Optimizing the built environment to foster urban vitality is essential for effective urban planning and sustainable development. Previous studies have predominantly focused on analyzing the relationship between the built environment and urban vitality at either a macro or micro-scale, often assuming a predefined linear relationship. In this study, we investigate the potential non-linear interactions between the built environment and urban vitality by employing an interpretable spatial machine learning framework that integrates the XGBoost model with the SHapley Additive exPlanations (SHAP) algorithm. Additionally, we analyze the determinants of urban vitality across both micro and macro-scales using multi-source data, semantic segmentation models, and street view imagery. Our findings reveal the following key insights: (1) the distribution of urban vitality exhibits spatial heterogeneity within the main urban area of Shanghai, with high vitality areas concentrated in the Huangpu District and at intersections with neighboring districts, demonstrating a decline from the center to the periphery; (2) the XGBoost model outperforms other comparative models, showcasing superior capabilities in simulating and predicting urban vitality; (3) among the various built environment factors influencing urban vitality, building coverage, population density, and distance to the CBD exert the most significant effects, while the green view index and the number of bus stops contribute relatively less; (4) all built environment factors demonstrate nonlinear impacts and exhibit certain threshold effects on urban vitality. The analytical outcomes of this study provide valuable insights for optimizing the spatial layout and resource allocation within urban settings, offering references for urban planning and sustainable development initiatives.

This paper aims to investigate the characteristics of discretionary lane change (LC) duration on freeways based on an enriched dataset that contains the LC vehicle trajectories of 2905 passenger cars and 433 heavy vehicles. The LC duration is comprehensively analysed, and four stochastic LC duration models are established according to vehicle type and LC direction. LC duration varies with vehicle type and LC direction. Considering driver heterogeneity, accelerated failure time (AFT) models with fixed parameters, latent classes, and random parameters were established in this paper. The results show that drivers of heavy vehicles display greater heterogeneity and that vehicle types and LC directions have significant influence on the LC duration. The results of this study are helpful for understanding the mechanism of the LC process and the influence of LC on traffic flow and improving the safety of lane-changing behaviours of connected and autonomous vehicles.

In this paper, a comprehensive analysis of merging behavior on the freeway under different traffic densities has been conducted. First, the influence of traffic density on merging parameters (namely merging position, merging speed, merging duration, and accepted gap) was analyzed by mathematical statistics, Mann-Whitney-U Test, and distribution fitting. It was found that the merging position, merging speed, merging duration, and accepted gap are all significantly influenced by traffic density and the best-fitted distributions of merging parameters could be different under different traffic densities. Then, to incorporate the dynamic effects of traffic density on merging behavior, a cell-based traffic density was introduced in this paper and logistic regression was used to model the gap selection behavior. The results showed that traffic density has a significant influence on gap selection behavior during the merging process. The effects of traffic density in logistic regression showed that merging drivers tend to choose a location with lower traffic density during the gap selection process. Moreover, introducing the cell-based traffic density can also improve the predictive accuracy of the logistic regression model.

In order to investigate the heterogeneity in merging behaviors on freeways, a novel data mining tool, called two-step cluster analysis, is applied to the merging maneuvers (namely, initial speed, merging speed, and merging position). Merging maneuvers of 370 drivers collected from the NGSIM dataset are automatically and optimally segmented into four clusters (Early Merging Drivers at High Speed, Early Merging Drivers at Low Speed, Late Merging Drivers at Low Speed, and Late Merging Drivers at High Speed) by the two-step cluster analysis. Hypothesis test confirms the significant differences in merging maneuvers between different clusters. The clustered data are used to find the best corresponding fitting distributions. Seven distributions (Normal, Log-normal, Student’s t, Logistic, Log-Logistic, Gamma, and Weibull) are considered for each cluster and the Kolmogorov-Smirnov test statics are used to select the best fitted distributions. It is found that merging drivers may merge either early or late, under congestion or uncongested traffic condition. Further analysis of merging durations shows that Late Merging Drivers use significantly shorter time than Early Merging Drivers to finish the merging maneuver, no matter if they are at high or at low speed. Hypothesis test of accepted lead gaps and lag gaps indicate that merging drivers are more sensitive to the lag gaps under congestion. The proposed method can automatically identify the heterogeneity in merging drivers and the results obtained in this paper can be used to enhance the accuracy of the merge behavior models in microscopic simulation software.