Crash and Driving Data Analysis

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UMTRI experts have access to petabytes of efficient and high-quality naturalistic data sets, crash data for the U.S. and several states, as well as the largest set of connected vehicle data in the world. This data will better inform manufacturers, and policy makers in their pursuit of a national connected vehicle network and AV deployment.

The Center for the Management of Information for Safe and Sustainable Transportation (CMISST) Group gathers, combines, and analyzes all types of transportation datasets to answer pressing questions in transportation safety and efficiency.

CMISST has one of the largest, most efficient and high-quality naturalistic sets of data around. In fact, they have the largest set of connected vehicle data in the world. Combined with our state-of-the-art data management systems and research expertise, CMISST experts are leading the way to a better understanding of factors that impact transportation safety and mobility, and identify the most effective crash and injury countermeasures.

Selected Publications

Bálint A, Flannagan CA, Leslie A, Klauer S, Guo F, & Dozza M. (2020). Multitasking additional-to-driving: Prevalence, structure, and associated risk in SHRP2 naturalistic driving data. Accident Analysis and Prevention 137.
Benedetti M, Klinich KD, Manary MA, Flannagan CA (2017) Predictors of restraint use among child occupants. Traffic Injury Prevention 18(8):866-860.
Benedetti M, Klinich KD, Manary MA, Flannagan CA. (2019) Factors affecting child injury risk in motor-vehicle crashes. Stapp Car Crash Journal 63.
Blower D, Flannagan C, Geedipally S, Lord D, & Wunderlich R. (2019) Identification of Factors Contributing to the Decline of Traffic Fatalities in the United States from 2008 to 2012. National Academies Press.
Buckley L, Bingham C R, Flannagan C A, Carter P M, Almani F, and Cicchino J B (2016) Observation of motorcycle helmet use rates in Michigan after partial repeal of the universal motorcycle helmet law. Accident Analysis & Prevention, 95, 178-186.
Carter PM, Buckley L, Flannagan CA, Cicchino JB, Hemmila M, Bowman PJ, and Bingham CR (2017) The impact of Michigan’s partial repeal of the universal motorcycle helmet law on helmet use, fatalities, and head injuries. American Journal of Public Health 107(1):166-172.
Flannagan C, Selpi, Boyraz P, Leslie A, Kovaceva J, & Thomson R. (Mar 2019) Analysis of SHRP2 Data to Understand Normal and Abnormal Driving Behavior in Work Zones: Final Report. Federal Highway Administration. FHWA-HRT-20-010. https://www.fhwa.dot.gov/
Flannagan CA & Leslie A. (2020). Crash Avoidance Technology Evaluation Using Real-World Crash Data (No. DOT HS 812 841). United States. Department of Transportation. National Highway Traffic Safety Administration.
FlannaganCA, Bálint A, Klinich KD, Sander U, ManaryMA, CunyS, McCarthy M, Phan V, Wallbank C, Green PE, Sui B, Forsman A, Fagerlind H. (2018) Comparing motor-vehicle crash risk of EU and US vehicles, Accident Analysis and Prevention 117:392-397. DOI:10:1016/j.aap.2018.01.003
Klinich KD, Bowman P, Flannagan CA, Rupp JD (2016) Injury Patterns in Motor-Vehicle Crashes in the United States 1998-2014, UMTRI 2016-06, University of Michigan Transportation Research Institute.
Kostyniuk LP, St. Louis RM, Zakrajsek J, Stanciu S, Zanier N, and Molnar LJ. (2017) Societal Costs of Traffic Crashes and Crime in Michigan: 2017 Update (UMTRI-2017-01). Ann Arbor, MI: University of Michigan Transportation Research Institute.
Newnam S, Blower D, Molnar LJ, Eby DW, and Koppel S. (2018) Exploring crash characteristics and injury outcomes: an analysis of truck-involved crash data in the US. Safety Science 106:140-145.
Rafei A, Flannagan CA, & Elliott MR. (2020) Big Data for Finite Population Inference: Applying Quasi-Random Approaches to Naturalistic Driving Data Using Bayesian Additive Regression Trees. Journal of Survey Statistics and Methodology 8(1):148–180.
Tan YV, Flannagan CA, and Elliott MR (2019) “Robust-squared” imputation models using BART. Journal of Survey Statistics and Methodology
Wu J, Shan C, Chou CC, Hu J, Cao L, Jiang B (2018) Age effects on injury pattern of rear-seat child occupants in frontal crashes. International Journal of Vehicle Safety, 10(3-4): 288-300.
Yu B, Bao S, Chen Y (2019) Quantifying Visual Road Environment to Establish a Speeding Prediction Model: An Examination Using Naturalistic Driving Data. Accident Analysis & Prevention 129: 289-298.