Dual Stream Meta Learning for Road Surface Classification and Riding Event Detection on Shared Bikes

Abstract

Road surface condition monitoring and bike riding event detection are crucial in densely populated cities for travel efficiency and rider safety. However, most current approaches are either costly, unreliable in different scenarios, or not adaptable in new environments. This article proposes a novel automated approach leveraging widely used shared bikes to intelligently detect road surface conditions and riding events suitable for interactive Internet of Things (IoT) cities. We propose a novel dual stream meta learning approach to solve the reliability problem when bike types for the training and testing are different with a limited set of new samples and the self-adaptive problem when classifying new classes without retraining the model, both via dual stream meta learning. Results demonstrate the feasibility of the proposed IoT-based solution with 98.9% accuracy for road surface conditions and 99.6% accuracy for riding events via the proposed dual stream deep learning method in the conventional scenario. With few samples per class, the proposed method is more reliable than other commonly used approaches in the different-bike scenario (e.g., proposed 92.4% versus random forest 74.6%). In cases of predicting new classes, the algorithm is 95.6% accurate using only one sample per class without explicit training (compared to 78.0% for$K $-nearest neighbor). This article proposes a robust IoT framework for smart cities involving road surface conditions and rider events which could be critical for many applications, including city mapping, shared bike rental maintenance and rider performance, and city maintenance services.

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