A5076897689- Transportation and Mobility Innovations
- Cloud Computing and Resource Management
- Urban Transport and Accessibility
- Smart Parking Systems Research
- Human-Automation Interaction and Safety
- Interconnection Networks and Systems
- Elevator Systems and Control
- Distributed and Parallel Computing Systems
- Academic Publishing and Open Access
- Transportation Planning and Optimization
- Traffic control and management
- Scientific Computing and Data Management
- Embedded Systems Design Techniques
- Electric Vehicles and Infrastructure
- Safety Warnings and Signage
- Traffic and Road Safety
- Big Data and Business Intelligence
- Parallel Computing and Optimization Techniques
Technical University of Munich
2020-2024
Zuse Institute Berlin
2006
This study presents an approach to collect and classify usage data of public charging infrastructure in order predict based on socio-demographic within a city. The comprises acquisition two-step machine learning approach, classifying predicting behavior. Data is acquired by gathering information points from publicly available sources. first step identifies four relevant patterns the gathered using agglomerative clustering approach. second utilizes Random Forest Classification factors spatial...
Abstract. Fully autonomously driving vehicles are expected to be a widely available technology in the near future. Privately owned cars, which remain parked for majority of their lifetime, may therefore capable independently during usual long parking periods (e.g. owners working hours). Our analysis aims focus on potential privately shared car concept as transition period between present usages cars towards transportation paradigm autonomous vehicles. We propose two methods field...
OpenCUBE aims to develop an open-source full software stack for Cloud computing blueprint deployed on EPI hardware, adaptable emerging workloads across the continuum. prioritizes energy awareness and utilizes open APIs, Open Source components, advanced SiPearl Rhea processors, RISC-V accelerator. The project leverages representative workloads, such as cloud-native workflows of weather forecast data management, molecular docking, space weather, evaluation validation.