Synthesis of Automated Multimodal Data Collection Techniques and Applications in the US

With an increase in multimodal fatalities and data-driven transportation funding, state agencies are required to justify their investments with robust quantitative data and measures. Thus, there is a critical need for accurate, reliable, and comprehensive information about multimodal travel movements to support the planning, design, and management of smart infrastructure as part of a larger regional surface transportation system. Irrespective of the application, collecting observational data for multiple modes is considered a challenging task due to the unorganized nature of multimodal traffic compared to vehicular traffic. In this study, the research team identifies the state-of-the-art and the state-of-the-practice multimodal data collection techniques used for safety analysis. Based on that, the research team conducts a nationwide outreach effort consisting of interviews with subject matter experts and Metropolitan Planning Organizations and Departments of Transportation employees. This paper summarizes the study development methodology, as well as questions and lessons learned from one-on-one interviews. Additionally, it includes a summary and conclusions regarding video analytics and nationwide best practices of multimodal data collection, analytics, and applications to safety and operations. The outcomes of this study and interviews aim to guide decision makers including the city of Austin regarding the deployment of detection technologies.