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Detection and Estimation of Key Parameters in Traffic Models Using Data Mining Tools

Moadab, Amir Hossein | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52425 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Khedmati, Majid
  7. Abstract:
  8. Nowadays, investigating the factors affecting traffic models from different aspects such as metropolitan planning according to the present conditions can help high-level decision-makers and also, at the micro-level, help the travelers to make appropriate decisions for scheduling affairs, route selection, and vehicle type selection. Given the importance of this topic, a framework will be presented in this study that will evaluate the impact of some identified factors such as travel distance, climate, and urban events, and then all these factors will be presented in mathematical formulas. In the end, based on the model, the travel time will be predicted. In this framework, gene expression programming has been used as one of the most potent methods of machine learning that has not been used in prior studies to predict travel time. In this study, to evaluate the performance of the proposed method, data of the New York Yellow Taxi Global Positioning System in 2016 were used, which analyzed gene expression programming, travel time in different models with different functions and finally presented the best model. Consequently, to achieve an optimal model, a recursive state is also considered that changes the parameters by adjusting the function. The results of the optimal model show that travel distance is more important than other parameters at travel time, which is evident not only in gene expression programming model but also in other comparative models such as support vector machine and neural network
  9. Keywords:
  10. Urban Traffic ; Data Mining ; Travel Time Estimation ; Gene Expression Programming ; Gene Expression Data

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