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Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction.

, , , , , , and . Comput. Environ. Urban Syst., (2022)

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Sensitivity of measuring the urban form and greenery using street-level imagery: A comparative study of approaches and visual perspectives., , , and . Int. J. Appl. Earth Obs. Geoinformation, (August 2023)Coupling graph deep learning and spatial-temporal influence of built environment for short-term bus travel demand prediction., , , , , , and . Comput. Environ. Urban Syst., (2022)Incorporating multimodal context information into traffic speed forecasting through graph deep learning., , , and . Int. J. Geogr. Inf. Sci., 37 (9): 1909-1935 (September 2023)Correction: Yang et al. Detecting Spatiotemporal Features and Rationalities of Urban Expansions within the Guangdong-Hong Kong-Macau Greater Bay Area of China from 1987 to 2017 Using Time-Series Landsat Images and Socioeconomic Data. Remote Sens. 2019, 11, 2215., , , , , , , and . Remote. Sens., 14 (16): 4108 (2022)OCD: Online Crowdsourced Delivery for On-Demand Food., , , , , and . IEEE Internet Things J., 7 (8): 6842-6854 (2020)Sensing urban soundscapes from street view imagery., , , , and . Comput. Environ. Urban Syst., (2023)Detecting Spatiotemporal Features and Rationalities of Urban Expansions within the Guangdong-Hong Kong-Macau Greater Bay Area of China from 1987 to 2017 Using Time-Series Landsat Images and Socioeconomic Data., , , , , , , and . Remote. Sens., 11 (19): 2215 (2019)Developing a multiview spatiotemporal model based on deep graph neural networks to predict the travel demand by bus., , , , and . Int. J. Geogr. Inf. Sci., 37 (7): 1555-1581 (2023)Deep online recommendations for connected E-taxis by coupling trajectory mining and reinforcement learning., , , , , , , and . Int. J. Geogr. Inf. Sci., 38 (2): 216-242 (February 2024)Optimizing Living Material Delivery During the COVID-19 Outbreak., , , , , , and . IEEE Trans. Intell. Transp. Syst., 23 (7): 6709-6719 (2022)