CETOC Research

Year 1 Research In Progress:

CETOC-23-R-01: Longitudinal Analysis of Transit's Land Use Multiplier in Three Regions

CETOC-23-R-02: Quantifying the Influences of Telecommuting on VMT and Transit Usage

CETOC-23-R-03: Examining the Role of Transit Investments on Opportunity Outcomes

CETOC-23-R-04: Understanding Transit User Experience and Expectations in Underserved Communities

CETOC-23-R-05: Using Machine Learning to Understand the Built Environment's Influence on 15-Minute Transit-Oriented Communities

CETOC-23-R-06: 50 Years of Trends in Station Areas Across the United States

CETOC-23-R-07: Shared Micromobility as a Last-Mile Complement to Public Transit

CETOC-23-R-08: Analyzing Transit-Based Evacuation Demand in Hurricanes

CETOC-23-R-09: Gentrification, Displacement, and GHG Emissions at Transit-Oriented Communities

CETOC-23-R-10: Is Transit-Oriented Development Affordable for Low- and Moderate-Income Households
(In Terms of Housing & Transportation)?

Year 2 Research In Progress:

CETOC-24-R-01: Mobility Hub Usage in West Palm Beach

CETOC-24-R-02: Re-examining TODs through the Lens of Disability and Care Responsibilities: How Street and Network Structure Perpetuate Inequity of Access and Opportunity

CETOC-24-R-03: Identifying TOD-Capable Locations Using D Variables: Flipping the Recipe on Making the TOD Cake

CETOC-24-R-04: Useful Transit: Bridging the Gap Between the Vision and Reality of Transit-Oriented Communities

CETOC-24-R-05: High-Resolution Measurement of Transit Riders’ Extreme Heat Exposure Across U.S. Cities

CETOC-24-R-06: Inventorying Bus Stop Amenities Across the United States Using Google Street View Images and Computer Vision

CETOC-24-R-07: A Deep Learning Approach for Detecting Built Environment in Transit-Oriented Developments

CETOC-24-R-08: Mobility and Accessibility of Transit-Dependent and Transportation-Disadvantaged (TD2) Population During Hurricanes

CETOC-24-R-09: Updating and Expanding the Nation’s Most Comprehensive Database of Household Travel Survey Data and Related Built Environmental Data

CETOC-24-R-10: Developing a Scalable Evaluation Framework and Dashboard for Implementing and Monitoring Equitable Transit-Oriented Communities

CETOC-24-R-11: New Transit, Bike Infrastructure, and Green Space: Do They Have a Multiplying Effect on Gentrification and Displacement?

CETOC-24-R-12: Transit-Oriented Development (TOD) Formation along Bus Rapid Transit (BRT) Lines: Database Development, Analysis, and Identification of High-Impact Policy, Design, and Service Characteristics

CETOC-24-R-13: What Makes Affordable Housing Affordable: Mechanisms Used to Produce Affordable Housing Near Transit in the US

See "Education & Outreach" for our Year 2 Funded Education Projects

CETOC-Funded Peer-Reviewed Papers:

Dai, Y., Liu, L., Wang, K., Li, M., Yan, X. (2025). Using computer vision and street view images to assess bus stop amenities. Computers, Environment and Urban Systems, 117, 102254.

Huang, E., Yin, Z., Broaddus, A., Yan, X. (2024). Transit and shared e-scooter integration: Travel behavior insights from Los Angeles and Washington D.C. Travel Behavior and Society, 34, 100663.

Lyons, T., Ewing, R., Tian, G. (2025). Coverage vs frequency: Is spatial coverage or temporal frequency more impactful on transit ridership? Journal of Transport Geography, 122, 104058.

Qian, Y., Polimetla, T., Sanchez, T., Yan, X. (2025). How do transportation professionals perceive the impacts of AI applications in transportation? A latent class cluster analysis. Transportation.

Su, L., Yan, X., & Zhao, X. (2024). Spatial equity of micromobility systems: A comparison of shared E-scooters and docked bikeshare in Washington DC. Transport Policy, 145, 25-36.

Tian, G., & Danton, B. (2024). Studying Understudied Populations’ Travel Behaviors with a Machine Learning Approach: A Focus on Hispanic and Latinx Households. Journal of Planning Education and Research.

Tian, G., Danton, B. Ewing, R. & Li, B. (2024). Varying influences of the built environment on household travel in the United States – An update with 36 diverse regions and machine learning. Cities, 155, 105490.

Tian, G., Danton, B., Li, B., Gopu, V., & Codjoe, J. A. (2024). Understanding household VMT generation: A comparative analysis with traditional statistical models and a machine-learning approach. Journal of Transport and Land Use, 17(1), 881–901.

Xu, Y., Ke, Q., Zhang, X., & Zhao, X. (2024). ICN: Interactive Convolutional Network for Forecasting Travel Demand of Shared Micromobility. GeoInformatica, 1-16.

Yin, Z., Rybarczyk, G., Zheng, A., Su, L., Sun, B. Yan, X. (2024). Shared micromobility as a first- and last-mile transit solution? Spatiotemporal insights from a novel dataset. Journal of Transport Geography, 114, 103778.

Zhang, X., Ke, Q., & Zhao, X. (2024). Travel Demand Forecasting: A Fair AI Approach. IEEE Transactions on Intelligent Transportation Systems, 25(10), 14611-14627.

Zhang, X., Zhou, Z., Xu, Y., & Zhao, X. (2024). Analyzing spatial heterogeneity of ridesourcing demand determinants using explainable machine learning. Journal of Transport Geography, 114, 103782.

Resources for PIs:

CETOC Year 3 Request for Proposals

CETOC Data Management Plan

CETOC Final Report Template