scholarly journals Combined Prediction Model of Death Toll for Road Traffic Accidents Based on Independent and Dependent Variables

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Feng Zhong-xiang ◽  
Lu Shi-sheng ◽  
Zhang Wei-hua ◽  
Zhang Nan-nan
Keyword(s):  
Linear Regression ◽  
Prediction Model ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Weight Coefficient ◽  
Multivariate Linear Regression ◽  
Road Traffic Accidents ◽  
Linear Regression Models ◽  
Combined Model ◽  
Death Toll

In order to build a combined model which can meet the variation rule of death toll data for road traffic accidents and can reflect the influence of multiple factors on traffic accidents and improve prediction accuracy for accidents, the Verhulst model was built based on the number of death tolls for road traffic accidents in China from 2002 to 2011; and car ownership, population, GDP, highway freight volume, highway passenger transportation volume, and highway mileage were chosen as the factors to build the death toll multivariate linear regression model. Then the two models were combined to be a combined prediction model which has weight coefficient. Shapley value method was applied to calculate the weight coefficient by assessing contributions. Finally, the combined model was used to recalculate the number of death tolls from 2002 to 2011, and the combined model was compared with the Verhulst and multivariate linear regression models. The results showed that the new model could not only characterize the death toll data characteristics but also quantify the degree of influence to the death toll by each influencing factor and had high accuracy as well as strong practicability.

scholarly journals The association between meteorological variables and road traffic injuries: a study from Macao

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6438 ◽  
Author(s):  
Chon-Fu Lio ◽  
Hou-Hon Cheong ◽  
Chon-Hou Un ◽  
Iek-Long Lo ◽  
Shin-Yi Tsai
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Meteorological Factors ◽  
Linear Regression Analysis ◽  
World Health ◽  
Road Traffic Accidents ◽  
Road Traffic Injuries ◽  
Windy Weather

Objective Correlation analysis and multiple linear regression analysis were conducted to estimate the influence of meteorological factors on road traffic injuries stratified by severity. Crash rate was defined as mean monthly road traffic accidents per 1,000 vectors. Design Ecological time-series study. Setting Macao traffic accident registry database between January 1st, 2001 and November 31st, 2016. Participants In total, 393,176 traffic accidents and 72,501 cases of road traffic injuries (RTIs) were enrolled; patients’ severity was divided into mild injury, required hospitalisation, and death. Exposure Variation of monthly meteorological factors. Main outcome measure Weather-condition-related road traffic accidents, injuries, and deaths. Results Windy weather significantly correlated with increased number of traffic accidents among all transport vectors (r = .375 to .637; p < 0.001). Multiple linear regression showed temperature (B = 0.704; p < 0.05) and humidity (B =  − 0.537; p < 0.001) were independent factors for mild injury. The role of windy weather was relatively more obvious among patients with severe injuries (B = 0.304; p < 0.001) or those who died (B = 0.015; p < 0.001). A longer duration of sunshine was also associated to RTI-related deaths (B = 0.015; p < 0.001). In total, 13.4% of RTIs were attributable to meteorological factors and may be preventable. Conclusion The World Health Organization stated that RTIs are a major but neglected public health challenge. This study demonstrates meteorological factors have significant effects on any degree of RTIs. The results may not be generalized to other climates or populations while the findings may have implications in both preventing injuries and to announce safety precautions regarding trauma and motor vehicle collisions to the general public by public agencies.

A road traffic accidents prediction model for traffic service robot

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chaohui Zhang ◽  
Yijing Li ◽  
Tian Li
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Social Robots ◽  
Service Robot ◽  
Road Traffic Accidents ◽  
Information Communication ◽  
Content Type ◽  
Time Autonomous

PurposeIn recent years, the demand for road traffic has continued to increase, but the casualties and economic losses caused by traffic accidents have also remained high. Therefore, the use of social service robots to manage, supervise and warn real-time traffic information has become an inevitable trend of traffic safety management.Design/methodology/approachIn order to explore the inherent objective development law of road traffic accidents, in this paper, the factor analysis (FA) is used to explore the main influencing factors of traffic accidents, then the random forest algorithm is applied to build an FA–RF-based road traffic accident severity prediction model to predict two- and three-category accidents.FindingsBy comprehensively comparing the classification results of the two- and the three-category accident prediction, it also finds that due to the intersection between injuries and fatalities and the lack of necessarily external environmental information, the FA–RF model has a large degree of misjudgment for injuries and fatalities. Therefore, it is recommended to establish a real-time autonomous information communication mechanism between different kinds of social robots, which can improve the prediction of traffic accidents.Originality/value(1) A fusion model of FA–RF is considered to predict traffic accidents, which can be applied in traffic service robot. (2) It is recommended to establish a real-time autonomous information communication mechanism between different kinds of social robots, which can improve the prediction of traffic accidents.

