AI in mobility

Introduction

Artificial intelligence is revolutionising urban mobility and public transport. AI is disrupting the mobility sector, transforming the way we move around cities and use public transport. From journey planning and traffic management to optimising transport networks, AI offers innovative solutions for smoother, more efficient and sustainable urban mobility.

1/ AI makes it easier to get around town :

  • Optimised journey planning : There are applications on the market that integrate AI to analyse traffic in real time and suggest optimised multimodal routes (public transport, cycling, walking) to users. Other applications use AI to provide real-time information on public transport timetables, disruptions and routes.
  • Intelligent traffic management : Intelligent systems analyse traffic flows in real time and adapt road signs to keep traffic moving. AI solutions can predict areas of congestion and suggest alternative routes for drivers and public transport users alike.
  • Optimised public transport networks : AI can be used to analyse public transport use and adjust services according to demand. Demand prediction systems can be used to optimise the number of buses, trains and metros in circulation.
  • Safer, more accessible transport : AI systems can detect dangerous behaviour and alert drivers and passengers in real time. Computer vision technologies can better identify pedestrians and cyclists on the road and improve public transport safety.
  • Intelligent and sustainable mobility solutions : car-sharing and car-pooling platforms, for example.

2/ AI offers many advantages for urban mobility and public transport :

  • Reduce journey times and traffic congestion
  • Improved road safety and public transport
  • Reduce pollutant emissions
  • Optimisation of transport costs
  • Increased accessibility to mobility for all

3/ However, the use of AI also raises challenges :

  • Ethical and data security issues
  • Impact on employment in the transport sector
  • Risks of cyber attacks and hacking
  • Need for appropriate regulation

4/ In addition to the examples given above, here are some concrete applications of AI in urban mobility and public transport :

  • Intelligent chatbots can answer users’ questions and help them plan their journeys.
  • Facial recognition systems can be used to control access to public transport.
  • Intelligent sensors can collect data on the urban environment and air quality.

5/ AI is not limited to public transport and traffic management :

It can also be used to optimise mobility in cities in a number of other ways :

  • Smart parking : Platforms can be used to geolocate and reserve parking spaces in real time.
  • Autonomous vehicles : Autonomous shuttles are already on the road in some cities, offering an alternative to traditional public transport.
  • Drones and autonomous robots are being used to deliver parcels in certain contexts.

6/ Here are some other areas where AI is being used to improve mobility :

  • Robotics : Robots are used for parcel delivery, street cleaning and infrastructure maintenance.
  • Drones : Drones are used for traffic monitoring, medicine delivery and mapping.
  • Blockchain : Blockchain can be used to secure transactions and guarantee the transparency of mobility data.

Conclusion

In conclusion, AI is a powerful lever for transforming urban mobility and public transport. AI has the potential to revolutionise the way we move around cities and dramatically improve citizens’ quality of life By tackling the challenges and seizing the opportunities, AI can contribute to smarter, safer urban mobility and help create more liveable cities.

At ALLONIA, there are concrete use cases : www.allonia.com

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The platform has been designed to meet any use case, while ensuring the ability to manage an increasing quantity and complexity of data. ALLONIA ensures complete data security, complying with rigorous data protection standards. What’s more, its structure is entirely hosted in France.

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The platform offers a simple, collaborative workspace. The management of models and pipelines is customised, enabling their performance to be tracked, compared and verified throughout their lifecycle.

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