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SHRP 2 L10: Project Summary, Results and Recommendations

SHRP 2 L10: Project Summary, Results and Recommendations. Work performed by: Virginia Tech Transportation Institute Center for Sustainable Mobility and SAIC Presented by: Hesham Rakha Professor, Charles E. Via Jr. Dept. of CEE at Virginia Tech Director, Center for Sustainable Mobility at VTTI.

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SHRP 2 L10: Project Summary, Results and Recommendations

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  1. SHRP 2 L10: Project Summary, Results and Recommendations Work performed by: Virginia Tech Transportation Institute Center for Sustainable Mobility and SAIC Presented by: Hesham Rakha Professor, Charles E. Via Jr. Dept. of CEE at Virginia Tech Director, Center for Sustainable Mobility at VTTI

  2. Presentation Outline • Study objective • Study approach • Datasets • Proposed approach for modeling travel time reliability • Study findings • Study recommendations

  3. Study Objective • Determine the feasibility of using in-vehicle video data to make inferences about driver behavior that would allow investigation of the relationship between observable driver behavior and non-recurring congestion to improve travel time reliability

  4. Approach Overview

  5. Dataset Findings • Key domestic and international studies where in-vehicle video cameras were used to collect data were investigated • A total of 16 datasets were initially identified • Studies ranged from 1997 to 2010 • A list of qualified data sets was identified for further data reduction and analysis • This list was composed of 7 studies • The studies were then rated based on several criteria

  6. Dataset Scoring

  7. Data Reduction: Overview100-Car Study • Dataset 7 Terabytes • Critical events identified using pre-defined trigger criteria values • Event video data were reviewed • 90-s epoch for each event (from 60 s prior) • Valid events were classified • non-conflict, proximity events, crash-relevant, near-crash, and crash • 69 crashes and 761 near-crashes identified

  8. Data Reduction: Event Triggers100-Car Study

  9. Data Reduction: Conflict Types100-Car Study

  10. Data Reduction: Weather Conditions100-Car Study • Clear weather • 78 percent of crash and near-crash events • Rainy conditions • 12 percent crashes and 8 percent near-crashes • Cloudy weather • 13 percent crashes and 9 percent near-crashes • Only one crash had snow as an associated factor

  11. Modeling Travel Time ReliabilityCurrent Methodologies • A set of Specific, Measurable, Achievable, Results-oriented, and Timely (SMART) performance measures are required • State-of-practice • 90th or 95th percentile travel times • Buffer index • Planning time index • Frequency with which congestion exceeds a threshold

  12. Modeling Travel Time ReliabilityEmpirical Observations

  13. Modeling Travel Time ReliabilityProposed Approach • Travel time variability can be modeled considering the underlying traffic states • Mixed distribution • Model parameters can be estimated using the Expectation Maximization algorithm Where: λ=(λ1, λ2,…, λn) ≡ vector of mixture coefficients θk=(θk1, θk2,…, θkI) ≡ vector of model parameters for the kth component dist.; fk(T|θk) ≡ density function for the kth component distribution

  14. Modeling Travel Time ReliabilityProposed Approach

  15. Modeling Travel Time ReliabilityProposed Approach

  16. Findings • It is feasible to identify driver behavior prior to near-crashes and crashes from video data • Naturalistic data can quantify impacts of crashes on traffic conditions when integrated with external data sources • Increased integration of naturalistic data with weather, construction, incident and traffic volume data is required

  17. Findings • The majority of crashes or near-crashes have the potential to be prevented if appropriate instrumentation is installed to issue warnings to drivers in a timely fashion • For example, in the 100-car study approximately 60% of the crash and near-crash events were associated with driver errors • Naturalistic driving data can be used to characterize typical levels of travel time variability • Understand causes for variability including differences in departure times and routes

  18. Findings Orange: Limited data coverage - Red: Trucks - Blue: Limited population coverage

  19. Recommendations • Better identify drivers in vehicle • A formal statement in the contract to make the contract signer the exclusive driver • A touch-screen device can be installed onboard to collect information before/after each trip • To identify reasons for change in departure time • Reminds driver that study is not naturalistic • Data collection system could run for an additional 10 minutes after the engine is turned off when an accident occurs

  20. Recommendations • To improve the linking of vehicle data with external data, it is ideal to standardize the format for time and location information • Additional analysis of existing data • Variability in driver departure times, trip travel times, and route choices • Additional analysis is required to study the impact of incidents on travel time distributions and travel time reliability

  21. Recommendations • The Strategic Highway Research Program S07 study will provide additional data • Larger sample size

  22. Questions ?

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