This project was funded by the National Institute for Transportation and Communities (NITC).
Multimodal transportation systems - Planning, Traffic safety, Transportation -- Data processing, Traffic monitoring, Travel time (Traffic engineering)
Multimodal transportation systems (e.g., walking, cycling, automobile, public transit, etc.) are effective in increasing people’s travel flexibility, reducing congestion, and improving safety. Therefore, it is critical to understand what factors would affect people’s mode choices. With advanced technology, such as connected and automated vehicles, cities are now facing a transition from traditional urban planning to developing smart cities. To support multimodal transportation management, this study will serve as a bridge to connect speed management strategies of conventional corridors to connected vehicle corridors. This study consists of three main components. In the first component, the impact of speed management strategies along traditional corridors was evaluated. To do so, a study corridor in Pima County, AZ, was selected, and using the data collected from smart sensors, the mobility and safety impact of a specific speed management strategy was explored. The results of this component showed a positive impact of SFS on both mobility and safety along traditional corridors. In the second component, the impacts of the specific speed management strategies, signal retiming and coordination, on transit signal priority (TSP) was studied. A connected corridor in Salt Lake City, UT, was selected as the study corridor. The results of this component showed TSP has great potential to reduce bus delays at intersections, improve transit operational reliability, and consequently increase transit ridership with improved service. Finally, in the third component, the feasibility of using controller event-based traffic data for estimating multimodal signal performance measures was investigated. Four intersections on Ina Rd., Pima County were selected as the study locations. The results of this component showed the proposed delay estimation method was able to capture and track the actual delay fluctuation during the day with an average of 10% of mean absolute error. The research outcomes of this study will help decision-makers understand the data and infrastructure needs in supporting future multimodal planning, operation, and safety tasks.
Wu, Y., Yang, X., Kothuri, S., Karimpour, A., Wang, Q., Anderson, J. Data-Driven Mobility Strategies for Multimodal Transportation. NITC-RR-1298. Portland, OR: Transportation Research and Education Center (TREC), 2021. https://dx.doi.org/10.15760/trec.262