Istrazivanja i projektovanja za privreduJournal of Applied Engineering Science


DOI: 10.5937/jaes0-40553 
This is an open access article distributed under the CC BY 4.0
Creative Commons License

Volume 21 article 1077 pages: 346-352

Mostafa K. Ardakani*
Information Systems and Business Analytics Kent State University, OH 44240, USA

Timothy Bonds
Senior Business Analyst, State Farm Mutual Automobile Insurance Company, Florida, USA

This research employs a series of machine learning methods to predict the direction of lane change. The response is a binary variable indicating changing the lane to the left or to the right. The employed methods include Decision Tree, Discriminant Analysis, Naïve Bayes, Support Vector Machine, k-Nearest Neighbour and Ensemble. The results are compared to the conventional logistic regression method. Both performance criteria and computational times are reported for comparison purposes. A design of experiments is run to test 25 classification methods at ratios of 25%, 50%, and 75% right to left lane change data. Furthermore, samples are validated by cross and holdback validation methods. RUSBoosted trees, an ensemble method, shows improvement over logistic regression. This research provides valuable insights on lane change behaviour, including trajectories and driving styles, which falls into the field of microscopic lane change study.

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