Finding Factors and Vehicles Involved in Two-Vehicle Accidents Through the Use of Social Network Analysis

Social Network Analysis (SNA) has emerged as a new paradigm to effectively represent complex patterns of relationships between all categories of social groups. It helps to find the structure of ties and its impact on individuals, groups or even incidents. This article is a similar attempt to explore...

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Vydáno v:2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) s. 541 - 546
Hlavní autoři: Ashraf, Imran, Hur, Soojung, Park, Yongwan
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: New York, NY, USA ACM 31.07.2017
Edice:ACM Conferences
Témata:
accident fatalities
Accidents
Applied computing
Applied computing > Law, social and behavioral sciences
Applied computing > Law, social and behavioral sciences > Sociology
degree centrality
eigenvector centrality
Electronic mail
Human-centered computing
Human-centered computing > Collaborative and social computing
Human-centered computing > Collaborative and social computing > Collaborative and social computing design and evaluation methods
Human-centered computing > Collaborative and social computing > Collaborative and social computing design and evaluation methods > Social network analysis
Human-centered computing > Collaborative and social computing > Collaborative and social computing theory, concepts and paradigms
Human-centered computing > Collaborative and social computing > Collaborative and social computing theory, concepts and paradigms > Social networks
Information systems
Information systems > Data management systems
Information systems > Data management systems > Database design and models
Information systems > Data management systems > Database design and models > Graph-based database models
Information systems > Information systems applications
Information systems > Information systems applications > Data mining
Information systems > World Wide Web
Information systems > World Wide Web > Web applications
Information systems > World Wide Web > Web applications > Social networks
Networks
Networks > Network types
Networks > Network types > Overlay and other logical network structures
Networks > Network types > Overlay and other logical network structures > Online social networks
Public transportation
social network analysis
Social network services
traffic violations
Vehicle crash testing
degree centrality
accident fatalities
accidents
traffic violations
social network analysis
eigenvector centrality
ISBN:1450349935, 9781450349932
ISSN:2473-991X
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Popis
Shrnutí:Social Network Analysis (SNA) has emerged as a new paradigm to effectively represent complex patterns of relationships between all categories of social groups. It helps to find the structure of ties and its impact on individuals, groups or even incidents. This article is a similar attempt to explore the vehicles and vehicle-related violations leading to accidents through the use of SNA. SNA is performed on accident data of New York for 2016. SNA measures including degree centrality, betweenness centrality and eigenvector centrality are used to probe into the impact of ties between actors of accidents. The empirical analysis shows that 'Passenger vehicle' with degree centrality of 7513 has the highest degree of accidents, explaining 41.12% of total accidents. In addition, it is involved in 57.42% of accidents when accidents occur between same types of vehicles. 'Sport utility/station wagon' and 'taxi' rank second and third in this category with degree values of 4657 and 1454 respectively. It is also found that 'driver inattention' holds the pivotal place when violations leading to accidents are concerned. It accounts for 19.09% accidents in general and 44.58% when accidents, where both parties commit the same violation, are considered. 'Failure to yield right-of-way' and 'following too closely' are ranked second and third. Research also finds that Manhattan area of New York is marked by elevated number of accidents.
ISBN:1450349935
9781450349932
ISSN:2473-991X
DOI:10.1145/3110025.3116210