This study investigates the wind field characteristics above a submerged rectangular bluff body subjected to the combined effects of wind and wave action. The study uses the moving measuring point method to assess these characteristics. Three heights of the bluff body above the water surface were measured, defined as dimensionless heights based on the ratio of the height of the bluff body exposed to the water surface to the wave height, where the wave height was 3 cm. The heights in ascending order were 1.67, 2.50, and 3.33. Wind field measurements were taken at the same height as the bluff body at three downstream positions, with dimensionless lengths defined as the ratio of the distance from the leading edge of the bluff body to the wavelength of 76.50 cm, in descending order of 0.05, 0.10, and 0.15. The results indicate the following: ① The wind speed above the bluff body exhibits an obvious acceleration effect, which becomes more pronounced as the height of the bluff body increases at the same downstream position and measurement height; ② The wind speed acceleration effect is more significant when the measurement point is closer to the leading edge of the bluff body, at the same height above the water; ③ A comparison of the wind speeds at three different heights of the bluff body with those without the bluff body reveals that the acceleration effect of the bluff body on wind speed reaches its maximum when the ratio of the length from the leading edge of the bluff body to the wave height is 0.05, with acceleration ratios of 23.37%, 27.53%, and 38.14%, respectively; ④ Turbulence intensity is found to be only weakly influenced by the wind speed, where a higher turbulence intensity is observed when the bluff body is exposed to the water surface, particularly near the wall at the same downstream position.

Floating bridges have good application prospects in deep-water areas with weak soil foundations, and the dynamic characteristics of their floating foundations are significantly influenced by the mooring system. A 1∶70 scaled hydrodynamic model test was conducted for three types of floating foundations: inclined cables, tension legs, and combination systems. The dynamic characteristics of different floating foundations were investigated and compared based on the natural frequency, amplitude response operator (RAO), and power spectral density, and the applicable wave environments of the mooring systems were specified. Based on a hydrodynamic numerical simulation, a parametric analysis of the combination mooring system was conducted, considering different inclined cable-to-tension leg stiffness ratios and the overall mooring stiffness. Furthermore, the influence of the mooring stiffness setting on this type of floating foundation was analyzed. The results showed that the inclined system had a broad frequency range of high response, and the motion coupling of each degree of freedom was significant, suitable only for weak-wave areas. For the tension leg system, the low-frequency horizontal motion was significant, which could reach 2.3 times of those of the other two types of systems under the same irregular wave. This type of system is unsuitable for water with low-frequency swells. The peak RAO of the horizontal displacement for the combined system was significant; however, a high response occurred in a narrow frequency range. By adjusting the cable stiffness, the horizontal resonance frequency could be tuned far from the wave peak frequency, which could effectively reduce the structural dynamic response. This system is suitable for a wide range of applications. This study provides a reference for future research and applications of floating foundations of deep-water bridges.

Recent national strategies for developing comprehensive multidimensional transportation systems in China have prompted bridge construction projects to connect islands and crossing straits along the coast. Deepwater floating bridges have attracted considerable attention as potential technical solutions. Analyzing the structural failure probability of deep-water floating bridges under extreme wave loads is important for conceptual design. Accordingly, a computational framework was established in this study for estimating the extreme response and structural failure probabilities of floating structures under narrow-banded random waves using an active learning approach. A deep-water floating bridge segment finite element model was developed for the dynamic analysis. Active learning was employed to examine failure probability learning patterns under various failure modes based on displacement and tension leg forces with different thresholds, and finally obtain failure probability predictions. The active learning method requires only limited samples to achieve convergent failure probability predictions, requiring a low computational cost. The active learning framework was applied to both the single-and multi-objective failure criteria. For the floating bridge studied, under random waves with a return period of 100 years, the failure probability based on the midspan displacement was 2.5% at the threshold ratios of w_S/w_B=2.2 and 1.3% based on the tension leg force at the threshold ratio of F_S/F_B=2.5. These rapid computational failure probability estimates can provide information for feasibility assessments, designs, disaster prevention, and protection technologies for future deepwater floating bridge projects.

