Researchers from University of Zurich Describe Findings in Urban Systems (Predicting Individuals’ Car Accident Risk By Trajectory, Driving Events, and Geographical Context): Urban Research – Urban Systems

2022 APR 06 (NewsRx) -- By a News Reporter-Staff News Editor at Ecology Daily News -- Researchers detail new data in Urban Research - Urban Systems. According to news reporting from Zurich, Switzerland, by NewsRx journalists, research stated, “With the prevalence of GPS tracking technologiees, car insurance companies have started to adopt usage-based insurance policies, which adapt insurance premiums according to the customers’ driving behavior. Although many risk models for assessing an individual driver’s accident risk based on the history of driving trajectories, driving events, and exposure records exist, these models do not take the geographical context of the driven trajectories and driving events into account.”

Financial support for this research came from European Commission.

The news correspondents obtained a quote from the research from the University of Zurich, “This study explores the influence of enriching the existing purely driving-behavior-based feature set by multiple geographical context features for the task of differentiating between accident and accident-free drivers. Prediction performances of five machine learning classifiers-logistic regression, random forest, XGBoost, feed-forward neural networks (FFNN), and long short-term memory (LSTM) networks-were evaluated on the usage records of over 8000 vehicles in one year from Italy. The results show that the inclusion of geographical information such as weather, points of interest (POIs), and land use can increase the relative predictive performance in terms of AUC by up to 8%, among which land use is the most informative. For the data of this study, XGBoost generally yielded the best performance and made most use out of the geographical information, while logistic regression is only slightly outperformed by more complex models if the proposed geographical information is not available. LSTM did not outperform the other methods, possibly due to the small volume of training data available.”

According to the news reporters, the research concluded: “The results outline the potential of including the geographical context in usage-based car insurance risk modeling to improve the accuracy, leading to fairer usage-based insurance policies.”

This research has been peer-reviewed.

For more information on this research see: Predicting Individuals’ Car Accident Risk By Trajectory, Driving Events, and Geographical Context. Computers Environment and Urban Systems, 2022;93. Computers Environment and Urban Systems can be contacted at: Elsevier Sci Ltd, The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, Oxon, England.

Our news journalists report that additional information may be obtained by contacting Cheng Fu, University of Zurich, Dept. of Geography, Zurich, Switzerland. Additional authors for this research include Livio Bruehwiler, Robert Weibel, Haosheng Huang and Leonardo Longhi.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.compenvurbsys.2022.101760. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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