The SmartDrive AI project aims to modernise accident reporting and management for public transport operators by replacing outdated paper-based systems with an easy-to-use digital solution. The inspiration came from everyday challenges faced by drivers and accident managers who rely on manual processes that are slow, error-prone and difficult to manage – especially in areas with poor internet connectivity.
The project’s main goal is to improve safety, efficiency and decision-making by enabling real-time accident documentation, even offline, and streamlining the way reports and documents are handled. Using a mobile app tailored for public transport operations, SmartDrive AI allows drivers to quickly collect and upload key accident data – such as reports, photos, and videos – all linked to a unique case ID. Accident managers can access all information instantly, and the system can automatically send reports to insurance providers.
Another key feature is built-in analytics, which help operators identify accident patterns and high-risk areas. This empowers transport authorities to take proactive steps toward improving road safety.
The pilot was developed and led by Mapular UG, a location intelligence company based in Berlin, Germany. It is being implemented in close partnership with Rhein-Erft-Verkehrsgesellschaft (REVG), a public transport operator in North Rhine-Westphalia, one of Germany’s most densely populated regions. The project builds on a successful rollout in Graz, Austria, and runs throughout 2025.
By solving a real and widespread problem in public transport, SmartDrive AI aims to become a scalable, cost-effective solution that improves safety and operations for transit systems across Europe.
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SmartDrive AI addresses 96% delays in public transport accident reporting caused by paper-based workflows, limited connectivity, and fragmented document and insurance processes.
SmartDrive AI digitises reporting with an offline-capable app, automates insurance workflows, and centralises data to reduce delays, errors and administrative burden.
Faster, more accurate accident reporting, reduced administrative workload, improved road safety through predictive analytics, and a scalable model for public transport operators across Europe.
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