Obtaining data on active travel activities such as walking, jogging, and cycling are important for refining sustainable transportation systems (STS). In order to provide an accurate and privacy-preserving sensing solution, a deep learning (DL)-enhanced distributed acoustic sensing (DAS) system for recognizing active travel activities is proposed. By leveraging the ambient vibrations captured by DAS, this scheme infers motion patterns without relying on image-based or wearable devices, thereby addressing privacy concerns. We conduct real-world experiments in two geographically distinct locations and collect a comprehensive dataset to evaluate the performance of the proposed system. To address the generalization challenges posed by heterogeneous deployment environments, we propose two solutions based on network availability: 1) an Internet-of-Things (IoT) scheme based on federated learning (FL) is proposed, and it enables geographically different DAS nodes to be trained collaboratively to improve generality; 2) an off-line initialization approach enabled by meta-learning is proposed to develop high-generality initialization for DL models and to enable rapid model fine-tuning with limited data samples, facilitating generalization in newly established or isolated DAS nodes. Experimental results of the walking and cycling classification problem demonstrate the performance and generality of the DL-enhanced DAS system, paving the way for practical, large-scale DAS monitoring of active travel.

A distributed acoustic sensor (DAS)-enabled smart vehicle monitoring system is investigated in this article to detect the type and passenger occupancy of vehicles for intelligent transportation systems (ITSs). Accurate detection of the number of occupants and the vehicle type is critical for ITS to monitor the occupancy of vehicles, improve vehicle operation efficiency, and achieve intelligent traffic management. We have developed several deep learning (DL) algorithms for occupancy detection and vehicle type discrimination according to the signals provided by DAS. To be more specific, a novel type of basic neural network structure, namely sparse residual (SR) block is proposed, and several DL models are developed for DAS signals based on the basic SR block unit. The proposed DL approaches are tested using a unique dataset collected from a road experiment. The test results indicate that 1) the proposed SR network (SR-Net) and Alex SR (Alex-SR) network can achieve detection accuracy of over 90%; 2) the proposed models exhibit superior convergence, stability, and accuracy than the conventional residual network (ResNet); and 3) the proposed DL solutions have superiority in terms of complexity and model size compared to benchmarks.

Active travel is an essential component in intelligent transportation systems. Cycling, as a form of active travel, shares the road space with motorised traffic which often affects the cyclists' safety and comfort and therefore peoples' propensity to uptake cycling instead of driving. This paper presents a unique dataset, collected by cyclists across London, that includes video footage, accelerometer, GPS, and gyroscope data. The dataset is then labelled by an independent group of London cyclists to rank the safety level of each frame and to identify objects in the cyclist's field of vision that might affect their experience. Furthermore, in this dataset, the quality of the road is measured by the international roughness index of the surface, which indicates the comfort of cycling on the road. The dataset11https://github.com/Chiayen0503/AllTheDocks_Dataset/tree/main will be made available for open access in the hope of motivating more research in this area to underpin the requirements for cyclists' safety and comfort and encourage more people to replace vehicle travel with cycling.