In times of climate change and urbanization, the transportation transition is high on the political agenda. However, while electric buses and emission-free trains are the focus, a crucial aspect is often overlooked: the working conditions of driving personnel. Without a radical restructuring of shift scheduling that considers the desire for flexibility and autonomy, the transportation transition cannot succeed. A promising approach is the use of AI-supported job-sharing platforms.

In many transit companies worldwide, rigid schedules remain the norm. Dispatchers set the shifts of driving staff, with personal needs or preferences of employees rarely considered. This approach leads to dissatisfaction and increasing turnover, as the flexibility that is increasingly common in other industries is missing.

Studies have shown that job satisfaction is strongly linked to the ability to shape one's own work schedule, proactively even in shift work. To meet this need, some transit companies have begun developing internal solutions that allow employees to swap shifts with each other or make individual adjustments in dialogue with shift planning.

Limited Options for Small Companies

Yet, options remain limited with internal solutions, particularly for smaller companies. Additionally, the potential of increasingly in-demand personnel such as high-speed train or cargo transport drivers has not been fully tapped; idle times of 30 to 40 percent, during which the driver is not actually driving, are not uncommon.

Scheduling models that can cross or connect company boundaries can be highly effective in increasing efficiency. Additionally, different licenses are required for various train models, along with route knowledge, which further reduces flexibility and interchangeability. Here, vehicle manufacturers are called upon to simplify operating standards so that the potential of driving personnel can be better utilized, especially since a transition to fully autonomous driving, particularly for high-speed trains, is still a long way off.

Innovative platforms are stepping in, enabling employees to plan their shifts themselves. Using artificial intelligence and machine learning, these systems can make personalized suggestions that take into account both operational requirements and individual preferences of employees. The platforms analyze historical data, current traffic flows, and individual schedules, creating dynamic shift plans based on real-time information.

Boom in Job-Sharing Platforms

Job-sharing platforms are experiencing a massive boom worldwide. According to an EU study, around 28.4 million people worked via digital platforms in 2022, compared to around 29 million people currently employed in industry. The trend on digital platforms continues, with expected growth of over 50 percent, and it is predicted that soon around 43 million people within the EU will be booking their work through job-sharing platforms.

Most of the work consists of services such as taxi driving, delivery, domestic services, or trades, but more and more professionals are also seeking an autonomous path via digital platforms, which can lead to increased efficiency and productivity if the systems interact well.

Job-sharing platforms not only offer employees more flexibility and autonomy but also contribute to increased efficiency. The ability to design their own work schedule leads to higher job satisfaction, better work-life balance, and thus stronger employee retention. For transit companies, this means fewer sick days, more stable scheduling, and ultimately greater operational efficiency. According to a study, the introduction of such platforms in other industries has already led to a reduction in turnover by up to 25 percent.

Practical Examples and Early Successes

In France, the rail operator SNCF is testing autonomous trains that not only optimize train operations but also enable more flexible shift scheduling for personnel. Using AI, operations are continuously monitored and adjusted in real-time, making the schedule more dynamic and adaptable. In Japan, Hitachi uses an AI-based system for station management, which optimizes staff deployment in real time based on passenger volume.

The transportation transition requires not only emission-free vehicles but also innovative work models. Job-sharing platforms enable transit companies to work more efficiently without ignoring the needs of personnel. This could be a crucial lever to successfully tackle the challenges of the transportation transition.

With the growing use of job-sharing platforms, labor law issues also arise. Social contributions and retirement security are particularly important topics. In flexible work models, ensuring these continuously can become complex.

Clear Legal Frameworks Needed

Platforms could be required to offer a form of basic retirement security. Tax issues and minimum wage regulations must also be clarified to ensure fair working conditions. In Germany, models like the "midijob" already offer a solution for low-income workers, where social contributions are reduced without affecting retirement entitlements. Looking ahead, it is important to establish clear labor law frameworks to ensure that platform workers enjoy the same protection as permanently employed staff.

It is now up to politics and business to set the right course. Financial incentives are needed to invest in such platforms, as well as clear regulatory frameworks that promote the use of AI in public transportation. The transportation transition can only succeed if technological innovation and humane working conditions go hand in hand.

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