Spatiotemporal Modeling on Foot Traffic Data to Unlock Auto Insurance Geo-risks

Foot traffic data is captured by various sources, such as smartphone APP, telematics devices in the vehicle, which can help insurance monitor policy holders’ behavior. It is beneficial for insurance companies to price the risk accurately and accelerate the underwriting process. On the other hand, policyholders are given incentives for good driving behavior. There are various state-of-art techniques to extract useful information from the high-dimensional foot traffic data, including spatial and temporal analysis, and geospatial analysis.

In this project, we intend to create spatial and temporal models to identify the policyholders’ driving behavior in certain CBG (Census Block Group) or city/county levels and provide guidance for auto insurance geo-risk. Currently, we are investigating the association between accident and foot traffic based on the 2018-2019 vehicle accident report from Indiana state.

Supervisors: Zhiyu (Frank) Quan

Graduate Supervisor: Changyue Hu