Crash prediction and risk assessment with individual mobility networks

Guidotti R., Nanni M.

Car insurance  Crash prediction  Mobility data model  Mobility data mining  Individual mobility network 

The massive and increasing availability of mobility data enables the study and the prediction of human mobility behavior and activities at various levels. In this paper, we address the problem of building a data-driven model for predicting car drivers' risk of experiencing a crash in the long-Term future, for instance, in the next four weeks. Since the raw mobility data, although potentially large, typically lacks any explicit semantics or clear structure to help understanding and predicting such rare and difficult-To-grasp events, our work proposes to build concise representations of individual mobility, that highlight mobility habits, driving behaviors and other factors deemed relevant for assessing the propensity to be involved in car accidents. The suggested approach is mainly based on a network representation of users' mobility, called Individual Mobility Networks, jointly with the analysis of descriptive features of the user's driving behavior related to driving style (e.g., accelerations) and characteristics of the mobility in the neighborhood visited by the user. The paper presents a large experimentation over a real dataset, showing comparative performances against baselines and competitors, and a study of some typical risk factors in the areas under analysis through the adoption of state-of-Art model explanation techniques. Preliminary results show the effectiveness and usability of the proposed predictive approach.

Source: MDM 2020 - 21st IEEE International Conference on Mobile Data Management, pp. 89–98, Online conference, 30/06/2020 - 03/07/2020

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