Journal articles: 'Rear-end collisions Prevention'

1

Zhang, Wenhui, Tuo Liu, and Jing Yi. "Exploring the Spatiotemporal Characteristics and Causes of Rear-End Collisions on Urban Roadways." Sustainability 14, no. 18 (September 19, 2022): 11761. http://dx.doi.org/10.3390/su141811761.

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Rear-end collisions are caused by drivers misjudging urgent risks while following vehicles ahead in most cases. However, compared with other accident types, rear-end collisions have higher preventability. This study aims to reveal the prone segments and hours of rear-end collisions. First, we extracted 1236 cases from traffic accident records in Harbin from 2015 to 2019. These accidents are classified as property damage accidents, injury accidents and fatal accidents according to the collision severity. Second, density analysis in GIS was used to demonstrate the spatial distribution of rear-end collisions. The collision spots considering the density and severity were visually displayed. We counted the hourly and seasonal distribution characteristics according to the statistical data. Finally, LightGBM and random forest classifier models were used to evaluate the substantial factors affecting accident severity. The results have potential practical value in rear-end collision warning and prevention.

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Minusa, Shunsuke, Kei Mizuno, Daichi Ojiro, Takeshi Tanaka, Hiroyuki Kuriyama, Emi Yamano, Hirohiko Kuratsune, and Yasuyoshi Watanabe. "Increase in rear-end collision risk by acute stress-induced fatigue in on-road truck driving." PLOS ONE 16, no. 10 (October 21, 2021): e0258892. http://dx.doi.org/10.1371/journal.pone.0258892.

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Increasing road crashes related to occupational drivers’ deteriorating health has become a social problem. To prevent road crashes, warnings and predictions of increased crash risk based on drivers’ conditions are important. However, in on-road driving, the relationship between drivers’ physiological condition and crash risk remains unclear due to difficulties in the simultaneous measurement of both. This study aimed to elucidate the relationship between drivers’ physiological condition assessed by autonomic nerve function (ANF) and an indicator of rear-end collision risk in on-road driving. Data from 20 male truck drivers (mean ± SD, 49.0±8.2 years; range, 35–63 years) were analyzed. Over a period of approximately three months, drivers’ working behavior data, such as automotive sensor data, and their ANF data were collected during their working shift. Using the gradient boosting decision tree method, a rear-end collision risk index was developed based on the working behavior data, which enabled continuous risk quantification. Using the developed risk index and drivers’ ANF data, effects of their physiological condition on risk were analyzed employing a logistic quantile regression method, which provides wider information on the effects of the explanatory variables, after hierarchical model selection. Our results revealed that in on-road driving, activation of sympathetic nerve activity and inhibition of parasympathetic nerve activity increased each quantile of the rear-end collision risk index. The findings suggest that acute stress-induced drivers’ fatigue increases rear-end collision risk. Hence, in on-road driving, drivers’ physiological condition monitoring and ANF-based stress warning and relief system can contribute to promoting the prevention of rear-end truck collisions.

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Yan, Yu, Jing Huang, Fan Li, and Lin Hu. "Investigation of the Effect of Neck Muscle Active Force on Whiplash Injury of the Cervical Spine." Applied Bionics and Biomechanics 2018 (2018): 1–10. http://dx.doi.org/10.1155/2018/4542750.

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The objective of the present study is to investigate the influence of neck muscle activation on whiplash neck injury of the occupants of a passenger vehicle under different severities of frontal and rear-end impact collisions. The finite element (FE) model has been used as a versatile tool to simulate and understand the whiplash injury mechanism for occupant injury prevention. However, whiplash injuries and injury mechanisms have rarely been investigated in connection with neck active muscle forces, which restricts the complete reappearance and understanding of the injury mechanism. In this manuscript, a mixed FE human model in a sitting posture with an active head-neck was developed. The response of the cervical spine under frontal and rear-end collision conditions was then studied using the FE model with and without neck muscle activation. The effect of the neck muscle activation on the whiplash injury was studied based on the results of the FE simulations. The results indicated that the neck active force influenced the head-neck dynamic response and whiplash injury during a collision, especially in a low-speed collision.

