Cities across the world have invested heavily in electronic ticketing systems. From London’s Oyster card to New York’s OMNY and Paris’s Navigo, digital fare systems have become a defining feature of modern public transport.

Despite these investments, many transit agencies continue to face persistent challenges, including revenue losses, congestion during peak travel periods, and declining passenger satisfaction.

The issue is not that electronic ticketing technology has failed.

According to Samuel Yaw Larbi, a manager of fleet and public transport operations and a chartered transport and logistics professional with more than a decade of experience managing large-scale public transportation systems in West Africa, the deeper problem lies in how these systems are designed and managed.

“Electronic ticketing systems generate enormous amounts of data,” Larbi explains.

“But in many cases, that data is reviewed after the fact rather than being used to guide real-time decisions. In fact, many large transit agencies continue to rely heavily on retrospective analysis rather than real‑time predictive systems. As a result, issues such as fare evasion, service bottlenecks, and overcrowding are often identified only after they have already disrupted service.”

Larbi’s work focuses on addressing this gap through the responsible integration of artificial intelligence into transit and logistics systems.

His professional background combines operational leadership with applied research in digital transformation and supply chain management. Having overseen transport operations serving millions of passengers, he brings a practitioner’s perspective to problems that are often discussed in abstract or technical terms.

Modern artificial intelligence systems have the capacity to analyze passenger flows, payment behaviour, and operational performance as they occur.

Machine learning tools can help detect unusual fare patterns, anticipate equipment failures before they lead to service interruptions, and forecast demand across routes and times of day.

These capabilities allow operators to act proactively rather than responding after inefficiencies have already occurred.

The implications extend beyond operational efficiency. Revenue losses from fare evasion and service inefficiencies remain a significant concern for many metropolitan transport authorities.

For passengers, these issues often translate into long boarding times, missed connections, and unpredictable travel experiences. From Larbi’s perspective, these challenges are not isolated.

They reflect digital systems that have been implemented without sufficient intelligence behind them.

A notable element of Larbi’s work is its grounding in real operational environments.

His research examines how data-driven management approaches can improve system transparency, reduce inefficiencies, and support more sustainable transport operations.

In recent years, his peer-reviewed work has explored digital transformation, supply chain resilience, and the governance challenges associated with advanced analytics in public systems.

Equity and ethics are central to his body of literatures published. AI-powered ticketing and pricing systems, if poorly designed, can reinforce social and economic disadvantage. Dynamic pricing models, for example, may disproportionately affect lower-income communities if fairness and transparency are not incorporated into system design.

Larbi’s frameworks stress the importance of ethical safeguards, accountability, and human oversight in public-sector technology adoption.
“These ideas are increasingly reflected in policy discussions and pilot initiatives worldwide.

Transport authorities in Europe, North America, and Asia are exploring predictive demand modeling, intelligent maintenance scheduling, and real-time service optimization. While the underlying technologies are available, successful implementation continues to depend on institutional readiness and operational expertise.”

An outlook . Larbi shared on the future transport operations.

Larbi’s work suggests that the next stage of smart city transit will not be defined by new payment media, but by how intelligently existing systems are connected, governed, and used.

Tools such as digital simulations allow agencies to model changes before deploying them system-wide, reducing risk and improving public confidence.

As cities confront ageing infrastructure, fiscal pressure, and environmental obligations, expectations for efficient and equitable transit systems continue to rise.

The central question is no longer whether the tools exist, but whether they are applied in ways that genuinely improve everyday mobility.

“The goal is not technology for its own sake,” Larbi says. “It is building systems that work better for people, day in and day out.”