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Trajectory Analysis at Intersections

Trajectory Analysis at Intersections

Leitung:  Zourlidou
E-Mail:  zourlidou@ikg.uni-hannover.de
Jahr:  2019

Objective

Road intersections are locations where different movement patterns are observed: traffic participants go ahead, turn right or left, stop due to traffic lights, wait other traffic participants to finish their manoeuvres, accelerate before traffic light turn "red", decelerate to check the traffic from nearby roads, etc.  Moreover, every intersection is regulated variously (traffic-light-controlled, uncontrolled, yield-controlled, stop-controlled), so we expect different movement patterns to be observed at different junctions.

The aim of this thesis is the implementation of a method that detects and recognises these movement patterns in terms of their geometrical and spatiotemporal components, that is in the first case the geometric paths that vehicles follow driving through the intersections and in the second case the way that they follow these geometric paths e.g. how fast they drive, if they have to stop before turn, etc. Machine learning methods such as clustering techniques can be used to resolve these objectives.

In total under this thesis work, the student will have the opportunity to experiment with known Machine Learning  and Trajectories Analysis methods, using geodata (GPS vehicle trace data) and studying their applicability on a realistic scenario. 

Tasks and time schedule

Literature review on the topic

Algorithms’ implementation

Experiments and testing

Writing thesis documentation

Data and Tools

Dataset: GPS trace data for training and testing of the algorithm(s)

Tools: PostgreSQL database with PostGIS extension (tutorial of installation and usage is provided), Python and relevant packages

Prerequisites

Programming: knowledge of a programming language (preferably Python) as the tasks are heavily dependent on implementing algorithms

Data Analysis/Machine Learning/Databases: prior experience is desirable but not mandatory

Interest and willingness to work on a Machine Learning related topic

Ability to work independently

Contact

M.Sc. Stefania Zourlidou - zourlidou@ikg.uni-hannover.de - Tel. 0511.762-19435 - Office 615
- Institut für Kartographie und Geoinformatik, Appelstraße 9a, 30167 Hannover