Integrating safety into the fundamental relations of freeway traffic flows: A conflict-based safety assessment framework. (December 2021)

• Record Type: Journal Article
• Authors: Mohammadian, Saeed; Haque, Md. Mazharul; Zheng, Zuduo; Bhaskar, Ashish
• Is Part Of: Analytic methods in accident research. Volume 32(2021)
• Publication Date: 2021-12
• Subjects: Rear-end crash risk -- Traffic conflict -- Conflict-based safety assessment -- Macroscopic modeling -- Traffic flow modeling; Accidents -- Research -- Methodology -- Periodicals; Accidents -- Prevention -- Periodicals; 363.100721
• DOI: 10.1016/j.amar.2021.100187
• Languages: English
• ISSNs: 2213-6657

Abstract

Highlights: Measures the safety of macroscopic states from underlying car-following interactions. Assesses time spent in conflict (TSC) using various rear-end conflict measures. TSC based on the proportion of stopping distance (PSD) is flexibly linked with macroscopic traffic states. A hybrid framework combining probabilistic and machine learning models to estimate safety. A pathway for trade-offs between congestion management and rear-end crash prevention in freeways. Abstract: Numerous statistical and data-driven modeling frameworks have estimated rear-end crashes and crash-prone events from macroscopic traffic states which are at the heart of traffic flow modelling and control. However, existing frameworks focus on critical events and exclude a vast majority of safer interactions, which are essential information with respect to identifying the trade-offs between congestion management and rear-end crash prevention. This study proposes a flexible conflict-based framework to extract safety information from freeway macroscopic traffic state variables (i.e., speed and density) by utilizing the information from all underlying car-following interactions. Time spent in conflict ( TSC ) is introduced as the total time spent by all vehicles in rear-end conflicts based on a given conflict measure and a threshold to be determined flexibly. Using the NGSIM vehicle trajectory dataset, we show that the proportion of stopping distance ( PSD ) is more desirable than several event-based conflict measures (e.g., time to collision) for describing TSC based on macroscopic state variables. Besides, it is shown that PSD provides explicit safety information about the entire travel time for each macroscopic state because it applies to all car-following interactions. This paper proposes a hybrid methodological framework combining probabilistic and machine learning models to develop the relationships between safety and macroscopic state variables within a flexible conflict-based safety assessment framework. At first, probabilistic and Machine learning models are separately developed to estimate PSD -based TSC using only macroscopic stte variables. Each approach is evaluated comprehensively against empirical observations using the NGSIM vehicle trajectory dataset. While the machine learning approach has better predictive accuracy for a fixed rear-end conflict threshold (i.e., PS D cr ), the probabilistic approach has a better explaining capability and captures TSC using flexible conflict thresholds. Utilizing the advantages of these two approaches, the proposed hybrid framework satisfactorily predicts TSC corresponding to PSD < P S D cr for a wide range of thresholds based only on macroscopic state variables. This paper provides an endogenous safety dimension to the fundamental relations of freeway traffic flows that can be utilized to balance freeway traffic flow efficiency and safety. For instance, control studies can utilize the proposed framework to minimize total travel time while also minimizing total time spent in conflict for crash-prone situations such as shockwaves and traffic oscillations.