Analysis of volatility in driving regimes extracted from basic safety messages transmitted between connected vehicles
DOI: 10.1016/j.trc.2017.08.004
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Summary
This study addresses the need for a fundamental understanding of instantaneous driving decisions to improve hazard anticipation and notification systems in connected vehicle environments. Specifically, it investigates "driving volatility," defined as the extent of speed variations and extreme longitudinal maneuvers like hard acceleration or braking. The research aims to characterize distinct, unobserved driving regimes within typical driving cycles, quantify the volatility associated with each regime, and identify the correlates that influence regime switching. This work is motivated by the potential of connected vehicle technologies to enhance safety, energy efficiency, and emissions by providing real-time alerts based on a deeper understanding of driver behavior relative to local traffic states. The researchers utilized Basic Safety Message (BSM) data from the Safety Pilot Model Deployment in Ann Arbor, Michigan. The dataset comprised nearly 1.4 million records from 184 trips undertaken by 71 instrumented vehicles equipped with vehicle-to-vehicle and vehicle-to-infrastructure communication systems. A detailed analysis focused on 43 randomly selected trips, totaling 714,340 BSM records. The methodology employed two- and three-regime Dynamic Markov Switching models to estimate instantaneous driving decisions. These models treated driving behavior as a time-series process switching between unobserved regimes, allowing for abrupt changes in intercepts and variances. The analysis mapped these regimes to instantaneous driving contexts, such as the number of surrounding objects and distance to the closest object, to determine how local traffic states correlate with specific driving behaviors. The results identified acceleration and deceleration as two distinct regimes in the two-regime model, revealing that drivers decelerate at higher rates than they accelerate and that braking is significantly more volatile than acceleration. The more generic three-regime model specification identified high-rate acceleration, high-rate deceleration, and cruise/constant speed as the three distinct regimes characterizing a typical driving cycle. The study found that, on average, drivers decelerate at a higher rate than they accelerate when in high-rate regimes. Furthermore, instantaneous driving decisions were found to be more volatile in both high-rate acceleration and high-rate deceleration regimes compared to the cruise/constant regime. The models successfully mapped these regime changes to combinations of local traffic states surrounding the vehicle. The significance of this study lies in its contribution to the analysis of short-term driving volatility and the probabilistic mapping of driving regime changes to local traffic conditions. By distinguishing normal from anomalous driving and characterizing the volatility of specific maneuvers, the findings provide a basis for improved real-time alerts, warnings, and control assistance applications. The use of rigorous dynamic Markov switching models on large-scale, real-world connected vehicle data offers a novel approach to understanding microscopic driving behavior, which is critical for developing effective cooperative intelligent transportation systems and enhancing transportation safety.
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-06-19 |
| archive | success | unpaywall | — | — | 2 | 2026-06-26 |
| extract | success | pdftotext | — | — | 2 | 2026-06-26 |
| clean | success | clean | — | — | 1 | 2026-06-26 |
| chunk | success | chunk | — | — | 1 | 2026-06-26 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-06-26 |
| enrich | success | semantic_scholar | — | — | 4 | 2026-06-26 |
| promote | success | — | — | — | 1 | 2026-06-19 |
| summarize | success | llm | qwen3.6-27b-prismaquant | summ-v5 | 1 | 2026-06-26 |
| tag | success | vector_similarity | — | — | 6 | 2026-06-26 |
| verify | success | — | — | — | 1 | 2026-06-26 |
Summary generated by qwen3.6-27b-prismaquant on 2026-06-26; verification: verified.
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