Special Issue "Multi-Sensor Positioning for Navigation in Smart Cities"
Deadline for manuscript submissions: 30 November 2022 | Viewed by 73
Interests: multimodal transportation; smart cities; modelling of passenger flows; smartphone-based navigation algorithms
Interests: indoor positioning; crowdsourcing; smartphone positioning; pedestrian dead reckoning; Wi-Fi positioning; sensor fusion
The mobility of people and goods plays an important role in the life, work, prosperity, and cohesion of the citizens of smart cities. The biggest part of this transportation is carried out using motorized vehicles that make a large contribution (82 %) to greenhouse gas emissions. Additionally, the continuing growth in the demand for transport and changes in mobility behaviour lead to increasing conflicts over the use of limited space, where pedestrians, cyclists, and motorized vehicles compete for the use of the roads.
New user-centred mobility concepts that complement existing public transport, with the use of e- scooters, bicycles, and walking, are the key to solving these issues in smart cities. These concepts require robust individual positioning to provide advanced, seamless navigation across all transport modes, enabling a frictionless coexistence of active and motorized transport modes, and fostering sustainable mobility options.
Navigation in urban spaces, such as train stations or airports, is of key importance in understanding the needs, preferences, behaviours, and activities of users and crowds in each area. To achieve seamless navigation, the successful detection of different urban scenarios is needed, e.g., indoor/outdoor detection, to adapt the sensors and algorithms depending on the scenario.
Artificial intelligence (AI) can provide a significant boost for understanding mobility and behavioural patterns, as well as for the protection of e-scooters, cyclists, and pedestrians in urban environments. For the application of AI in safety-critical applications, new methods of validation and training are required. Access to city-wide information provides a significant amount of data, but it introduces new challenges for data handling and mining that need to be addressed. The analysis of big data and the methods for data-driven research should be used to gain high-quality data, dedicated to the training of AI for transport applications.
Dr. Estefania Munoz Diaz
Dr. Francisco Zampella
Dr. Elizabeth Colin
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- machine learning
- awareness and context detection
- big data
- data mining