TRB 2016 Blue Ribbon Committee
16th National Transportation Planning Applications Conference

Using FARS Data to help Automated Vehicles Policy Making

Corresponding Author: Krishnan Viswanathan, Cambridge Systematics, Inc.

Presented By: Krishnan Viswanathan , Cambridge Systematics, Inc.


On Sept 23, 2016 the National Highway Traffic Safety Administration (NHTSA) released a request for comments on “Federal Automated Vehicles Policy” (Docket No. NHTSA-2016-0090). Despite important safety technologies becoming standard equipment, NHTSA’s data suggest that 94% of crashes can be tied to a human choice or behavior . The advent of highly automated vehicles (HAVs) offers hints at mitigating the risk of crashes due to human choice or behavior. Therefore, NHTSA is issuing this document – “Federal Automated Vehicles Policy” – as Agency guidance rather than in a rulemaking in order to speed the delivery of an initial regulatory framework and best practices to guide manufacturers and other entities in the safe design, development, testing, and deployment of HAVs and also to ensure that premature, static regulatory requirements do not hinder innovation and diffusion of the dynamic technologies that are being developed in the industry. NHTSA is dividing the task of facilitating the safe introduction and deployment of HAVs into four sections: (1) Vehicle Performance Guidance for Highly Automated Vehicles; (2) Model State Policy; (3) NHTSA’s Current Regulatory Tools; and (4) New Tools and Authorities. The focus of this presentation is on using Fatality Analysis Reporting System (FARS) data to help model state policy.

As part of this research, data from 2014 & 2015 FARS will be evaluated to identify the environmental, roadway, person, and vehicle characteristics which cause accidents of varying severity. The reason for using data from 2014 onwards is because it includes information about crashes between motor vehicles and pedestrians, people on personal conveyances and bicyclists. The intent of this research is to identify and quantify the various measures that cause accidents and provide a data driven framework to help states formulate policies for HAVs. Given the demographic and geographic variability between states and regions, this research will also identify common and distinct measures across these demographic and geographic dimensions to help policy development.


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