Long-term traffic modelling is fundamental to transport planning, but existing approaches often trade off interpretability, transferability, and predictive accuracy. Classical travel demand models provide behavioural structure but rely on strong assumptions and extensive calibration, whereas generic deep learning models capture complex patterns but often lack theoretical grounding and spatial transferability, limiting their usefulness for long-term planning applications. We propose DeepDemand, a theory-informed deep learning framework that embeds key components of travel demand theory to predict long-term highway traffic volumes using external socioeconomic features and road-network structure. The framework integrates a competitive two-source Dijkstra procedure for local origin-destination (OD) region extraction and OD pair screening with a differentiable architecture modelling OD interactions and travel-time deterrence. The model is evaluated using eight years (2017-2024) of observations on the UK strategic road network, covering 5088 highway segments. Under random cross-validation, DeepDemand achieves an R2 of 0.718 and an MAE of 7406 vehicles, outperforming linear, ridge, random forest, and gravity-style baselines. Performance remains strong under spatial cross-validation (R2 = 0.665), indicating good geographic transferability. Interpretability analysis reveals a stable nonlinear travel-time deterrence pattern, key socioeconomic drivers of demand, and polycentric OD interaction structures aligned with major employment centres and transport hubs. These results highlight the value of integrating transport theory with deep learning for interpretable highway traffic modelling and practical planning applications.

翻译：长期交通建模是交通规划的基础，但现有方法往往在可解释性、可迁移性和预测精度之间进行权衡。经典出行需求模型提供了行为结构，但依赖强假设和大量标定；而通用深度学习模型能捕捉复杂模式，但常缺乏理论基础和空间可迁移性，限制了其在长期规划应用中的有效性。我们提出了DeepDemand，一个理论驱动的深度学习框架，该框架嵌入了出行需求理论的关键组成部分，利用外部社会经济特征和路网结构预测长期高速公路交通流量。该框架集成了用于局域起讫点（OD）区域提取和OD对筛选的竞争性双源Dijkstra算法，以及一个对OD交互作用和出行时间阻抗进行建模的可微架构。该模型利用英国战略公路网（涵盖5088个高速公路路段）2017-2024年八年的观测数据进行评估。在随机交叉验证下，DeepDemand的R²达到0.718，MAE为7406辆车，优于线性回归、岭回归、随机森林和引力模型基线。在空间交叉验证下，模型性能依然强劲（R²=0.665），表明其具有良好地理可迁移性。可解释性分析揭示了一个稳定的非线性出行时间阻抗模式、关键的社会经济需求驱动因素，以及与主要就业中心和交通枢纽相一致的多中心OD交互结构。这些结果突显了将交通理论与深度学习相结合，用于可解释高速公路交通建模和实际规划应用的价值。