Journal of Transport and Land Use

Urban visual AI for travel behavior prediction: A comparative analysis of satellite and street view imagery

Jinghao Cao

New Jersey Institute of Technology

Yuebing Liang

Tsinghua University

Meiqing Li

University of Central Florida

Shenhao Wang

University of Florida

DOI: https://doi.org/10.5198/jtlu.2026.2873

Keywords: Travel behavior, AI, Computer vision, Satellite images, Street view images

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Abstract

The increasing availability of satellite and street-view images offers new opportunities for visual AI in commuting mode share analysis. However, few studies have systematically examined how different visual representations of the built environment encode census tract-level commuting mode shares. This study evaluates the predictive and interpretative performance of satellite and street-view imagery for modeling census tract-level commuting mode shares in Florida, USA. We apply semantic segmentation to extract land-use and streetscape features from both imagery types and integrate them with socio-demographic data in multiple machine learning models. We further apply SHAP (SHapley Additive exPlanations) to interpret how visual features contribute to model predictions across commuting modes. Results show that satellite imagery generally outperforms street-view imagery in terms of out-of-sample R², although the magnitude of improvement varies across models, travel modes, and satellite zoom levels. For the tree-based models, the relative improvement of satellite imagery over street-view imagery typically ranges from approximately 2% to 8%. Lower zoom levels with a broader spatial context better predict driving and transit use, whereas higher zoom levels with finer details improve walking and biking prediction. Despite the performance differences, SHAP analysis reveals largely consistent predictive associations: lower-density and more vegetated environments are associated with higher car dependence, while denser and pedestrian-oriented environments are associated with higher transit and active mode shares. This study demonstrates the value of interpretable GeoAI in linking visual urban form to commuting mode shares. The results highlight the importance for planners of using both satellite and street-view imagery, as the two data sources offer complementary capacities for interpreting neighborhood-level commuting mode-share patterns. The proposed approach can help practitioners compare neighborhoods and flag areas where commuting patterns or visible built-environment conditions warrant closer transportation or land-use analysis.

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References

Albert, A., Kaur, J., & Gonzalez, M. C. (2017). Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1357–1366). https://doi.org/10.1145/3097983.3098070

Ayush, K., Uzkent, B., Burke, M., Lobell, D., & Ermon, S. (2020). Generating interpretable poverty maps using object detection in satellite images. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI-20 (pp. 4410–4416). IJCAI. https://doi.org/10.24963/ijcai.2020/608

Bagwari, N., Kumar, S., & Verma, V. S. (2023). A comprehensive review on segmentation techniques for satellite images. Archives of Computational Methods in Engineering, 30(7), 4325–4358. https://doi.org/10.1007/s11831-023-09939-4

Berriel, R. F., Lopes, A. T., De Souza, A. F., & Oliveira-Santos, T. (2017). Deep learning-based large-scale automatic satellite crosswalk classification. IEEE Geoscience and Remote Sensing Letters, 14(9), 1513–1517. https://doi.org/10.1109/LGRS.2017.2719863

Biljecki, F., & Ito, K. (2021). Street view imagery in urban analytics and GIS: A review. Landscape and Urban Planning, 215, 104217. https://doi.org/10.1016/j.landurbplan.2021.104217

Cai, Q., Abdel-Aty, M., Zheng, O., & Wu, Y. (2022). Applying machine learning and Google street view to explore effects of drivers’ visual environment on traffic safety. Transportation Research Part C: Emerging Technologies, 135, 103541. https://doi.org/10.1016/j.trc.2021.103541

Cervero, R. (2002). Built environments and mode choice: Toward a normative framework. Transportation Research Part D: Transport and Environment, 7(4), 265–284. https://doi.org/10.1016/S1361-9209(01)00024-4

Cervero, R., & Kockelman, K. (1997). Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part D: Transport and Environment, 2(3), 199–219. https://doi.org/10.1016/S1361-9209(97)00009-6

Chen, L. C., Zhu, Y., Papandreou, G., Schroff, F., & Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. In European Conference on Computer Vision (pp. 833–851). Springer International Publishing. https://doi.org/10.1007/978-3-030-01234-2_49

Cheng, L., Chu, S., Zong, W., Li, S., Wu, J., & Li, M. (2017). Use of tencent street view imagery for visual perception of streets. ISPRS International Journal of Geo-Information, 6(9), 265. https://doi.org/10.3390/ijgi6090265

Crane, R. (2000). The influence of urban form on travel: An interpretive review. Journal of Planning Literature, 15(1), 3–23. https://doi.org/10.1177/08854120022092890

Ding, C., Cao, X., & Wang, Y. (2018). Synergistic effects of the built environment and commuting programs on commute mode choice. Transportation Research Part A: Policy and Practice, 118, 104–118. https://doi.org/10.1016/j.tra.2018.08.041

