# Highly Accurate Deep Learning Models for Estimating Traffic Characteristics from Video Data
## 5 Discussion

Table 5: Model performance comparison for different tracking models in the tested scenario. preliminary version of DeepSORT, lacking feature extraction ability in complex scenarios. The Kalman filter is a statistical model and has pre-assumptions to be satisfied, which restricts its application in random motion scenarios. The Kalman filter is also the algorithm integrated into both FairMOT and DeepSORT models. Limitations of the model mainly revolve around its deficiency of detection inaccuracy on distant vehicles and distorted camera angles. Due to the precision limitation on low-resolution footage, the model's inaccuracy increases significantly as distance increases, reducing the model's reliability on cameras positioned further from traffic. Moreover, the suppression effect of the PID kernel function lessens over distance. The suppression effect significantly falls off after \(\leq\)50 m, after which the model is no longer able to predict vehicle speed at a satisfactory accuracy. Distorted camera angles also influence speed estimation accuracy, since they increase difficulties in converting image coordinates into spatial coordinates through mathematical derivation. Errors may be generated. Reducing detected distance and choosing a front camera angle are effective ways to profoundly increase model performance by minimizing environmental noises. In terms of the crash prediction model, setting dynamic traffic features as input variables achieves significantly higher accuracy than only considering the historical crash numbers, weather, and holiday conditions. This is because crash characteristics are related to dynamic traffic features. Causal inference of crash occurrence with influencing traffic dynamic parameters can be analysed with the assistance of video processing techniques on pre-crash scenario analysis in future studies. Future improvements in this study intend to focus on estimating traffic crash probability by utilizing the vehicle speed determined in this study, and the aim is to devise a crash prediction framework in contributing to proactive traffic accident prevention. Specific research points include:  
1. The lane detection method can be integrated into the model. More detailed dynamic traffic parameters such as lane changing times and lane speed variances could be incorporated into the crash prediction model to enhance the accuracy of real-time crash prediction models. 2. The causal effect of crash occurrence with dynamic traffic parameters could be analysed. Deep learning computer vision techniques could be applied to extract traffic parameters relating to pre-crash scenarios. The relationship between crash occurrence with influencing variables can be built and the influence of individual variables on crashes can be investigated. 3. With involvement of traffic operation parameters and consideration of the importance of the temporal spatial structure in hourly traffic crash prediction, our ongoing research proposes a new joint model by combining the time-series generalized regression neural network (TGRNN) model and the binomially weighted convolutional neural network (BWCNN) model. The joint model aims to capture all these characteristics in short-term crash prediction.