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Yang, Xianfeng (Terry), Gang-Len Chang, Zhao Zhang, and Pengfei (Taylor) Li. "Smart Signal Control System for Accident Prevention and Arterial Speed Harmonization under Connected Vehicle Environment." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 5 (March 27, 2019): 61–71. http://dx.doi.org/10.1177/0361198119837242.

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The intent of this paper is to develop a system that can integrate connected vehicle (CV) data and traffic sensor information to concurrently address the need to improve urban arterial safety and mobility. Under the mixed traffic pattern of CVs and human-driven vehicles (HVs), the system aims to achieve three primary objectives: proactively preventing rear-end collision, reactively protecting side-street traffic from red-light-running vehicles, and effectively facilitating speed harmonization along local arterials. The embedded safety function will integrate CV and roadside sensor data to compute the distribution of dilemma zones for vehicles of different approaching speeds in real-time. Such data fusion will enable the proposed system to offer the advice of either “stop” or “go” to both CVs and HVs so as to prevent rear-end collisions and side-angled crashes. Given the locations and speeds of CVs, and the number of vehicles monitored by sensors, the proposed system can further compute the time-varying intersection queue length. Then the embedded mobility function will optimize the arterial signal plan in real-time and produce the speed advisory for approaching vehicles to facilitate their progression through intersections. Results from extensive simulation experiments confirm the effectiveness of the proposed system in both reducing potential intersection crash rates and improving arterial progression efficiency. The proposed control framework also proves the effectiveness of using dilemma zone protection sensors for traffic mobility improvement.

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Giummarra, Melita J., Ben Beck, and Belinda J. Gabbe. "Classification of road traffic injury collision characteristics using text mining analysis: Implications for road injury prevention." PLOS ONE 16, no. 1 (January 27, 2021): e0245636. http://dx.doi.org/10.1371/journal.pone.0245636.

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Road traffic injuries are a leading cause of morbidity and mortality globally. Understanding circumstances leading to road traffic injury is crucial to improve road safety, and implement countermeasures to reduce the incidence and severity of road trauma. We aimed to characterise crash characteristics of road traffic collisions in Victoria, Australia, and to examine the relationship between crash characteristics and fault attribution. Data were extracted from the Victorian State Trauma Registry for motor vehicle drivers, motorcyclists, pedal cyclists and pedestrians with a no-fault compensation claim, aged > = 16 years and injured 2010–2016. People with intentional injury, serious head injury, no compensation claim/missing injury event description or who died < = 12-months post-injury were excluded, resulting in a sample of 2,486. Text mining of the injury event using QDA Miner and Wordstat was used to classify crash circumstances for each road user group. Crashes in which no other was at fault included circumstances involving lost control or avoiding a hazard, mechanical failure or medical conditions. Collisions in which another was predominantly at fault occurred at intersections with another vehicle entering from an adjacent direction, and head-on collisions. Crashes with higher prevalence of unknown fault included multi-vehicle collisions, pedal cyclists injured in rear-end collisions, and pedestrians hit while crossing the road or navigating slow traffic areas. We discuss several methods to promote road safety and to reduce the incidence and severity of road traffic injuries. Our recommendations take into consideration the incidence and impact of road trauma for different types of road users, and include engineering and infrastructure controls through to interventions targeting or accommodating human behaviour.

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Bian, Chentong, Guodong Yin, Liwei Xu, and Ning Zhang. "REAR-END COLLISION ESCAPE ALGORITHM FOR INTELLIGENT VEHICLES SUPPORTED BY VEHICULAR COMMUNICATION." Transport 37, no. 6 (December 31, 2022): 398–410. http://dx.doi.org/10.3846/transport.2022.18172.