To study the force mechanism and force calculation method of a multibox girder under wave surges, a series of wave surges were experimentally generated using dam-break waves, and a series of hydrodynamic interaction experiments between these wave surges and multibox girder were performed. The characteristics of the entire interaction process, pressure time-history characteristics at different positions on the outer surface of the girder, and the evolutions of the impact peaks of horizontal force, vertical force, and moment, as a function of the initial water depth, incoming wave height, and girder elevation, were systematically presented and analyzed. Based on the experiments conducted in this study, a calculation method is proposed for the impact peaks of forces and moment of multibox girder under wave surges. The results indicate that the wavefront profile gradually varies with the ratio of the incoming wave height to the initial water depth. The impact peaks of horizontal force, vertical force, and moment on the girder are only generated by the water pressures on the front side of the girder and the front part of the girder bottom. There are obvious differences in pressure time histories at different positions on the front, back, upper, and downward sides of the girder. The horizontal impact peak force increased as the deviation between the midpoint of the girder and the midpoint of the maximum wave height decreased. When the girder was located at the upper and middle parts of the incoming wave height, the vertical impact peak force increased when the girder elevation decreased, and this situation varied for small girder elevations. The proportion of hydrostatic component in vertical impact peak force was larger than that in horizontal impact peak force. The proposed calculation method can provide a reference for the rapid calculation of wave surge forces on multibox girders.

The resistance of a structure to blasts is a critical aspect that should not be overlooked in the design of submerged floating tunnels (SFTs). To improve the blast resistance capabilities and optimize the structural form of SFTs, the LS-DYNA software was employed to establish a fluid-structure interaction model under underwater explosion conditions. Three representative structural forms were selected (concrete structure, steel plate + concrete composite structure, and foam aluminum + concrete composite structure), and by mapping two-dimensional underwater explosion shock waves onto three-dimensional bodies, calculations of the dynamic response of SFTs to underwater explosions were conducted. The evaluation of the structure's blast resistance performance was based on indices such as structural displacement, pressure, and effective stress, analyzing the impact of different structural forms and protection methods on blast resistance. The following results were obtained: ① In the analysis of blast resistance with respect to concrete thickness, when the concrete tube body thickness ranges from 1.1 to 1.43 m, the blast resistance of the submerged tunnel significantly improves with increased thickness. However, beyond this range, the performance shows a saturation trend, and the influence of the thickness on the blast resistance becomes limited. ② In the analysis of blast resistance with steel plate protection, the overall protective performance of steel-reinforced concrete is superior to that of an external steel plate, effectively reducing the range of high stress, although its impact on the range of displacement deformation is not significant. Internal steel-plate protection is not suitable for external underwater explosions. The peak stress and displacement values in steel-reinforced concrete structures show reductions of 16.10% and 17.63%, respectively, compared to those of concrete structures. ③ In the analysis of blast resistance with external foam aluminum protection, the porous nature of foam aluminum materials allows for the effective absorption and dispersion of the explosive energy acting on the tube body. The peak displacement and stress values are reduced by 23.11% and 25.40%, respectively, compared with those of concrete structures, suggesting that the overall blast resistance performance is superior to that of other forms of protection.

To explore the key issues and future directions in the study of asphalt pavement textures, this paper provides a comprehensive review of the research progress in three-dimensional (3D) reconstruction and evaluation methods of asphalt pavement textures, both domestically and internationally. First, 3D reconstruction methods of asphalt pavement textures are systematically reviewed. The 3D reconstruction techniques for asphalt pavement surface tomographic images are summarized, and active laser scanning and passive image-based methods for the 3D reconstruction of pavement surface textures are introduced. The algorithm principles and characteristics of the monocular, binocular, and multi-view image depth estimations are compared, and the application of pavement texture 3D generation technology is analyzed. Subsequently, based on computational principles, the pavement surface texture evaluation methods are categorized into the geometric statistical index method, spectral index method, fractal index method, and image feature method, and the corresponding evaluation indices are further classified into 2D evaluation indices based on the pavement surface profile and 3D evaluation indices based on the pavement surface texture. Multidimensional and multiscale analyses are conducted on the applicability conditions, advantages, and limitations of the different evaluation indices. Finally, future research directions concerning the evaluation and reconstruction techniques of pavement surface textures are discussed, and the development trends of intelligence, digitization, and informatization in the evaluation and 3D reconstruction of asphalt pavement textures are anticipated. This study provides a reference and source of inspiration for academic research on the functionality of pavement surfaces and development of modern high-quality pavements.

