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

通过交叉学科技术框架重构、分阶段问题解耦与策略优化、政策导向的未来领域研判, 系统地综述地理空间人工智能(GeoAI)在交通需求预测中的代表性应用进展。针对交通需求预测四阶段(交通生成、交通分布、交通方式划分和交通流分配)的共性特征及差异化需求, 重点分析交通需求预测面临的复合技术难题, GeoAI集成空间表征学习、空间显式建模与隐式建模、模型评估与解释等技术, 形成提升预测精度和可靠性的高效解决方案。GeoAI弥补了传统预测模型在处理高维、多模态复杂数据时的局限, 增强了模型鲁棒性和时空预测能力。面对大数据多模态、交通系统耦合和时空关系演化等挑战, 未来研究方向应聚焦于优化多模态交通数据要素治理体系、构建交通领域大模型跨任务自适应学习框架, 为交通强国和数字中国战略下的交通需求预测理论研究与应用实践提供科学参考。