Leveraging text mining approach to explore research roadmap and future direction of wrong-way driving crash studies

Abstract

This study delves into the wrong-way driving (WWD) research performed in the United States over the past five decades. The study employs a text network to summarize and synthesize major themes, data sources, and collaboration efforts from 123 previously published WWD articles collected from various online databases. The analysis shows that the majority of WWD studies were conducted between 2014 and 2020. Throughout the entire period, the primary focuses have been on WWD countermeasures, freeways, exit ramps, contributing factors, driver behavior, and detection systems. Over time, there has been a noticeable shift in the key themes, whereby earlier studies focused on freeways and driver behaviors, while the latter delved into exit ramps, limited access, and contributing factors among others. Most studies were from Florida, Texas, Illinois, and Alabama, with little collaboration of researchers across the States, except for Alabama and Illinois. Further, across the four States, there observed variations in the interests. Studies from Texas and Florida were centered on WWD countermeasures, detection systems, and the use of connected vehicles, while Illinois and Alabama investigated WWD crashes on exit ramps and interchanges. Despite the development and deployment of various countermeasures, there is still a need to develop crash modification factors to examine the effectiveness of the countermeasures. Furthermore, as technology continues to advance, connected and autonomous technology is expected to play a significant role in mitigating the WWD problem. The findings are essential for transportation agencies to evaluate the mitigation efforts and direct resources toward the right course of action.