Affected by factors such as land use and road maintenance, the spatial distribution of urban pavement performance exhibits significant spatial heterogeneity. Understanding the differences and causes behind the spatial distribution of urban pavement performance is crucial for allocating maintenance resources and formulating maintenance plans. Unlike existing studies that primarily focus on the impact of natural factors like climate on pavement performance deterioration, this study emphasizes exploring the influencing factors and distribution characteristics of spatial heterogeneity in urban pavement performance under human activities, further categorizing differentiated routine maintenance zones. Utilizing weekly updated fine-grained pavement performance data, this research employs a geographical detector model to identify key factors affecting pavement performance distribution and their interactions. Subsequently, according to the identified significant factors, four types of road segments with distinct spatial distribution characteristics and corresponding maintenance zones were divided by two-stage clustering analysis. Results indicate that human factors such as maintenance frequency and traffic facilities significantly affect the spatial heterogeneity in pavement performance distribution. The interactions between factors can substantially enhance their explanatory power regarding the spatial distribution of pavement performance. Moreover, the division of road segments based on significant factors accurately characterizes their spatial distribution features and different maintenance needs, which further supports the division of differentiated maintenance zones. These conclusions can provide support for formulating data-driven and scientific maintenance decisions, aiding in the optimization of limited maintenance resources to improve maintenance efficiency and effectiveness.

This study discusses the internal relationship between the composition characteristics and pavement performance of hot mix asphalt (HMA) and the influence of the cornerstone of plastic mastic without mastic leakage on the design, construction, and pavement performance of asphalt mixtures during current HMA molding or construction processes. Based on the theoretical innovation of fluid mastic and principle of fluid mastic filling of the voids in the aggregate, a theoretical framework of the Fluid Mastic Asphalt (FMA) mixture and the Mastic Flow for Filling (MaFF) method were proposed, and various forming processes of FMA were explored. Several typical FMA mixtures were designed and formed, and their road performances were evaluated and verified. The results show that owing to the fluid mastic, FMA facilitates the formation of a tightly embedded skeleton, and the internal air voids are close to zero, forming a skeleton-ultra-dense structure. Therefore, FMA can prevent water damage to asphalt pavements with good rutting resistance performance and improve the ability of asphalt pavements to resist fatigue, reflective cracking, and aging. Additionally, the fluid mastic allows the surface structure of the FMA to be flexibly designed to realize pavement skid resistance and anti-skid durability. The asphalt content of FMA must be increased to enable mastic flow, which leads to difficulties in construction when using the current process. However, FMA are expected to significantly improve the performance of asphalt mixtures and solve the problems of pavement water damage and cracking, while ensuring anti-rutting. The FMA theory and its MaFF method break through the current HMA theoretical system and design framework. This can significantly improve the durability of pavements, reduce life cycle costs, and resource consumption. Hence, it can lead to far-reaching influence on the asphalt pavement industry, which is worthy of further study.

Soil arching is a primary load-transfer mechanism in pile-supported embankments. For embankments supported by a composite foundation with rigid friction piles, cement soil piles, and gravel piles, the settlement of the pile head under embankment fill and localized vehicle loads significantly impacts the soil arching of the embankment. However, traditional trapdoor models usually use fixed supports, which do not account for the influence of pile head (or arch foot) settlement on the soil arch shape and load transfer of the embankments. To address this, a multispan spring-based trapdoor test apparatus with movable arch feet was designed in this study. The vehicle loads were simulated with localized surface loads, and movable blocks with springs of varying stiffnesses were used to model the piles and the soil between the piles. Fifteen orthogonal spring-based trapdoor tests and one reference test with a fixed trapdoor were conducted. Under the self-weight of the fill and localized loads, the earth pressure in the affected area of the soil arching and the displacement of the arch foot and trapdoor were monitored using earth pressure cells and laser displacement meters, respectively. The evolution process of the soil arch morphology was reproduced using digital image correlation (DIC) technology. The test results show that the arch foot settlement weakens soil arching. The weakening effect varies across different arching states and becomes more prominent with an increase in trapdoor stiffness. Additionally, the arch foot settlement under the fill weight significantly decreased the soil arching height, restricted the upward propagation of the differential settlement within the embankment, and expanded the affected width of the soil arch, causing a transformation from a steep to a flat soil arch. Under a localized load, arch foot settlement significantly reduces the stability of the mid-span soil arch, promotes load transfer to the adjacent span, and mobilizes the soil arching of the adjacent span to carry the load.

