The University of Tennessee, Knoxville

Transportation Engineering and Science Program

The principal idea behind pursuing multi-dimensional activities undertaken in this NSF project is to understand (and where possible reduce) "driving volatility" in instantaneous driving decisions and increase driving and locational stability. Consequently, driving volatility was operationalized at both the driver level, when drivers undertake trips, and at the network level. Driving volatility helps us understand instantaneous driving decisions in a connected vehicle environment.

The project team has expanded knowledge about instantaneous driving decisions by making conceptual, methodological, and empirical contributions. Methodologically, we introduce the applications of Dynamic Markov Switching models, Markov-Decision Processes, Inverse Reinforcement Learning, and hierarchical models to large-scale CAV data. Such techniques are contributing to the scientific analysis of real-world connected vehicle data, and extracting actionable information embedded in such data.

Initially the concept of driving volatility was operationalized at the trip level as the percentage of time a driver's acceleration or braking is more than the mean plus one or two standard deviations above the accelerations or braking of a large sample at that speed. In terms of developing new ideas, we have worked on driving volatility by understanding acceleration and braking regimes in instantaneous trip-level driving decisions. A rich CAV database was used to empirically explore regimes using Markov switching models and more broadly the microscopic decisions made by drivers. Questions answered include when these regimes change and quantifying the volatility associated with each regime. Another new idea is extending volatility to specific locations in a network, termed "location-based volatility." For location-based volatility, a unique database was assembled by integrating CAV data with crash and inventory data at intersections. This enabled associating location-based volatility with real-life crashes, generating new insights. Continuing analysis of high resolution connected vehicle data, the Markov Decision Process and Inverse Reinforcement Learning are applied to explain observed driving behavior. Personally optimal policies that maximize the expected sum of rewards for individual drivers are explored in terms of drivers making decisions about acceleration, braking, and maintaining speed.

The new ideas are meant to enhance technology development, by developing tools and algorithms that address research needs in connected and automated vehicle space. The study is focusing on science and engineering of new CAV systems and results-oriented technology development (Figure 1). In particular, by developing and applying new data analytic techniques, the study enables new discoveries and generation of actionable information for innovative real-life applications. Ultimately, the fundamental knowledge generated can provide a strong behavioral basis for CAV applications including hazard notification and real-time or predictive driver feedback. Overall, the basic methods for extracting useful information from CAV and related data are developed that will enhance safety, fuel efficiency and reduce emissions.

In this study, the dynamics of driving regimes extracted from Basic Safety Messages transmitted between connected vehicles are analyzed at a microscopic level. We generate new knowledge for connected vehicle technologies by explicitly investigating time-series instantaneous driving decisions of connected vehicle drivers, and mapping such decisions to instantaneous driving contexts. This analysis is important because driving decisions primarily depend on surrounding traffic states, and a detailed understanding of driving decisions can significantly help us with better anticipating hazardous situations and providing warnings and alerts to drivers. By introducing application of Markov-Switching models to large-scale CAV data, we characterize typical driving cycle into different regimes. The exhaustive analysis generates meaningful information about the existence of different regimes, when do the regimes change and how long they last, and key correlates associated with each regime. Finally, the study provides a new fundamental framework for making short-term instantaneous driving and regime predictions at specific instances in time, which is critical for developing connected vehicle hazard anticipation and notification systems.

A key idea explored in this study is to establish context-relevant alert, warning, and control assist thresholds based on extreme event information. A rigorous analytical methodology introduces speed varying thresholds to account for different driving situations and contexts. The study generates new knowledge by making sense of high-frequency geo-referenced connected vehicle data, and extracts, through data analytic techniques, critical information about extreme events from connected vehicle data.

From a driver behavior perspective, an in-depth understanding of drivers' policies related to instantaneous driving decisions made on different roadway functional classifications can help improve the effectiveness of transportation systems. As such, by examining trip-level microscopic instantaneous driving decisions, we study and analyze drivers' personally optimal policies on a specific trip. Given the availability of large-scale high-resolution connected vehicle data, instantaneous driving decisions are treated as realization of an optimal driver policy in a Markov Decision Process framework. The proposed framework allows defining the progression of different driving states over time in terms of objects surrounding a vehicle. Different actions (i.e. acceleration, braking, or maintaining constant speed) can be taken by drivers, and a value is derived for each action, which can be quantified in terms of accumulated discounted rewards (Figure 6). Personally optimal policies are inferred from data when the expected sum of rewards is maximized for individual drivers, given the progression of different states. To the best of our knowledge, the Markov Decision Process and inverse reinforcement techniques have not been applied to naturalistic CV data to help extract human drivers' personally optimal policies, with the aim of answering fundamental questions regarding individual driver decision mechanisms. In a broader sense, the study outcomes will eventually assist drivers in making more informed and calmer driving decisions, helping to improve the effectiveness of the transportation system.

As a proactive safety measure and a leading indicator of safety, we expand the concept of volatility to specific locations by quantifying variability in instantaneous driving decisions at intersections. Traditionally, evaluation of intersection safety has been largely reactive, based on historical crash frequency data. The developed conceptual framework uses emerging CAV data and historical crash data for proactive identification of intersections with high levels of variability in instantaneous driving behaviors prior to the occurrence of crashes. As an empirical contribution, significant efforts went into assembling a unique database that integrates intersection crash and inventory data with more than 65 million real-world Basic Safety Messages transmitted between connected vehicles and roadside units. Such a unique empirical database facilitates rigorous analysis for making new discoveries. Additionally, the techniques used to extract useful information from integrated CAV, crash, and inventory data provide useful knowledge for enhancing proactive intersection safety.

Overall, by studying driving volatility from different perspectives, the team is creating a new analytical framework to extract useful information from raw CAV data with the purpose of enhancing safety and fuel consumption efficiency. As the project progresses further, apart from the new knowledge generated regarding instantaneous driving volatility and key associated factors, newly developed algorithms will generate substantial new knowledge based on CAV data science.


The University of Tennessee, Knoxville. Big Orange. Big Ideas.

Knoxville, Tennessee 37996 | 865-974-1000
The flagship campus of the University of Tennessee System