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Qin Shi

Researcher at Hefei University of Technology

Publications -  13
Citations -  107

Qin Shi is an academic researcher from Hefei University of Technology. The author has contributed to research in topics: Crash & Poison control. The author has an hindex of 6, co-authored 11 publications receiving 72 citations.

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Identification methods of key contributing factors in crashes with high numbers of fatalities and injuries in China.

TL;DR: Speeding and overloading of passengers were the primary contributing factors, featuring in up to 66.3 and 32.6% of accidents, respectively, and two secondary contributing factors were road related: lack of or nonstandard roadside safety infrastructure and slippery roads due to rain, snow, or ice.
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Applying latent class analysis to investigate rural highway single-vehicle fatal crashes in China.

TL;DR: Analysis of the single-vehicle crash data of rural highways in Anhui Province, China from 2014 to 2017 can facilitate the development of cost-effective policies or countermeasures for reducing the severity of single-Vehicle crashes in rural highways.

Identification methods of key contributing factors in crashes with high numbers of fatalities and injuries in China

TL;DR: Wang et al. as mentioned in this paper identified the main factors contributing to these road traffic crashes and proposed preventive measures to reduce their number, which would be more effective to investigate contributing factors and characteristics of SRTCs and PSRTCs as a whole.
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Differences in Factors Affecting Various Crash Types with High Numbers of Fatalities and Injuries in China

TL;DR: Findings indicated that intersections were more likely to have side impact SRTCs and PSRTCs, especially with poor visibility at night, and driver distraction is an important risk factor for head-on crashes, while vertical alignment and roadside safety rating are positively associated with single-vehicle crashes.
Journal ArticleDOI

Robust kinematics design of MacPherson suspension based on a double-loop multi-objective particle swarm optimization algorithm:

TL;DR: The ADAMS simulation results indicated that, compared with the vehicle with the original hard-point coordinates, the double-loop multi-objective particle swarm optimization algorithm and the genetic algorithm can effectively reduce the variation ranges of front wheel alignment parameters, regardless of the values of the mechanical parameters.