The dynamic characteristics of embankment filling are crucial for the design of road durability. However, research on the dynamic characteristics of predisintegrating carbonaceous mudstone embankment filling is lacking. Therefore, the dynamic characteristics and cumulative plastic strain changes of predisintegrating carbonaceous mudstone embankment filling were studied via dynamic triaxial tests. The effects of varying the confining pressure σ₃, water content w, static deviatoric stress σ_s, compaction degree K, and fractal dimension D on the backbone curve, dynamic modulus E_d, damping ratio λ, cumulative plastic strain ε_p, and critical dynamic stress σ_cri of predisintegrating carbonaceous mudstone embankment filling were analyzed. An empirical model for ε_p of predisintegrating carbonaceous mudstone embankment filling was established, and the rationality of this model was verified. The results show that the backbone curve of the sample is nonlinear. The dynamic stress σ_d at the vertex first increases and then decreases with increasing D. For a D of 2.53, the maximum σ_d is 111.9 kPa. The dynamic strain ε_d at the vertex increases monotonically with increasing D. With an increase in ε_d, the E_d of the sample gradually decreases and then tends to stabilize. λ increases with an increase in ε_d. The smaller the value of ε_d of the sample during failure, the greater is the corresponding λ. E_d and λ exhibit different development rules with varying σ₃, w, σ_s, K, and D values. ε_p of the sample is inversely proportional to σ₃ and K and proportional to w and σ_s. σ_cri has a strong linear positive correlation with σ₃, a nonlinear positive correlation with K and D, and a nonlinear negative correlation with w and σ_s. The relative standard deviation of the calculated data of the empirical model for ε_p of predisintegrating carbonaceous mudstone embankment filling established in this study from the experimentally measured data is between 0.03 and 0.21, indicating good prediction capability for ε_p of the predisintegrating carbonaceous mudstone embankment filling. The results of this study can provide a reference for analyzing the dynamic characteristics of predisintegrating carbonaceous mudstone embankments.

In recent years, with the development of water conservation projects, a large number of dredged silt foundations have been generated in coastal cities in China. Dredged silt has poor engineering properties and requires treatment prior to its use in construction. The widely used traditional vacuum-preloading method has poor effectiveness in treating dredged silt foundations owing to issues such as clogging. The improved horizontal combined vertical drainage plate (PHD & PVD) method has problems such as an insufficient reinforcement effect and clogging in the last duration. To further improve the reinforcement effect of the PHD and PHD & PVD vacuum preloading method, this study proposes a new method in which air is periodically introduced during the vacuum preloading process to promote drainage. It is called the PHD and PHD & PVD vacuum preloading-aeration method. Four sets of indoor model experiments were conducted using self-made glass model boxes. Parameters such as drainage volume, pore water pressure, and surface settlement were monitored during the tests, and the soil moisture content and shear strength were tested after treatment. The effects of different ventilation durations on the reinforcement of dredged silt using the PHD and PHD & PVD vacuum preloading-aeration method were compared and analyzed. The experimental results show that, compared with the conventional PHD and PHD & PVD vacuum preloading, the intermittent ventilation PHD and PHD & PVD vacuum preloading group increased the discharge of dredged silt by 6.60% to 14.03%, the settlement increased by 9.30% to 25.66%, and the overall average shear strength of the cross plate increased by 10.08% to 29.74%. The best consolidation effect was achieved when air was introduced for 1 h/day. Compared to the conventional PHD and PHD & PVD vacuum preloading method, the clogging phenomenon near the drain can be effectively reduced, and the consolidation efficiency is improved. In addition, micro-experiments confirmed that intermittent ventilation can effectively alleviate the problem of particle accumulation around the drain and improve the reinforcement effect of dredged silt. This study verified the effectiveness of the PHD and PHD & PVD vacuum preloading-aeration method, which will aid in the future development and application of dredging silt treatment technologies.

