Research

Traffic forecasting applies machine learning and deep learning techniques to accurately predict future traffic conditions, helping cities reduce congestion and improve daily travel for everyone. Traffic forecasting is about using smart technology like machine learning and deep learning to predict traffic patterns before they happen. By analyzing data from sensors, GPS, and historical trends, these methods can forecast where traffic will be heavy and when, making it easier to plan for smoother, faster commutes. Also, these predictions help cities manage traffic more efficiently, cut down on delays, and improve road safety, while also enabling real-time decision-making to prevent congestion. As cities get smarter and technology advances, traffic forecasting continues to improve, offering more accurate estimations and valuable insights into the road networks’ mechanics and spatiotemporal relations that benefit everyone on the road.

Traffic Management combines artificial intelligence, simulation models, and optimization techniques to improve traffic flow, reduce congestion, and create safer roads for all. Traffic Management harnesses the power of artificial intelligence, simulation models, and optimization strategies to improve how we move through cities. By using advanced algorithms, like reinforcement learning, cities can adapt to real-time traffic conditions, optimize signal timings, and dynamically manage road usage.

Traffic modeling refers to the process of simulating and analyzing traffic flow and the interactions between vehicles on road networks. It involves the use of various car-following models to predict driving behavior and understand how drivers react in different traffic conditions. These models are essential for studying mobility patterns, especially in complex environments like shared spaces, where interactions between vehicles, pedestrians, and cyclists are more intricate. With the rise of autonomous vehicles, traffic modeling is evolving to account for these new agents and their impact on traffic dynamics. The goal is to create accurate simulations that help optimize traffic management and improve road safety.

Transport planning involves the development of strategies and policies to ensure efficient, safe, and sustainable transportation systems. It encompasses a range of considerations, including parking management, integration of new mobility services, and the coordination of both physical and digital infrastructure. As urban mobility evolves, micromobility options such as e-scooters and bike-sharing systems have become essential components, requiring careful planning to address their impact on existing infrastructure. Additionally, Intelligent Transportation Systems (ITS) play a crucial role in optimizing traffic flow, improving safety, and enhancing user experience by utilizing advanced technologies for real-time data collection and management. Effective transport planning ensures a balanced approach to these emerging challenges.