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To reduce rear-end collision risks and improve traffic safety, a novel rear-end collision escape algorithm is proposed for intelligent vehicles supported by vehicular communication. Numerous research has been carried out on rear-end collision avoidance. Most of these studies focused on maintaining a safe front clearance of a vehicle while only few considered the vehicle’s rear clearance. However, an intelligent vehicle may be collided by a following vehicle due to wrong manoeuvres of an unskilled driver of the following vehicle. Hence, it is essential for an intelligent vehicle to maintain a safe rear clearance when there is potential for a rear-end collision caused by a following vehicle. In this study, a rear-end collision escape algorithm is proposed to prevent rear-end collisions by a following vehicle considering both straight and curved roads. A trajectory planning method is designed according to the motions of the considered intelligent vehicle and the corresponding adjacent vehicles. The successive linearization and the Model Predictive Control (MPC) algorithms are used to design a motion controller in the proposed algorithm. Simulations were performed to demonstrate the effectiveness of the proposed algorithm. The results show that the proposed algorithm is effective in preventing rear-end collisions caused by a following vehicle.

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Behbahani, Hamid, Navid Nadimi, Hooman Alenoori, and Mina Sayadi. "Developing a New Surrogate Safety Indicator Based on Motion Equations." PROMET - Traffic&Transportation 26, no. 5 (October 31, 2014): 371–81. http://dx.doi.org/10.7307/ptt.v26i5.1388.

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Collision avoidance system (CAS), with the help of surrogate safety measures is a beneficial tool for reducing driver errors and preventing rear-end collisions. One of the most well-known surrogate safety measures to detect rear-end conflicts is Time-to-collision (TTC). TTC refers to the time remaining before the rear-end accident if the course and the speed of vehicles are maintained constant. Different surrogate measures have been derived from TTC; however, the most important are Time Exposed Time-to-collision (TET) and Time Integrated Time-to-collision (TIT). In this paper a new surrogate safety measure based on TTC notion has been developed. This new indicator merges TET and TIT into one measure and gives a score between 0 and 100%, as the probability of collision. Applying this indicator in CAS as a safety measure will be more useful than TET&TIT, to reduce driver errors and rear-end collisions.

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HIROSE, Toshiya, Takafumi KAWAKAMI, Nobuyo KASUGA, and Toichi SAWADA. "Evaluation of Preventing Rear-end Collision System." Transactions of the Japan Society of Mechanical Engineers Series C 73, no. 725 (2007): 244–50. http://dx.doi.org/10.1299/kikaic.73.244.

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9

Xi, Jianfeng, Hongyu Guo, Jian Tian, Lisa Liu, and Weifu Sun. "Analysis of influencing factors for rear-end collision on the freeway." Advances in Mechanical Engineering 11, no. 7 (July 2019): 168781401986507. http://dx.doi.org/10.1177/1687814019865079.

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Rear-end collision accounts for the main type of traffic accidents occurring on the freeway. In order to extract the significant influence factors of rear-end collision on the freeway, this study utilized the data of freeway traffic accidents between 2010 and 2015 in China. First, based on quasi-induced exposure theory, the information of driver, vehicle, and road environment was analyzed. Gender, age, driving age, vehicle safety, load, weather, fatigue, driving speed, road alignment, accident time, and visibility were selected as the important factors that might affect rear-end collision. Second, based on logistic regression model, the influencing factors analysis model of freeway rear-end collision was established. In the regression analysis, the possible important factors selected were taken as the independent variables, and the accident responsibility was taken as the dependent variable. Then, the factors that had significant influence on rear-end collision were selected from candidate independent variables by stepwise regression method. Finally, the specific influence of driving age, load, weather, accident time, visibility, fatigue, and driving speed on rear-end collision occurring on the freeway was discussed. The analysis results were explained according to the odds ratio. The research results of this article can provide guidance for the prevention of rear-end collision on the freeway and theoretical support for the development of freeway early warning system.

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HUANG, SHYH-CHOUR, and RONALD L. HUSTON. "INFLUENCE OF THE HEAD RESTRAINT POSITION ON DYNAMIC RESPONSE OF THE HEAD/NECK SYSTEM UNDER WHIPLASH LOADING." Biomedical Engineering: Applications, Basis and Communications 15, no. 04 (August 25, 2003): 164–69. http://dx.doi.org/10.4015/s1016237203000250.

