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Driver Behavior


UMTRI researchers use multiple strategies to investigate driver behavior, including testing in simulators, on the MCity test track, and on-road naturalistic studies.  Recent topics of interest are distraction, variations with driver age, trust and acceptance of advanced driving systems, and driver workload. UMTRI also has a focus on older drivers with details found here.

Recent Publications

  • Bao S, Funkhouser D, Buonarosa ML, Gilbert M, LeBlanc D, & Ward N. (2020). Human factors research on seat belt assurance systems (Report No. DOT HS 812 838). Washington, DC: National Highway Traffic Safety Administration.
  • Feng F, Bao S, Sayer JR, Flannagan C, Manser M, and Wunderlich R. (2017) Can vehicle longitudinal jerk be used to identify aggressive drivers? An examination using naturalistic driving data. Accident Analysis & Prevention 104:125-136. DOI: 10.1016/j.aap.2017.04.012
  • Flannagan C, Bálint A, and Bärgman J. (in press) What are drivers doing when they aren’t on the cell phone? Transportation Research Part F.
  • Flannagan C, Bärgman J, & Bálint A. (2019) Replacement of distractions with other distractions: a propensity-based approach to estimating realistic crash odds ratios for driver engagement in secondary tasks. Transportation research part F: traffic psychology and behaviour63, 186-192. DOI: 10.1016/j.trf.2019.04.013
  • Flannagan MJ. (2020) A market-weighted description of tungsten-halogen and LED headlighting patterns in the U.S. (Report No. UMTRI-2019-5). Ann Arbor: The University of Michigan Transportation Research Institute.
  • Jones MLH, Ebert SM, Reed MP. (in press) Sensations associated with motion sickness response during passenger vehicle operations on a test track. SAE International Journal of Advances and Current Practices in Mobility 1(4):1398-1403. DOI:10.4271/2019-01-0687
  • Li G, Li SE, Cheng B, and Green P. (2017) Estimation of driving style in naturalistic highway traffic using maneuver transition probabilities. Transportation Research Part C: Emerging Technologies, 74:113-125. DOI: 10.1016/j.trc.2016.11.011
  • Li G, Wang Y, Zhu F, Sui X, Wang N, Qu X, & Green P. (2019) Drivers’ visual scanning behavior at signalized and unsignalized intersections: A naturalistic driving study in China. Journal of Safety Research71, 219-229. DOI: 10.1016/j.jsr.2019.09.012
  • Li G, Zhu F, Qu X, Cheng B, and Green P. (2019) Driving Style Classification Based on Driving Operational Pictures. IEEE Access. DOI: 10.1109/ACCESS.2019.2926494
  • Liao Y, Li G, Li S, Cheng GB, and Green P. (2018) Understanding driver response patterns to mental workload increase in typical driving scenarios. IEEE Access 6(1): 35890-35900. DOI: 10.1109/ACCESS.2018.2851309.
  • Lin BTW (2017) Book Review: Human Factors in Automotive Engineering and Technology. Ergonomics in Design, 25(2), 26-27.
  • Lin BTW, Kang T-P, and Green P. (2016) Drivers’ Responses to Augmented Reality Warnings for Crash Scenarios at Urban Signalized Intersections (technical report UMTRI -2017-06), Ann Arbor, MI: University of Michigan Transportation Research Institute.
  • Lin BTW, Kang T-P, Green P, and Jeong H. (2016) Analysis and Modeling of Drivers’ Responses at Urban Signalized Intersections (Report No. UMTRI-2016-13), Ann Arbor, MI: University of Michigan Transportation Research Institute (UMTRI)
  • Molnar LJ, Pradhan AK, Eby DW, Ryan L, St. Louis RM, Zakrajsek J, Ross B, Lin BT, Liang C, Zalewski B, and Zhang L. (2017) Age-Related Differences in Driver Behavior Associated with Automated Vehicles and the Transfer of Control between Automated and Manual Control: A Simulator Evaluation. Report No. UMTRI-2017-4. Ann Arbor, MI: University of Michigan Transportation Research Institute.
  • Molnar LJ, Ryan LH, Pradhan AK, Eby DW, St. Louis RM, and Zakrajsek J. (2018) Understanding trust and acceptance of automated vehicles: An exploratory simulator study of transfer of control between automated and manual driving. Transportation Research Part F: Psychology and Behaviour, 58: 319-328. DOI: 10.1016/j.trf.2018.06.004
  • Reagan IJ, Brumbelow ML, Flannagan MJ, and Sullivan JM. (2017) High beam headlamp use rates: Effects of rurality, proximity of other traffic, and roadway curvature. Traffic Injury Prevention 18(7):716-723. DOI: 10.1080/15389588.2016.1228921
  • Reed MP, Ebert SM, Jones MLH, and Park B-KD. (2019) Comparison Across Vehicles of Passenger Head Kinematics in Abrupt Vehicle Maneuvers. Traffic Injury Prevention. 20(sup2):S128-S132, DOI: 10.1080/15389588.2019.1689566
  • Shen J, Li G, Yan W, Tao W, Xu G, Diao D, and Green P. (2018) Nighttime driving safety improvement via image enhancement for driver face detection. IEEE Access 6:45625-45634. DOI: 10.1109/ACCESS.2018.2864629
  • Sullivan JM, Flannagan MJ, Pradhan AK, and Bao S. (2016) Literature Review of Behavioral Adaptation to Advanced Driver Assistance Systems. Washington, D.C.: AAA Foundation for Traffic Safety.
  • Tan V, Elliott M, and Flannagan CA. (2017) Development of a real-time prediction model of driver behavior at intersections using kinematic time series data, Accident Analysis & Prevention 106:428-436. DOI: 10.1016/j.aap.2017.07.003
  • Tan V, Flannagan C, and Elliott M. (2018) Predicting human-driving behavior to help driverless vehicles drive: random intercept Bayesian additive regression trees. Statistics and Its Interface 11(4): 557–572. DOI: 10.4310/SII.2018.v11.n4.a1
  • Xiong H, Narayanaswamy P, Bao S, Flannagan C, and Sayer J. (2016) How do drivers behave during indecision zone maneuvers? Accident Analysis and Prevention 96:274-279. DOI:10.1016/j.aap.2015.04.023.