Usage Based Insurance

The idea of Pay-As-You-Drive-And-You-Save [PAYDAYS]

Traditional insurance models are based on a combination of static factors such as the driver’s socio-demographic profile and vehicle information in conjunction with driving history. With the advent of new mobile technologies, some insurance companies have begun exploring a new auto insurance model known as Usage-Based-Insurance [UBI] or Pay-As-You-Drive-And-You-Save [PAYDAYS], which aims to incorporate individualized, real-world dynamic driving patterns into actuarial pricing. While most existing UBI or PAYDAYS efforts can be considered a major leap forward, the majority of researchers and insurers currently rely solely on user GPS trajectories, which only measure the specific driver’s performance–such as the number of miles driven, travelling speeds, and hardness of braking–without considering other critical risk factors in the surrounding environment that may also contribute to crash risk, i.e. the contextual-sensitive risk factors.

Research Approach

The research team proposed that the consideration of these contextual risk factors would offset risk for automobile insurers providing PAYDAYS coverage and lead to greater savings for low-risk drivers operating under such a policy. In September of 2013, The Federal Highway Administration contracted a research project to prove their PAYDAYS theory and study the implications of such an approach to insurance companies’ premium calculation model.

In this research project, we collected individualized driving behavior data from the smartphone GPS module, combined with geographical network information and dynamic traffic conditions, to identify driving risk factors and evaluate driving behaviors under various contexts. The main contribution and research findings from this research project include:

  1. Through a smartphone app, we collected a total of 131,537 trips taken by 503 panel members over an 18-month period, which corresponds to 1,090,136 miles traveled.
  2. Analysis definitively proved that driving behavior is context-sensitive, particularly with regard to the traffic conditions and the roadway geometry surrounding the vehicle of interest.
  3. Through comparison to prior studies without context information, our analysis demonstrated the benefits of utilizing context-relevant information in the driving behavior assessment process.
  4. The developed model found that the rates insurers charge PAYDAYS customers to be off by as much as 25%-31% in either direction–undercharging or overcharging drivers. By applying a driver’s individual risk assessment, insurers are able to offer safer drivers a more competitive rate while charging less-safe drivers a rate more appropriate for their level of risk.

​The findings of our study can further existing knowledge about driving exposure factors that are closely linked to crash risk, help insurers restructure their existing pricing models to allow for variation in premiums based on individualized driving characteristics, and provide the actuarial foundation for advanced forms of PAYDAYS insurance pricing.

Executive Summary Video

Relevant Publications

  1. Yuluen Ma, Xiaoyu Zhu, Xianbiao Hu*, Yi-Chang Chiu. The Use of Context-Sensitive Insurance Telematics Data in Auto Insurance Rate Making. Transportation Research Part A: Policy and Practice. Volume 113, July 2018, Pages 243-258
  2. Xianbiao Hu*, Xiaoyu Zhu, Yuluen Ma, Yi-Chang Chiu, Qing Tang. Advancing Usage Based Insurance – A Contextual Driving Risk Modeling and Analysis Approach. IET Intelligent Transport Systems. 2018. (Excellent paper award, 2018 World Transport Convention).
  3. Zhu, Xiaoyu, Yifei Yuan, Xianbiao Hu, Yi-Chang Chiu, and Yu-Luen Ma. “A Bayesian Network model for contextual versus non-contextual driving behavior assessment.” Transportation Research Part C: Emerging Technologies 81 (2017): 172-187.
  4. Xiaoyu Zhu, Xianbiao Hu, Yu-Luen Ma, Yi-Chang Chiu. Contextual Driving Risk Analysis Using Dynamic Smartphone-Based Data: The Potential for Usage Based Insurance. Transportation Research Procedia. 2016.
  5. Xianbiao Hu*, Yi-Chang Chiu, Yu-Luen Ma, Lei Zhu. Studying Driving Risk Factors using Multi-Source Mobile Computing Data. International Journal of Transportation Science and Technology, vol. 4 no. 3, 2015, pp. 295-312.
  6. Xiaoyu Zhu, Xianbiao Hu, Yi-Chang Chiu. Design of Driving Behavior Pattern Measurements Using Smartphone Global Positioning System Data. International Journal of Transportation Science and Technology. Vol 2, no. 4. 2013, pp 269-288.