SOLP

Congested roads and an insufficient number of parking spaces cause truck drivers, who have to observe rest periods, to negligently mis-park with often fatal results. In contrast to static approaches, which do not sufficiently take into account the intentions of drivers and trucking companies, SOLP dynamically determines a prognosis for the approach to potentially usable parking spaces based on the interdependence of traffic volume, parking space availability, time/cost economics, and legal requirements.

The project goal of SOLP is to research and develop an AI-based recommendation system for truck parking lots. The system is based on traffic flow, weather, telematics and parking lot occupancy data. The innovation consists of assigning an actual available truck parking space in the route path, which can be approached directly and expeditiously, taking into account legal requirements.

The optimization of parking space allocation by SOLP leads to quicker approaches to actually available parking spaces, reduces the risk of congestion and thus leads to a significant improvement in traffic flow. CO₂ emissions are avoided by reducing search approaches. The allocation optimized by SOLP also prevents accidents caused by wrong parking in parking lot entrances, which in turn relieves the police and emergency services. The decision support relieves truck drivers physically and mentally, reduces stress and improves mental state and concentration. Overall, SOLP leads to a reduction in costs for trucking companies and carriers.

• BLUE Consult GmbH
• Fraunhofer HHI
• KRAVAG und SVG Assekuranz Service GmbH