A real-time autonomous highway accident detection model based on big data processing and computational intelligence

Abstract

Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the others that are stuck behind the wheels. Early detection of an accident can save lives, provides quicker road openings, hence decreases wasted time and resources, and increases efficiency. In this study, we propose a preliminary real-time autonomous accident-detection system based on computational intelligence techniques. Istanbul City traffic-flow data for the year 2015 from various sensor locations are populated using big data processing methodologies. The extracted features are then fed into a nearest neighbor model, a regression tree, and a feed-forward neural network model. For the output, the possibility of an occurrence of an accident is predicted. The results indicate that even though the number of false alarms dominates the real accident cases, the system can still provide useful information that can be used for status verification and early reaction to possible accidents.

A Review on Road Accident Detection Using Data Mining Techniques

“…The comparison of the efficient techniques is carried out in terms of accuracy, precision, recall and f-measure. The survey concludes that the preliminary real time autonomous accident detection system [7] method was efficient in terms of accuracy and Naïve Bayes, J48, Random Forest algorithm, Apriori association rule mining [11] method was efficient in terms of precision, recall and F-measure. This survey also helps in deriving the motivation for our future research work.…”

“…X axis denotes the methods and Y axis denotes the accuracy in %. The graph clearly shows that the preliminary real time autonomous accident detection system [7] has high accuracy than other methods.…”

“…For accident detection, a preliminary real time autonomous accident detection system [7] was proposed. For the accident detection, data were collected from the sensors and it was integrated with the event log to extract the most discriminative features.…”

“…The Preliminary real time autonomous accident detection system [7] has better accuracy of 99.79% than other methods, Naïve Bayes, J48, Random Forest algorithm, Apriori association rule mining [11] method has better precision of 0.983 than other methods, Naïve Bayes, J48, Random Forest algorithm, Apriori association rule mining [11] method has better recall of 98.3 than other methods and Naïve Bayes, J48, Random Forest algorithm, Apriori association rule mining [11] method has better fmeasure of 98.3 than other methods. …”

“…The methods considered for analysis are Long Short-Term Memory, Deep Belief Network, supervised Latent Dirichlet Allocation, Support Vector Machine [4], Hybrid Clustering, association rule mining [5], Random Forest, AdaBoostM1, Naïve Bayes, J48, PART [6], Preliminary real time autonomous accident detection system [7], Naïve Bayes, C4.5, C&RT, RndTree, Decision List, rule induction, random tree [8], Multi-class Support Vector Machine [9], Naïve Bayes, J48, Random Forest algorithm, Apriori association rule mining [11], Decision Trees, Neural Networks [12], K means clustering, Decision tree [14] and Support Vector Machines [15]. The comparison is done by the experimental results of the methods in terms of accuracy, precision, recall and F-measure.…”