Content

Part 1: ITS Belgium conference visit

• In order to broaden your overview of the ITS domain, we visit the ITS Belgium conference in Brussels (http://its.be/).

Part 2: exercises exploring open data and some developer tools

• Introduction to ITS CreaLab (http://www.itscrealab.be/)
• introduction to open data sources (OpenStreetMap, Flanders Open Data Portal,…)
• introduction to developer tools (QGIS, Google API’s, Matlab, Python)

Part 3: conceive and create your own ITS application; build a proof of concept

• You dispose of many public open data sets, open source tools, and - through IST CreaLab - some data and tools that the L-Mob research group at KUL-CIB acquired and/or developed over the years. Use this as your playground to develop your own ITS application!
• Position your app carefully within the transportation domain: identify the stakeholders that will use your service and their user needs (required service, operational context and constraints, willingness to pay,…). Translate those into a system architecture of your app. Build a proof of concept of some key components of your application.
• Pitch your idea and proof-of-concept demo as part of the exam!

Optional: guest lectures

• depending on the amount of students and availability of speakers, we may invite some guest lecturers on some special topic within ITS. These lectures may be co-ordinated with other courses within the Log&Traf master.

Course material

Presentation slides, papers, ITS CreaLab tools and data.

Content

The goal of the lectures is to illustrate how sensing data & data processing, combined with knowledge of the transportation system, enables ITS services. To this end, some in-depth methods are discussed from a subset of ITS services all belonging to traffic operations management services (whereas, ofcourse, ITS is much broader than just that).

Lectures cover the following topics:

Part 1.       ITS domain definition, overview, architectures and EU policy

Part 2.       traffic data collection, data processing, data fusion and state estimation

• a.       cumulative representation of traffic data and feature extraction
• b.       travel time estimation algorithms
• c.       adaptive smoothing method
• d.       Kalman filters for traffic models
• e.       KF applications to traffic state prediction and traffic data fusion: motorway and urban network case studies

Part 3.       traffic management and control

• a.       Characteristics of the road traffic system, relevant for dynamic traffic management (DTM) à understand the system you are controlling, identify bottlenecks and understand how they are utilized
• b.       Traffic management as a region wide ITS service; defining the objective function for the DTM problem à know who controls what and for which objectives
• c.       Some selected traffic control tools à understand the power and limitations of controls and why

                                                    i.      ramp metering (ALINEA, HERO)

                                                   ii.      variable speed limits (MOW, MTFC, SPECIALIST)

                                                  iii.      signal control

Part 4.       intelligent vehicles

• a.       case study: A58 Spookfiles project
• b.       autonomous robots
• c.       autonomous driving: some realism in the hype

Course material

Lecture notes, papers.

Language of instruction: more information

English

Format: more information

Lectures