Time Distribution and Analysis of Traffic Accident Fatalities

2013 ◽  
Vol 361-363 ◽  
pp. 2308-2314
Author(s):  
Lv Wen Qu ◽  
Mei Wei Dai ◽  
Fu Jian Wang ◽  
Zhi Yuan Zeng
Keyword(s):  
Traffic Safety ◽  
Time Distribution ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Statistical Data ◽  
Road Traffic ◽  
Road Traffic Accidents ◽  
Safety Education ◽  
Death Toll ◽  
High Incidence

According to hourly statistical data of road traffic accidents death toll during 1999~2004 of Zhejiang province, we found the death toll concentration phenomenon which is called phenomenon of 17 to 20 o'clock. And we discussed causes of high incidence of traffic accidents during 17 to 20 o'clock and put forward the corresponding countermeasures to reduce traffic accidents, according to the distribution pattern of deaths gender, age, travel purpose during these hours. The results of this research provide the foundation for reinforcing the traffic safety education and management.

ADHD is a Risk Factor of Road Crashes: A Purposive Study of Finding Relation between ADHD and Road Traffic Accident in Bangladesh

2018 ◽  
Vol 9 (08) ◽  
pp. 20531-20536
Author(s):  
Nusrat Shamima Nur ◽  
M. S. l. Mullick ◽  
Ahmed Hossain
Keyword(s):  
Risk Factor ◽  
Road Traffic Accident ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Fatality Rate ◽  
Road Traffic Accidents ◽  
Road Crashes ◽  
Road Crash ◽  
Day By Day

Background: In Bangladesh fatality rate due to road traffic accidents is rising sharply day by day. At least 2297 people were killed and 5480 were injured in road traffic accidents within 1st six months of 2017.Whereas in the previous year at 2016 at least 1941 people were killed and 4794 were injured within the 1st six months. No survey has been reported in Bangladesh yet correlating ADHD as a reason of impulsive driving which ends up in a road crash.

Positive Aspects of COVID 19 Pandemic: A Blessing in Disguis

2020 ◽  
Vol 11 (SPL1) ◽  
pp. 187-191
Author(s):  
Anjankar Ashish P ◽  
Anjankar Vaibhav P ◽  
Anjankar Anil J ◽  
Kanyal Lata
Keyword(s):  
Air Pollution ◽  
Heart Attack ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Community Action ◽  
Crime Rates ◽  
Road Traffic Accidents ◽  
Positive Effects ◽  
Positive Side ◽  
The World

COVID 19 is undeniably one of the deadliest diseases that humanity has ever seen. It continues to affect the lives and livelihood of people appallingly across the world. Maximum discussions focus towards the apprehension of catching the infection, dwelling in homes, overpopulated nursing homes and shut down of all kinds. But, here let’s discuss the positive side of COVID 19 pandemic.As COVID 19 has spread its influence all over the world, affected countries have either announced lockdown or have implemented severe restrictions in their respective countries. Because of this, everyone dwells in their homes. Thus, exercising social distancing and functioning from home. All of the above is directed at restricting the transmission of coronavirus and expectantly ostracising the fatality from COVID 19. These transformations have also brought about some unanticipated emanations; some good things have come out of the pandemic as well. Positive effects of COVID 19 are seen on reduced road traffic, and road traffic accidents lowered levels of air pollution which has to lead to lowered heart attack rates and rejuvenating environment. Crime rates have fallen, and expenses are reduced in most places. Community action, communication amongst families, behaviour, sanitation, hygiene, online and distance education has positively impacted by COVID 19 pandemic. COVID 19 despite a bane for humans, can be thought of a boon for living beings. The habitats and elements have been purified with the stringent use of petrochemical products. To breathe fresh air and to consume purified water is a boon by itself. Now, it is time for humans to lead a caring life to every bounty bestowed on them by Nature. This thoughtful and considerate life will give hope for a healthy, stress-free life.

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