This study addresses the route stowage planning problem in inland container shipping using a multi-stage stochastic programming model (SPM). The model dynamically optimises stack occupancy and deviations between adjacent stages’ stowage plans by way of rolling scheduling, explicitly integrating dynamic uncertainties like stochastic container volume variations and specific seasonal waterway constraints. A robust optimisation approach with interval estimation converts the SPM into a mixed-integer programming model (MIPM) for mathematical solver accessibility. An adaptive reinforcement learning framework based on proximal policy optimisation (PPO) is proposed, featuring enhanced policy updates and adaptive exploration. Computational results show that both MIPM and PPO outperform Deep Q Network algorithms across scales. For large-scale problems, PPO achieves solutions within 30 s on average, matching or surpassing MIPM in efficiency. PPO maintains stability under ≤10% demand perturbations but degrades at 15% due to complexity. Hyperparameter analysis confirms the balanced configurations optimise exploration trade-offs, ensuring convergence reliability.

Railway transportation systems are essential for sustainable mobility, yet they are also noteworthy sources of noise and vibration, leading to adverse impacts on human health, structural integrity and environmental quality. This review synthesises the current state of research on strategies to mitigate noise and vibration on railways, encompassing both vehicle-based and infrastructure-based solutions. The discussion includes track modifications, rail dampers, resilient fasteners, ballast mats, floating slab tracks, train geometry optimisation and vehicle suspension design, as well as emerging materials and passive/active control methods. Special attention is given to the mechanisms of noise generation, vibration propagation and the effectiveness of mitigation measures under different operational conditions. Comparative analyses highlight the advantages, limitations and cost-effectiveness of various approaches, with emphasis on their applicability in rural, urban and high-speed rail contexts. Finally, the review identifies knowledge gaps and future research directions, including novel materials, predictive modelling and integrated system-level solutions. The findings provide a comprehensive foundation for engineers, government representatives and researchers aiming to design quieter and more vibration-resilient railway systems.

The study of lane-changing behaviour is crucial for road efficiency, safety, and traffic flow, especially at highway interchanges where lane-changing actions significantly impact capacity and safety. In mixed traffic with human-driven and autonomous vehicles (AVs), the traditional model of Minimising Overall Braking Induced by Lane changes (MOBIL) fails to account for human decision making imprecision. This study is an analysis of lane-changing characteristics using naturalistic driving data and enhances MOBIL by integrating dynamic fuzzy thresholding and collaborative decision making. A fuzzy logic-based lane-changing model for human drivers is developed, incorporating multivehicle data and lane-changing urgency, and has been adapted for AVs. Using Simulation of Urban Mobility (SUMO)–Python microsimulation, results show that a 50% AV penetration rate improves interchange efficiency by 15% and reduces conflicts by 20%, compared with human-only scenarios. These findings aid in optimising traffic management for mixed-traffic interchanges.

Both geometric changes and new technologies are being implemented throughout the United States with the potential to significantly improve the operational capabilities and safety of underperforming corridors. Despite the potential upsides, there are significant up-front costs that can prohibit the widespread adoption of new technologies. This research explores the use of anonymous probe vehicle (APV) speed data and crash data to visualise and rank intersection performance. A methodology was created to produce visually intuitive performance metrics to highlight underperforming road segments. Spearman’s rank correlation (SRC) is utilised to determine road segments with a significant correlation between the observed congestion and the number of crashes, using 360 million APV speed records and 12 000 crash records along a 32 mile (51.5 km) segment of US Route 1 in New Jersey, USA. The study proposes a methodology to analyse the corridor response to crash incidents using vectors to visualise changes in speeds around an incident. With the new visualisation technique and SRC, capital improvement decisions are supported by data-driven funding allocation methods.

Freeway on-ramp merging zones are critical bottlenecks where frequent lane changes and decelerations often lead to congestion and capacity drops. Although differential variable speed limit (DVSL) strategies have shown potential in mitigating these problems, their effectiveness significantly declines under high-density conditions due to speed limit failure, a situation in which drivers are unable to comply with posted speed limits because of excessive congestion. To address this challenge, this study proposes a multi-zone DVSL (MDVSL) control strategy within a multi-lane cell transmission model framework. The main innovation lies in the introduction of a dynamic upstream buffer zone that is activated when potential speed limit failure is detected. By harmonising speeds across lanes in this buffer zone, the strategy facilitates anticipatory lane changes from the merging lane, improving traffic smoothness and reducing congestion in the primary control zone. A predictive control algorithm is employed to dynamically optimise lane-specific speed limits, aiming to minimise total travel time (TTT). Simulation results demonstrate that the proposed MDVSL strategy effectively reduces the occurrence of speed limit failure, enhances traffic flow stability and achieves a 25.6% reduction in TTT compared to the uncontrolled strategy.

