How many are enough?: Investigating the effectiveness of multiple conflict indicators for crash frequency-by-severity estimation by automated traffic conflict analysis. (May 2022)

• Record Type: Journal Article
• Authors: Arun, Ashutosh; Haque, Md. Mazharul; Washington, Simon; Sayed, Tarek; Mannering, Fred
• Is Part Of: Transportation research. Volume 138(2022)
• Publication Date: 2022-05
• Subjects: Traffic conflict techniques -- Crash severity -- Crash-conflict relationship -- Peak over threshold -- Extreme value copulas -- Multivariate modelling; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2022.103653
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Multivariate Extreme value models based on Gumbel Copulas are used to estimate crash frequency-by-severity from traffic conflict indicators. Traffic conflict indicators are extracted from videos by applying an automated computer vision technique. The accuracy and precision of crash predictions are not proportional to the number of conflict indicators used in the extreme value models. Modified Time to Collision (MTTC) and Deceleration Rate to Avoid a Crash (DRAC) is the best combination of indicators for rear-end crash frequency estimation. A trivariate model with MTTC, DRAC and Delta-V efficiently estimates crash frequency-by-severity. Abstract: Traffic conflict techniques are a viable alternative to crash-based safety assessments and are particularly well suited to evaluating emerging technologies such as connected and automated vehicles for which crash data are sparsely available. Recently, the use of multiple traffic conflict indicators has become common in methodological studies, yet it is often difficult to determine which conflict indicators are appropriate given the application context, and the net benefit, in terms of improved crash prediction accuracy, of considering additional conflict indicators. Addressing these concerns, this study investigates the potential benefits of multiple conflict indicators for conflict-based crash estimation models by using a multivariate extreme value modeling framework (with Gumbel-Hougaard copulas) to estimate crash frequency by severity. The selected conflict indicators include Modified Time-To-Collision (MTTC), Deceleration Rate to Avoid a Collision (DRAC), Proportion of Stopping Distance (PSD) and expected post-collision change in velocity (Delta-V). The proposed framework was applied to estimate the total, severe (Maximum Abbreviated Injury Scale ≥ 3; MAIS3+), and non-severe (MAIS < 3) rear-end crash frequencies at three four-legged signalized intersections in Brisbane, Australia. Rear-end traffic conflicts were extracted from video data using state-of-the-art Computer Vision analytics. Results show that the prediction performance improvements are not necessarily proportional to the number of conflict indicators used in extreme value models. MTTC and DRAC, combined with the severity indicator Delta-V, were the most suitable predictors of rear-end crashes at signalized intersections. Results suggest that instead of adding more and more conflict indicators, careful selection of compatible conflict indicators (considering their functional differences and empirical correlations) is the best way to enhance the predictive performance of conflict-based models.