Paper Info
Title
Crash Prediction and Risk Assessment with Individual Mobility Networks
Abstract
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.
Year
DOI
Venue
2020
10.1109/MDM48529.2020.00030
2020 21st IEEE International Conference on Mobile Data Management (MDM)
Keywords
DocType
ISSN
Mobility Data Model,Crash Prediction,Individual Mobility Network,Mobility Data Mining,Car Insurance
Conference
1551-6245
ISBN
Citations 
PageRank 
978-1-7281-4664-5
0
0.34
References 
Authors
19
2
Name
Order
Citations
PageRank
Riccardo Guidotti111224.81
Mirco Nanni2141284.47