Prefabricated bridge deck panels (PBDPs) are easily constructed, are associated with lower pollution levels, and cause minimum disruptions in existing traffic and the surrounding environment. Owing to these advantages, they are gradually becoming the research focus of bridge engineering. Configurations and materials of joints have significant effects on the speed of construction, integrity, and durability of PBDPs. Wet joints, with their small amounts of grout, high tolerance for construction errors, and superior mechanical properties, have gradually become commonly used joint types for PBDPs. Herein, previously published research studies on wet joints for PBDPs are systematically summarized. The configurations and performance of common wet joints are introduced first. The designs and behaviors of wet joints with high-performance materials are then clarified. The prediction models for the capacity of bar connections and post-tensioned connections subjected to bending moments, shear, and tension are discussed. The practical engineering values of the wet joints are demonstrated based on engineering practices. Finally, the development trends and future research directions of wet joints for PBDPs are pointed out. Based on high-performance materials, the novel configurations of the wet joints associated with superior performance, simple and convenient construction approaches, and economic efficiency, constitute the premise of future studies. Working mechanisms, failure modes, and performance prediction models of novel wet joints subjected to bending moment, shear, and fatigue loading are the basis for future works. In addition, a practical engineering-oriented design method of wet joints for PBDPs is the focus to be investigated. This paper can provide a broad view and technical support for the application of PBDPs.

In this study, a method for accelerating the vehicle-bridge coupling vibration analysis based on an improved Gibbs-Poole-Stockmeyer algorithm was proposed to address the efficient analysis requirements of kilometer-scale dual-layer random vehicle-bridge coupled vibrations to reduce the storage and computational costs of the bridge subsystem. First, the improved Gibbs-Poole-Stockmeyer algorithm was tested using a structural discretization model to verify its accuracy. Subsequently, the improved method was applied to optimize the storage analysis of a super-long-span dual-layer steel-truss bridge model, while incorporating the existing analysis system for random vehicle flow over a bridge response analysis. Finally, the calculation efficiencies were compared. The results show that when the same bandwidth is obtained as in existing research methods, the improved Gibbs-Poole-Stockmeyer algorithm can achieve a lower tree width without the need for backtracking during node numbering. The proposed method improves the storage space of the long-span dual-layer steel-truss bridge model, and the maximum bandwidth of the stiffness matrix and the length of the one-dimensional variable-bandwidth storage array are significantly reduced. Under equivalent computational conditions, the improved analysis system significantly improves computational efficiency in performing transient analysis and handling high-traffic vehicle-bridge coupled vibration analysis compared to existing analysis systems. The proposed method significantly increases the processing capacity and computational efficiency of existing analysis systems for large-scale finite element models.

Integral abutment bridges commonly employ flexible reinforced concrete (RC) piles to mitigate structural lateral deformations. However, RC piles have a limited capacity for horizontal deformation and are susceptible to cracking and are difficult to retrofitting under seismic effects. Therefore, the use of flexible abutments to accommodate structural lateral deformations has emerged as a promising development direction for integral abutment bridges. To investigate the dynamic interaction mechanisms, failure patterns, and seismic response characteristics of an integral bridge with a flexible abutment, a shaking table test was conducted on a flexible abutment-RC pile-soil system using the Maluanshan Integral Abutment Bridge in Shenzhen as an example. The experimental results reveal that under minor seismic actions, the integral abutment bridge with a flexible abutment-RC pile structure does not exhibit cracking and only experiences soil settlement. As the ground motion intensifies, minor cracks appear at the boundary between the abutment top and girder, as well as at a depth of -1.69 m in the pile foundation. However, these micro-cracks were nearly automatic closed after the seismic motion ceases. During seismic events, owing to the lateral movement of the soil behind the abutment, the first-order vibration mode of the flexible abutment-RC pile-soil system involves the outward protrusion of the abutment and pile foundation together. This behavior explains the “bulging” failure observed in several thin-walled abutments in the practical engineering. The maximum acceleration and displacement responses occur at the bottom of the abutment, rather than at the top of the abutment or pier. When the abutment bottom experiences peak displacement, the deformation of the flexible abutment-pile-soil system is dominated by the first-order vibration mode, with characteristics of the second-order mode. By contrast, when the abutment top experiences peak displacement, the third-order vibration mode is dominant. At both aforementioned instances of peak displacements, the abutment body exhibits considerable shear deformation, with the shear displacement angle between the elastic and plastic displacement angles.

