Shujuan Chen

Yue Li, Shujuan Chen, Akihiro Shimoda et al.

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.

Yue Li, Shujuan Chen, Ying Jin

Logit stochastic user equilibrium (SUE) captures imperfect route cost perceptions, but its practical solvers rely on route enumeration, which becomes prohibitive on large networks, or on link-based heuristics without convergence guarantees. We develop DiLLSUE, a differentiable solver for the link-based logit SUE that requires no route enumeration, no training data, and no network-specific tuning. Its inner loading algorithm batches all destinations into fixed-shape tensor operations, yielding, to our knowledge, the first GPU implementation of link-based logit SUE. Four outer solvers -- successive averages, self-regulating averaging, Anderson mixing, and implicit-function-theorem Newton -- are benchmarked under identical settings, and a family of acyclicity filters provides fast approximations with a quantified speed-accuracy trade-off. On five standard benchmark networks, GPU and CPU executions agree to within $10^{-4}$\% mean absolute percentage error, and the computed equilibrium converges monotonically to the independently computed Wardrop equilibrium, reaching 0.33\% error on Sioux Falls, where the zero-training solver is more accurate than published trained surrogates.

Shujuan Chen, Yue Li, Ying Jin

Active mobility is widely promoted for sustainable and healthier living, but whether it translates into equitable mental health benefits across individuals and places over time remains unknown. Using causal machine learning and causal deep learning in 264168 UK adults, we find substantial inequalities in individualized effects of active mobility on anxiety, depression, and common mental disorders. These inequalities widen over time and are strongly structured by urban context. For example, anxiety risk at follow-up ranges from a 40.6% reduction to a 10.1% increase across individuals, versus a 10.4% reduction to a 0.1% increase at baseline. Benefits are greatest in greener, safer, less polluted, and less deprived neighborhood environments, with 81.8% of individuals experiencing above-average benefits and mean anxiety risk reduced by 26.4%, versus 10.4% of individuals and 7.4% reduction in the least supportive environments. Urban compact form further modifies these effects through nonlinear interactions with neighborhood environments, amplifying benefits only under supportive conditions. Despite these strong environmental gradients, genetic moderation is negligible. These findings suggest universal active mobility promotion could widen health inequalities if individual and contextual differences are not accounted for.