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The objective of this paper is to present modeling and simulation of the effect of head restraint position on head/neck dynamics in rear-end motor vehicle collisions. Although individual injury tolerance levels vary, it is believed that properly positioned head restraints can be beneficial in reducing injury. The paper discusses the effects of restraint positioning by simulating a series of rear-end collisions using a finite-segment (lumped-mass) model of the human frame. It is found that proximity of the restraint to the head is the principal factor in preventing harmful whiplash motion. The findings suggest that "smart" head restraints could therefore significantly reduce whiplash induced injuries.

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11

Cao, Qing Gui, Lu Hua Zhao, Nai Xiu Gao, and Ping Chen. "System Safety Analysis and Strategy Research on Truck Rear-End Accidents." Applied Mechanics and Materials 253-255 (December 2012): 1700–1704. http://dx.doi.org/10.4028/www.scientific.net/amm.253-255.1700.

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The paper is intended to analyze trucks rear-end accident by applying fishbone diagram and fault tree analysis methods and study the countermeasures. And applying fishbone diagram to analyze the cause of trucks rear-end collision accidents systematically and logically and classify the accidents cause. The paper drew the fault tree of trucks rear-end collision accidents, sought the way of controlling the accidents through least path-set of fault tree, and found the main factors that influence the truck traffic safety according to the result of structure importance level. According to the analysis of the fishbone diagram and fault tree results, the paper researched the preventive measures of truck traffic accidents, and draws up safety checklist to ensure the effective implementation of the measures.

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12

Chen, Yen-Hsiang, Sung Yoon Park, Gang-Len Chang, and Minseok Kim. "Preventing Intersection Rear-End Collisions with an Optimized Dynamic Two-Stage Actuated Control." Journal of Transportation Engineering, Part A: Systems 147, no. 9 (September 2021): 04021049. http://dx.doi.org/10.1061/jtepbs.0000541.

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13

Chen, Lien-Wu, Yu-Hao Peng, and Yu-Chee Tseng. "An Infrastructure-less Framework for Preventing Rear-End Collisions by Vehicular Sensor Networks." IEEE Communications Letters 15, no. 3 (March 2011): 358–60. http://dx.doi.org/10.1109/lcomm.2011.011811.100519.

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14

Qu, Xiao, and Wei Gang Zheng. "The Design of Ultrasonic Ranging Car Collision Prevention Alarm System." Applied Mechanics and Materials 457-458 (October 2013): 1631–34. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1631.

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This article in view of the situation of highway automobile rear-end collision accident frequency, designed and developed based on the ultrasonic ranging car anti collision warning system, its main purpose is to get the driver more parking time, thereby reducing the occurrence of accident. The design on the basis of the minimum safety distance model, and puts forward the system solution: by ultrasound to measure the distance of the two cars, using this system as the control core, for the analysis of all kinds of information, when driving distance less than the minimum safety distance control will alarm and stop the car.

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15

Iwashita, Yohei, Motonori Ishibashi, Yasuhiko Miura, and Masashi Yamamoto. "Changes of Driver Behavior by Rear-end Collision Prevention Support System in Poor Visibility." International Journal of Automotive Engineering 3, no. 3 (2012): 89–95. http://dx.doi.org/10.20485/jsaeijae.3.3_89.

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16

Karyemsetty, Nagarjuna, and Kontham Raja Kumar. "Road Safety: An Accident Prevention Using Intelligent Vehicular Network." International Journal of Safety and Security Engineering 10, no. 5 (November 30, 2020): 631–38. http://dx.doi.org/10.18280/ijsse.100507.

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The increasing rate of road fatalities has demanded the attention of the researchers, scientists, Industry and government organizations and technologies. The impact of accidents is simulated by rear-end collision with parameters such as vehicle position, direction, speed, inter-vehicle distance, and relative speeds, etc. Open source simulators have to be adopted to study and analyze various collision scenarios in vehicular networks. Safety mechanism proposed to minimize the possibility of accidents and mitigate the effect of the escalating incident. The proposed mechanism estimates the point of intersection, time to collision, and time to avoid accidents. Using parameters, the proposed mechanism able to determine accidents with 92.6% accuracy. The remaining 7.4% cases enable the passive safety system to help the people to stay alive, minimize the damage in case an accident.