Ultrahigh-performance concrete (UHPC) specimens containing different amounts and particle sizes of a superabsorbent polymer (SAP) were preloaded to induce cracking to investigate the effect of the SAP on the self-healing performance of UHPC cracks with different widths in different curing environments. The initial crack widths were measured, and the specimens were then cured in three environments: water, water-dry cycle, and room-temperature-drying environments. The self-healing effect of the UHPC was evaluated from four perspectives: changes in crack width, recovery of mechanical properties, variations in ultrasonic velocity, and microscopic product analysis. The test results show that water curing best improves the crack-healing effect of UHPC, followed by water-dry cycle curing, whereas room-temperature-drying curing was the worst. In addition, UHPC exhibits a self-healing ability. Under water and water-dry cycle curing conditions, microcracks with crack widths smaller than 50 μm can basically achieve self-healing, and adding the SAP has a minimal effect. For macrocracks with crack widths exceeding 50 μm, adding SAP can significantly improve the crack-width-healing effect of UHPC. Under water-cured conditions, cracks with widths of 50-150 μm can be completely closed, and the healing rate of cracks with widths between 150 and 200 μm can reach 50%. The SAP minimally influences the initial compressive strength of the UHPC, and the flexural strength decreases with increasing SAP content. The effects of SAP dosage and particle size on the mechanical property recovery rate and ultrasonic velocity recovery rate of UHPC are consistent. The healing products of the UHPC cracks are primarily calcium carbonate and C—S—H gel. The addition of SAP promotes the hydration of the cementitious material, prolongs the duration of the self-healing reaction, and increases the content of healing substances at cracks.

To investigate the horizontal bearing characteristics of bridge pile foundations on steep slopes via karst caves, a centrifugal model test of transverse axially loaded pile foundations is conducted based on the Guangna Highway project, considering the effects of steep slopes and karst caves. Under a certain foundation slope, cave size, and location, the length-to-diameter (L/D) ratios of the pile foundation are set to 6.5, 9.0, 11.5, and 14.0, and the horizontal load on the top of the pile is applied step-by-step. The effects of changes in L/D ratio on the horizontal ultimate bearing capacity of the pile foundation, pile bending moment, and soil resistance on the pile side are analyzed, and the location range and distribution law of the maximum values of the bending moment and soil resistance are determined. The results show that the horizontal ultimate bearing capacity of the pile foundation increases with the L/D ratio when the pile foundation passes through the karst cave in steep slope areas under an L/D ratio of less than 14.0. The pile bending moment increases and then decreases to zero along the depth of the pile foundation, and the maximum value appears in the range of 3.5-4.5 times the pile diameter from the pile top. The maximum value of the bending moment increases gradually as the L/D ratio increases from 6.5 to 14.0. The soil resistance on the pile side increases and then decreases to zero along the depth of the pile foundation, and the maximum value of the soil resistance appears in the range of 2.5-4.5 times the pile diameter from the pile top. With an increase in the L/D ratio, the maximum value of the soil resistance on the pile side decreases gradually, and the position of the maximum value of the soil resistance gradually decreases. When the L/D ratio is less than or equal to 11.5, the pile foundation exhibits the bearing characteristics of a rigid pile, whereas when the L/D ratio is greater than 11.5, the foundation exhibits the characteristics of an elastic pile. The soil resistance in front of the pile is lower than that behind the pile at different L/D ratios. The results of this study can provide a reference for calculating the horizontal bearing capacity of pile foundations in steep-slope karst areas.

