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Honors Thesis


Route prediction is an important tool in understanding the trajectories of vehicles. This information can be used to make intelligent decisions in city planning such as traffic avoidance and pedestrian safety. Machine Learning can predict these routes by learning common patterns from previous vehicle paths. These vehicle paths are plentiful due to the abundance of IoT devices and advancements in computer vision. The most suitable machine learning algorithm that could accurately predict destinations was determined by passing vehicle data to three popular machine learning algorithms: Decision Trees, Artificial Neural Networks, and Naïve-Bayes. The training data for these algorithms was converted into a suitable format using the Pandas python library. Moreover, the hyperparameters for each algorithm were tuned to maximize the accuracy of the prediction. Respectively, decision trees were observed to provide the highest accuracy of 99.7% with the Naïve-Bayes and ANN showing 99.5% and 98%

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