Abstract
Walk Bike Shaler, with the support of the Township of Shaler, will partner with CMU researchers to inform and contribute to the Better Boulevard initiative. CMU, providing urban design and computer vision expertise, will deploy cameras for visual data, develop computer vision algorithms to analyze data, and develop mobility/urban design recommendations from the data and responsive to community priorities. Shaler’s pattern of a long Main Street in a suburban context is a mobility/urban design case study relevant to numerous municipalities in Southwestern PA and nationally.
Description
Walk Bike Shaler, with the support Shaler Township civic and commercial leadership, has developed an ongoing initiative to build a “Better Boulevard” along its long Main Street, Mount Royal Boulevard, looking to incorporate mobility and urban design principles of Complete Streets and Complete Communities. Despite its status as Shaler's primary cultural, transit, and economic corridor, Mount Royal Blvd has safety and equity issues that impact its socioeconomic potential. Residents who rely on non-motorized transportation and public transit are faced with dangerous walking, biking, and transit stop conditions that disproportionately affect elders, people with disabilities, transit riders, school children, and low-income individuals.
Better Boulevard Analytics will provide CMU urban design and computer vision expertise, in support of the Better Boulevard initiative, deploying cameras for visual data, develop computer vision algorithms to analyze data. This work will contribute to data-based decision-making in developing mobility/urban design recommendations for a Complete Boulevard. It will build on and contribute to the work of Walk Bike Shaler and its partner, which has included study, analysis, presentations and feedback from community and Planning Commission sessions. Individuals supportive of this work include elected officials, business groups, property holders, regional organizations and neighboring communities.
The ongoing work provides the basis for the research, as decisions are made regarding camera deployment that will provide the most useful data and analysis for mobility and urban design recommendations. This work includes identifying different focus areas for the study, moving from north to south from the Hampton Handshake, to the Northern Plateau, to the Central Cultural District, to the Hilltop Shops, to the Southern Shopping District, and ending at the Etna Handshake. They have identified different uses, distinct mobility patterns, and variable concentrations of challenges and goals in these areas, offering a framework for the Better Boulevard Analytics project. Primary among these is the recognition that there are mobility challenges the length of the Boulevard, and also specific district ones. One critical finding is that priorities include: better connecting students from the Middle School to the Library and other assets in the Cultural District; improving pedestrian, bike, and other non-motorized connections between the Cultural District and the Hilltop Shops; and providing safer, more equitable connections for elderly and lower income residents living close to the Boulevard, but unable to access services without a car, throughout and especially in the Southern Shopping and Hilltop Shops areas.
In brief, community, business. and municipal leaders are seeking innovative mobility infrastructure solutions that manage local topographical ROW, and policy challenges to address unsafe and inequitable mobility conditions. The Better Boulevard Analytics Project will support evidence-based strategies that support public transit and non-motorized mobility, ultimately leading to a Better Boulevard, improving residents' safety and access to healthcare, cultural, food, economic, and municipal resources and services. In addition, it will inform models for similar land use and mobility conditions in SW Pennsylvania and nationally.
Timeline
Timeline:
• July 1, 2021 – August 31, 2021: Scoping Meeting with Partners and stakeholders, reviewing study area, study area priorities, and updating in response to response to ongoing “Better Boulevard” project. Begin process for intersection instrumentation. Site visits, equipment determination/evaluation, and stress testing Confirm siting for cameras.
• September 1, 2021 – October 31, 2021: Instrument street with cameras.
• November 1, 2021: Begin data collection and algorithm development.
• November 1, 2021 – January 31, 2022: Continue algorithm development and aggregate statistics.
• February 1, 2022 – February 29, 2022: Meet with stakeholders to present preliminary results and obtain feedback.
• March 1, 2022– May 31, 2022: Revise methods based on feedback
• May 1 - May 31, 2022; Meet with stakeholders to further report findings and recommendations.
• June 1, 2022 – June 30, 2022: Final Report
Strategic Description / RD&T
Deployment Plan
General
Video cameras will be installed on property owned by Shaler and local businesses along Mt Royal Blvd. Participating businesses will be identified by Shaler collaborators. Deployment locations will be determined through on-site visits and meetings with key stakeholders. Resources for physical installation and property access will be provided by Shaler. We have experience installing infrastructure cameras from past/ongoing deployments through projects with Heinz Endowments, Metro21, and Mobility21. We have ongoing industrial collaborations with Bosch, Intel, and Nvidia for hardware support, and Comcast for internet support.
