Integrating learning-based solutions in intelligent transportation systems: a conceptual framework and case studies validation

Urbanization has led to significant traffic congestion, presenting challenges for traditional traffic management systems that rely on static and rule-based approaches. These systems struggle to adapt to real-time changes in traffic patterns, resulting in inefficiencies and delays. Intelligent Transportation Systems (ITS), leveraging advanced technologies such as sensors, communication networks, and data analytics, offer promising solutions. This study aims to develop and validate a conceptual framework integrating deep learning, reinforcement learning, and transfer learning into ITS for dynamic and adaptive traffic management. An explorative literature review identifies key constructs, including real-time data collection, data preprocessing, adaptive signal control, and predictive analytics. The framework is validated through case studies from Singapore, Los Angeles, and Rio de Janeiro, demonstrating practical implementation and impact. The findings highlight the potential of learning-based ITS solutions to enhance traffic flow, reduce congestion, and improve urban transportation networks, contributing to the broader vision of smart cities.

http://dx.doi.org/10.1080/23311916.2024.2427235