Traffic21/Mobility21 University Transportation Center Deployment Partner Consortium Symposium
November 3, 2022
The Traffic21/Mobility21 Deployment Partner Consortium is engaged for identifying real-world transportation needs, research project development and deployment, technology licensing and commercialization, student recruitment for jobs and internships, class and capstone projects.
Putting our research, development and deployment approach into action – the annual deployment partner symposium provides an opportunity for interaction and discussion among researchers, students and deployment partners.
Over 130 attendees from academia, community, government, and industry participated in the event.
Thursday, November 3, 2022
Welcome & Program Updates – Presentation
Panel: Driving the Economic Engine of the Region – Presentations
- Scott Andes, Program Lead $1 Billion Build Back Better Regional Competition, U.S. Economic Development Administration
- Richard Ezike, Program Communications Specialist, Joint Office of Energy and Transportation
- Ellie Ezzell, Economic Development Specialist, RIDC of Southwestern PA
- Karen Lightman, Executive Director, Metro21 – Moderator
Lunch & Keynote Speaker – Presentation
Carl Andersen, Technical Director, Operations Research and Development, FHWA, Turner Fairbank
Panel: UTC Impacts from the Partners’ Perspectives – Presentations
- Rajeev Chhajer, Group Leader – Connected Technologies Research / Associate Director – 99P Labs, Honda
- Kelly Maurer, Director of Public Works, Cranberry Township
- Ben Schmidt, Global Chief Technology Officer, RoadBotics by Michelin
- Stan Caldwell, Executive Director, Mobility21 – Moderator
UTC Research/Education Showcase & Networking Reception
- Analysis of the Potential for Micromobility to Replace Short Car Trips in Urban Areas, And Impacts on Congestion – Corey Harper and student, Zhufeng Fan (CMU): Transportation is a basic social and economic need but those mobility options conceived a generation ago may not be economically or environmentally sustainable with rising urban populations. According to the U.S. Department of Transportation (USDOT), the average U.S. household produces about 9.5 trips a day. About half of these trips are within three miles, but fewer than 2 percent of those trips are made by bicycle. Private vehicles like cars, pick-up trucks, and SUVs, account for almost 50 percent short distance trips (i.e., trips within 3 miles), in most U.S. metro areas. As a result, commuters are spending increased amounts of time in congestion, which has associated costs such as wasted fuel and emissions. Micromobility (defined as shared bikes, e-bikes and e-scooters) represents a significant opportunity to replace short distance trips made by personally owned vehicles (POVs) and provide first-and last-mile solutions for underserved public transit riders. The purpose of this research is to estimate the number of short distance POV trips that could be replaced by micromobility options and the resulting environmental benefits, and to develop policy recommendations that could assist policymakers in better understanding where the greatest opportunities for expanding active transportation exist.
- BusEdge – Christoph Mertz and student, Anurag Ghosh (CMU): The BusEdge platform is an edge-cloud system to analyze visual data stream freshly-captured from the transit buses in a scalable and responsive manner. It includes components that execute on the bus (the edge) and on a central server (the cloud) for different kinds of real-time and non-real time road scene understanding efficiently. This data stream and the processing platform enables us to perform city scale spatio-temporal analysis. For example, we can detect which garbage cans are full and need intervention from the agency. Furthermore, we are able to detect and track long term changes in the built environment, we can detect construction zones and changes in sidewalk and inform HD map updates for autonomous vehicles.
- Can Ridesharing Help the Disadvantaged Get Moving – Lee Bransetter, Beibei Li and student, Seth Chizeck (CMU): Research undertaken by co-PIs Lee Branstetter and Beibei Li has used randomized controlled trials to measure the impact of access to smartphone-enabled new transportation options, including ridehailing, on the geographic and socioeconomic mobility of low-income residents of the Pittsburgh region. Research team members will describe two projects and results to date.
- CMU Transportation Club – Maggie Harger (CMU): The CMU Transportation Club aims to connect students to opportunities for networking, career development, research and education in the transportation sector. During their poster session, they will be presenting on past activities the club has hosted for students interested in the field of transportation. Members of the club will also be in attendance to share their experience and how they have developed professionally as a result of the club.
- Controlled Deployment of Analytical Solutions for Essential Transportation Services in Low-Income Neighborhoods – Peter Zhang (CMU): Heritage Community Transportation (HCT) provides essential transportation service in low-income neighborhoods in east Pittsburgh. We are collaborating with HCT for another year (July 2022 – June 2023) to fully roll out the deployment in a controlled way, which will help evaluate technical solutions on the ground and adjust quickly to ensure the overall success and continuity of service change. This project also provides opportunities for faculty and students to learn and conduct research in an under-studied area.
- Creating and Integrating Solutions for the ‘Complete Trip’ – Stephen Smith (CMU): This project is developing a smartphone app that integrates capabilities for accessible routing, safe intersection crossing, real-time traveler-to infrastructure communication, and autonomous wheelchair navigation to provide ‘complete trip’ support for pedestrians with disabilities.
- Determining traffic volumes using video imagery obtained from transit buses in regular service – Mark McCord (The Ohio State University): Vehicles appearing in video imagery from cameras installed on transit buses for non-traffic purposes can be used to estimate traffic volumes over urban networks on a regular basis. A method to estimate traffic volumes from transit bus platforms is developed and applied on The Ohio State University roadway network on a regular basis. Vehicle miles traveled are monitored over time. Comparisons to road tube data and known changes in traffic by year and by time-of-day support the promise of the approach and demonstrate much better accuracy than results obtained when using data from a popular on-demand location-based-service data integrator.
