovskis, Olegas, Edgar Sokolovskij i Vilius Bartulis. "INVESTIGATING TRAFFIC ACCIDENTS: A COLLISION OF TWO MOTOR VEHICLES". TRANSPORT 25, nr 2 (30.06.2010): 105–15. http://dx.doi.org/10.3846/transport.2010.14.
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Traffic safety may be ensured by normal operation of all elements of the system, including the driver, a motor vehicle and transportation medium. Insufficient safety of some particular elements of this system (the lack of discipline of the participants of traffic, poor technical state of a motor vehicle or road, etc.) are the main causes of traffic accidents. Statistical data on traffic accidents in 2000–2009 in Lithuania is presented. Collisions of motor vehicles in 2009 make one of the largest proportion of all traffic accidents ‐ 33.4%. In 2009 drivers, were the main traffic accident perpetrators ‐ 73.6%. The paper considers some major aspects of motor vehicle collision simulation based on the application of PC‐CRASH software, allowing researchers to analyze the changes in the direction of motor vehicle motion in the case of a collision and the influencing factors. This type of traffic accident simulation consists in studying the circumstances of collision, reconstructing the processes, calculating the pre‐impact speed of motor vehicles and deter‐ mining various parameters of motor vehicles’ movement at different stages of traffic accident development.
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Pressney, I., B. Stedman i LJ King. "Significant breast injury after road traffic collision". Trauma 16, nr 1 (11.11.2013): 45–47. http://dx.doi.org/10.1177/1460408613484684.
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Alsaleh, Rushdi, Kawal Walia, Ghoncheh Moshiri i Yasmeen T. Alsaleh. "Traffic Collision Severity Modeling Using Multi-Level Multinomial Logistic Regression Model". Applied Sciences 15, nr 2 (16.01.2025): 838. https://doi.org/10.3390/app15020838.
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This study investigates the various factors contributing to the severity of traffic collisions, with specific attention given to elements such as the involvement of pedestrians and cyclists, the roles played by motor vehicles, prevailing weather conditions, road characteristics, and geographical contexts. Drawing from a comprehensive dataset from the Virginia Department of Transportation, encompassing over 500,000 data points, this study utilizes two statistical models. Specifically, it utilizes Multinomial Logistic Regression and Multi-Level (Mixed Effect) Multinomial Logistic Regression, which accounts for group-level heterogeneity, to explore the intricate interplay between various factors and collision severity outcomes. The findings underscore the superiority of the Multi-Level Multinomial Logistic Regression model over the standard Multinomial Logistic Regression model in capturing road user severity. Furthermore, this paper highlights the heightened odds of fatalities associated with the presence of vulnerable road users, such as pedestrians and cyclists. Collisions involving unbelted drivers exhibited odds ratios exceeding 10, indicating a substantially elevated likelihood of severe outcomes compared to their belted counterparts.
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Oţăt, Oana Victoria, i Victor Oţăt. "Determining the Projection Throw Distance of a Pedestrian upon the Collision with a Vehicle". Applied Mechanics and Materials 822 (styczeń 2016): 461–71. http://dx.doi.org/10.4028/www.scientific.net/amm.822.461.
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Recent statistics on the main causes of road traffic accidents and subsequently the main causes of victims’ injuries which may ultimately lead to fatalities, indicate that within urban areas the first place is occupied by prohibited road-crossing, accounting for over 20% of the causes for road traffic accidents. Hence, one of the major direct consequences arising is the severe injury of the pedestrians-up to their fatalities-due to prohibited road crossing through unauthorized places. Thus, the present paper underpins a research study on the vehicle-pedestrian collision, based on a statistical analysis of the pedestrian model, most frequently involved in road traffic accidents. The statistical analysis will be then reinforced by a theoretical study regarding this type of collision. In order to validate the mathematical model, applied to determine the projection throw distance of a pedestrian, we have also proposed a virtual simulation of the vehicle - pedestrian collision, described within the last section of the present research paper.
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Branzi, Valentina, Monica Meocci, Lorenzo Domenichini i Margherita Calcinai. "A Combined Simulation Approach to Evaluate Overtaking Behaviour on Two-Lane Two-Way Rural Roads". Journal of Advanced Transportation 2021 (20.08.2021): 1–18. http://dx.doi.org/10.1155/2021/9973138.
