A big data exploration approach to exploit in-vehicle data for smart road maintenance

In modern Smart Cities, pervasive collection of sensor-based and IoT data streams is a challenging opportunity for improving mobility resilience. Among the potential applications, sensor-based data streams provide valuable information about the quality of the area-wide road surface. Modern vehicle black boxes are also able to estimate the type of anomaly (e.g., bump, hole, rough ground, depression), based on real-time analysis of acceleration data streams. Road maintainers may use all this information to improve monitoring and maintenance activities. However, the volume of data streams, the variety of road network and different degrees of seriousness of detected anomalies call for methods to support maintainers in the exploration of available data. To this aim, in this paper, we propose a methodological approach, based on big data exploration techniques. The approach is grounded on: (i) a multi-dimensional model, apt to organise data streams according to different dimensions and enable data exploration; (ii) data summarisation techniques, based on an incremental clustering algorithm, to simplify the overall view over massive data streams and to cope with their dynamic nature; (iii) a measure of relevance, to focus the attention on road portions that present critical conditions. The innovative contributions regard the formalisation of the exploration methodology, the definition of exploration scenarios, based on road maintainers’ goals and the measure of relevance, and an extensive experimentation on a real world case study, addressed in a research project on smart and resilient mobility. Experimental results show how relevance evaluation is able to efficiently attract the road maintainers’ attention on road portions that present the most critical conditions and the proposed incremental clustering algorithm outperforms existing algorithms in the literature.