AI based Traffic Estimation
Deep Learning Approach for Spatial Extension of Traffic Sensor Points in Urban Road Network
SUMO / TRACI / VEINS / INET / OMNET++ Programming
If you have found the following documents useful, please, cite one of our papers in your publication: FROM HERE!
1)
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The beginning steps of SUMO TRACI programming with Matlab Simple example code with Matlab |
The beginning steps of SUMO TRACI programming with Python Simple example code with Python |
2) Manual for SUMO/MATLAB/VEINS/INET/OMNET++ programming and interfacing with EXAMPLE CODES
3) Tutorial for SUMO - VEINS programming (Open Source vehicular network simulation framework):
"Vehicle-in-the-Loop" testing environment for autonomous cars
Vehicle-In-the-Loop Test Environment for Autonomous Driving with SUMO Microscopic Traffic Simulation
Vehicle-in-the-Loop (ViL) simulation test in ZalaZone test track - Autonomous Valet Parking Demo
Related publicatinos:
https://ieeexplore.ieee.org/document/8519486
https://content.sciendo.com/view/journals/ttj/20/2/article-p153.xml
Bus Bunching Control
On busy lanes, where public transport buses are frequent, bus bunching is a common phenomenon:
Bunching leads to non-homogeneous utilization of buses and therefore degradation of service quality.
The objective is to (i) adhere buses to a timetable and (ii) guarantee equal headways.
The proposed control methodology is a shrinking horizon model predictive velocity control.
Different control strategies are considered:
(i) timetable tracking: buses priorize the timetable over the headway reference.
(ii) headway tracking: buses try to keep equal headways.
(iii) balanced: both objectives are equally important.
The controllers are simulated in VISSIM in a realistic traffic setting (Göteborg):
The system was evaluated in the event of severe perturbation
(Xdes: desired position, Xref: reference position, ETD: estimated time of departure):
Related Publication:
Varga B, Tettamanti T and Kulcsár B (2018), "Optimally combined headway and timetable reliable public transport system", Transportation Research Part C: Emerging Technologies. Vol. 92, pp. 1 - 26.
https://doi.org/10.1016/j.trc.2018.04.016