ABSTRACT
Public transportation provides vital connectivity to people with disabilities, facilitating access to work, education, and health services. While modern navigation applications provide a suite of information about transit options—including real-time updates about bus or train arrivals—they lack data about the accessibility of the transit stops themselves. Bus stop features such as seatings, shelters, and landing areas are critical, but few cities provide this information. In this demo paper, we introduce BusStopCV, a Human+AI web prototype for scalably collecting data on bus stop features using real-time computer vision and human labeling. We describe BusStopCV’s design, custom training with the YOLOv8 model, and an evaluation of 100 randomly selected bus stops in Seattle, WA. Our findings demonstrate the potential of BusStopCV and highlight opportunities for future work.
- 2016. Stations & Stops. https://nacto.org/publication/transit-street-design-guide/stations-stops/Google Scholar
- Marc A. Adams, Christine B. Phillips, Akshar Patel, and Ariane Middel. 2022. Training Computers to See the Built Environment Related to Physical Activity: Detection of Microscale Walkability Features Using Computer Vision. International Journal of Environmental Research and Public Health 19, 8 (April 2022), 4548. https://doi.org/10.3390/ijerph19084548Google ScholarCross Ref
- Saleema Amershi, Dan Weld, Mihaela Vorvoreanu, Adam Fourney, Besmira Nushi, Penny Collisson, Jina Suh, Shamsi Iqbal, Paul N. Bennett, Kori Inkpen, Jaime Teevan, Ruth Kikin-Gil, and Eric Horvitz. 2019. Guidelines for Human-AI Interaction. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, Glasgow Scotland Uk, 1–13. https://doi.org/10.1145/3290605.3300233Google ScholarDigital Library
- Apple. 2023. Maps - Apple. https://www.apple.com/maps/Google Scholar
- The MARTA Army. [n. d.]. Operation Bus Stop Census. https://www.martaarmy.org/stop-censusGoogle Scholar
- Saurav Dev Bhatta and Matthew P. Drennan. 2003. The economic benefits of public investment in transportation: A review of recent literature. Journal of Planning Education and Research 22, 3 (2003), 288–296. ISBN: 0739-456X Publisher: Sage Publications Thousand Oaks, CA.Google ScholarCross Ref
- Michael Duan, Shosuke Kiami, Logan Milandin, Johnson Kuang, Michael Saugstad, Maryam Hosseini, and Jon E. Froehlich. 2022. Scaling Crowd+AI Sidewalk Accessibility Assessments: Initial Experiments Examining Label Quality and Cross-city Training on Performance. In The 24th International ACM SIGACCESS Conference on Computers and Accessibility. ACM, Athens Greece, 1–5. https://doi.org/10.1145/3517428.3550381Google ScholarDigital Library
- Google. 2023. Google Maps. https://www.google.com/mapsGoogle Scholar
- Andrea Hamre and Ralph Buehler. 2014. Commuter Mode Choice and Free Car Parking, Public Transportation Benefits, Showers/Lockers, and Bike Parking at Work: Evidence from the Washington, DC Region. Journal of Public Transportation 17, 2 (June 2014), 67–91. https://doi.org/10.5038/2375-0901.17.2.4Google ScholarCross Ref
- Kotaro Hara, Shiri Azenkot, Megan Campbell, Cynthia L. Bennett, Vicki Le, Sean Pannella, Robert Moore, Kelly Minckler, Rochelle H. Ng, and Jon E. Froehlich. 2015. Improving Public Transit Accessibility for Blind Riders by Crowdsourcing Bus Stop Landmark Locations with Google Street View: An Extended Analysis. ACM Transactions on Accessible Computing 6, 2 (March 2015), 1–23. https://doi.org/10.1145/2717513Google ScholarDigital Library
- Kotaro Hara, Vicki Le, and Jon Froehlich. 2013. Combining crowdsourcing and google street view to identify street-level accessibility problems. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems(CHI ’13). Association for Computing Machinery, New York, NY, USA, 631–640. https://doi.org/10.1145/2470654.2470744Google ScholarDigital Library
- Kotaro Hara, Jin Sun, Robert Moore, David Jacobs, and Jon Froehlich. 2014. Tohme: detecting curb ramps in google street view using crowdsourcing, computer vision, and machine learning. In Proceedings of the 27th annual ACM symposium on User interface software and technology(UIST ’14). Association for Computing Machinery, New York, NY, USA, 189–204. https://doi.org/10.1145/2642918.2647403Google ScholarDigital Library
- Maryam Hosseini, Mikey Saugstad, Fabio Miranda, Andres Sevtsuk, Claudio T. Silva, and Jon E. Froehlich. 2022. Towards Global-Scale Crowd+ AI Techniques to Map and Assess Sidewalks for People with Disabilities. arXiv preprint arXiv:2206.13677 (2022).Google Scholar
