Rapid urbanisation and increasing mobility needs mean that urban transport is at a turning point. The integration of AI into existing transport systems increases the efficiency and performance of these systems without having to replace them completely. This allows cities to stay at the cutting edge of technology while optimising existing infrastructures. 

Currently, almost 40 percent of all people in the EU live in cities, and an additional 35 percent live in suburbs or smaller towns. In this context, transport plays a crucial role, especially in terms of environmental impact. In 2021, a total of 3,472 million tonnes of carbon dioxide were emitted in the EU, of which 740 million tonnes were attributable to the combustion of fossil fuels in transport alone. Although the electrification of mass mobility can help to reduce these figures, there is still further potential for improvement. This can be realised through the use of Artificial Intelligence. These systems not only optimise the flow of traffic, but also increase safety on the roads. The planned introduction of autonomous vehicles will further increase the need for dynamic interaction with traffic systems, making the use of AI indispensable. Furthermore, the combination of AI with technologies such as 5G and the Internet of Things (IoT) could revolutionise urban transport systems. 

Use cases for the application of Artificial Intelligence in traffic management 

  1. Adaptive traffic control 

    AI can analyse real-time data to optimise traffic lights and traffic flows. By adapting signalling times to the current traffic volume, congestion is reduced and traffic efficiency improved. 

  2. Traffic forecasting and planning 

    AI algorithms use historical and current traffic data to recognise traffic patterns and make precise predictions about future traffic volumes. This information ensures better planning and optimised traffic management to avoid bottlenecks. 

  3. Intelligent traffic monitoring 

    By analysing data from surveillance cameras and sensors, AI can detect unusual traffic situations, such as traffic jams or accidents, in real time. This then enables traffic control centres to react quickly to divert traffic flows or coordinate emergency services. 

  4. Optimisation of public transport 

    AI will help to optimise public transport timetables and routes by analysing demand patterns and adjusting the availability of transport accordingly. This will reduce waiting times for passengers and improve the utilisation of transport capacity. 

  5. Environmental monitoring and management 

    By analysing traffic flows and vehicle types, AI helps to monitor and manage environmental impacts such as air pollution and noise. For example, environmental zones can be dynamically adapted to reduce emissions. 

  6. Car park management 

    In future, AI will be used in car park guidance systems to show drivers the availability of parking spaces in real time and guide them to the nearest free parking spaces. This reduces the time and fuel consumption required to find a parking space. 

  7. Traffic flow optimisation for emergency operations 

    AI can be used to control the flow of traffic during emergency operations so that emergency vehicles have the fastest possible route through the traffic. 

Enriching existing systems with AI: the best way to proceed 

Integrating Artificial Intelligence into existing transport systems requires strategic planning and implementation to reap the full benefits and minimise the challenges. Here are practical tips for successful implementation: 

  1. Inventory and needs analysis

    Analysis of existing systems 
    Determine the current state of transport systems and infrastructure, including hardware and software. Identify weaknesses and areas that could benefit from AI. 

    Target definition 
    Define clear goals for the integration of AI, e.g. improving transport efficiency, reducing emissions or increasing safety.

  2. Data management 

    Data access and integration 
    Ensure that you have access to relevant data sources, such as traffic flow data, weather data or information from social media. The integration of information from different sources is crucial for the effectiveness of your AI application. 

    Data quality and preparation 
    Invest in the preparation of data, as its quality directly influences the performance of AI systems. This includes data cleansing, normalisation and enrichment. 

  3. Selection of technology

    AI platforms and tools 
    Select suitable AI platforms and tools that are tailored to the specific requirements and objectives. Consider both commercial and open source options. 

    Compatibility 
    Make sure that the selected AI solutions are compatible with existing systems or that interfaces can be created for integration. 

  4. Pilot projects and scaling

    Pilot projects 
    Start with pilot projects in limited areas to test the feasibility and benefits of AI integration. This will allow you to gain insights and adjust the strategy before a broader implementation. 

    Scaling 
    After successful pilot projects, plan the gradual scaling of the AI applications. Take into account the technical infrastructure, the required investment and the training needs of staff. 

  5. Partnerships and collaborations 

    Collaboration with technology providers and consultants 
    Seek collaboration with experienced technology companies to gain access to expert knowledge and advanced solutions. 

    Stakeholder involvement 
    Involve all relevant stakeholders, including the public, in the process at an early stage. Communicate transparently about goals, approaches and expected benefits.

  6. Security and data protection

    Data protection guidelines 
    Ensure that all AI applications comply with the applicable data protection guidelines. The protection of personal data and compliance with the General Data Protection Regulation (GDPR) or similar regulations are essential. 

    Security measures 
    Implement robust security measures to ensure the integrity of traffic systems and the security of data. Also consider the defence against cyber attacks.

  7. Ongoing evaluation and adaptation

    Monitoring and evaluation 
    Establish systems for continuous monitoring of AI applications and regularly evaluate their performance and effectiveness. 

    Agility: 
    Be ready to make adjustments based on lessons learnt. The technology landscape and traffic management requirements are constantly evolving and agility is critical to ensure long-term success.

Conclusion

The integration of AI into existing transport systems offers a unique opportunity to overcome the challenges of urban mobility without completely replacing the existing infrastructure. By optimising traffic flow, reducing emissions and improving safety, AI can make a significant contribution to sustainable urban development. Examples from cities such as Pittsburgh and Singapore show that the path to a more efficient and environmentally friendly future is already underway. The continuous development and implementation of AI technologies in traffic management promises to fundamentally change the way we move in and around cities. 

We are always ready to advise you on how to integrate AI into your existing IT systems. Get in touch with our experts!

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#IoT #Solutions #Trends #UseCases

About the author

Mathias Herrmann

CEO at ALLEHERZEN

Mathias Herrmann is an internet entrepreneur going back to the Internet’s early days with a deep interest in digital and future technologies. For over 20 years, he has been helping companies make the most of their data by forging innovative solutions – without forgetting the people behind the data.

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