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17

Gentle, C. R., W. Z. Golinski, and F. Heitplatz. "Computational studies of ‘whiplashg’ injuries." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 215, no. 2 (February 1, 2001): 181–89. http://dx.doi.org/10.1243/0954411011533742.

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The term ‘whiplash’ was initially used to describe injuries to the neck caused by the head being forced backwards during a rear-end collision in cars without head restraints. The addition of head restraints in the 1970s was expected to solve this problem by preventing excessive extension of the neck but experience suggests the problem still exists. This paper reviews available experimental studies of whiplash and uses the data to construct a finite element model which is capable of dynamically simulating whiplash collisions and predicting the forces in all the relevant neck ligaments. For the first time, it is shown that trauma occurs long before the head hits the head restraint as a result of displacement between the head and the torso caused by the head's inertia leading to markedly different acceleration histories. It is concluded that experimental and computational studies must be used together to produce progress in biomechanical studies.

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18

Scott, J. J., and Robert Gray. "A Comparison of Tactile, Visual, and Auditory Warnings for Rear-End Collision Prevention in Simulated Driving." Human Factors: The Journal of the Human Factors and Ergonomics Society 50, no. 2 (April 2008): 264–75. http://dx.doi.org/10.1518/001872008x250674.

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May, Jennifer F., Carryl L. Baldwin, and Raja Parasuraman. "Prevention of Rear-End Crashes in Drivers with Task-Induced Fatigue through the Use of Auditory Collision Avoidance Warnings." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 50, no. 22 (October 2006): 2409–13. http://dx.doi.org/10.1177/154193120605002213.

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20

Ho, Cristy, Nick Reed, and Charles Spence. "Assessing the effectiveness of “intuitive” vibrotactile warning signals in preventing front-to-rear-end collisions in a driving simulator." Accident Analysis & Prevention 38, no. 5 (September 2006): 988–96. http://dx.doi.org/10.1016/j.aap.2006.04.002.

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21

Wang, Ke, Qingwen Xue, and Jian John Lu. "Risky Driver Recognition with Class Imbalance Data and Automated Machine Learning Framework." International Journal of Environmental Research and Public Health 18, no. 14 (July 15, 2021): 7534. http://dx.doi.org/10.3390/ijerph18147534.

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Identifying high-risk drivers before an accident happens is necessary for traffic accident control and prevention. Due to the class-imbalance nature of driving data, high-risk samples as the minority class are usually ill-treated by standard classification algorithms. Instead of applying preset sampling or cost-sensitive learning, this paper proposes a novel automated machine learning framework that simultaneously and automatically searches for the optimal sampling, cost-sensitive loss function, and probability calibration to handle class-imbalance problem in recognition of risky drivers. The hyperparameters that control sampling ratio and class weight, along with other hyperparameters, are optimized by Bayesian optimization. To demonstrate the performance of the proposed automated learning framework, we establish a risky driver recognition model as a case study, using video-extracted vehicle trajectory data of 2427 private cars on a German highway. Based on rear-end collision risk evaluation, only 4.29% of all drivers are labeled as risky drivers. The inputs of the recognition model are the discrete Fourier transform coefficients of target vehicle’s longitudinal speed, lateral speed, and the gap between the target vehicle and its preceding vehicle. Among 12 sampling methods, 2 cost-sensitive loss functions, and 2 probability calibration methods, the result of automated machine learning is consistent with manual searching but much more computation-efficient. We find that the combination of Support Vector Machine-based Synthetic Minority Oversampling TEchnique (SVMSMOTE) sampling, cost-sensitive cross-entropy loss function, and isotonic regression can significantly improve the recognition ability and reduce the error of predicted probability.

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Thenmozhi, R., and S. Govindarajan. "Cluster Based Architecture for Preventing Accident and Rear-End Collision in VANET." Indian Journal of Science and Technology 9, no. 48 (December 27, 2016). http://dx.doi.org/10.17485/ijst/2016/v9i48/91034.

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Journal articles on the topic 'Rear-end collisions Prevention'

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