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Guest blog: Empowering the future of transportation with Generative AI
In an era defined by digital transformation, many transport authorities are presented with both immense opportunities and challenges to overcome in order to deliver services capable of meeting modern demands. The ability to harness the power of data and AI is becoming a critical route to achieving this which we set out in this article.
The power of data in transportation
Data has become the lifeblood of modern transportation. Whether it's traffic flow, vehicle maintenance, public transit, or infrastructure planning, data fuels decision-making processes. GenAI (Generative AI), powered by machine learning and deep learning techniques, takes data utilisation to the next level by enabling innovative problem-solving, automation and predictive analytics.
Transport is already a data-rich sector. Transport for London (TfL) was one of the first public transport authorities to embrace this truth, opening market access to its 80 different types of data through open APIs. It encourages 13,000 developers to produce customer-facing products to allow users to receive information about London’s transport network through their channel of choice; from getting real-time traffic information to finding charging stations for zero emission capable (ZEC) vehicles within the city. Approximately 600 apps were developed and are powered by these data feeds with 42% of Londoners relying on these applications as part of their daily commute. By using machine learning models, GenAI can take this even further, dynamically analysing the data to identify patterns, predict future transportation trends, such as traffic congestion, service disruptions, or station overcrowding.
GenAI use cases in transportation
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Urbanisation: Many transportation agencies are dealing with changes in population demographics, including urbanisation and an aging population. In densely populated urban areas, it is typical to see a greater reliance on public transport, walking, cycling, eScooters, and ride-sharing services driven by convenience, cost and environmental concerns. Growing urban populations often require transportation agencies to expand and improve public transit systems to meet increased demand. This includes expanding subway, bus, and light rail networks. With GenAI, transport agencies can ensure networks are planned and operated with the greatest efficiency. For example, it can provide suggestions to reroute buses or manage traffic signals more efficiently during peak hours and assist in integrating data from various modes of transportation, including buses, trains, bicycles, and even ride-sharing services. Additionally, machine learning algorithms can automatically analyse user feedback and reviews to identify common issues and suggestions for improvement. This feedback can be invaluable for transportation agencies in making continuous route and traffic optimisation.
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Ageing population: An ageing population typically experiences reduced physical mobility and increased demand for accessible transportation options, such as paratransit services and senior transportation programmes. Senior citizens often require transportation for medical appointments and healthcare services which can strain existing transportation services and necessitate specialised medical transportation options. To support an aging population, transport agencies may need to invest in infrastructure improvements like accessible public transit stations, pedestrian-friendly features, and age-friendly urban planning. GenAI can create personalised experiences for senior citizens based on their historical usage (medical appointments and healthcare services) and preferences, this can include customised route recommendations, real-time updates, and tailored notifications to improve the user's experience. GenAI can also make apps more inclusive by providing features like speech-to-text, text-to-speech, and other accessibility enhancements, ensuring that a wider range of users can benefit from these apps. Moreover, GenAI can further enhance the user experience by implementing Natural Language Processing (NLP) algorithms to interpret user queries and provide more accurate and context-aware responses. This can be especially helpful for senior citizens and chatbots or voice assistants integrated into public transport apps.
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Predictive maintenance: With the proliferation of IoT devices, cameras, drones and other monitoring devices deployed on bridges, roads and railways, huge volumes of data is collected in real-time or at regular intervals to capture changes and anomalies. By integrating this data from multiple sources into a centralised data repository, utilising machine learning algorithms to build predictive models, employing anomaly detection techniques to identify deviations from expected sensory data patterns, an alter system that triggers notifications can detect potential or predictive maintenance needs. This proactive approach ensures safety and minimises disruptions caused by unexpected infrastructure failures.
While GenAI holds immense promise, it's essential to acknowledge that its success hinges on data availability. Democratising data within the transportation agencies ecosystem is crucial. This means breaking down silos, promoting data sharing, and adopting a Modern Data Strategy: creating a centralised repository known as a data lake.
Looking ahead
Many transportation agencies stand at the cusp of a data-driven revolution, with GenAI as a potent ally. By harnessing the power of GenAI to address critical use cases and democratising data through the creation of a data lake, transport agencies can not only enhance safety and efficiency but also pave the way for a more sustainable and intelligent transportation ecosystem. In an age where data is the differentiator, embracing GenAI and democratising data are essential steps toward a brighter transport future.
On 16 November 2023, techUK is hosting a roundtable, in partnership with AWS, to discuss the future of transport through data and AI. Click here for more information and register your interest in attending.
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