Machine Learning for Intelligent Transportation Systems

Dr. Mohammad Bawaneh

For English BSc and MSc students


Nowadays, the smart city concept is becoming more actual than ever as cities are growing and becoming more and more crowded as a result of urbanization and growth of the world population. The smart city can make intelligent responses to different kinds of needs, including daily livelihood, environmental protection, public safety and city services, and industrial and commercial activities. Among the different notable goals of smart cities, the construction of intelligent transportation systems could have a significant impact to residents of future cities. Advanced Traffic Management Systems (ATMSs) and Intelligent Transportation Systems (ITSs) integrate information, communication and other technologies and apply them in the field of transportation to build an integrated system of people, roads and vehicles. These systems constitute a large, full-functioning, real-time, accurate and efficient transportation management framework.

In ATMSs and ITSs it is a fundamental challenge to predict the next possible states of traffic with high precision, because this information helps to prevent unlikely events like traffic jams or other anomalies on roads. Therefore the research topic focuses on the investigation of prediction methods and techniques, appropriate for optimizing the transport systems, using different sensor information. Machine learning methods for this purpose will be analyzed and utilized. 

Specifically, you will be involved in one or more of the following Machine Learning based tasks:

  • Data Cleaning and Preprocessing.
  • Traffic Prediction.
  • Congestion Detection and Prediction.
  • Roads Network Clustering.
  • Or you can suggest your own idea for a project on this topic. It is welcome and encouraged.


Required skills:

  • Basics of Python programming language.
    • Knowledge of one or more of the following Python libraries: (Pandas, NumPy, Matplotlib, Tslearn, Keras, TensorFlow, Scikit-learn).
  • Theoretical basics of Data Analysis and Machine Learning.