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A significant percentage of road fatalities and injuries occur in the nonmotorway rural road network. One of the main causes of accidents on these roads is represented by overtaking, as, by its nature, it involves a risk of a head-on collision with oncoming traffic. The paper describes a combined simulation approach (driving simulator and traffic microsimulation) designed to examine the influence of different traffic conditions on passing manoeuvres on two-lane two-way rural roads. The main focus was the evaluation of the end of the passing manoeuvre because it reflects the risk of a head-on collision. In addition, the study aimed to assess the usefulness of the proposed combined approach in the ability to proactively and quickly diagnose traffic safety problems and consequently to evaluate appropriate solutions. The data collected with an interactive driving simulator on a sample of 54 participants have been used to adjust some input data of the traffic microsimulation software. A specific situation consisting of a stationary heavy vehicle obstructing the entire lane was repeated in both experiments. The analyses focused on time-to-collision (TTC), defined as the remaining gap between the passing vehicle and the oncoming vehicle at the end of the passing manoeuvre. The results showed that the type of manoeuvre performed is significantly influenced by the traffic condition. Furthermore, the manoeuvre is influenced by the gap between two successive vehicles in the opposite lanes. Focusing on the end of the manoeuvre, it was found how a traffic increase leads to a significant reduction of the TTC values. Furthermore, the comparative analysis conducted between the data recorded following the combined approach and those obtained using exclusively the input data of the microsimulation software supports the usefulness of the proposed methodology for conducting road safety analyses, especially in complex traffic environments where drivers’ behaviour plays a decisive role.
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Jima, Debela, i Tibor Sipos. "The Impact of Road Geometric Formation on Traffic Crash and Its Severity Level". Sustainability 14, nr 14 (11.07.2022): 8475. http://dx.doi.org/10.3390/su14148475.
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Road infrastructure has an impact on the occurrence of road traffic crashes. The aim of this study was to analyze the impact of road geometric formation on road traffic crashes. Based on the nature, convenience, and availability of data, the study used Budapest city road traffic crash data from 2017 to 2021. For organizing, analysis, and modeling, the study used Microsoft-Excel, the Statistical Package for Social Science, and Quantum Geographic Information System. Relative frequency distribution, Multinomial Logistic Regression, Multilayer Perceptron Artificial Neural Network, and Severity Index were used for the analysis. Both inferential and descriptive statistics are used to describe and summarize the study outcome. Multicollinearity tests, p-value, overdispersion, percent of incorrect error, and other statistical model testes were undertaken to analyze the significance of the data and variable for modeling and analysis. A large number of crashes were observed in straight and one-lane road geometric formationsr890. However, the severity level was high at the horizontal curve and in all three lanes of the road. The regression model indicated that light conditions, collision type, road geometry, and speed had a significant effect on traffic accidents at a p-value of 0.05. A collision between the vehicle (rear end collision), and a vehicle with a pedestrian was the probable cause of the crash. The Multilayer Perceptron Artificial Neural Network indicated that horizontally curved geometry has a positive and strong relationship with road traffic fatalities. The primary reasons for the occurrences of a road traffic crash at an intersection, horizontal curve, and straight road geometric formation were the improper use of road traffic signs, road pavement condition, and stopping sight distance problems, respectively. The hourly distribution showed that from 16:01 to 17:00 time interval was a peak hour for the occurrences of road traffic crashes. Whereas, driver plays vital role and responsible body for the occurrences of crashes at all geometric formations.
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Linn, Nyi Nyi, Kai Liu i Qiang Gao. "A Contention-Free Cooperative MAC Protocol for Eliminating Heterogenous Collisions in Vehicular Ad Hoc Networks". Sensors 23, nr 2 (16.01.2023): 1033. http://dx.doi.org/10.3390/s23021033.
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In vehicular ad hoc networks (VANETs), efficient data dissemination to a specified number of vehicles with minimum collisions and limited access delay is critical for accident prevention in road safety. However, packet collisions have a significant impact on access delay, and they may lead to unanticipated link failure when a range of diversified collisions are combined due to complex traffic conditions and rapid changes in network topology. In this paper, we propose a distributed contention-free cooperative medium access control (CFC-MAC) protocol to reduce heterogenous collisions and unintended access delay in stochastic traffic scenarios. Firstly, we develop a cooperative communication system model and cooperative forwarding mechanism to explore the optimum road path between the source and destination by identifying the potential cooperative vehicles. Secondly, we propose a vectorized trajectory estimation mechanism to suppress merging collisions by identifying the relative velocity of vehicles with different speeds in a specific time interval. Based on the case study, typical heterogeneous collisions and aggregated heterogeneous collisions at dissociated positions and associated positions are investigated. In both cases, we propose the corresponding collision-resolving mechanisms by methodically recapturing the colliding time slot or acquiring the available free time slots after identifying the access vehicles and comparing the received signal strengths. Performance analysis for collision probability and access delay is conducted. Finally, the simulation results show that the proposed protocol can achieve deterministic access delay and a minimal collision rate, substantially outperforming the existing solutions.
