ENHANCING ACCIDENT INVESTIGATION USING TRAFFIC CCTV FOOTAGE

This Culminating Experience Project investigated how the densenet-161 model will perform on accident severity prediction compared to proposed methods. The research questions are: (Q1) What is the impact of usage of augmentation techniques on imbalanced datasets? (Q2) How will the hyper parameter tuning affect the model performance? (Q3) How effective is the proposed model compared to existing work? The findings are: Q1. The effectiveness of our model depends on the implementation of augmentation techniques that pay attention to handling imbalanced datasets. Our dataset poses a challenge due to distribution of classes in terms of accident severity. To address this challenge directly we utilize an augmentation process that involves applying transformations to the data. By applying these transformations our aim is to create a training set. This enables our model to grasp and capture the nuances of classes resulting in enhanced prediction accuracy and improved generalization abilities. Q2. Adjusting the settings of algorithms to enhance their performance is an aspect of machine learning known as fine tuning hyperparameters. In one of our experiments, we successfully increased our model\u27s accuracy by 2%, which was quite an improvement. Prior to tweaking the hyperparameters the model only achieved a 90% accuracy rate. This remarkable disparity truly emphasizes the impact that hyperparameter tuning can have on a model\u27s performance. By adjusting these parameters, we were able to unlock the hidden potential of our algorithm and enhance its ability to identify patterns and subtle details within the data. This entire process exemplifies how meticulous fine tuning of hyperparameters can lead to advancements in machine learning outcomes. Q3. The study’s findings show that the current work has achieved an accuracy rate of 88%. However, when we implemented the model, we observed an improvement with accuracy reaching 95%. This increase of 4% is quite notable. Reflects an enhancement in performance. It\u27s clear that the densenet-161 model excels at classifying data, which suggests its effectiveness in applications. This substantial boost in accuracy has ranging implications from improving the reliability of diagnoses to enhancing the precision of image recognition systems. These findings highlight the importance of utilizing models like densenet-161 to achieve levels of accuracy emphasizing their potential for profound advancements in fields reliant on precise data classification and analysis. The conclusions are: Q1. This method can help prevent bias in Favor of the majority class and balance the data. Q2. Hyper parameter tuning helps to improve accuracy. Q3. Densenet-161 model able to achieve a 95% accuracy. Further research topics that our study raises are the prospect of evaluating and training our model with a bigger set of data and fine-tuning other hyperparameters for even greater performance

Intelligent Real-Time Decision Support Systems for Road Traffic Management. Multi-agent based Fuzzy Neural Networks with a GA learning approach in managing control actions of road traffic centres.

The selection of the most appropriate traffic control actions to solve non-recurrent traffic congestion is a complex task which requires significant expert knowledge and experience. In this thesis we develop and investigate the application of an intelligent traffic control decision support system for road traffic management to assist the human operator to identify the most suitable control actions in order to deal with non-recurrent and non-predictable traffic congestion in a real-time situation. Our intelligent system employs a Fuzzy Neural Networks (FNN) Tool that combines the capabilities of fuzzy reasoning in measuring imprecise and dynamic factors and the capabilities of neural networks in terms of learning processes. In this work we present an effective learning approach with regard to the FNN-Tool, which consists of three stages: initializing the membership functions of both input and output variables by determining their centres and widths using self-organizing algorithms; employing an evolutionary Genetic Algorithm (GA) based learning method to identify the fuzzy rules; tune the derived structure and parameters using the back-propagation learning algorithm. We evaluate experimentally the performance and the prediction capability of this three-stage learning approach using well-known benchmark examples. Experimental results demonstrate the ability of the learning approach to identify all relevant fuzzy rules from the training data. A comparative analysis shows that the proposed learning approach has a higher degree of predictive capability than existing models. We also address the scalability issue of our intelligent traffic control decision support system by using a multi-agent based approach. The large network is divided into sub-networks, each of which has its own associated agent. Finally, our intelligent traffic control decision support system is applied to a number of road traffic case studies using the traffic network in Riyadh, in Saudi Arabia. The results obtained are promising and show that our intelligent traffic control decision support system can provide an effective support for real-time traffic control