Computational framework for reinforcement learning in traffic control
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Updated
Mar 17, 2024 - Python
Computational framework for reinforcement learning in traffic control
Veins - The open source vehicular network simulation framework.
Reinforcement Learning environments for Traffic Signal Control with SUMO. Compatible with Gymnasium, PettingZoo, and popular RL libraries.
A framework where a deep Q-Learning Reinforcement Learning agent tries to choose the correct traffic light phase at an intersection to maximize traffic efficiency.
Official github page of UCF SST CitySim Dataset
We have used Deep Reinforcement Learning and Advanced Computer Vision techniques to for the creation of Smart Traffic Signals for Indian Roads. We have created the scripts for using SUMO as our environment for deploying all our RL models.
Containerised SUMO. Use sumo, sumo-gui and TraCI with Docker. 🐳 🚗
An Activity-based Multi-modal Mobility Scenario Generator for SUMO. This project is available in the Eclipse SUMO contributed tools section (https://github.com/eclipse/sumo/tree/master/tools/contributed) under the name SAGA (SUMO Activity GenerAtion).
A model problem for big data self adaptive systems using SUMO and TraCI
Adaptive real-time traffic light signal control system using Deep Multi-Agent Reinforcement Learning
FMU data standard and data export with rich metadata in the FMU context
InTAS is the Ingolstadt traffic scenario for SUMO, which was designed, developed and validated with real traffic data from measuring points.
We provide an open source software package for AV based simulation and testing running a docker container
TrafficSenseMSD is an open-source traffic light performance tuning and optimization tool.
Unified All-in-one Monero, Sumokoin, Aeon and other Cryptonote miner
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