Project: #367 Design and Demonstration of an Arterial-friendly Local Ramp Metering Control System Progress Report - Reporting Period Ending: March 30, 2022 Principal Investigator: Sean Qian Status: Active Start Date: July 1, 2021 End Date: June 30, 2022 Research Type: Advanced Grant Type: Research Grant Program: FAST Act - Mobility National (2016 - 2022) Grant Cycle: 2021 Mobility UTC Progress Report (Last Updated: March 16, 2022, 3:44 p.m.) % Project Completed to Date: 67 % Grant Award Expended: 67 % Match Expended & Document: 67 USDOT Requirements Accomplishments This research project addresses two problems for an integrated TSMO system: ahead-of-curve prediction and system-level signal and ramp metering coordination. We propose to develop theories, models and algorithms of machine learning to predict traffic patterns in real time being a typical recurrent pattern or non-recurrent pattern, and to optimize the timing plans for both ramp metering and street signals in the TSMO system. Prediction and operational strategies are intimately coupled. The prediction will be made by a machine that learns not only historical traffic patterns but also real-time data (possibly from multiple sources). Operational strategies are made and updated in real time to achieve management goals (e.g. minimization of total travel time) as a result of ahead-of-curve prediction of network impacts. In particular, the research will fuse multiple data sources related to highways and local street/intersections; develop an efficient network-level modeling framework enabled and validated by multi-source data; make real-time optimal signal plans and ramp metering plans; and finally quantify the network benefits of operational strategies to improve mobility/safety. Task 1 (100%): Identify and process (pre-covid) various data sources for in-depth data analytics and system Control. Completed by Aug 31, 2021 Task 2 (100%): literature review on coordinated ramp metering and arterial signal timing. Completed by July 31, 2021 Task 3 (100%): Develop a dynamic network model for the TSMO 1 system. Completed Dec 31, 2021 Task 4: (20%) Develop control strategies for ramp metering and local signal synchronization, to be completed by May 31, 2021 We have been actively collaborating with Morgan State University on this research effort, developing additional models on analyzing flooding impact to roads in the TSMO 1 area. The collaboration help train one phd student in MSU and two phd students at CMU. Impacts We worked closely with the Office of the Transportation Mobility and Operations (OTMO) (formerly the Office of CHART and ITS Development) at MDOT to implement this research results. We had three meetings with OTMO along with several consultant firms that work for OTMO. We also met with OTMO project managers, engineers and TSMO staff who provides feedback/comments for this quarter, to ensure the model development and testing are consistent with MDOT’s view, and the tasks are aligned with the partners’ needs. We plan to commercialize this research upon its completion through Sean Qian's spinoff firm TraffiQure LLC. Other n/a Outcomes New Partners n/a Issues n/a