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The dynamic and unpredictable nature of road traffic necessitates effectiveaccident detection methods for enhancing safety and streamlining trafficmanagement in smart cities. This paper offers a comprehensive exploration studyof prevailing accident detection techniques, shedding light on the nuances ofother state-of-the-art methodologies while providing a detailed overview ofdistinct traffic accident types like rear-end collisions, T-bone collisions,and frontal impact accidents. Our novel approach introduces the I3D-CONVLSTM2Dmodel architecture, a lightweight solution tailored explicitly for accidentdetection in smart city traffic surveillance systems by integrating RGB frameswith optical flow information. Our experimental study's empirical analysisunderscores our approach's efficacy, with the I3D-CONVLSTM2D RGB + Optical-Flow(Trainable) model outperforming its counterparts, achieving an impressive 87\%Mean Average Precision (MAP). Our findings further elaborate on the challengesposed by data imbalances, particularly when working with a limited number ofdatasets, road structures, and traffic scenarios. Ultimately, our researchilluminates the path towards a sophisticated vision-based accident detectionsystem primed for real-time integration into edge IoT devices within smarturban infrastructures.

Smart City Transportation: Deep Learning Ensemble Approach for Traffic Accident Detection