- Dynamic Coupling Strategy for Interdependent Network Systems Against Cascading Failures – Carlee Joe-Wong, and student, I-Cheng Lin (CMU): In networked systems, initial failures at only a small part of the network may trigger a sequential failure process called cascading failures. The vulnerability is further exacerbated in modern systems that consist of multiple networks. We study the robustness of such systems against cascading failures and develop a dynamic coupling coefficient strategy that adjusts the portion of load being redistributed internally and externally during the cascading failure process. We show our step-wise optimization (SWO) strategy significantly improves the robustness compared to prior work that mainly focuses on static coupling coefficients. This is shown through extensive simulations of different network topologies.
- F1tenth Demo – Rahul Mangharam and student, Ahmad Amine (University of Pennsylvania): We will be showcasing F1tenth as an educational, research, and development platform. The F1TENTH Autonomous Vehicle System is a powerful and versatile open-source platform for autonomous systems research and education. We will be demonstrating the use of this platform through a sample race where two cars will be racing head-to-head autonomously. https://f1tenth.org/
- Low Dimensional Representation and Analysis of Traffic Mobility – Vijayakumar Bhagavatula and student, Dereje Shenkut (CMU): A deeper understanding of mobility patterns enables a better design of vehicular networks. One type of relevant traffic data is the set of vehicle mobility traces from a fleet of taxis. Previous works in this area were mainly conducted by doing analysis in the spatio-temporal domain. However, using spatio-temporal analysis for high-volume and high-dimensional transportation data makes it difficult to discern underlying patterns as the representation is noisy and garbled. To solve this problem, spectral analysis can be used to reduce the dimensionality of the mobility data. This technique helps find the existence of a stable, low-dimensional spectral structure (which may not be clearly visible in spatio-temporal analysis). The proposed spectral analysis approach enables the low-dimensional representation of transportation data, providing interpretable insights into urban mobility and potentially tackling practical problems like traffic anomaly detection.
- Mobility Data Analytics Center – Sean Qian and student, Bin Gui (CMU): Leverage multi-source emerging data to develop smart decision-making systems for public agencies and mobility service providers. https://mac.heinz.cmu.edu/
- Safety-Cost Aware Inspection Strategy for Commercial Vehicle Fleets – Pingbo Tang and student, Ying Shi (CMU): This project intends to enhance the safety of fleets of commercial trucks and trailers without compromising mobility and operating costs. Various agencies inspected safety components, such as tires, brakes, and lights through planned or roadside inspections. Targeting these inspections toward safety-critical vehicles and components can ensure safety without losing mobility. We partner with two Pennsylvania firms to create vehicle deterioration digital twins that use inspection histories to guide effective telematics for predictive operations of truck fleets. https://sites.google.com/andrew.cmu.edu/trsafety/home
- Spectrum sharing for C-V2X – Jon Peha and student, Bodong Shang (CMU): Cellular vehicle-to-everything (C-V2X) allows vehicles to communicate with each other and roadside infrastructure. Currently, the amount of spectrum available for C-V2X is 30 MHz. However, there are use cases that require more bandwidth to provide the needed data rate, reliability, and/or latency. One way to meet those needs is for C-V2X devices to also use the adjacent unlicensed band, but with today’s technology, this would yield inadequate performance. This project enables C-V2X use of unlicensed bands by designing novel spectrum-sharing algorithms that improve performance for both C-V2X and Wi-Fi. A system-level simulator was developed to evaluate the protocol designs.
- The Potential of Smart Curbspace to Reduce Congestion and Fuel Consumption – Jeremy Michalek, Constantine Samaras, Kate Whitefood and students, Aaron Burns and Connor Forsythe (CMU): We are exploring the benefits of a new smart cities technology concept called Smart Curbspace which is similar to an Airbnb reservation system for vehicles and curbside parking spaces. Our goal is to optimally schedule and shift parking reservation requests such that we can reduce the number of double parked vehicles which have negative impacts for the surrounding environment. In the first stage of our research we focused specifically on delivery vehicle arrivals and found that a Smart Curbspace system with several parking spaces can reduce double parking and associated vehicle delay and fuel consumption. Our subsequent research funded by Mobility21 expands our user set to include more curbspace users, e.g. ride hailing, passenger vehicles, etc., more parking spaces and a new optimization model to allow for scalability.
- Traffic21 Policy Papers – Stan Caldwell & Chris Hendrickson (CMU): Informed, research-driven policymaking is central to improving our transportation sector. Our expertise in this area helps guide civic leaders to harness the power of intelligent transportation systems, while managing the inherent risks and protecting constituents. Based in the rigorous, cutting-edge findings of CMU faculty and drawing upon Heinz College’s expertise in data-driven public policy, our policy papers are a roadmap for the future of transportation legislation. https://www.cmu.edu/traffic21/research-and-policy-papers/index.html
- TraffiQure Technologies – Sean Qian (CMU): A CMU technology spinoff firm, uses Artificial Intelligence and Machine Learning technologies to provide services or products that make effective decisions in transportation based on massive data. TraffiQure is actively working for federal and state agencies on addressing data needs for multi-modal transportation planning and operation. www.traffiqure.com
Photos from the event: