CA-ICA Traffic Optimization is a smart transportation research project that applies Cultural Algorithm (CA) and Imperialist Competitive Algorithm (ICA) to optimize urban traffic systems, public transportation networks, and bus allocation through AI-driven simulation models.

ITS Autonomous Vehicle Demo is an experimental intelligent transportation system project that demonstrates autonomous vehicle technologies such as lane detection, obstacle recognition, vehicle navigation, and real-time driving simulation using AI and computer vision techniques.

Framework for classifying how network slicing policies impact Intelligent Transportation Systems (ITS) applications, enabling dynamic resource allocation in vehicular networks.

Advanced autonomous driving perception suite comparing classical computer vision and deep learning based lane/road detection pipelines, including HybridNets ONNX multitask inference, U-Net road segmentation, curved lane tracking, and real-time GPU-accelerated autonomous road analysis.

DeepTrafficQ is a reinforcement learning-based traffic signal control system that uses Deep Q-Networks (DQN) to minimize vehicle waiting times at a 4-way intersection. By leveraging Q-learning with experience replay and a convolutional neural network (CNN), the agent dynamically adjusts traffic light phases to optimize traffic flow.

Tesi di Laurea Triennale in Informatica, Università degli Studi di Padova, A.A. 2019-2020.

Production-grade real-time smart-parking platform (ITS): concurrency-safe booking, dynamic surge pricing, live WebSocket updates, payments & ANPR. Flask · PostgreSQL · Redis · Celery · Docker — 88% tested, full CI/CD, live on Render, built to scale horizontally.

AI-powered highway traffic shockwave detection and prediction system using MT-STNet deep learning model with real-time monitoring and intelligent decision support