Loading...

Traffic Congestion Prediction based on Tehran Traffic Data using Machine-Learning Algorithms

Shabani, Zeinab | 2022

0 Viewed
  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 57601 (19)
  4. University: Sharif University of Technology
  5. Department: Computer Engineering
  6. Advisor(s): Motahari, Abolfazl
  7. Abstract:
  8. Metropolitan development and increase in urban population cause a significant rise in intra-city travels and transportation, leading to many problems such as heavy traffic and air pollution, loud noise, and waste of time and human resources. To tackle these problems, it is essential to study the reasons for arising traffic and ways to reduce it. There is a line of research on traffic prediction as one of the potential solutions to the above-mentioned problem. In this research, we intend to analyze the traffic data of Tehran to find the correlation between the traffic density of different streets and provide a method for short-term traffic prediction. This research could be beneficial from two aspects: First, it helps users choose routes with less traffic, therefore, reducing the duration of intra-city travels by short-term traffic prediction. Second, identifying the factors causing the traffic enables policymakers and authorities to make appropriate macro decisions toward reducing urban traffic. The focus of this study is traffic prediction based on traffic trends using the implementation and comparisons of different models. In addition, novel frameworks such as the point process have been studied and implemented
  9. Keywords:
  10. Traffic Congestion ; Machine Learning ; Neural Network ; Traffic Density Estimation ; Traffic Data ; Traffic Prediction

 Digital Object List

 Bookmark

...see more