Algorithm

Engineering

Philosophy

Transport engineering

Association rule learning

Apriori algorithm

Traffic accident

Association (psychology)

Data mining

Computer science

Algorithm design

Lift (data mining)

Epistemology

Road traffic

IEEE Access

The traffic accidents on urban roads are result of joint actions between multiple factors, namely, human, vehicle, road and environment. To identify the key causes to such accidents, it is necessary to mine the association rules between relevant risk factors out of the statistics on these accidents. Considering the multiple layers and dimensions of accident data, this paper improves the Apriori algorithm to mine the association rules between risk factors, and probes deep into the causes of traffic accidents on urban roads. According to the layer and dimension of specific attributes, the parameters like support, confidence and lift were adjusted to find the qualified association rules between risk factors. The results were further screened to obtain a series of meaningful association rules. The research results enable the traffic department to formulate pertinent accident control measures, and promote the traffic safety on urban roads.

Cause Analysis of Traffic Accidents on Urban Roads Based on an Improved Association Rule Mining Algorithm