In order to reduce the occurrence of aircraft waiting and conflicts, a prediction method combining time series features and convolutional neural network/long short-term memory (CNN-LSTM) network attention mechanism is proposed. Design. Appropriate spatiotemporal factors influencing traffic patterns were first selected based on ground traffic data, and then a CNN-LSTM network model was developed. The experimental outcomes indicate that the proposed model had an average absolute error of 0.7032, 0.7387, 1.0102 and 3.6534 for airport departure flow, departure queue length, airport surface traffic (AST) density and departure flight taxiing time, respectively. The root mean square errors were 1.0658, 0.9562, 1.2437, and 4.9242, respectively. The predicted values of traffic situation were between [23.51, 1.57], with significant fluctuations. Therefore, the proposed model was able to accurately predict the AST situation, improve flight operation efficiency and contribute to the healthy growth of the civil aviation industry.

Demand-responsive transit systems have emerged as a vital solution for enhancing public transportation efficiency; however, they suffer from inefficiencies in operations. Implementing flexible origin–destination solutions can help minimise travel time, thereby enhancing overall operational performance. The unrestricted choice of pick-up and drop-off points introduces substantial complexity in vehicle scheduling and routing optimisation. Thus, this paper introduces an adaptive large neighbourhood search (ALNS) algorithm to optimise the bus scheduling process by dynamically responding to demand fluctuations and optimising candidate pick-up/drop-off points. Roulette destroy and best repair operators are applied with random operators for getting out of local optima. Simulated annealing acceptance is activated when the best repair operator is used. Experimental validation on the Wangjing road network demonstrates that incorporating candidate points leads to a significant improvement of 11% in system efficiency. In addition, the proposed destroy and repair method upon ALNS algorithm further improves efficiency by an 17%. In sensitivity analysis, candidate point demands are capable of making extra service possible in traffic jams and saving more time compared to traditional cases. These results validate the proposed approach and highlight the potential of the adaptive algorithm in addressing the challenges of demand-responsive bus scheduling.

To advance high-quality development in the Yellow River basin’s ‘Ji-shape’ bend metropolitan area (YRB-JBMA), the authors constructed a transportation–environment–economy evaluation system. Using coupling coordination and GM(1,1) models, spatiotemporal coupling characteristics (2012–2021) were assessed and coupling coordination degree (CCD) was predicted. Results indicate: (a) all subsystems show positive trends, with ecological environment performing best; (b) CCD evolved from near imbalance to marginal coordination, exhibiting a multicentric-edge spatial pattern centred on Ordos/Hohhot/Taiyuan/Yinchuan; (c) during forecasting, YRB-JBMA’s overall CCD improved continuously, with Xinzhou emerging as the city with the highest CCD by the end of the forecast, while Shizuishan and Wuzhong remained near imbalance. Path recommendations are proposed to coordinate transport development with ecological conservation.

In this paper, a computational model was constructed to investigate the vibrations of a maglev vehicle–guideway bridge system under vertical earthquake. The maglev vehicle was modelled as a multi-rigid body with 10 degrees of freedom, while the guideway bridge was simulated as a Bernoulli–Euler beam. The seismic acceleration, which was expressed as a non-stationary random process, was artificially synthesised based on the response spectrum. To simulate the interactions between a running maglev vehicle and the guideway bridge, an actively controlled magnetic suspension model was developed by employing a proportional derivative (PD) controller. A TR06 maglev train running on a simply supported box-section bridge was introduced as the example, and its dynamic behaviour was compared with those under non-seismic conditions. Results demonstrate that the vertical earthquake significantly amplifies the key dynamic characteristics of the system, including levitation gap fluctuations, car body and guideway vibrations. Furthermore, parametric studies were conducted to evaluate the influences of train speed, seismic intensity and seismic travelling wave velocity on the system’s vibration responses. The findings reveal that the seismic intensity was the dominant factor, and to ignore the effect of the seismic travelling wave velocity could lead to underestimating the dynamic responses of the coupled system when maglev vehicles move on bridges.