- Junehyung Jeon and Ayoung Woo. 2023. Deep learning analysis of street panorama images to evaluate the streetscape walkability of neighborhoods for subsidized families in Seoul, Korea. Landscape and Urban Planning 230 (Feb. 2023), 104631. https://doi.org/10.1016/j.landurbplan.2022.104631Google ScholarCross Ref
- Jiyun Lee, Donghyun Kim, and Jina Park. 2022. A Machine Learning and Computer Vision Study of the Environmental Characteristics of Streetscapes That Affect Pedestrian Satisfaction. Sustainability 14, 9 (Jan. 2022), 5730. https://doi.org/10.3390/su14095730 Number: 9 Publisher: Multidisciplinary Digital Publishing Institute.Google ScholarCross Ref
- Todd Litman. 2012. Evaluating public transportation health benefits. Victoria Transport Policy Institute Victoria, BC, Canada.Google Scholar
- Gerhard Neuhold, Tobias Ollmann, Samuel Rota Bulo, and Peter Kontschieder. 2017. The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes. In 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, Venice, 5000–5009. https://doi.org/10.1109/ICCV.2017.534Google ScholarCross Ref
- National Association of City Transportation Officials. [n. d.]. Stations & Stops | National Association of City Transportation Officials. https://nacto.org/publication/transit-street-design-guide/stations-stops/Google Scholar
- Roboflow. 2023. Roboflow: Give your software the power to see objects in images and video. https://roboflow.com/Google Scholar
- Manaswi Saha, Michael Saugstad, Hanuma Teja Maddali, Aileen Zeng, Ryan Holland, Steven Bower, Aditya Dash, Sage Chen, Anthony Li, Kotaro Hara, and Jon Froehlich. 2019. Project Sidewalk: A Web-based Crowdsourcing Tool for Collecting Sidewalk Accessibility Data At Scale. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems. ACM, Glasgow Scotland Uk, 1–14. https://doi.org/10.1145/3290605.3300292Google ScholarDigital Library
- Wayne Sarasua, Robert Awuah-Baffour, Bradley University, Mark Fawley, TRW, Carlton Byars, MARTA, Jeffery Orton, and Utah Transit Authority. 1997. Design and Development of a Bus Stop Inventory to Support an Intelligent Transportation System: The MARTA Experience. Journal of Public Transportation 1, 4 (Dec. 1997), 61–80. https://doi.org/10.5038/2375-0901.1.4.4Google ScholarCross Ref
- Ultralytics. 2023. Home - Ultralytics YOLOv8 Docs. https://docs.ultralytics.com/Google Scholar
- Ultralytics. 2023. Train. https://docs.ultralytics.com/modes/trainGoogle Scholar
- King County Washington. 2017. Transit Stops for King County Metro / transitstop point. https://gis-kingcounty.opendata.arcgis.com/datasets/kingcounty::transit-stops-for-king-county-metro-transitstop-point/aboutGoogle Scholar
- Galen Weld, Esther Jang, Anthony Li, Aileen Zeng, Kurtis Heimerl, and Jon E. Froehlich. 2019. Deep Learning for Automatically Detecting Sidewalk Accessibility Problems Using Streetscape Imagery. In The 21st International ACM SIGACCESS Conference on Computers and Accessibility. ACM, Pittsburgh PA USA, 196–209. https://doi.org/10.1145/3308561.3353798Google ScholarDigital Library
- Haiying Zhou, Kun-Mean Hou, Decheng Zuo, and Jian Li. 2012. Intelligent Urban Public Transportation for Accessibility Dedicated to People with Disabilities. Sensors 12, 8 (Aug. 2012), 10678–10692. https://doi.org/10.3390/s120810678 Number: 8 Publisher: Molecular Diversity Preservation International.Google ScholarCross Ref
Index Terms
- BusStopCV: A Real-time AI Assistant for Labeling Bus Stop Accessibility Features in Streetscape Imagery
Recommendations
On the Need of Trustworthy Sensing and Crowdsourcing for Urban Accessibility in Smart City
Special Issue on Connected CommunitiesMobility in urban environments is an undisputed key factor that can affect citizens’ well-being and quality of life. This is particularly relevant for those people with disabilities or with reduced mobility who have to face the presence of barriers in ...
A Feasibility Study of Using Google Street View and Computer Vision to Track the Evolution of Urban Accessibility
ASSETS '18: Proceedings of the 20th International ACM SIGACCESS Conference on Computers and AccessibilityPrevious work has explored scalable methods to collect data on the accessibility of the built environment by combining manual labeling, computer vision, and online map imagery. In this poster paper, we explore how to extend these methods to track the ...
Urban and Building Accessibility Diagnosis using `Accessibility App? in Smart Cities
SMARTGREENS 2017: Proceedings of the 6th International Conference on Smart Cities and Green ICT SystemsIn the context of economic and technological changes arised from globalization, cities face the challenge of conceiving models capable of combining both competitiveness and sustainable urban development. The increasing body of knowledge in the field of ...
Comments