An Overview of the Effectiveness of Graph Learning Methods for Traffic Demand Forecasting

Traffic demand forecasting plays a crucial role in intelligent transportation systems and is a fundamental aspect of smart cities. The spatial-temporal nature of this task poses significant challenges for forecasting models, especially when it comes to extracting spatial features from complex graphs. To effectively capture these intricate spatial patterns, previous studies have explored a variety of methods for constructing a graph from spatial data. In this study, we present a thorough survey and taxonomy of existing research based on various graph construction methods, including static, adaptive, and dynamic approaches. To thoroughly evaluate these models and methodologies, we conduct experiments on seven real-world datasets. Among these, two are widely recognized benchmarks, while the other five have been newly collected and processed by us from government open data platforms. Our findings enable us to analyze and compare the strengths and limitations of various approaches. We also identify emerging trends and assess the current effectiveness of these methods. Finally, we propose potential research directions and opportunities for future work in this field.