Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles |
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Affiliation: | 1. University of Tennessee, Knoxville, TN 37996, USA;2. Civil and Environmental Engineering Department, University of Tennessee, Knoxville, TN 37996, USA;1. Department of Civil and Environmental Engineering, The University of Tennessee, 311 John Tickle Building, Knoxville, TN 37996, United States;2. Department of Civil and Environmental Engineering, The University of Tennessee, 322 John Tickle Building, Knoxville, TN 37996, United States;3. Center for Transportation Research, The University of Tennessee, 309 Conference Center Building, Knoxville, TN 37996, United States;4. Department of Civil and Environmental Engineering, The University of Tennessee, 320 John Tickle Building, Knoxville, TN 37996, United States;1. Department of Civil & Environmental Engineering, The University of Tennessee, Knoxville, TN 37996, USA;2. Center for Transportation Research, The University of Tennessee, Knoxville, TN 37996, USA;1. State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 10084, China;2. Honda R&D Co. Ltd., Automobile R&D Center, Tochigi 321-3393, Japan;1. Department of Civil & Environmental Engineering, The University of Tennessee, United States;2. Department of Civil and Environmental Engineering & Senior Fellow, Howard H. Baker, Jr. Center for Public Policy, The University of Tennessee, United States;3. Travel Demand Modeler, Virginia Department of Transportation (VDOT), United States;1. Oak Ridge National Laboratory, Oak Ridge, 37830, TN, United States;2. Virginia Transportation Research Council, Charlottesville, VA, 22903, United States;3. University of Tennessee, Knoxville, TN, United States |
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Abstract: | Driving volatility captures the extent of speed variations when a vehicle is being driven. Extreme longitudinal variations signify hard acceleration or braking. Warnings and alerts given to drivers can reduce such volatility potentially improving safety, energy use, and emissions. This study develops a fundamental understanding of instantaneous driving decisions, needed for hazard anticipation and notification systems, and distinguishes normal from anomalous driving. In this study, driving task is divided into distinct yet unobserved regimes. The research issue is to characterize and quantify these regimes in typical driving cycles and the associated volatility of each regime, explore when the regimes change and the key correlates associated with each regime. Using Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan, two- and three-regime Dynamic Markov switching models are estimated for several trips undertaken on various roadway types. While thousands of instrumented vehicles with vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) communication systems are being tested, nearly 1.4 million records of BSMs, from 184 trips undertaken by 71 instrumented vehicles are analyzed in this study. Then even more detailed analysis of 43 randomly chosen trips (N = 714,340 BSM records) that were undertaken on various roadway types is conducted. The results indicate that acceleration and deceleration are two distinct regimes, and as compared to acceleration, drivers decelerate at higher rates, and braking is significantly more volatile than acceleration. Different correlations of the two regimes with instantaneous driving contexts are explored. With a more generic three-regime model specification, the results reveal high-rate acceleration, high-rate deceleration, and cruise/constant as the three distinct regimes that characterize a typical driving cycle. Moreover, given in a high-rate regime, drivers’ on-average tend to decelerate at a higher rate than their rate of acceleration. Importantly, compared to cruise/constant regime, drivers’ instantaneous driving decisions are more volatile both in “high-rate” acceleration as well as “high-rate” deceleration regime. The study contributes to analyzing volatility in short-term driving decisions, and how changes in driving regimes can be mapped to a combination of local traffic states surrounding the vehicle. |
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Keywords: | Connected vehicle Basic safety messages Instantaneous driving decisions Driving regimes Markov-switching dynamic regressions |
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