Real-time modelling for smart traffic management systems with proactive traffic control and demand optimization
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Authors
Menichetti, D.
Journal Title
Journal ISSN
Volume Title
Publisher
Southern African Transport Conference
Abstract
Historically, cities have managed the live transport network through staff monitoring
CCTVs and social media feeds, with Network Monitoring Operators (NMOs) and transport
engineers manually implementing new plans and signal changes to ease network issues.
In recent years, the evolution of technologies (e.g. sensors, connectivity, computational
capacity, etc.) have allowed the adoption of more pro-active approaches in monitoring and
influencing the transport network and the behaviour of its users. These approaches
leverage on systems based on dynamic transport models and machine learning algorithms
that can ingest data, estimate and predict traffic as well as perform simulations in real time.
The goal of such predictive system is to evaluate in real time the effects on traffic caused
by changes to the transport network such as planned works, accidents, and traffic lights
configurations, including the synchronization effects between adjacent intersections such
as the so-called green wave phenomenon. With these augmented capabilities, traffic
managers and operators can take advantage of simulation, evaluation and visualization
capabilities to test and monitor any traffic optimization strategies in an integrated and
centralized environment, enabling its users to evaluate their effectiveness before
proceeding with their application on the ground. In our presentation, through a practical
application, we will explain the specifics of this system, the symposium of benefits of the
real time traffic models for NMOs, and how they are integrated with live data assets
including traffic signals.
Description
Papers presented at the 40th International Southern African Transport Conference on 04 -08 July 2022
Keywords
Network Monitoring Operators (NMOs), Smart Traffic