Mission Statement

Mobility and transportation has been an essential part of humankind existence. It is the key factor of the evolution of technology and economy. The traffic and mobility problems are the results of concentration of population and markets. Hence, the challenges in mobility are related to many factors and actors such vehicle, safety, sustainability, traffic jams, incident, human behaviour, etc. From this perspective our ITS Team focuses on contributing on the following fields:

  1. Mobility Mining
  2. Vehicular Networking
  3. Advanced Travel Information Systems
  4. Advanced Driver Assistance Systems


Urban Mobility and Spatio-temporal Analysis

  • Community detection

    This is about using spatio-temporal mobility analysis in order to detect communities in the mobile cellular networks using CDR data.

  • Mobility patterns

    Conducting patterns extraction based mobility characteristics from the trajectory records.

Mobility prediction

  • Mobility prediction

    Our aim is to try to predict the movement of people via the use of any mean of data sources that has a spatio-temporal aspect such as GPS, CDR, VLR, etc.


  • Indoor pedestrian simulation

    Simulating pedestrian movement for mobility analysis and emergency evacuation situtation.

  • Urban mobility simulation

    Simulating road traffic in urban areas for mobility analysis and addressing issues related to urbanization.

  • Mobile network simulation for road traffic applications

    Simulating mobile networks to generate data logs that can help in investigating the usages and integration of mobile data in transportation and smart mobility.

Localization and tracking

  • Mobile positioning

    In this topic, we are trying to use CDR data as a mean to localize and position the mobile users in the mobile networks.

  • Map-matching

    We are investigating the use of map-matching techniques to solve issues related to GPS errors, positioning accuracy, or noise in the geolocation data.

  • Mobility episode detection 

    This topic is related to extract mobility episode from the cell based trajectories extracted from the CDR data.

Travel information

  • Travel time estimation and prediction

    Developing techniques and algorithms to increase the accuracy of travel time estimation and prediction for navigation and fleet management purposes.

  • Traffic status estimation

    Investigating algorithms and methods for estimating the road traffic status, congestion detection, incident detection, etc.

Outdoor and Indoor Mapping

  • Indoor Mapping

    The idea is to build an understanding of the indoor environment by creating a 3D representation model of the surrounding using sensors.

  • Outdoor Mapping

    The use of sensors such as cameras, lasers, radars, ridars to create a model of the outdoor environment for the purpose of navigation and SLAM.

Detection and Recognition

  • Pedestrian detection

    This topic is about the use of cameras and sensors to identify and detect pedestrians, which is one of the important features in ADAS systems for safety purposes.

  • Vehicle detection and recognition

    Detecting vehicles and recognizing them is a key component for future  autonomous vehicle navigation systems.

  • Characters recognition

    Recognizing the handwritten characters is the ability of making the machine recognize the characters which has impact on artificial intelligence applications such as autonomous vehicles, signature verification, location based services, etc.


  • IoV

    Investigating the usage of internet of vehicles for improving the V2V and V2I solutions and also computing strategies for real time systems.

  • V2V and V2I

    Conducting research on the enhancement and usage of V2V and V2I communications for the purpose of information exchange and cooperative probes.

Mobility Big Data

  • Data Warehousing

    • Collecting the data from live streams
    • Collecting the data from the public data sources
    • Crawling the Web for spatio-temporal data
    • Designing the storage for redundant input streams
    • Introducing metada indexes for fast spatio-temporal lookups


  • Population Movement Analytics, Monitoring and Prediction Algorithms (PoMAMPA:2016-2019)
  • Predictive and Mining Algorithms for Behavioral Analysis and Performance Assessment (GoData:2017-2018)
  • Data Science Methods and Applications (DSMA: 2015-2020)
  • Large Real-time Location Data Algorithms (LR-LDA:2013-2015)
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