Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles. (November 2017)

• Record Type: Journal Article
• Authors: Khattak, Asad J.; Wali, Behram
• Is Part Of: Transportation research. Volume 84(2017)
• Page Start: 48
• Page End: 73
• Publication Date: 2017-11
• Subjects: Connected vehicle -- Basic safety messages -- Instantaneous driving decisions -- Driving regimes -- Markov-switching dynamic regressions; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2017.08.004
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Microscopic analysis of driving volatility in real-world connected vehicle environment. Driving task is divided into distinct yet unobserved regimes. Quantify the regimes, associated volatility, and regime switching durations. Map time-series instantaneous driving volatility to surrounding driving contexts. Sophisticated two- and three-regime Dynamic Markov Switching models. 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.