The Generation and Optimization of Alternative Data Element Sets for Crash Event Data Recorders on Large Trucks 2004-01-2716
This paper presents an optimization model that was developed to generate optimal alternative sets of data elements for crash event data recorders (EDRs) on commercial motor vehicles, since EDRs can provide important information about crashes to improve vehicle safety. The input data for this optimization model was from the following United States Department of Transportation (USDOT) reports: Development of Requirements and Functional Specifications for Crash Event Data Recorders (EDRs) on Commercial Motor Vehicles and EDR Volume II - Supplemental Findings for Trucks, Motorcoaches, and School Buses (May 2002).
The purpose of this optimization model was to provide useful and cost effective alternative sets of EDR data elements for a “real world” application. Using input data based on crashes, working group expert opinions, and estimated relative costs for specific data elements, an optimization model was developed to generate alternative sets of EDR data elements that would be useful to supplement the crash reconstruction process and to improve knowledge about crashes involving commercial motor vehicles. The optimization model also incorporated the Surrogate Worth Trade-off Method, which involves soliciting sequential articulation of expert or decision maker preferences as input for the model.
A crash EDR with optimized sets of data elements is a tool that can aid crash reconstructionists by providing information about the causes of crashes and by making it possible to better identify and address safety problems. Individual data elements recorded by an EDR are not intended to be utilized in isolation or as only one means to reconstruct crashes. The optimized alternative sets of crash data elements developed in this paper provide information relating to useful and cost effective EDR data elements as an aid to accident reconstruction, which could also be used for future designs of crash EDRs on large trucks.