Quarterly Deployment
Q1
• July 1, 2021 – August 31, 2021: Begin process for intersection instrumentation. Site visits, equipment determination/evaluation, and stress testing.
• Scoping Meeting with Partners and stakeholders, reviewing study area, study area priorities, and updating in response to ongoing Better Boulevard planning initiative. Confirm siting for cameras.
Q2
• September 1, 2021 – October 31, 2021: Instrument street with cameras.
• November 1, 2021: Begin data collection and algorithm development.
• November 1, 2021 – December 31, 2021: Continue algorithm development and aggregate statistics.
Q3
• January 1, 2022 – January 31, 2022; Continue algorithm development and aggregate statistics.
• February 1, 2022 – February 29, 2022: Meet with stakeholders to present preliminary results and obtain feedback.
• March 1, 2022– March 31, 2022: Revise methods based on feedback.
Q4
• April 1- May 31, 2022: Continue to revise methods based on feedback.
• May 1--May 31, 2022; Meet with stakeholders to further report findings and deployment recommendations.
• June 1, 2022 – June 30, 2022: Final Report.
Expected Outcomes/Impacts
Cameras will be deployed along the targeted area and computer vision algorithms will be developed to analyze the collected visual data. Results of the analyses will be aggregated and tabulated to identify mobility trends and problems, then used to inform urban planning recommendations. Any novel algorithms developed will be published along with experimental results. Algorithms will be thoroughly tested and analyzed with real data collected at the installation site. Datasets will be collected throughout the year in a variety of weather and lighting conditions. This unique dataset will be published on-line for use by researchers to advance the fields of computer vision, artificial intelligence, mobility and urban design and planning.
Results will be benchmarked on a concrete list of key stakeholders identified mission-critical tasks. Comprehensive testing will be performed in multiple lighting and weather conditions. The following will be delivered: 1) semi-annual and final reports, 2) database of collected (anonymized) visual data, 3) planning recommendations, 4) results published in top tier conferences and journals, 5) a project webpage
Expected Outputs
TRID
Individuals Involved
| Email |
Name |
Affiliation |
Role |
Position |
| rgastil@Andrew.cmu.edu |
Gastil, Ray |
Remaking Cities Institute |
PI |
Other |
| dnarapur@andrew.cmu.edu |
Narapureddy, Dinesh |
Carnegie Mellon University |
Other |
Student - PhD |
| gparmar@andrew.cmu.edu |
Parmar, Gaurav |
Carnegie Mellon University |
Other |
Student - PhD |
| squick@andrew.cmu.edu |
Quick, Stephen |
Carnegie Mellon University |
Co-PI |
Other |
| rtamburo@cmu.edu |
Tamburo, Robert |
Carnegie Mellon University |
Co-PI |
Other |
| kvuong@andrew.cmu.edu |
Vuong, Khiem |
Carnegie Mellon University |
Other |
Student - Masters |
Budget
Amount of UTC Funds Awarded
$167240.00
Total Project Budget (from all funding sources)
$334480.00
Documents
| Type |
Name |
Uploaded |
| Presentation |
Better_Boulevard_Analytics.presentation.pptx |
Dec. 17, 2020, 12:17 p.m. |
| Data Management Plan |
BetterBoulevard.Analytics.DataPlan.pdf |
Dec. 17, 2020, 3 p.m. |
| Progress Report |
372_Progress_Report_2021-09-30 |
Sept. 29, 2021, 6:18 p.m. |
| Data Management Plan |
DMP_updated.docx |
Jan. 10, 2022, 5:50 p.m. |
| Progress Report |
372_Progress_Report_2022-03-31 |
March 30, 2022, 11:11 a.m. |
| Progress Report |
372_Progress_Report_2022-09-30 |
Sept. 30, 2022, 5:23 a.m. |
| Final Report |
372_-_Final_Report.pdf |
Jan. 3, 2023, 9:06 a.m. |
Match Sources
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Partners
| Name |
Type |
| Walk BIke Shaler |
Deployment Partner Deployment Partner |
