Visualizing Temporal and Spatial Distribution Characteristic of Traffic Accidents in China

Yang, Yingliu; Jin, Lianghai

doi:10.3390/su142113706

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…There is currently no widely accepted concept for identifying Road Traffic Crash (RTC) hot spots [ 7 ]. The spatial statistics method has recently been used to understand the characteristics of the spatial and temporal distribution of road traffic crashes [ [17] , [18] , [19] , [20] ]. In order to identify hotspots with regard to time and compare the hotspots in terms of crash type and severity index, the crash pattern was analyzed at the TAZ level in Iran using the spatial autocorrelation method [ 21 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial pattern identification and crash severity analysis of road traffic crash hot spots in Ohio

Alam

Tabassum

2023

Heliyon

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Section: Literature Reviewmentioning

confidence: 99%

Spatial pattern identification and crash severity analysis of road traffic crash hot spots in Ohio

Alam

Tabassum

2023

Heliyon

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“…Ma et al [6] used density analysis to identify high accident areas based on the road traffic accident data in Wales in 2017, and outlier and hotspot analysis models to identify areas with high accident severity and determine the clustering characteristics of accidents. Yang et al [7] used 2002-2019 based on traffic accident data from 31 provinces in China and studied the spatial autocorrelation characteristics and spatial heterogeneity between provinces and regions using statistical analysis and spatial autocorrelation analysis.…”

Section: Study On Road Accident Characteristicsmentioning

confidence: 99%

Data-Driven Analysis of Fatal Urban Traffic Accident Characteristics and Safety Enhancement Research

Zhang

et al. 2023

Sustainability

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The occurrence of fatal traffic accidents often causes serious casualties and property losses, endangering travel safety. This work uses the statistical data of fatal road traffic accidents in Shenzhen from 2018 to 2022 as the basis to determine the characteristic patterns and the main influencing factors of the occurrence of fatal road traffic accidents. The accident description data are also analyzed using the analysis method based on Term Frequency-Inverse Document Frequency (TF-IDF) data mining to obtain the characteristics of accident fields, objects, and types. Furthermore, this work conducts a kernel density analysis combined with spatial autocorrelation to determine the hotspot areas of accident occurrence and analyze their spatial aggregation effects. A principal component analysis is performed to calculate the factors related to the accident subjects. Results showed that weak safety awareness of motorists and irregular driving operations are the main factors for the occurrence of accidents. Finally, targeted safety management strategies are proposed based on the analysis results. In the current data era, the research results of this paper can be used for the prevention and emergency of accidents to formulate corresponding measures, and provide a theoretical basis for decision making.

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“…Research has shown that there is a significant spatial autocorrelation between traffic accidents in various provinces and cities in China, which means that the number of traffic accidents and deaths is high with high aggregation, which leads to traffic congestion in populated areas, resulting in the waste of resources and inability to reflect sustainable development. Traffic accidents will not only threaten people's safety; improper emergency response will increase rescue time, cause secondary damage to the accident site, waste resources, and hinder sustainable development [3]. In the actual emergency disposal process of highway emergencies, the accident site is often complex, the response time of the emergency department is urgent, and there is a lack of adequate communication between multiple emergency departments, which makes the emergency departments unable to fully understand the progress of the accident site, resulting in poor cooperation between the emergency departments and low emergency rescue efficiency.…”

Section: Introductionmentioning

confidence: 99%

Collaborative Decision-Making Method of Emergency Response for Highway Incidents

Yao

Yan

et al. 2023

Sustainability

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With the continuous increase in highway mileage and vehicles in China, highway accidents are also increasing year by year. However, the on-site disposal procedures of highway accidents are complex, which makes it difficult for the emergency department to fully observe the accident scene, resulting in the lack of sufficient communication and cooperation between multiple emergency departments, making the rescue efficiency low and wasting valuable rescue time, and causing unnecessary injury or loss of life due to the lack of timely assistance. Thus, this paper proposes a multi-agent-based collaborative emergency-decision-making algorithm for traffic accident on-site disposal. Firstly, based on the analysis and abstraction of highway surveillance videos obtained from the Shaanxi Provincial Highway Administration, this paper constructs an emergency disposal model based on Petri net to simulate the emergency on-site disposal procedures. After transforming the emergency disposal model into a Markov game model and applying it to the multi-agent deep deterministic strategy gradient (MADDPG) algorithm proposed in this paper, the multiple agents can optimize the emergency-decision-making and on-site disposal procedures through interactive learning with the environment. Finally, the proposed algorithm is compared with the typical algorithm and the actual processing procedure in the simulation experiment of an actual Shaanxi highway traffic accident. The results show that the proposed emergency-decision-making method could greatly improve collaboration efficiency among emergency departments and effectively reduce emergency response time. This algorithm is not only superior to other decision-making algorithms such as genetic algorithm (EA), evolutionary strategy (ES), and deep Q network (DQN), but also reduces the disposal processes by 28%, 28%, and 42%, respectively, compared with the actual disposal process in three emergency disposal cases. In summary, with the continuous development of information technology and highway management systems, the multi-agent-based collaborative emergency-decision-making algorithm will contribute to the actual emergency response process and emergency disposal in the future, improving rescue efficiency and ensuring the safety of individuals. The on-site disposal procedure of freeway accidents is complicated, and the emergency response time is limited, which makes it difficult for emergency response departments to fully observe the accident scene, leading to the lack of sufficient communication and team cooperation among multiple emergency departments. This paper proposes a multi-agent-based collaborative emergency-decision-making algorithm for traffic accident on-site disposal. Firstly, through analyzing freeway surveillance videos obtained from the Shaanxi Provincial Freeway Administration, this paper constructs an emergency disposal model based on Petri net to simulate the emergency on-site disposal procedures. Then, an emergency-decision-making method based on a multi-agent deep deterministic policy gradient algorithm is proposed to optimize the emergency-decision-making and on-site disposal procedures. Finally, the proposed algorithm is compared with the typical algorithm in a simulation experiment of an actual Shaanxi freeway traffic accident, and the difference between the proposed algorithm and the actual processing procedure is also analyzed. The results show that the proposed emergency-decision-making method could greatly improve team collaboration efficiency among emergency departments and effectively reduce emergency response time. This algorithm is not only superior to other decision-making algorithms, but also reduces the disposal processes by 28%, 28%, and 42%, respectively, compared with the actual disposal process in the three studied cases. It is believed that the continuous development of information technology and freeway management systems will help to improve actual emergency response times and emergency drills in the future.

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Visualizing Temporal and Spatial Distribution Characteristic of Traffic Accidents in China

Cited by 5 publications

References 20 publications

Spatial pattern identification and crash severity analysis of road traffic crash hot spots in Ohio

Spatial pattern identification and crash severity analysis of road traffic crash hot spots in Ohio

Data-Driven Analysis of Fatal Urban Traffic Accident Characteristics and Safety Enhancement Research

Collaborative Decision-Making Method of Emergency Response for Highway Incidents

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