As expressway networks expand and road system complexity increases, the demand for accurate traffic assignment results continues to grow. However, traditional models struggle to capture the behavioral inertia of travelers, limiting assignment accuracy. This study proposes a novel traffic assignment model integrating graph convolutional networks (GCNs) with the multicriteria decision‐making method TODIM. Vehicle trajectory data from expressway toll gantries enable GCN to extract route choice inertia, whereas TODIM incorporates travel time, distance, and cost to simulate dynamic path selection. An iterative optimization process achieves traffic flow equilibrium. The model was validated on expressway networks in Jinan, Taian, and Weifang, Shandong Provinces. Results show that during peak hours, the GCN‐TODIM (G‐T) model outperforms stochastic user equilibrium (SUE), bounded rational user equilibrium (BR‐UE), and mean excess travel time (METT), reducing mean absolute error by 36.1%–52.4% and root‐mean‐square error by 27.3%–43.5% and improving R ² by 13.9%–16.6%. Similar improvements were seen off‐peak, confirming strong accuracy and robustness, especially in networks with many segments and ramps. Overall, the G‐T model offers an efficient, practical tool for traffic assignment, supporting more scientific highway planning and management strategies.

Time headway is a key parameter for describing car-following behavior and microscopic traffic flow characteristics, and it is important for traffic safety analysis, road design, and optimizing intelligent-driving strategies. Existing research offers limited insight into the heterogeneity of time headway under different vehicle types and lane conditions. It is particularly important to investigate how time headway distributions differ across lane–vehicle-type combinations on highways, as these differences can affect safety evaluation and operational performance. This study is based on drone-captured vehicle trajectories from the publicly available HighD dataset. We select 378,751 vehicle–frame trajectory records; these records are used to construct valid follower–leader pairs and derive time headway (THW) samples for distribution fitting. Eight subsets are formed by combining two lane positions (inner vs. outer) and four follower–leader vehicle-type pairs (car–car, car–truck, truck–car, truck–truck). Six candidate distributions (Lognormal, Log-logistic, Burr, Weibull, Gamma, and Logistic) are fitted using maximum likelihood estimation, and their fit is evaluated using Kolmogorov–Smirnov, Anderson–Darling, and Chi-square tests, which are fused via an entropy-weighted composite score for model ranking. Results show pronounced heterogeneity across lane–vehicle-type subsets: Inner-lane samples exhibit smaller and more concentrated time gaps, whereas outer-lane samples show larger mean gaps, stronger dispersion, and heavier upper tails. Overall, Lognormal(3P) is selected as the top-ranked model in 5 of 8 subsets (62.5%), while Burr(4P) (car–truck, outer lane), Gamma(3P) (truck–car, outer lane), and Weibull(3P) (truck–truck, inner lane) are optimal in the remaining subsets. These findings indicate that lane position and vehicle-type pairing materially affect THW distributional characteristics, providing quantitative guidance for lane- and vehicle-aware traffic modeling, safety-oriented assessment, and intelligent-driving strategy design.

Escalating urban traffic problems are impeding city development, underscoring the critical need to better coordinate built environments (BEs) with traffic system states (TSSs). However, the efficiency measurements in the current data envelopment analysis (DEA) model exhibit excessive dependence on input and output data. The determination of weight constraints is also based on subjective judgment. This causes the varying impacts of critical and noncritical indicators on interaction dynamics to be ignored. This study introduces an enhanced DEA model with spatially adaptive weights calibrated by geographically weighted regression (GWR). We propose a TSS indicator that integrates three critical dimensions: traffic efficiency, traffic safety, and travel comfort. In this study, Jinan City is selected as the research area. The coordination assessments refined by incorporating constraints reveal significant disparities: 7.66% are fully coordinated and 63.7% show coordinated conditions, while 28.61% exhibit limited or no coordination. Compared to conventional DEA, the GWR–DEA model demonstrates marginally improved performance, validating the effectiveness of optimized weighting constraints in spatial coordination analysis.