23 research outputs found
Harnessing Big Data for Characterizing Driving Volatility in Instantaneous Driving Decisions – Implications for Intelligent Transportation Systems
This dissertation focuses on combining connected vehicles data, naturalistic driving sensor and telematics data, and traditional transportation data to prospect opportunities for engineering smart and proactive transportation systems.The key idea behind the dissertation is to understand (and where possible reduce) “driving volatility” in instantaneous driving decisions and increase driving and locational stability. As a new measure of micro driving behaviors, the concept of “driving volatility” captures the extent of variations in driving, especially hard accelerations/braking, jerky maneuvers, and frequent switching between different driving regimes. The key motivation behind analyzing driving volatility is to help predict what drivers will do in the short term. Consequently, this dissertation develops a “volatility matrix” which takes a systems approach to operationalizing driving volatility at different levels, trip-based volatility, location-based volatility, event-based volatility, and driver-based volatility. At the trip-level, the dynamics of driving regimes extracted from Basic Safety Messages transmitted between connected vehicles are analyzed at a microscopic level, and where the interactions between microscopic driving decisions and ecosystem of mapped local traffic states in close proximity surrounding the host vehicle are characterized. Another new idea relates to extending driving volatility to specific network locations, termed as “location-based volatility”. A new methodology is proposed for combining emerging connected vehicles data with traditional transportation data (crash, traffic, road geometrics data, etc.) to identify roadway locations where traffic crashes are waiting to happen. The idea of event-based and driver-based volatility introduces the notion that volatility in longitudinal and lateral directions prior to involvement in safety critical events (crashes/near-crashes) can be a leading indicator of proactive safety.Overall, by studying driving volatility from different lenses, the dissertation contributes to the scientific analysis of real-world connected vehicles data, and to generate actionable knowledge relevant to the design of smart and intelligent transportation systems. The concept of driving volatility matrix provides a systems framework for characterizing the health of three fundamental elements of a transportation system: health of driver, environment, and the vehicle. The implications of the findings and potential applications to proactive network level screening, customized driver assist and control systems, driving performance monitoring are discussed in detail
Heterogeneous Ensemble Learning for Enhanced Crash Forecasts -- A Frequentest and Machine Learning based Stacking Framework
A variety of statistical and machine learning methods are used to model crash
frequency on specific roadways with machine learning methods generally having a
higher prediction accuracy. Recently, heterogeneous ensemble methods (HEM),
including stacking, have emerged as more accurate and robust intelligent
techniques and are often used to solve pattern recognition problems by
providing more reliable and accurate predictions. In this study, we apply one
of the key HEM methods, Stacking, to model crash frequency on five lane
undivided segments (5T) of urban and suburban arterials. The prediction
performance of Stacking is compared with parametric statistical models (Poisson
and negative binomial) and three state of the art machine learning techniques
(Decision tree, random forest, and gradient boosting), each of which is termed
as the base learner. By employing an optimal weight scheme to combine
individual base learners through stacking, the problem of biased predictions in
individual base-learners due to differences in specifications and prediction
accuracies is avoided. Data including crash, traffic, and roadway inventory
were collected and integrated from 2013 to 2017. The data are split into
training, validation, and testing datasets. Estimation results of statistical
models reveal that besides other factors, crashes increase with density (number
per mile) of different types of driveways. Comparison of out-of-sample
predictions of various models confirms the superiority of Stacking over the
alternative methods considered. From a practical standpoint, stacking can
enhance prediction accuracy (compared to using only one base learner with a
particular specification). When applied systemically, stacking can help
identify more appropriate countermeasures.Comment: This paper was presented at the 101st Transportation Research Board
Annual Meeting (TRBAM) by National Academy of Sciences in January 2022 in
Washington D.C. The paper is currently under review for potential publication
in an Impact Factor Journa
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Role of Multiagency Response and On-Scene Times in Large-Scale Traffic Incidents
Traffic incidents, often known as nonrecurring events, impose enormous economic and social costs. Compared with short-duration incidents, large-scale incidents can substantially disrupt traffic flow by blocking lanes on highways for long periods. A careful examination of large-scale traffic incidents and associated factors can assist with actionable largescale incident management strategies. For such an analysis, a unique and comprehensive 5-year incident database on East Tennessee roadways was assembled to conduct an in-depth investigation of large-scale incidents, especially focusing on operational responses, that is, response and on-scene times by various agencies. Incidents longer than 120 min and blocking at least one lane were considered large scale; the database contained 890 incidents, which was about 0.69% of all reported incidents. Rigorous fixed- and random-parameter, hazard-based duration models were estimated to account for the possibility of unobserved heterogeneity in large-scale incidents. The modeling results reveal significant heterogeneity in associations between operational responses and large-scale incident durations. A 30-min increase in response time for the first, second, and third (or more) highway response units translated to a 2.8%, 1.6%, and 4.2% increase in large-scale incident durations, respectively. In addition, longer response times for towing and highway patrol were significantly associated with longer incident durations. Given large-scale incidents, associated factors included vehicle fire, unscheduled roadwork, weekdays, afternoon peaks, and traffic volume. Notably, the associations were heterogeneous; that is, the direction could be positive in some cases and negative in others. Practical implications of the results for large-scale incident management are discussed