Ewing, R., & Cervero, R. (2010). Travel and the built environment: A meta-analysis. Journal of the American Planning Association, 76(3), 265–294. https://doi.org/10.1080/01944361003766766

Ewing, R., & Handy, S. (2009). Measuring the unmeasurable: Urban design qualities related to walkability. Journal of Urban Design, 14(1), 65–84. https://doi.org/10.1080/13574800802451155

Fan, Z., Zhang, F., Loo, B. P., & Ratti, C. (2023). Urban visual intelligence: Uncovering hidden city profiles with street view images. Proceedings of the National Academy of Sciences, 120(27), e2220417120. https://doi.org/10.1073/pnas.2220417120

Ganji, A., Zhang, M., & Hatzopoulou, M. (2022). Traffic volume prediction using aerial imagery and sparse data from road counts. Transportation Research Part C: Emerging Technologies, 141, 103739. https://doi.org/10.1016/j.trc.2022.103739

Gaw, L., Chen, S., Chow, Y., Lee, K., & Biljecki, F. (2022). Comparing street view imagery and aerial perspectives in the built environment. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 10, 49–56. https://doi.org/10.5194/isprs-annals-X-4-W3-2022-49-2022

Gebru, T., Krause, J., Wang, Y., Chen, D., Deng, J., Aiden, E. L., & Fei-Fei, L. (2017). Using deep learning and Google street view to estimate the demographic makeup of neighborhoods across the United States. Proceedings of the National Academy of Sciences, 114(50), 13108–13113. https://doi.org/10.1073/pnas.1700035114

Goel, R., Garcia, L. M., Goodman, A., Johnson, R., Aldred, R., Murugesan, M., Brage, S., Bhalla, K., & Woodcock, J. (2018). Estimating city-level travel patterns using street imagery: A case study of using Google street view in Britain. PloS One, 13(5), e0196521. https://doi.org/10.1371/journal.pone.0196521

Hamim, O. F., & Ukkusuri, S. V. (2024). Towards safer streets: A framework for unveiling pedestrians’ perceived road safety using street view imagery. Accident Analysis & Prevention, 195, 107400. https://doi.org/10.1016/j.aap.2023.107400

He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask r-cnn. In Proceedings of the IEEE International Conference on Computer Vision (pp. 2961–2969). https://doi.org/10.1109/ICCV.2017.322

Herold, M., Goldstein, N. C., & Clarke, K. C. (2003). The spatiotemporal form of urban growth: Measurement, analysis and modeling. Remote Sensing of Environment, 86(3), 286–302. https://doi.org/10.1016/S0034-4257(03)00075-0

Hipp, J. R., Lee, S., Ki, D., & Kim, J. H. (2022). Measuring the built environment with Google street view and machine learning: Consequences for crime on street segments. Journal of Quantitative Criminology, 38, 537–565. https://doi.org/10.1007/s10940-021-09506-9

Hong, J., Shen, Q., & Zhang, L. (2014). How do built-environment factors affect travel behavior? A spatial analysis at different geographic scales. Transportation, 41(3), 419–440. https://doi.org/10.1007/s11116-013-9462-9

Huang, Y., Sanatani, R. P., Liu, C., Kang, Y., Zhang, F., Liu, Y., Duarte, F., & Ratti, C. (2025). No “true” greenery: Deciphering the bias of satellite and street view imagery in urban greenery measurement. Building and Environment, 269, 112395. https://doi.org/10.1016/j.buildenv.2024.112395

Janzing, D., Minorics, L., & Blöbaum, P. (2020). Feature relevance quantification in explainable AI: A causal problem. International Conference on Artificial Intelligence and Statistics, 2907–2916. https://doi.org/10.48550/arXiv.1910.13413

Jean, N., Burke, M., Xie, M., Alampay Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790–794. https://doi.org/10.1126/science.aaf7894

Jing, C., Zhou, W., Qian, Y., & Yan, J. (2020). Mapping the urban population in residential neighborhoods by integrating remote sensing and crowdsourcing data. Remote Sensing, 12(19), 3235. https://doi.org/10.3390/rs12193235

Kashifi, M. T., Jamal, A., Kashefi, M. S., Almoshaogeh, M., & Rahman, S. M. (2022). Predicting the travel mode choice with interpretable machine learning techniques: A comparative study. Travel Behaviour and Society, 29, 279–296. https://doi.org/10.1016/j.tbs.2022.07.003

Kuang, W., & Dou, Y. (2020). Investigating the patterns and dynamics of urban green space in China’s 70 major cities using satellite remote sensing. Remote Sensing, 12(12), 1929. https://doi.org/10.3390/rs12121929

Larkin, A., Gu, X., Chen, L., & Hystad, P. (2021). Predicting perceptions of the built environment using GIS, satellite and street view image approaches. Landscape and Urban Planning, 216, 104257. https://doi.org/10.1016/j.landurbplan.2021.104257