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Agina, Samaa, Faeze Momeni Rad i Karim El-Basyouny. "Linking Deployment Outcomes to Program Impacts for Mobile Photo Enforcement". Safety 9, nr 4 (7.12.2023): 88. http://dx.doi.org/10.3390/safety9040088.
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Previous research has delved into the effectiveness of Mobile Photo Enforcement (MPE) in curbing speed violations and enhancing road safety. This present study extends this investigation to explore the potential influence of MPE deployment efforts on subsequent collision occurrences. Specifically, the research team applied survival analysis techniques to examine the connection between MPE deployment variables and the duration between collisions. K-M survival estimates were employed to assess the survivability of classified groups, categorized based on deployment hours, visits, the ratio of hours to visits, and traffic count. The findings illuminated that the ratio of hours to visits emerged as the most impactful variable in prolonging the time interval between collisions and diminishing the likelihood of collisions. Notably, the anticipated reduction in collision hazards, signifying the occurrence of collisions, exhibited variations ranging from 22% to 52%. The most substantial decreases were observed when deployment occurred in high-traffic locations. These outcomes reinforce the positive impact of deployed MPE hours and visits in extending the duration between consecutive collisions, thus reducing the risk of collision occurrences.
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Ma, Xiaolong, Qiang Yu i Jianbei Liu. "Modeling Urban Freeway Rear-End Collision Risk Using Machine Learning Algorithms". Sustainability 14, nr 19 (23.09.2022): 12047. http://dx.doi.org/10.3390/su141912047.
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A large amount of traffic crash investigations have shown that rear-end collisions are the main type collisions on the freeway. The purpose of this study is to investigate the rear-end collision risk on the freeway. Firstly, a new framework was proposed to develop the rear-end collision probability (RCP) model between two vehicles based on Generalized Pareto Distribution (GPD). Secondly, the freeway rear-end collision risk (F-RCR) was defined as the sum of the rear-end collision probability of each vehicle and divided into three levels which was high, median, and low rear-end collision risk. Then, different machine learning algorithms were used to model F-RCR under the condition of an unbalanced dataset. The result of the RCP model showed continuous change and can identify the dangerous condition quickly compared to the traditional models even when the speed of the leading vehicle is faster than the following vehicle. When the vehicle distribution was unbalanced on road and the speed difference between adjacent lanes and the traffic volume was large, F-RCR will increase. Multi-Layer Perceptron (MLP) was found to be more suitable for modeling F-RCR. The framework provided in this research was transferrable and can be used in the freeway proactive traffic safety management system.
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V. V. Pattabhiram, Yegulla, i P. Sravana. "Traffic safety and vulnerable road users – A case study in Hyderabad". i-manager's Journal on Structural Engineering 12, nr 2 (2023): 36. http://dx.doi.org/10.26634/jste.12.2.20151.
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Vulnerable Road Users (VRU) refers to individuals who are most at risk in traffic. This category mainly includes pedestrians and two-wheelers who lack the protection of an outside shield and face a greater risk of injury in any collision with a vehicle. Consequently, they are in dire need of protection against such collisions. According to the World Health Organization's Global Status Report on Road Safety, Vulnerable Road Users account for 47% of fatalities in India. This group encompasses motorized two-wheelers and three-wheelers (Auto Rickshaws). Over the past few decades, road "improvement" schemes have become common in urban areas. However, "improvement" often translates to road widening, raising concerns about the safety of such projects. Are they taking into account the needs of vulnerable road users? Furthermore, where have the footpaths gone that used to be on both sides of the road? It's concerning that public buildings such as schools, cinemas, and shopping malls, which attract many pedestrians, have direct access to main roads without any drop-off areas. Additionally, there are no speed-calming measures in place. Recent modifications in many of the city's junctions removed four-arm intersections, diverting traffic through U-turns. While this may work well for vehicular traffic, what about pedestrians? When the U-turn is located close to the junction, traffic from the approaching road has to weave through 3 or 4-lane traffic to reach the U-turn facility. How safe are these junctions when there is insufficient weaving distance? This paper focuses on the traffic safety challenges faced by pedestrians, considered vulnerable road users, in the Indian context. Specifically, it discusses recent developments in Hyderabad aimed at improving pedestrian safety, laws applicable to pedestrians, issues with enforcement, and recommendations based on IRC guidelines.
