Project: #367 Design and Demonstration of an Arterial-friendly Local Ramp Metering Control System Progress Report - Reporting Period Ending: March 31, 2023 Principal Investigator: Sean Qian Status: Active Start Date: July 1, 2021 End Date: May 31, 2023 Research Type: Advanced Grant Type: Research Grant Program: FAST Act - Mobility National (2016 - 2022) Grant Cycle: 2021 Mobility UTC Progress Report (Last Updated: April 8, 2023, 10:59 a.m.) % Project Completed to Date: 100 % Grant Award Expended: 100 % Match Expended & Document: 100 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: (100%) Develop control strategies for ramp metering and local signal synchronization, Completed 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. We have presented the results to MDOT three times since summer 2022 to disseminate this modeling results and help MDOT on real-world ramp metering deployment in their TSMO 1 system, as well as cost-benefit analysis. 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 An open-source software package for any general network with the implementation of ramp metering and local signal synchronization. This is shared on Github and open for public use. Outcomes New Partners n/a Issues n/a