Larsen, J., & El-Geneidy, A. (2011). A travel behavior analysis of urban cycling facilities in Montréal, Canada. Transportation Research Part D: Transport and Environment, 16(2), 172–177. https://doi.org/10.1016/j.trd.2010.07.011

Li, M., Sheng, H., Irvin, J., Chung, H., Ying, A., Sun, T., Ng, A. Y., & Rodriguez, D. A. (2023). Marked crosswalks in US transit-oriented station areas, 2007–2020: A computer vision approach using street view imagery. Environment and Planning B: Urban Analytics and City Science, 50(2), 350–369. https://doi.org/10.1177/23998083221112157

Li, W., Lai, Y., Xu, L., Xiangli, Y., Yu, J., He, C., Xia, G.-S., & Lin, D. (2023). OmniCity: Omnipotent city understanding with multi-level and multi-view images. In 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 17,397–17,407). IEEE. https://doi.org/10.1109/CVPR52729.2023.01669

Li, Z., Su, Y., Biljecki, F., & Zhao, W. (2026). BuildingMultiView: powering multi-scale building characterization with large language models and multi-perspective imagery. International Journal of Applied Earth Observation and Geoinformation, 146, 105034. https://doi.org/10.1016/j.jag.2025.105034

Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30. https://doi.org/10.48550/arXiv.1705.07874

Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019). Deep learning in remote sensing applications: A meta-analysis and review. ISPRS Journal of Photogrammetry and Remote Sensing, 152, 166–177. https://doi.org/10.1016/j.isprsjprs.2019.04.015

Naik, N., Philipoom, J., Raskar, R., & Hidalgo, C. (2014). Streetscore-predicting the perceived safety of one million streetscapes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (pp. 779–785). https://doi.org/10.1109/CVPRW.2014.121

Neupane, B., Horanont, T., & Aryal, J. (2021). Deep learning-based semantic segmentation of urban features in satellite images: A review and meta-analysis. Remote Sensing, 13(4), 808. https://doi.org/10.3390/rs13040808

Nguyen, Q. C., Khanna, S., Dwivedi, P., Huang, D., Huang, Y., Tasdizen, T., Brunisholz, K. D., Li, F., Gorman, W., Nguyen, T. T., & Jiang, C. (2019). Using Google street view to examine associations between built environment characteristics and US health outcomes. Preventive Medicine Reports, 14, 100859. https://doi.org/10.1016/j.pmedr.2019.100859

Nguyen, Q. C., Sajjadi, M., McCullough, M., Pham, M., Nguyen, T. T., Yu, W., Meng, H.-W., Wen, M., Li, F., Smith, K. R., Brunisholz, K., & Tasdizen, T. (2018). Neighbourhood looking glass: 360º automated characterisation of the built environment for neighbourhood effects research. Journal of Epidemiology and Community Health, 72(3), 260–266. https://doi.org/10.1136/jech-2017-209456

Nguyen, T. T., Nguyen, Q. C., Rubinsky, A. D., Tasdizen, T., Deligani, A. H. N., Dwivedi, P., Whitaker, R., Fields, J. D., DeRouen, M. C., Mane, H., Lyles, C. R., Brunisholz, K. D., & Bibbins-Domingo, K. (2021). Google street view-derived neighborhood characteristics in California associated with coronary heart disease, hypertension, diabetes. International Journal of Environmental Research and Public Health, 18(19), 10428. https://doi.org/10.3390/ijerph181910428

Nice, K. A., Thompson, J., Wijnands, J. S., Aschwanden, G. D., & Stevenson, M. (2020). The “Paris-end” of town? Deriving urban typologies using three imagery types. Urban Science, 4(2), 27. https://doi.org/10.3390/urbansci4020027

Ning, H., Ye, X., Chen, Z., Liu, T., & Cao, T. (2022). Sidewalk extraction using aerial and street view images. Environment and Planning B: Urban Analytics and City Science, 49(1), 7–22. https://doi.org/10.1177/2399808321995817

Novack, T., & Stilla, U. (2018). Classifying the built-up structure of urban blocks with probabilistic graphical models and Terrasar-x spotlight imagery. Remote Sensing, 10(6), 842. https://doi.org/10.3390/rs10060842

Pacifici, F., Chini, M., & Emery, W. J. (2009). A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification. Remote Sensing of Environment, 113(6), 1276–1292. https://doi.org/10.1016/j.rse.2009.02.014

Patil, G. R., Bhatia, S. Y., Yadav, M., Buddhiraju, K. M., & Rao, K. V. (2025). Spatiotemporal urban growth dynamics and its interactions with transportation networks in an unplanned urban conglomeration using satellite imagery: A case study of Mumbai Metropolitan Region, India. Land Use Policy, 157, 107617. https://doi.org/10.1016/j.landusepol.2025.107617

