Project: #318 Determining segment and network traffic volumes from video data obtained from transit buses in regular service: Developments and evaluation of approaches for ongoing use across urban networks Progress Report - Reporting Period Ending: Sept. 30, 2021 Principal Investigator: Mark McCord Status: Active Start Date: June 1, 2020 End Date: June 30, 2022 Research Type: Advanced Grant Type: Research Grant Program: FAST Act - Mobility National (2016 - 2022) Grant Cycle: Mobility21 - The Ohio State University Progress Report (Last Updated: Oct. 6, 2021, 2:12 p.m.) % Project Completed to Date: 40 % Grant Award Expended: 75 % Match Expended & Document: 75 USDOT Requirements Accomplishments Goals and Objectives • Refine approaches to determine traffic volumes and related network measures in urban areas from video sensors mounted on transit buses in operational service • Demonstrate performance of the method • Motivate practical use of the method and begin applications • Expand method to an approach for ongoing monitoring of traffic measures Accomplishments • Three new undergraduate students were recruited and trained in vehicle digitization by an existing undergraduate student; a new graduate student was brought up to speed on the project to replace the previous graduate student who graduated • Additional OSU CABS bus-based video imagery was collected on two separate days (6/24/2021 and 7/15/2021); these dates were selected to eventually allow volume comparisons with volumes from videos obtained in 2020 • Processing of video imagery into volume estimates was completed for the large data collection effort conducted in November 2020 and begun on other previously obtained video imagery • Alternative approaches to aggregating multiple volume estimates obtained from data on individual bus passes were developed and empirically evaluated; evaluation results demonstrate systematically better performance from the newly developed approaches • Efforts continued to develop approaches that yield estimates of uncertainty distributions of traffic volumes estimated from bus-based video data, and an approach was developed to evaluate the reasonableness of the approach; empirical results were conducted that indicate the promise of this approach • The quality of video-based estimates determined when aggregating multiple bus passes was seen to empirically improve (become closer to volumes obtained from road tubes or manual data collections) when video-based volumes on individual bus passes greater than an estimate of road capacity were excluded from aggregation • Additional comparisons were conducted that support the hypothesis that “average day” volume estimates would be improved with ongoing monitoring from buses in regular transit service • Empirical comparisons were begun to investigate the ability to detect spatial and time-of-day variability in a network of segments using video obtained from transit buses in regular operation • Traffic volume data were obtained from a vendor of traffic information through an agreement between the vendor and ODOT; the traffic volume data obtained are expected to allow comparisons with video-based volumes estimated and road-tube counts previously collected in conjunction with this project Training and professional development • Four graduate students and seven undergraduate students were involved with various efforts of this project • One undergraduate student continues to take responsibility for training students on various data processing task and supervising data processing assignments Dissemination • An M.S. thesis focused on this project was completed • A presentation was accepted for an October 2021 conference • Investigators meet regularly with administrators from The Ohio State University’s Transportation and Traffic Management (TTM) to update them on empirical results obtained; TTM provides the video imagery upon request of the investigators as a result of these meetings Plans for upcoming period • Process additional video imagery into digital location- and time-specific vehicle observations • Develop volume estimates from processed video imagery • Refine and validate approaches that produce point-estimates of video-based traffic volume estimates • Refine and validate approaches that produce estimates of uncertainty distributions of video-based traffic volumes • Validate benefits of estimating average day traffic volumes in a time-of-day period relative to estimating single day traffic volumes in the time-of-day period • Investigate the accuracy in estimating temporal and spatial patterns in traffic volumes from video imagery • Investigate the accuracy of estimating the spatial-temporal metric VMT using video-based volume estimates. • Compare video-based volume estimates with volume estimates provided by third party suppliers • Plan and implement coordinated video, manual, and road tube data collection • Continue interactions with OSU Transportation and Traffic Management (TTM) Impacts • Regular meetings with The Ohio State University (OSU) Transportation and Traffic Management (TTM) office continue to generate interest in the empirical traffic flow estimates being produced and motivate ongoing collaboration in project research and outreach efforts Other Physical collections: During the period video imagery was obtained for eventual processing Curricula: During the period, the motivation and implementation of this project were presented as a module in one undergraduate/graduate Civil Engineering class consisting of 28 students and in an undergraduate Civil Engineering class consisting of 66 students Outcomes New Partners We continued to work closely with Transportation and Traffic Management (TTM) at The Ohio State University (OSU). TTM oversees all OSU transportation operations, other than parking, and operates the Campus Area Bus Service (CABS), with a fleet of approximately fifty 40-foot transit buses serving close to 5 million passengers per year on fixed route scheduled services. Issues It appears that some charges made to matching funds should have been made to the award. We continue to work on correcting the cost-share related errors.