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might support or refute the hypothesis. This explanatory model used his group’s impact speed/injury model (Davis and Cheong, 2019) as a component and described fitting and testing an empiri- cal model relating vehicle impact speed to pedestrian injury severity.
And earlier this year, Gao and Davis along with John Hourdos and Indrajit Chatterjee (who completed his Ph.D. in 2016), published a paper describing the development and testing of a mech- anism for studying freeway rear-end crashes, including how the model could be extended to a population of vehicles equipped with intelligent cruise control and automatic brake assistance.
Five-Year Goals
Davis was recently reappointed as the Richard P. Braun/CTS Chair in Transpor- tation Engineering, and he has set some ambitious goals for his next five years as the Braun/CTS Chair.
Transferability
Primary on his list for the next five years is to develop his group’s theoretical insights regarding the transferability and external validity of crash modification
factors into an operational methodology. In 2019, Davis outlined a possible approach. Gao is extending those ideas and applying them to sight distance modifications at stop-controlled inter- sections for her doctoral thesis. Davis noted, “Although posing and coding explanatory models is not trivial, the main challenge is finding quality data for testing our explanatory models.”
One data source comes from crash reconstruction, and one emphasis will be on using data from instrumented crash tests to evaluate and validate
their reconstruction methods. Davis (2017) presented an example of how this can be done. Gao and Cheong are both working with data from crash tests collected by the Southwest Association of Technical Accident Investigators. The researchers will then apply their recon- struction methods to additional cases from the National Highway Traffic Safety Administration’s National Automotive Sampling System Crashworthiness Data System (NASS CDS database). One weakness of the NASS CDS database is its limited roadway information, partic- ularly regarding traffic control and sight distance. So, Davis plans to request data from other sources to supplement the NASS CDS database.
A second and potentially very helpful data source comes from studies of traffic conflicts and surrogate measures. The ability to extract surrogate crash measures from video has improved sub- stantially during recent years. Davis and Gao are investigating the usefulness and availability of surrogate measures for testing and validating their mech- anism models. Davis (2021) recently published a theoretical paper on using surrogate outcomes to estimate crash modification factors.
Bayesian Inference
Davis also plans to write a book on the specialized techniques for applying Bayesian inference to crash recon- struction that he and his research group have developed.
And beyond that? Davis predicts even more change: “In the foreseeable future, automated vehicles, micro-mobility,
and shared mobility will become more prevalent. Transportation engineers
will need competence in areas such as cyber-physical systems and data analytics. Relevant courses should be implemented now to ensure that transportation engineers are ready for that future.”
Davis, G. A., Chatterjee, I., Gao, J., & Hourdos, J. (2021). Traffic Density versus Rear-End Crash Risk on Freeways: Empirical Mod- el, Mechanism Model, and Transfer to Automated Vehicles. Journal of Transportation Engineering Part A: Systems, 147(4), [04021007]. https://doi.org/10.1061/JTEPBS.0000501
Davis, G. A. (2021). Mechanisms, mediators, and surrogate esti- mation of crash modification factors. Accident Analysis and Pre- vention, 151, [105978]. https://doi.org/10.1016/j.aap.2021.105978
Davis, G. A. (2019). Explaining crash modification factors: Why it's needed and how it might be done. Accident Analysis and Preven- tion, 131, 225-233. https://doi.org/10.1016/j.aap.2019.06.015
Davis, G. A., & Cheong, C. (2019). Pedestrian Injury Severity vs. Vehicle Impact Speed: Uncertainty Quantification and Calibration to Local Conditions. Transportation Research Record. https://doi. org/10.1177/0361198119851747
Davis, G. A. (2017). Bayesian Estimation of Drivers’ Gap Selec- tions and Reaction Times in Left-Turning Crashes from Event Data Recorder Pre-Crash Data. SAE Technical Papers, 2017-March (March). https://doi.org/10.4271/2017-01-1411
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