Navigating the Future: 7 ITS Trends and Expectations in 2024

The Intelligent Transport System (ITS) landscape is evolving rapidly, with 2024 poised to witness groundbreaking advancement, let’s explore some of the key trends I see shaping the future of transportation this year:  

1. Data Revolution in Mobility 

In today’s mobility sector, vast, complex, and fast-moving data systems are emerging as the lifeblood of contemporary ITS. New roadway sensors powered by leading-edge software are providing significantly greater datasets than ever before. They enable the real-time detection of vehicles and pedestrians, including trajectory, vehicle classification, near-miss events, red-light running, excessive vehicle queueing, and other critical roadway information. In 2024, cutting-edge technologies will push the boundaries of what is possible with ITS to include predictive analytics and traffic management. One example is edge computing or computing at the Cabinet Edge. All the data gathered by sensors and other devices requires an agile distributed approach to processing and computing. Dedicated platforms, which are located at the traffic cabinet and traffic controller, offer the unique benefit of near real-time control, given its proximity and very low latency of data exchange between sensors in the field and traffic control platforms. 

2. Connected Vehicles  

2024 promises to be a landmark year for Connected Vehicle (CV) technology. The widespread adoption of V2X (Vehicle-to-Everything) communications, enabling vehicles to communicate with each other, as well as with traffic signals and other elements of the transportation ecosystem, will expand the real-time exchange of data. The further integration of 5G networks plays an important role here, allowing vehicles to make split-second decisions and respond to changing road conditions with unparalleled accuracy. These technologies contribute to safer road conditions and optimized traffic flow. 

3. Artificial Intelligence in Traffic Management  

In 2024, AI's role in traffic management we be increasingly prominent, providing real-time data analysis, predictive modeling, and adaptive decision-making. Machine learning algorithms process vast amounts of historical and real-time data identifying patterns and trends, modelling incidents in the road network, and even forecasting traffic volumes, as well as congestion up to several hours in advance. These AI-driven tools enable traffic managers to respond swiftly to changing conditions, minimizing delays, reducing emissions, and enhancing overall traffic flow. Moreover, AI's ability to continuously learn from data allows for the refinement of traffic management strategies over time, ensuring a more adaptive and proactive approach to handling the complexities of modern urban mobility. 

4. Traffic Management to Multimodal Network Management 

Traditional traffic management systems focus on monitoring and optimizing the infrastructure for private vehicle flows. This car-centric view is undergoing a paradigm shift towards a more comprehensive and inclusive approach—multimodal network management. Recognizing the diverse modes of transportation available to individuals, including bicycles, public transit, and shared mobility services, Traffic Planners and Managers are embracing multimodal strategies to optimize the entire transportation network. This is even more pertinent in view of new mobility trends and technologies, connected and automated vehicles, new physical and digital infrastructures, and innovative services. 

The shift acknowledges that effective urban mobility solutions go beyond addressing vehicular traffic alone, prioritizing a holistic network management approach that accommodates the diverse needs of commuters and fosters a sustainable, interconnected urban ecosystem. 

5. Digital Twinning  

Digital Twinning (DT) technology offers the potential of a new strategic paradigm in roadway traffic management. In the past, traffic operation simulations, modeling, and analytics occurred separately offline and used limited traffic data. A digital twin of a physical roadway along with a mathematical model layer raises the decision-making capability bar to the next level by enabling modeling, simulation, testing, and validation in real-time. It will aggregate real-time data feeds and events, which provide information on the traffic state of the physical roadway, fusing various sources of information into one comprehensive view. This allows the model to use a variety of analytical, simulation, and AI tools to deliver measurable traffic predictions and forecasts. The coalescence of ITS innovations, machine learning, and information communication technologies are now providing the means to make DT technology a strategic reality, especially for traffic signal timing operations. DT and the capability to dynamically optimize traffic management will transform mobility. 

6. Open-Source Architecture Solutions  

Recent V2X systems have revealed significant gaps between existing industry standards and the forthcoming generation of new field devices, new datasets, data taxonomy, machine learning/predictive analytics, and other emerging ITS technologies. Newer technologies promise to improve roadway safety and mobility, but only if these technologies and datasets can be accessed by existing ITS systems and vice versa. The potential mobility capabilities of newer V2X systems depends greatly on the data exchange compatibilities between devices and systems.  

Open-Source, cloud-based architecture is an answer to mitigating the risk of bridging the standards gaps without returning to proprietary solutions of the past. An open-source architecture offers the same collaborative openness as standards, but can move faster, and be more responsive to change. An open-source architecture can also easily integrate and interface with future traffic control capabilities, including the anticipated datasets that will be produced from situational awareness and cooperative perception solutions, trajectory-aware sensing, and real-time modeling.   

7. Vision Zero Advances  

The Vision Zero initiative is a strategy aimed at eliminating all traffic fatalities and severe injuries, while increasing safe, healthy, equitable mobility for all, continues to be a driving force in shaping transportation policies. 2024, technology and data-driven strategies contribute to Vision Zero goals. Advanced Driver Assistance Systems (ADAS) are becoming more sophisticated, reducing the likelihood of accidents and enhancing road safety. Predictive analytics enable authorities and manufacturers to anticipate safety risks, analyzing historical data, weather conditions, and real-time traffic patterns to implement proactive preventive measures.