Interpretable long-term traffic modelling on national road networks using theory-informed deep learning

Abstract

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.

Figures (10)

• Figure 1: Framework overview.a, Demographic and socioeconomic features are harmonised to small-area (LSOA) units. b, A simplified road network is constructed with node features and ground truth attached. c, Traffic sensor records are matched and aggregated at edge level as ground truth. d, For each target edge, a local OD region is extracted and candidate OD pairs are screened. e, A deep learning model learns to predict traffic volume on the target edge. f, Model performance is evaluated using random and spatial cross-validation and compared with baseline models. g, Interpretability analyses reveal travel patterns, influential features, and spatial demand distributions.
• Figure 2: Data collection and preprocessing workflow.
• Figure 3: Construction and preprocessing of the UK national driving network. The raw OSM Great Britain extract is filtered to retain only drivable road segments, converted into a directed graph with key geometric and semantic attributes, simplified to a topologically consistent national-scale network, and exported for downstream spatial joins and modelling.
• Figure 4: Descriptive statistics and spatial visualisation of ground-truth traffic volumes (AADT).a, Spatial distribution of AADT, with high volumes concentrated on major corridors between large cities, particularly around London. b,c, AADT distributions by highway type. d, Distribution of the coefficient of variation (standard deviation divided by mean daily traffic). e, Relationship between AADT and the standard deviation of daily total traffic, indicating increasing variability with higher traffic volumes.
• Figure 5: Local OD region extraction and OD pair screening.a, Illustration of the competitive expansion process from the target edge, shown up to a 20-minute travel-time threshold for simplicity; points indicate potential origin and destination locations (LSOA centroids) identified during the search. b, Example of a valid OD pair whose shortest route necessarily traverses the target edge. c, Example of an invalid OD pair for which a faster bypass route exists, leading to exclusion.