Saelens, B. E., Sallis, J. F., & Frank, L. D. (2003). Environmental correlates of walking and cycling: Findings from the transportation, urban design, and planning literatures. Annals of Behavioral Medicine, 25(2), 80–91. https://doi.org/10.1207/S15324796ABM2502_03

Suel, E., Polak, J. W., Bennett, J. E., & Ezzati, M. (2019). Measuring social, environmental and health inequalities using deep learning and street imagery. Scientific Reports, 9(1), 6229. https://doi.org/10.1038/s41598-019-42036-w

Sun, B., Ermagun, A., & Dan, B. (2017). Built environmental impacts on commuting mode choice and distance: Evidence from Shanghai. Transportation Research Part D: Transport and Environment, 52, 441–453. https://doi.org/10.1016/j.trd.2016.06.001

Tapiador, F. J., Avelar, S., Tavares-Corrêa, C., & Zah, R. (2011). Deriving fine-scale socioeconomic information of urban areas using very high-resolution satellite imagery. International Journal of Remote Sensing, 32(21), 6437–6456. https://doi.org/10.1080/01431161.2010.512928

Wang, Q., Wang, S., Zheng, Y., Lin, H., Zhang, X., Zhao, J., & Walker, J. (2024). Deep hybrid model with satellite imagery: How to combine demand modeling and computer vision for travel behavior analysis? Transportation Research Part B: Methodological, 179, 102869. https://doi.org/10.1016/j.trb.2023.102869

Wang, R., Ren, S., Zhang, J., Yao, Y., Wang, Y., & Guan, Q. (2021). A comparison of two deep-learning-based urban perception models: which one is better? Computational Urban Science, 1(1), 3. https://doi.org/10.1007/s43762-021-00003-0

Wu, F., Li, W., & Qiu, W. (2023). Examining non-linear relationship between streetscape features and propensity of walking to school in Hong Kong using machine learning techniques. Journal of Transport Geography, 113, 103698. https://doi.org/10.1016/j.jtrangeo.2023.103698

Xie, M., Jean, N., Burke, M., Lobell, D., & Ermon, S. (2016). Transfer learning from deep features for remote sensing and poverty mapping. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1), pp. 3929–3935. https://doi.org/10.1609/aaai.v30i1.9906

Ye, J., Lv, Z., Li, W., Yu, J., Yang, H., Zhong, H., & He, C. (2024). Cross-view image geo-localization with Panorama-BEV co-retrieval network. In European Conference on Computer Vision (pp. 74–90). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-72913-3_5

Yi, Y., Zhang, Z., Zhang, W., Zhang, C., Li, W., & Zhao, T. (2019). Semantic segmentation of urban buildings from VHR remote sensing imagery using a deep convolutional neural network. Remote Sensing, 11(15), 1774. https://doi.org/10.3390/rs11151774

Yin, G., Huang, Z., Fu, C., Ren, S., Bao, Y., & Ma, X. (2024). Examining active travel behavior through explainable machine learning: Insights from Beijing, China. Transportation Research Part D: Transport and Environment, 127, 104038. https://doi.org/10.1016/j.trd.2023.104038

Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., Xu, H., Tan, W., Yang, Q., Wang, J., Gao, J., & Zhang, L. (2020). Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241, 111716. https://doi.org/10.1016/j.rse.2020.111716

Zhang, M., Quanxin, S. U. N., Jinchuan, C. H. E. N., & Jifu, G. U. O. (2008). Travel behavior analysis of the females in Beijing. Journal of Transportation Systems Engineering and Information Technology, 8(2), 19–26. https://doi.org/10.1016/S1570-6672(08)60015-X

Zhang, X., & Du, S. (2016). Learning selfhood scales for urban land cover mapping with very-high-resolution satellite images. Remote Sensing of Environment, 178, 172–190. https://doi.org/10.1016/j.rse.2016.03.015

Zhou, B., Zhao, H., Puig, X., Fidler, S., Barriuso, A., & Torralba, A. (2017). Scene parsing through ade20k dataset. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 5122–5130). IEEE. https://doi.org/10.1109/CVPR.2017.544

Zhou, B., Zhao, H., Puig, X., Xiao, T., Fidler, S., Barriuso, A., & Torralba, A. (2019). Semantic understanding of scenes through the ade20k dataset. International Journal of Computer Vision, 127(3), 302–321. https://doi.org/10.1007/s11263-018-1140-0

Zhou, H., He, S., Cai, Y., Wang, M., & Su, S. (2019). Social inequalities in neighborhood visual walkability: Using street view imagery and deep learning technologies to facilitate healthy city planning. Sustainable Cities and Society, 50, 101605. https://doi.org/10.1016/j.scs.2019.101605