The operation of the subway system necessitates a comprehensive understanding of passenger flow characteristics at station locations, as well as a keen awareness of the average travel distances at these stations. Moreover, the travel distances at the station level bear a direct relationship with the built environment composed of land use characteristics within the station’s catchment area. To this end, we selected the land use features within an 800 m radius of the station (land use area, distribution of points of interest, and the surrounding living environment) as the influencing factors, with the travel distances at peak hours on the subway network in Xi’an as the research subject. An improved SSA-XGBOOST-SHAP interpretable machine learning framework was established. The research findings demonstrate that the proposed enhanced model outperforms traditional machine learning or linear regression methods in terms of R-squared, MAE, and RMSE. Furthermore, the distance from the city center, road network density, the number of public transit routes, and the land use mix have a pronounced influence on travel distances, reflecting the significant impact that mature built environments can have on passenger attraction. Additionally, the analysis reveals a notable nonlinear relationship and threshold effect between the built environment variables comprising land use and the travel distances during peak hours. The research results provide data-driven support for operational strategy management and line capacity optimization, as well as theoretical underpinnings for enhancing the efficiency and sustainability of the entire subway system.

The COVID-19 pandemic and similar public health emergencies have significantly impacted global travel patterns. Analyzing the recovery characteristics of subway station-level passenger flow during the pandemic recovery phase can offer unique insights into public transportation operations and guide practical planning efforts. This pioneering study constructs a station-level passenger flow recovery resilience (PFRR) index during the rapid recovery phase using subway AFC system swipe data. Additionally, it develops an analytical framework based on a multiscale geographically weighted regression (MGWR) model, the improved gray wolf optimization with Levy flight (LGWO), and light gradient boosting machine (LightGBM) regression to analyze passenger flow resilience on weekdays and weekends in relation to land use-related built environment types. Finally, SHAP attribution analysis is used to study the nonlinear relationships between built environment variables and PFRR index. The results show significant spatial heterogeneity in the impact of commercial, recreational, and residential land, as well as POI (points of interest) of leisure and shopping on PFRR. On weekdays, the most relevant built environment variables for PFRR are POI of enterprises and shopping numbers. In contrast, the contribution of built environment variables affecting PFRR of weekend is more balanced, reflecting the recovery of non-essential travel on weekends. Most land use-related built environment variables exhibit nonlinear associations with PFRR values. The proposed analytical framework shows significant performance advantages over other baseline models. This study provides unique insights into subway passenger flow characteristics and surrounding land use-related development layouts under the impact of public health emergencies.

In high-density expressway environments, lane-changing (LC) maneuvers act as stochastic perturbations that compromise the hydrodynamic stability of traffic flow, leading to safety hazards and operational delays. While existing literature has extensively modeled crash severity in static complex environments (e.g., tunnels and mountainous terrains), there remains a critical deficiency in quantifying the dynamic, systemic risks induced by LC maneuvers under saturation conditions. To address this gap, this study proposes a novel Systemic Risk Assessment Framework. First, a Hidden Markov Model (HMM) is employed to decode the latent state transitions of following vehicles, quantifying the systemic consequence of LC maneuvers as “operational delay” based on traffic wave theory. Second, a Bayesian Network (BN) is constructed to infer the causal probability of risk, integrating geometric proxies such as insertion angle with kinematic variables. Validated with real-world trajectory data, the model achieves high accuracy in identifying risk accumulation precursors. This research contributes to the field of transportation systems by shifting the risk paradigm from static collision prediction to dynamic system reliability analysis, offering theoretical support for Connected and Autonomous Vehicle (CAV) decision logic.

This study addresses critical safety challenges in sustainable urban mobility by developing a probabilistic framework for lane-change risk prediction on congested expressways. Utilizing unmanned aerial vehicle (UAV)-captured trajectory data from 784 validated lane-change events, we construct a Bayesian network model integrated with an I-CH scoring-enhanced MMHC algorithm. This approach quantifies risk probabilities while accounting for driver decision dynamics and input data uncertainties—key gaps in conventional methods like time-to-collision metrics. Validation via the Asia network paradigm demonstrates 80.5% reliability in forecasting high-risk maneuvers. Crucially, we identify two sustainability-oriented operational thresholds: (1) optimal lane-change success occurs when trailing-vehicle speeds in target lanes are maintained at 1.0–3.0 m/s (following-gap < 4.0 m) or 3.0–6.0 m/s (gap ≥ 4.0 m), and (2) insertion-angle change rates exceeding 3.0°/unit-time significantly elevate transition probability. These evidence-based parameters enable traffic management systems to proactively mitigate collision risks by 13.26% while optimizing flow continuity. By converting behavioral insights into adaptive control strategies, this research advances resilient transportation infrastructure and low-carbon mobility through congestion reduction.

With the increase in travel costs brought about by motor vehicles and the enhancement of environmental awareness among travellers, the rise in bicycle usage has grown globally. However, cyclists are more vulnerable in the process of a crash with motor vehicles, and tend to have more serious injuries compared to motorists after a crash. Therefore, cycling safety needs to be taken seriously, and it is crucial to analyse the factors that influence the severity of injuries sustained by cyclists in collisions. In this paper, we investigated the bicycle-motor vehicle (BMV) crash statistics in North Carolina, U.S.A., conducted preliminary screening of the variables covered in the data, classified the data into clusters by applying the latent cluster analysis, and used the multinomial logit (MNL) model based on the latent category to explore the main influences on the severity of rider injuries in BMV crashes in each cluster and the mechanism of the factors’ effects. The results of this paper explain the contribution of the influencing factors to the severity of cyclists’ injuries under each cluster characteristic. The results can be used for the special management and optimisation of the causative factors affecting the severity of cyclists’ injuries under different cluster categories.

To mitigate urban congestion and promote sustainable mobility, the Pre-signal system has been widely adopted to exploit spatio-temporal resources at saturated intersections. However, existing studies predominantly focus on isolated intersection optimization. The application of pre-signals in arterial coordination remains challenging because the “secondary queuing” phenomenon in the sorting area disrupts the platoon dispersion characteristics relied upon by traditional green wave strategies. To address this gap, this paper proposes a hierarchical control architecture integrating “intra-intersection main-pre signal synergy” and “inter-intersection arterial coordination.” First, the spatio-temporal evolution mechanism of traffic flow under pre-signal conditions is analyzed. Subsequently, a Multi-Objective Optimization Problem (MOP) model targeting maximized throughput, minimized average delay, and reduced queue lengths is formulated. A Decomposition-Based Multi-Objective Evolutionary Algorithm (MOEA/D) is explicitly designed to solve this complex problem involving conflicting objectives. VISSIM-based simulation results demonstrate that the proposed method achieves a superior balance between operational efficiency and environmental performance. Compared with single-objective optimization, the proposed framework increases throughput by approximately 12% and reduces average vehicle delay by 6%. Furthermore, compared with uncoordinated control, the maximum queue length is reduced by 6–15%, while throughput is enhanced by 18–39%. This study provides a robust implementation framework for alleviating spillback risks and enhancing the sustainability of complex urban arterials.