An Artificial Intelligence based automated case-based reasoning (CBR) system for severity investigation and root-cause analysis of road accidents – Comparative analysis with the predictions of ChatGPT

Abstract

Road accidents have been progressively causing havoc in our society and certain preventive measures must be taken to reduce or possibly eliminate road accidents. The derivative of a road accident ranges from a mild injury to casualty. This research work mainly focusses on developing a novel case-based reasoning system to investigate and troubleshoot the cause of road accidents on a war-foot basis. First, the dominant attributes contributing to the cause of road accidents are identified and finalized as 28. A unique road accident dataset is developed which comprises of 1028 data collected from web resources, popular news magazines and extended further to large scale database of one-million cases by biased random number simulation. Each attribute is given a severity weightage of 1,2 and 3 for computing the net weighted score for a case in the database. Also, non-weighted scores are computed by introduction of a primary number dataset to maintain the uniqueness of the score which is further used for similarity analytics. Now, an accident news is randomly selected, and Rapid automatic keyword extraction (RAKE) schema is used as Natural language processor (NLP) for extracting the dominant keywords from the news articles. The extracted keywords are compared and further mapped into a factor-matrix comprising 28 attributes causing road accidents. Further, similarity analytics is performed to evaluate the severity scores and comparison of new cases. The system demonstrated high retrieval accuracy with all road accident cases collected from real world scenarios. This research has great prospects on troubleshooting road accident cases effectively and provides instant promising troubleshooting measures to prevent such accidents in the future. Also, the proposed framework might be useful for intelligent decision-making systems and automated driving systems. Based on the final outlook, a comprehensive framework for national road safety could be developed and passed as a valid law for implementation. Finally, the forecasted results of the proposed algorithm are compared with the predictions of Chat GPT program.