Analysis of AODV, DSDR, LAR, DSR and ZRP using OSM, SUMO, and NS2
A smart traffic light management dashboard using SUMO and reinforcement learning to simulate traffic flow, optimize signal timing, and improve vehicle movement at intersections.
An Intelligent Traffic Light Control system using Reinforcement Learning. Compares Deep Q-Network (DQN) and Tabular Q-Learning to optimize traffic flow in SUMO simulator.
Multi-agent reinforcement learning (PPO) for adaptive traffic signal control, built with SUMO and Stable-Baselines3
Deep Q-Learning (DQN) for traffic signal control in SUMO: a Gymnasium environment, max-pressure and fixed-time baselines, and a reproducible held-out evaluation. A clean starting point for RL course projects.
Repository to compare the quality of data generated from CARLA and SUMO simulators against real data from the UAH-DRIVESET-v1
Evaluating quality of data augmentation using CARLA and SUMO driving simulators for classifying normal and aggressive behaviors of UAH-driveset.
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