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Xian, Huacai, Yu Wang, Yujia Hou, Shunzhong Dong, Junying Kou i Huili Zeng. "Research on Influencing Factors of Urban Road Traffic Casualties through Support Vector Machine". Sustainability 14, nr 23 (5.12.2022): 16203. http://dx.doi.org/10.3390/su142316203.
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Urban road traffic safety has always been vital in transportation research. This paper analyzed the factors influencing the degree of traffic accident casualties on Jinan Jingshi Road and its branch roads, taking them as the study area for urban road traffic safety problems. Additionally, it used the application of Particle Swarm Optimization (PSO), a Support Vector Machine (SVM) model, and a recursive feature elimination (RFE) to rank the contribution degree of the influencing factors. The results showed that driving on rainy days has a high probability of casualties, while the type of collision was a minimum influence factor. Additionally, on rainy days, cars were accident-prone road vehicles, and 8:00–12:00 and 18:00–22:00 were accident-prone periods. Based on the results, preventive measures were further put forward regarding the driver, road drainage capacity, policy management, and autopilot technology. This study aimed to guide urban traffic safety planning and provide a basis for developing traffic safety measures.
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Petin, V. V., i A. V. Keller. "Description and tests of a control system for automatic emergency braking of a car with an algorithm for predicting the adhesion of automobile tires to the road surface". Journal of Physics: Conference Series 2061, nr 1 (1.10.2021): 012100. http://dx.doi.org/10.1088/1742-6596/2061/1/012100.
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Abstract Every year the number of cars in the world is steadily growing, which in turn leads to an increase in road accidents. Russia, as the largest country by area in the world, has a number of traffic-related features that are not typical for other regions, including changing road and climatic conditions. The sharp increase in the number of accidents in the first hours after precipitation is especially noticeable, that indicates the difficulty to adapt to the changing traffic situation for vehicle drivers. More than 60% of collisions, according to the traffic police databases for 2018-2019, occur due to incorrect predictions by drivers of the braking distance of their cars. As a rule, this can happen due to the inexperience of the driver, incorrect assessment of the current adhesion properties of the roadway, distraction to a mobile phone, the climate or multimedia system of the car, movement with insufficient visibility. Preventing collisions occurring in such scenarios is the main task of an intelligent driver assistance system such as the automatic emergency braking system. The main part of this system is an adequate assessment of dynamically changing road and climatic conditions and warning the driver of a possible collision hazard, as well as automatic action on the vehicle’s brake drive to prevent a collision. The article describes control algorithms for assessing and predicting dangerous situations.
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Sari, Novita, Siti Malkhamah i Latif Budi Suparma. "Prediction model of factors causing traffic accidents on rural arterial roads: A binary logistic regression approach". Journal of Infrastructure, Policy and Development 8, nr 6 (25.06.2024): 6692. http://dx.doi.org/10.24294/jipd.v8i6.6692.
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In rural areas, land use activities around primary arterial roads influence the road section’s traffic characteristics. Regulations dictate the design of primary arterial roads to accommodate high speeds. Hence, there is a mix of traffic between high-speed vehicles and vulnerable road users (pedestrians, bicycles, and motorcycles) around the land. As a result, researchers have identified several arterial roads in Indonesia as accident-prone areas. Therefore, to improve the road user’s safety on primary arterial roads, it is necessary to develop models of the influence of various factors on road traffic accidents. This research uses binary logistic regression analysis. The independent variables are carelessness, disorderliness, high speed, horizontal alignment, road width, clear zone, road shoulder width, signs, markings, and land use. Meanwhile, the dependent variable is the frequency of accidents, where the frequency of accidents consists of multi-accident vehicles (MAV) and single-accident vehicles (SAV). This study collects data for a traffic accident prediction model based on collision frequency in accident-prone areas. The results, road shoulder width, and road sign factor all have an impact on the frequency of traffic accidents. According to a realistic risk analysis, MAV and SAV have no risk difference. After validation, this model shows a confidence level of 92%. This demonstrates that the model generates estimations that accurately reflect reality and are applicable to a wider population. This research has the potential to assist engineers in improving road safety on primary arterial roads. In addition, the model can help the government measure the impact of implemented policies and engage the public in traffic accident prevention efforts.
