Identifying Patterns of Pedestrian Crashes in Urban Metropolitan Roads in India using Association Rule Mining

Sivasankaran, Sathish Kumar; Natarajan, Priyadarshini; Balasubramanian, Venkatesh

doi:10.1016/j.trpro.2020.08.102

Cited by 15 publications

(5 citation statements)

References 12 publications

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“…Since its proposal by Apwal et al [15], the apriori algorithm for association rule mining has been widely used in traffic safety research. For example, Sivasankaran et al used the apriori algorithm to mine the causes of motor vehicle and bicycle accidents in India from 2015 to 2016 [16]. Kong et al adopted a classification-based association algorithm to mine the association rules of traffic accidents caused by speeding on expressways in Michigan, United States [17].…”

Section: Association Analysismentioning

confidence: 99%

Research on intelligent construction method for traffic control scenarios based on traffic violation data mining

Yang¹,

Zhu²,

2023

3rd International Conference on Internet of Things and Smart City (IoTSC 2023）

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To meet the actual operational needs of public security traffic control departments, we propose an intelligent construction method for traffic control scenarios and introduce its construction process and application value. In this study, traffic control scenarios are split into several constituent elements. The associated elements are found and combined into scenarios based on actual data. To meet the computational efficiency challenges due to the rapid update of massive traffic violation data in the real world, this study applies ordering points to identify the clustering structure algorithm to achieve fast and efficient clustering of latitude and longitude data. Furthermore, the frequent pattern growth algorithm is improved by splitting the database and node exchange based on a Hash table to improve the association rule mining efficiency. The results reveal that both algorithms significantly improve efficiency and accuracy compared with traditional algorithms. By obtaining the refined influence range of traffic violation control scenarios through the clustering method, correlating the environmental factors of scenarios with control behaviors through association rules, and analyzing the intrinsic causes of scenarios based on identified indicators, the efficiency and accuracy of public security traffic control departments in finding and solving problems are effectively improved.

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Section: Association Analysismentioning

confidence: 99%

Research on intelligent construction method for traffic control scenarios based on traffic violation data mining

Yang¹,

Zhu²,

2023

3rd International Conference on Internet of Things and Smart City (IoTSC 2023）

View full text Add to dashboard Cite

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“…Kong et al (2021) utilized such method to investigate the association between near‐crash events, road geometry, and trip features. Sivasankaran et al (2020) used association rule mining to identify patterns of pedestrian crashes. Das et al (2018) employed association rules to determine the interactions between various factors that resulted in wrong‐way driving crashes.…”

Section: Literature Reviewmentioning

confidence: 99%

Identification of aggressive driving behavior of online car‐hailing drivers based on association classification

Wu,

Chen,

et al. 2023

Hum Ftrs & Erg Mfg Svc

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With the rapid development of online car‐hailing, the related crashes have become a key issue with public concern. Identifying and predicting aggressive driving behaviors is critical to reduce traffic crashes. In this study, we propose a method to recognize aggressive driving behavior based on association classification, with multisource features being employed, including driver emotion, vehicle kinematic characteristics, and road environment. The model performs best in a 10‐fold cross‐test when the minimum support and minimum confidence are set as 0.01 and 0.8, respectively. Besides, we also compare the performance of aggressive driving behavior recognition classifiers constructed using association classification with other rule‐based classification methods, including ID3, C4.5, CART, and Random Forest. The results show that association classification performs better than other classification competitors. Thirty‐six if–then rules generated by the association classification are used to analyze the influencing factors and associated mechanisms of aggressive driving behavior. It is found that aggressive driving behavior is highly correlated with driver anger and disgust emotions. Aggressive driving behavior is more likely to occur when no passengers are in the car than the case with passengers. Driver entertainment behavior and passenger interference also affect driving behavior. Moreover, drivers are prone to aggressive driving when making a U‐turn. This research not only proposed a new identification method for aggressive driving behavior but also provided a comprehensive understanding of the associated influencing factors which thus benefit the further research and development of safety assistance driving devices.

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“…The analysis focused on the RTA characteristics under different lighting conditions in which the Random Forest algorithm selected the significant variables, and the Apriori algorithm generated the association rules for the lighting conditions of daylight, dark with a streetlight, and dark without a streetlight. In Chennai, an urban Indian metropolitan, [16] examined the characteristics of vehicle-pedestrian crashes using the data obtained from the RADMS (road safety accident reporting database) for association rules generation. The association rules were classified into the fatal/grievous injury and simple injury/property damage only categories to analyse the RTA characteristics.…”

Section: Related Workmentioning

confidence: 99%

Comparative analysis of regional variations in road traffic accident patterns with association rule mining

Chai,

Lau,

Tee

et al. 2023

EAI Endorsed Trans Perv Health Tech

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INTRODUCTION: Road Traffic Accidents (RTAs) patterns discovery is vital to formulate mitigation strategies based on the characteristics of RTAs.OBJECTIVES: Various studies have utilised Apriori algorithm for RTA pattern discovery. Hence, this work aimed to explore the applicability of FP-Growth algorithm to discover and compare the RTA patterns in several regions.METHODS: Orange data mining toolkit is used to discover RTA patterns from the open-access RTA datasets from Addis Ababa city (12,317 samples), Finland (371,213 samples), Berlin city-state (50,119 samples), New Zealand (776,878 samples), the UK (1,048,575 samples), and the US (173,829 samples).RESULTS: There are similarities and differences in RTA patterns among the six regions. The five common factors contributing to RTAs are road characteristics, type of road users or objects involved, environment, driver’s profile, and characteristics of RTA location. These findings could be beneficial for the authorities to formulate strategies to reduce the risk of RTAs.CONCLUSION: Discovery of RTA patterns in different regions is beneficial and future work is essential to discover the RTA patterns from different perspectives such as seasonal or periodical variations of RTA patterns.

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Identifying Patterns of Pedestrian Crashes in Urban Metropolitan Roads in India using Association Rule Mining

Cited by 15 publications

References 12 publications

Research on intelligent construction method for traffic control scenarios based on traffic violation data mining

Research on intelligent construction method for traffic control scenarios based on traffic violation data mining

Identification of aggressive driving behavior of online car‐hailing drivers based on association classification

Comparative analysis of regional variations in road traffic accident patterns with association rule mining

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