AI for Smart Mobility

Data, AI & Machine Learning Engineering Solution

context

Smart Mobility has emerged in recent years as a revolution in urban transportation, quickly becoming a key element for the sustainability of city centers. By seamlessly integrating IoT, real-time data, and AI, this innovative approach has been reshaping how people commute and travel for leisure, leading to more efficient, integrated, and sustainable transportation systems.

The growing complexity arising from evolving technologies and infrastructure poses significant challenges for new Smart Mobility operators. A well-structured technological approach is needed to effectively manage and integrate different data streams, ensure robust information security, and maintain consistent service reliability. Competitive advantage increasingly depends on innovation and, in return, on the ability to address the evolving needs of final users.

pain points

High implementation and maintenance costs impact the financial sustainability of Smart Mobility solutions
Quality and reliable data for AI models is crucial to avoid issues such as data drift and inaccurate predictions
Real-time data integration and transformation are needed for the swift deployment of AI models
Transparency in data management and AI algorithms is to be ensured to achieve compliance and meet stakeholders’ expectations

solutions

Predictive Maintenance for Vehicles and Infrastructure

Leveraging the proprietary Radicalbit platform, Bitrock integrates real-time data from IoT sensors with AI models to proactively detect potential deterioration or failures. This enables preemptive maintenance of vehicles and infrastructure, preventing costly breakdowns, extending asset lifespan, and enhancing safety. By optimizing maintenance schedules, this approach reduces downtime and saves resources, while ensuring efficient and sustainable transportation operations.

Fleet Distribution Forecasting

Bitrock develops decision support systems that forecast vehicle demand for shared mobility operators. These predictions are based on real-time data such as weather conditions and disruptive events like accidents and road closures altering the ordinary fleet distribution. Foreseeing car distribution and demand based on traffic patterns and extraordinary occurrences, smart mobility operators can thus proactively adjust vehicle repositioning and pricing.

Multimodal Trip Planning

Bitrock enables the evolution of commuting with AI-powered trip planning systems. These analyze real-time traffic data, weather conditions, and available transportation options – including cars, bikes, and public transit – to recommend the most efficient routes to users. The AI also factors in potential delays and suggests alternative solutions by integrating shared mobility services with public transport into a cohesive plan. This personalized and multi-modal approach empowers travelers and commuters to reach their destinations – be it home or work – more quickly and safely.

benefits

Significant cost reduction through preventive maintenance of vehicle fleets
Increased operational efficiency thanks to accurate forecasts of vehicle demand and distribution
Improved user experience with personalized and real-time recommendations
Greater environmental sustainability through route optimization and reduction of CO2 emissions

Technology Stack and Key Skills

IoT and advanced sensors for real-time data collection
Data processing systems for the integration of multimodal data
MLOps platform for deployment and monitoring of AI models
Skills in Data Engineering and MLOps
Expertise in smart mobility and urban mobility systems

AI for Smart Mobility

context

pain points

solutions

Predictive Maintenance for Vehicles and Infrastructure

Fleet Distribution Forecasting

Multimodal Trip Planning

benefits

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