Open AccessAutomatic classification of traffic incident's severity using machine learning approaches
Author(s) -
Nguyen Hoang,
Cai Chen,
Chen Fang
Publication year - 2017
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2017.0051
Subject(s) - incident management , computer science , incident report , outlier , machine learning , control (management) , anomaly detection , process (computing) , operator (biology) , artificial intelligence , data mining , computer security , biochemistry , chemistry , repressor , transcription factor , gene , operating system
During daily work at a Transport Management Centre (TMC), the operators have to record and process a large volume of traffic information especially incident records. Their tasks involve manual classification of the data and then decide appropriate operations to clear the incidents on time. A real‐time automatic decision support system can minimise an operator's responded time and hence reduce congestion. Besides standard descriptions (e.g. incident location, date, time, lanes affected), severity is an important criteria that operators have to evaluate based on all available information before any control commands can be issued. The NSW TMC and the research organisation Data61 in Sydney have collaborated to discover and visualise frequent patterns in historical incident response records, leading to the automatic classification of severity levels among past incidents using advanced machine learning, active learning and outlier detection techniques. The experiments were executed using 4 years TMC's incident logs from 2011 to 2014 which includes >40,000 records. The classification model achieved nearly 90% accuracy in five‐fold cross‐validation and is expected to help the TMC to improve its procedures, response plans, and resource allocations.