Zourlidou - Research Projects

Mobility

  • Traffic Regulator Detection and Identification from Crowdsourced Data
    Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this research is motivated by this fact.
    Team: Zourlidou, Sester
    Year: 2020

Bachelor Theses

  • Crowdsourcing turning restrictions from OpenstreetMap
    Road intersections are locations where different movement patterns are observed: traffic participant go ahead, turn right or left, according both to their needs and most importantly to the traffic restrictions applied everytime at the current location (traffic signs). The aim of this thesis is the implementation of a method, where vehicles trajectories acquired from OpenstreetMap (OSM) are analysed in terms of the turning possibilities that drivers have at each intersection location. Final objective is to find out what kind of turning restrictions are found at those locations, like those shown on the figure right.
    Leaders: Zourlidou
    Year: 2019

Master Theses

  • Trajectory Analysis at Intersections
    In this thesis, we focus on trajectories at different intersections with various regulated types (traffic-light-controlled, priority/yield- controlled, uncontrolled) and test some methods to detect and recognise movement patterns, in terms of their geometrical and spatio-temporal components. That is, in the first case the geometric paths that vehicles follow while driving through the intersections and in the second case the way that they follow these geometric paths.
    Leaders: Zourlidou
    Team: Chenxi Wang
    Year: 2020
  • Travel Delay Analysis Using VISSIM and Pattern Recognition at Regulated Junctions
    This thesis explores the travel time and travel delay at T- and four-way junctions under different regulator settings (yield/priority traffic signs and uncontrolled junctions), conducting experiments both with simulation originated and real data. First this thesis uses VISSIM simulation software to estimate the delay and stop time at yield controlled and uncontrolled T- and and Four-way intersections. At the second part of the thesis, the same objective is being pursued by using real data.
    Leaders: Zourlidou
    Team: Qingyuan Wang
    Year: 2019
  • Pattern Recognition of Movement Behavior for Intersection Classification using GPS Trace Data
    The aim of this thesis is to classify different regulator types of traffic road intersections based on GPS trace data. To reach this aim a variety of features is calculated to describe the driving behavior at intersections. These are derived from the measured units of the GPS trace data that compose an individual’s movement trajectory.
    Leaders: Zourlidou
    Team: Jens Golze
    Year: 2019
  • Crowdsourcing turning restrictions from OpenstreetMap
    Road intersections are locations where different movement patterns are observed: traffic participant go ahead, turn right or left, according both to their needs and most importantly to the traffic restrictions applied everytime at the current location (traffic signs). The aim of this thesis is the implementation of a method, where vehicles trajectories acquired from OpenstreetMap (OSM) are analysed in terms of the turning possibilities that drivers have at each intersection location. Final objective is to find out what kind of turning restrictions are found at those locations, like those shown on the figure right.
    Leaders: Zourlidou
    Year: 2019
  • Traffic-sign Recognition from Street-level Photos: a Deep Learning Approach
    The scope of this thesis is the recognition of traffic-signs from street-level images. A state-of-the-art deep learning detection algorithm is used, the Single Shot Multi-box detector (SSD) and within the thesis its performance is validated experimentally by examining different training strategies.
    Leaders: Prof. Dr. Bernhard Roth, Co-advisor: Prof. Monika Sester, Supervisor: Stefania Zourlidou
    Team: Qifa Bao
    Year: 2018
    Lifespan: 2018