Digital technologies increasingly shape various aspects of our lives, changing tools and approaches, minds and attitudes. In this context, the transport sector plays a crucial role in improving citizens' quality of life, impacting both private and public transport systems. It is essential to recognize the steady rise in the number of vehicles on the roads. While this growth can address certain well-being needs, it also presents challenges that may impede the effective fulfilment of these needs, particularly concerning capacity and timely service. This issue is particularly acute in densely populated urban areas, where traffic congestion can lead to delays in transportation, goods delivery, and commuting. The environmental ramifications of rising vehicle numbers must also be considered, as road congestion contributes significantly to air pollution due to emissions. The objective of this study is to identify and learn lessons from the implementation of various strategies aimed at reducing transport congestion, identify opportunities for optimizing private vehicle traffic flow in large cities, and propose a model that balances the interests of private car owners, municipal authorities, and urban residents’ well-being. Lessons learned might suggest further model development that would integrate a time-based financial incentive system, such as the introduction of city road tolls in designated metropolitan areas, along with recommendations for enhancing roadway infrastructure and traffic management, including intelligent transportation systems. The methodology leverages data from traffic management service providers, cartographic resources, and other relevant traffic flow and satellite information. It takes into account population movement patterns, driving habits, travel time preferences, various toll fee payment options, and motivational incentives. Additionally, the study incorporates insights and best practices derived from case studies of cities globally.