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Elena Esposito

Publications and source records attributed to Elena Esposito.

2 recordsLinked to original sources

Can Telematics Improve Driving Style? The Use of Behavioural Data in Motor Insurance

Motor insurance can use telematics data not only to understand the individual driving style, but also to implement innovative coaching strategies that feed back to the drivers, through an app, the aggregated information extracted from the data. The purpose is to encourage an improvement in their driving style. Precondition for this improvement is that drivers are digitally engaged, that is, they interact with the app. Our hypothesis is that the effectiveness of current experimentations depends on the integration of two distinct types of behavioural data: behavioural data on driving style and behavioural data on users' interaction with the app. Based on the empirical investigation of the dataset of a company selling a telematics motor insurance policy, our research shows that there is a correlation between engagement with the app and improvement of driving style, but the analysis must distinguish different groups of users with different driving abilities, and take into account time differences. Our findings contribute to clarify the methodological challenges that must be addressed when exploring engagement and coaching effectiveness in proactive insurance policies. We conclude by discussing the possibility and difficulties of tracking and using second-order behavioural data related to policyholder engagement with the app.

cs.CY

Adaptive machine learning strategies for network calibration of IoT smart air quality monitoring devices

Air Quality Multi-sensors Systems (AQMS) are IoT devices based on low cost chemical microsensors array that recently have showed capable to provide relatively accurate air pollutant quantitative estimations. Their availability permits to deploy pervasive Air Quality Monitoring (AQM) networks that will solve the geographical sparseness issue that affect the current network of AQ Regulatory Monitoring Systems (AQRMS). Unfortunately their accuracy have shown limited in long term field deployments due to negative influence of several technological issues including sensors poisoning or ageing, non target gas interference, lack of fabrication repeatability, etc. Seasonal changes in probability distribution of priors, observables and hidden context variables (i.e. non observable interferents) challenge field data driven calibration models which short to mid term performances recently rose to the attention of Urban authorithies and monitoring agencies. In this work, we address this non stationary framework with adaptive learning strategies in order to prolong the validity of multisensors calibration models enabling continuous learning. Relevant parameters influence in different network and note-to-node recalibration scenario is analyzed. Results are hence useful for pervasive deployment aimed to permanent high resolution AQ mapping in urban scenarios as well as for the use of AQMS as AQRMS backup systems providing data when AQRMS data are unavailable due to faults or scheduled mainteinance.

eess.SP