What is Q-Traf ?
Q-Traf is a trip-based macroscopic traffic model which calculates and draws traffic flow maps (QGis layers), based on Origin-Destination (O/D) trip matrices, by calculating the best route for each vehicle (“assignment”). “Traffic assignment” is the final step of the “4-steps” urban / transportation modelling.
Q-Traf can manage large scale network, with more than 30 000 links, 2 000 zones, dozens of matrices and 10 different vehicle categories.
Apart from the demand (trips matrices), a large scale road network can be modelling in a day or less if a GIS road layer is already available.
Why Q-Traf ?
There are already a lot of traffic or trips programs.
The strengths of Q-Traf are :
- All the road network modelling and data visualization is done with QGis
- Free , without commercial purpose.
- Simple. As it could be
- No coding required.
- Simple open file formats only : “*.csv” (parameters and matrices) and “*.geojson” (QGis layers)
- Portable. No installation required.
- Lightweight. Only 5 Mo
A brief history
Q-traf has been developed over the years, starting in 2014 with a simple Dijkstra algorithm and delay(flow) functions.
Q-Traf has been developed through years of case studies, aiming to do the better use of QGis (for all the modelling and graphic visualization) and to remain the most user-friendly possible.
Performance and limitations
Q-Traf runs on Windows 10 and 11.
It can handle up to :
- 30 000 links
- 2 000 zones
- 4 main categories of road vehicles (car, truck, bus, pedestrian), 9 sub-types, and 16 matrices
- Park & Ride (car-bus trip)
- 40 scenarios (sets of networks + matrices, for comparison)
- LHD and RHD
- Day and hour analysis
- provides traffic flow, density, per vehicle type and PCU (Passenger Car Unit), zone analysis
(computing 20 000 links and 2 000 zones requires 2 Go of free RAM)
The graphical results are only limited by what a Gis can do.
Traffic assignment – Method and principles
The basis of the program are :
- Method of assignment : MSA (Method of Successive Averages) algorithm. “All or nothing” (for 1 iteration) to “user equilibrium” (the more iterations)
- link delay / performance functions
- generalised travel cost (based on delay, distance and penalties)
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