Autonomic Transport Management Systems—Enabler for Smart Cities, Personalized Medicine, Participation and Industry Grid/Industry 4.0

By J. Schlingensiepen, F. Nemtanu, Rashid Mehmood, L. Mccluskey. 2016

68 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Automotive Engineering, Strategy and Management

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Chau Yuen
2. Yuren Zhou
3. Meng Zhang
4. Billy Pik Lik Lau
5. U-Xuan Tan
6. Marakkalage Sumudu Hasala
7. Naveed Ul L. Hassan

In Automotive Engineering

1. Chau Yuen

Citations in the corpus

• A survey of data fusion in smart city applications (2019, background): “…indoors or outdoors, location-based services (e.g. route planning and navigation) can be provided to end users by fusing the location sequences with other information sources such as geographic information system (GIS) data, real-time traffic data, and user preference data [136], [137], [187]–[190].”
• Exploring the influence of big data on city transport operations: a Markovian approach (2017, background): “approaches that could improve efficiency and reliability and hence the sustainability of city transport and operations systems (Schlingensiepen et al., 2014 ; Schlingensiepen et al., 2016) .”
• UbeHealth: A Personalized Ubiquitous Cloud and Edge-Enabled Networked Healthcare System for Smart Cities (2018, background): “This is due to the fact that smart cities are driven by, or involve, integration of multiple city systems such as transport, healthcare, and operations research [17]–[20], with the aim to provide its citizens a high quality of life.”