Understanding Crash Mechanisms and Selecting Interventions to Mitigate Real-Time Hazards on Urban Expressways

Abstract

The concept of dynamic black spot identification has matured substantially over the past decade. Rather than being confined to improving prediction accuracy, the concept has expanded to understanding the crash mechanism by using sensor data and devising real-time countermeasures. A major drawback of most studies has been considering a crash as a generic mechanism throughout the expressway. Consequently, proposed countermeasures, such as warning messages, variable speed limits, and ramp metering, have been independent of location. Some studies have incorporated ramps as variables, but little progress has been made in investigating variation in the crash mechanism or evaluating the effectiveness of countermeasures separately for the basic freeway segments and ramp vicinities. This paper divides expressways into five segments: basic freeway, upstream and downstream of exits, and entrance ramps. The paper uses high-resolution detector data and advanced ensemble learning methods such as random forest and classification and regression trees to identify important factors and understand the crash mechanism. The outcome advocates lane-based speed management to improve the safety of basic freeway segments. Although ramp flows were found to contribute to crashes in ramp vicinities, the flows caused little concern for basic freeway segments. The findings will be valuable for researchers in predicting real-time crashes, investigating crash mechanisms, and designing proactive countermeasures.