Intelligent Mobility
Summary
Intelligent mobility encompasses the integration of advanced sensing, artificial intelligence and connectivity to optimise the movement of people and goods across diverse transport modes. Spanning road, rail, air and water, it transforms traditional infrastructures into responsive, data-driven systems that improve safety, efficiency and sustainability. Key enablers include real-time data acquisition via cameras, radar, LIDAR and GPS; machine learning algorithms for perception, prediction and decision-making; and low-latency communication networks. Intelligent mobility supports dynamic traffic management, predictive maintenance, autonomous operation and multimodal coordination. By embracing sensor fusion, edge computing and cloud analytics, cities can reduce congestion, lower emissions and enhance user-centred services such as shared mobility, on-demand transit and smart freight logistics. This systematic approach underpins a transition to resilient, accessible and environmentally responsible transport ecosystems.
Research from Nature Portfolio
Researchers have refined urban traffic monitoring by combining a two-stage deep segmentation network with object detection. Using a Faster R-CNN backbone enhanced by active-net deformable models and adaptive background filtering, this approach achieves high-precision vehicle segmentation and rapid inference on dash-cam video streams. It performs robustly under complex occlusions and variable lighting, supporting scalable smart-city traffic management.
A novel free-flow tolling system automates axle counting on highways using a cascade of YOLO-based wheel detectors and a passage tracking module. By reconstructing complete vehicle profiles from sequential frames, the system reliably enumerates axles in real time. With over 99% precision and recall, it streamlines toll collection, alleviates infrastructure bottlenecks and exemplifies intelligent road pricing through real-time AI.
Intelligent Mobility publication trend
The graph below shows the total number of articles in intelligent mobility across all publications each year (not limited to Nature Index journals).
Technical terms
Convolutional Neural Network (CNN): A deep learning model applying convolutional filters to extract spatial features from images.
Faster R-CNN: A two-stage object detection framework that first generates region proposals and then refines object classes and bounding boxes.
You Only Look Once (YOLO): A single-shot object detector that predicts bounding boxes and class probabilities in one forward pass.
Edge computing: Distributed data processing performed at or near the data source to minimise latency and bandwidth use.
Sensor fusion: The integration of data from multiple sensor modalities (e.g., cameras, radar, LIDAR) to enhance perception accuracy.
References
- Real-Time Traffic Flow Statistics Based on Dual-Granularity Classification. International Journal of Network Dynamics and Intelligence (2023).
- YOLOv7-RAR for Urban Vehicle Detection. Sensors (2023).
- MEGF-Net: multi-exposure generation and fusion network for vehicle detection under dim light conditions. Visual Intelligence (2023).
- A deep learning-based approach for axle counter in free-flow tolling systems. Scientific Reports (2024).
- Smart traffic management of vehicles using faster R-CNN based deep learning method. Scientific Reports (2024).
About these summaries
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