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Isradi, Muhammad, Hermanto Dwiatmoko, Joewono Prasetijo, Andri Irfan Rifai, Zaffan Farhana Zainal, Guohui Zhang i Hendy Yusman Firdaus. "Identification of hazardous road sites: a comparison of blackspot methodology of Narogong Road Bekasi and Johor Federal Roads". SINERGI 28, nr 2 (4.05.2024): 347. http://dx.doi.org/10.22441/sinergi.2024.2.014.
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A traffic accident is an unexpected and inadvertent road event involving a vehicle with or without other road users that results in human injuries and possibly property damage. This study identifies and analyses the characteristics of Indonesian and Malaysian road traffic incidents. The method used was the identification of crash data coordinates and the Equivalent Accident Number (EAN) score per road segment. Accident characteristics are based on the type of road traffic accident, and collision is the most common type of accident. Speed data of two vehicles, cars and motorcycles, was collected through the test driver method by using the same vehicles for cars and motorcycles and following their normal driving habits. From the analysis results, some factors causing accidents on the Bekasi Narogong Road Section are the lack of traffic signs, road surface conditions, and lack of clear road markings. While on the FT050 Johor federal road, every kilometre per segment and along the segment, motorcycles are faster than cars in terms of speed.
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Mohammed, Ali Ahmed, Kamarudin Ambak, Ahmed Mancy Mosa i Deprizon Syamsunur. "A Review of the Traffic Accidents and Related Practices Worldwide". Open Transportation Journal 13, nr 1 (30.06.2019): 65–83. http://dx.doi.org/10.2174/1874447801913010065.
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A traffic accident, a traffic collision or crash occurs when a vehicle collides with another vehicle, pedestrian, animal, road barriers, or any stationary obstruction such as a tree or a utility pole. Traffic collisions may result in injury, death, vehicle damage and possession damage. Motor vehicle collisions cause death and disability as well as a financial burden. Traffic accidents cause many losses especially of human life, property damages, and loss of resources. Indeed, even in strife influenced countries such as Afghanistan, Libya, Pakistan, and Yemen, road traffic remains the most common cause of fatal injuries, causing between two and eight times more fatalities than war and lawful mediation. The World Health Organization (WHO) 2013 assessed the traffic casualty rate in the Eastern Mediterranean Region (EMR) to be the second most elevated rate universally after the African Region and extending a few other countries in the region. The aim of this paper was to enrich the global highway safety knowledge by revealing the catastrophic impact of traffic accidents on the economy of the societies and the safety of the common worldwide.
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Seefong, Manlika, Panuwat Wisutwattanasak, Chamroeun Se, Kestsirin Theerathitichaipa, Sajjakaj Jomnonkwao, Thanapong Champahom, Vatanavongs Ratanavaraha i Rattanaporn Kasemsri. "Big Data Analytics with the Multivariate Adaptive Regression Splines to Analyze Key Factors Influencing Accident Severity in Industrial Zones of Thailand: A Study on Truck and Non-Truck Collisions". Big Data and Cognitive Computing 7, nr 3 (21.09.2023): 156. http://dx.doi.org/10.3390/bdcc7030156.
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Machine learning currently holds a vital position in predicting collision severity. Identifying factors associated with heightened risks of injury and fatalities aids in enhancing road safety measures and management. Presently, Thailand faces considerable challenges with respect to road traffic accidents. These challenges are particularly acute in industrial zones, where they contribute to a rise in injuries and fatalities. The mixture of heavy traffic, comprising both trucks and non-trucks, significantly amplifies the risk of accidents. This situation, hence, generates profound concerns for road safety in Thailand. Consequently, discerning the factors that influence the severity of injuries and fatalities becomes pivotal for formulating effective road safety policies and measures. This study is specifically aimed at predicting the factors contributing to the severity of accidents involving truck and non-truck collisions in industrial zones. It considers a variety of aspects, including roadway characteristics, underlying assumptions of cause, crash characteristics, and weather conditions. Due to the fact that accident data is big data with specific characteristics and complexity, with the employment of machine learning in tandem with the Multi-variate Adaptive Regression Splines technique, we can make precise predictions to identify the factors influencing the severity of collision outcomes. The analysis demonstrates that various factors augment the severity of accidents involving trucks. These include darting in front of a vehicle, head-on collisions, and pedestrian collisions. Conversely, for non-truck related collisions, the significant factors that heighten severity are tailgating, running signs/signals, angle collisions, head-on collisions, overtaking collisions, pedestrian collisions, obstruction collisions, and collisions during overcast conditions. These findings illuminate the significant factors influencing the severity of accidents involving trucks and non-trucks. Such insights provide invaluable information for developing targeted road safety measures and policies, thereby contributing to the mitigation of injuries and fatalities.