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Driving volatility captures the extent of speed variations when a vehicle isbeing driven. Extreme longitudinal variations signify hard acceleration orbraking. Warnings and alerts given to drivers can reduce such volatilitypotentially improving safety, energy use, and emissions. This study develops afundamental understanding of instantaneous driving decisions, needed for hazardanticipation and notification systems, and distinguishes normal from anomalousdriving. In this study, driving task is divided into distinct yet unobservedregimes. The research issue is to characterize and quantify these regimes intypical driving cycles and the associated volatility of each regime, explorewhen the regimes change and the key correlates associated with each regime.Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment inAnn Arbor, Michigan, two- and three-regime Dynamic Markov switching models areestimated for several trips undertaken on various roadway types. Whilethousands of instrumented vehicles with V2V and V2I communication systems arebeing tested, nearly 1.4 million records of BSMs, from 184 trips undertaken by71 instrumented vehicles are analyzed in this study. Then even more detailedanalysis of 43 randomly chosen trips (N = 714,340 BSM records) that wereundertaken on various roadway types is conducted. The results indicate thatacceleration and deceleration are two distinct regimes, and as compared toacceleration, drivers decelerate at higher rates, and braking is significantlymore volatile than acceleration. Different correlations of the two regimes withinstantaneous driving contexts are explored. With a more generic three-regimemodel specification, the results reveal high-rate acceleration, high-ratedeceleration, and cruise/constant as the three distinct regimes thatcharacterize a typical driving cycle. (Continued)

Analysis of Volatility in Driving Regimes Extracted from Basic Safety Messages Transmitted Between Connected Vehicles