Getting Data-Ready For AI: How Public Transit Can Thrive in An AI World

This article was written by Miki Szikszai, CEO of Snapper Services.

Artificial Intelligence (AI) has the potential to transform public transit. Applied correctly, AI could enhance operational efficiency, support with optimisation efforts and improve the passenger experience. Tasks like recommending schedules or routes in line with evolving community needs can become seamless. It could also support with maximising resource allocation, reducing emissions and promoting alternative transportation modes such as micromobility and Mobility-as-a-Service (MaaS) platforms.  

There are real productivity gains to be had with AI. It’s not mere hype – it’s a tangible reality that is being applied across various industries. By leveraging AI-powered tools, public transit organisations can accelerate the rate at which they improve the passenger experience. The cognitive burden of sifting through vast amounts of data to highlight key trends is taken care of, enabling operators to focus on strategic decision making.

These tools are already improving productivity in many sectors and chances are your team is already using them in some capacity. But while the benefits of AI are clear, its potential depends on accurate transit data and addressing outstanding strategic issues.

Navigating the AI adoption curve

There will be an adoption curve for AI in all industries and public transit is no exception. While recent technological phenomena like smartphones were driven by consumer demand, businesses are motivated to implement AI to automate processes, boost productivity and reduce costs. Public transit organisations must determine the optimal approach to AI adoption, ranging from leveraging off-the-shelf AI solutions to developing in-house capabilities.

Assessing which solution is right for your organisation can be a challenge. While there is a spectrum of approaches, it’s likely that larger corporations with access to substantial capital will be the first to invest in the development of in-house AI capabilities. Collaborating with trusted technology providers can help navigate the complexities of AI adoption and ensure alignment with organisational goals.

Preparing for an AI-driven future

It’s likely AI will become an increasingly critical part of transit operations, particularly as automated technology matures. Organisations can begin laying the groundwork now to prepare for an AI-dominant future. Key considerations include safeguarding data integrity, anticipating customer expectations for AI-enabled services and maintaining agility.

In other words, to thrive in an AI-driven landscape, organisations must prioritise data protection, embrace customer-centric AI solutions and foster a culture of continuous learning and adaptation. These proactive measures will enable organisations to effectively leverage AI and deliver value-driven solutions that meet evolving passenger needs.

Balancing sustainability and efficiency

While AI offers significant productivity benefits, there are real concerns surrounding its energy-intensive nature. The dilemma presents a complex obstacle for transit operations prioritising net zero.  Organisations must strike a balance between harnessing AI’s efficiency gains and mitigating its potential environmental impact.

As organisations explore AI-driven solutions, it’s essential to evaluate the trade-offs and ensure that the results achieved through AI justify the associated energy costs. By integrating AI insights with sustainability initiatives, transit personnel can align efficiency gains with environmental control, driving a more sustainable future for public transit. The emergence of on-device AI tools may represent a more balanced path, with substantially lower energy requirements.

Adopting a targeted and regulated approach

A targeted and regulated approach to AI adoption is essential for organisations seeking to harness its potential responsibly. Rather than indiscriminately applying AI across all operations, organisations should focus on integrating AI where it can enhance monitoring, management and passenger experiences without compromising data integrity or public trust.

Regulatory frameworks play a vital role in guiding responsible AI adoption, ensuring compliance with privacy regulations and ethical considerations. By adopting this approach, organisations can mitigate risks associated with AI implementation while maximising benefits for passengers and stakeholders.

The role of accurate transit data

Access to accurate and comprehensive transit data is key to preparing for AI advancements. Today, standardised data can provide a clear understanding of network performance, enabling operators to identify inefficiencies and opportunities for improvement. In an AI context, this standardisation is key to enabling AI solutions to be deployed at scale and with high levels of accuracy.

Obtaining and managing this data effectively can be challenging. Operators may be producing data from different hardware sources and authorities don’t always have capacity to work through the range of available insights to find hidden optimisation opportunities. This makes tools that remove the burden of navigating complex data streams invaluable.

Accessible data tools can bridge the gap between current systems and AI technology, offering a near-term solution without overhauling existing procedures. Data standardisation, visualisation and analysis tools can help operators make better decisions and optimise their transit services. To prepare for the new era of AI, authorities and operators will need to prioritise access to effective data and user-friendly tools to gain a comprehensive understanding of their network.

The future of AI in transit

The potential developments of AI in transit are vast and promising, with AI technologies evolving to enable more sophisticated analyses. In the years to come, AI is expected to become increasingly dynamic, empowering operators to address complex challenges with greater precision.

AI could play a crucial role in supporting multimodal journey-planning initiatives that contribute to transportation sustainability goals. By integrating usage patterns and rider behaviour across public transit, ridesharing, active travel and micromobility services, AI can seamlessly integrate data insights leading to an efficient, user-centred transport network.

The role of data literacy and human intervention

While AI holds tremendous promise for the future of transit, it’s crucial to recognise that AI doesn’t replace human intervention but rather extends the abilities of the planners, schedulers, and analysts already in our industry.

AI systems are only as effective as the data available. While AI can sift through vast amounts of information to identify trends and prioritise issues, human oversight is still necessary to ensure the accuracy and relevance of AI-driven insights. Trained individuals with a deep understanding of transit networks play a crucial role in validating AI outputs and making informed decisions that will ultimately impact passengers.

Conclusion

Navigating the integration of AI in public transit demands a strategic approach rooted in trust, collaboration and responsible innovation. By embracing AI with optimism and practicality, organisations can enhance passenger experiences, optimise network performance and foster sustainable growth. Collaborative efforts among operators, authorities, and technology providers are crucial for shaping an AI-driven future that aligns with business goals and emphasises passenger satisfaction.

With strategic planning and trustworthy advice, public transit organisations can leverage AI to revolutionise operations, delivering efficient, reliable, and sustainable transportation solutions. In this evolving AI landscape, prioritising data integrity, balancing efficiency with sustainability, and maintaining regulatory compliance will position organisations for success, unlocking the full potential of AI to drive innovation and deliver exceptional value to passengers and stakeholders alike.

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