This paper provides a comprehensive review of the technological advancements in geospatial artificial intelligence (GeoAI) and its applications in traffic demand forecasting. It systematically analyzes the evolution of GeoAI technologies, with a particular focus on its role in addressing the challenges inherent in the four key stages of traffic demand forecasting: traffic generation, traffic distribution, traffic mode choice, and traffic flow assignment. Through the reconstruction of interdisciplinary frameworks, the decomposition of traffic demand forecasting problems into manageable phases, and the optimization of corresponding strategies, this review highlights how GeoAI integrates spatial representation learning, explicit and implicit spatial modeling, and advanced model evaluation techniques to improve prediction precision and reliability. The application of GeoAI has yielded substantial improvements in the accuracy of traffic forecasts, overcoming the limitations of traditional predictive models that often struggle with the complexity of high-dimensional, multimodal data. By enhancing spatiotemporal prediction capabilities and facilitating a more comprehensive understanding of traffic dynamics, GeoAI has been shown to enhance the robustness of predictive models, enabling more effective traffic management and policy formulation. Looking forward, the paper outlines key directions for future research in GeoAI for traffic demand forecasting. These include the optimization of multimodal traffic data governance, the development of large-scale generative models tailored to the transportation domain, and the establishment of cross-task adaptive learning frameworks. Addressing challenges such as data heterogeneity, traffic system coupling, and the dynamic evolution of spatiotemporal relationships will be crucial for advancing the field. Ultimately, these innovations will support China’s national strategy of building a strong transportation country, delivering key theoretical and practical insights for intelligent transportation systems and sustainable urban mobility.

geospatial artificial intelligence (GeoAI), traffic demand forecasting, graph neural networks, data elements, multimodal large models