Under the backdrop of rapid intelligentization and informatization development in bus systems, bus en-route operation control has gradually become an important research highlight in bus operation optimization. Such research helps to enhance the efficiency of bus operation and passenger travel experiences, and it provides strong support for the development of the public transportation industry. To analyze the problems and challenges in bus en-route operation control, this study systematically reviewed and analyzed the status and development achievements of relevant research in terms of research direction, scope, and practical application to provide a clear research context, theoretical framework, and methodological guidance for subsequent researchers. This study followed the research approach of “state diagnosis-control optimization”, considered a bus bunching incident as an example of a poor en-route operation state, provided a focused summary of the influencing factors that lead to such incidents, and addressed other possible poor en-route operation states as well as related identification and diagnosis technologies. Hence, a comprehensive examination of the methodologies employed in bus en-route operation control is undertaken. First, the methodologies and limitations of existing research on the optimization objectives, control objects, and constraint conditions are analyzed. This is followed by a comprehensive analysis of the control strategies from disparate dimensions, including stop control and interval control, as well as an examination of the characteristics and applicable scenarios of the various control strategies. Furthermore, this paper presents a summary of commonly used optimization model-solving algorithms and discusses their applicable conditions and optimization effects. Finally, this paper offers a prospective outlook on future research directions and development trends to provide new ideas and system optimization directions for future work to improve bus operation efficiency and service quality.

Estimating and predicting road capacity in highway weaving areas is challenging, owing to complex vehicle dynamics. Hence, this study introduces a Bayesian inference method to estimate road capacity based on the lane-changing frequencies of mixed vehicle platoons composed of multivehicle platoons and individual vehicles. This study aims to quantify the impact of frequent lane changes on traffic flow and road capacity. First, a method is proposed to identify vehicle platoons using data from upstream and downstream loop detectors. Then, the traffic flow is modeled based on platoon proportions. An analysis of real-world data from weaving areas reveals that as the lane-changing frequency increases, the average headway within and between platoons, as well as that for individual vehicles, decreases or increases. Consequently, a heteroscedastic normal distribution model in which the mean and standard deviation decrease polynomially with the average headway is proposed. Bayesian inference and Markov Chain Monte Carlo techniques were used to derive the posterior distribution of the model parameters to enable road capacity estimation. The results indicate that the lane-changing frequency significantly affects headway and road capacity. Lower lane-changing frequencies enhance the road throughput. For example, when the lane-changing frequency of some road sections is reduced by 33.3%, road capacity can be increased by 85.7%. The proposed method provides new strategies for traffic flow management based on probabilistic inference. Therefore, with the future support of connected and automated vehicles, the proposed method can quantitatively optimize traffic efficiency in highway weaving areas.

Ride comfort is a crucial factor influencing the acceptance and trustworthiness of autonomous driving technology, which is the foundation of high-quality autonomous driving services. How to continuously improve ride comfort while ensuring safety and efficiency is a key challenge for the application of autonomous vehicles. However, traffic conditions, pavement quality, and acceleration and deceleration of vehicles have an impact on passenger sensation, leading to difficulties in discerning the factors contributing to passenger comfort in complex traffic environments. This further results in speed planning biases and affects vehicle control effectiveness. Therefore, understanding the causal relationship between pavement quality, traffic conditions, speed decisions, and comfort sensation is crucial for enhancing the ride comfort of autonomous driving. In this regard, based on the vehicle-road-cloud integration architecture, this study proposes an intelligent decision-making and control framework for ride comfort improvements in autonomous driving. First, we consider onboard units and edge clouds as intelligent agents. Then, an intelligent speed planning model is constructed for autonomous driving based on deep reinforcement learning, utilizing counterfactual reasoning and expert recommendations to increase training samples in two directions and enhance the understanding of the driving environment and tasks. In experiments, a simulation environment was established using pavement data from Shanghai and traffic data from Next Generation Simulation (NGSIM). Speed control models were trained and tested under different pavement and traffic conditions. The experimental results show that with sufficient training samples, the counterfactual reasoning model can analyze the causal relationships between pavement and traffic conditions, speed planning decisions, and decision rewards. The counterfactual reasoning model analyzes the importance of states and clarifies the focusing points of speed planning at different stages. On the premise of driving safety and efficiency, the intelligent speed planning model trained with counterfactual reasoning and expert recommendation can decrease the longitudinal jerk and annoyance rate by 25.71% and 18.89%, respectively, compared to traditional reinforcement learning models. This results in a significant improvement in autonomous driving comfort, with interpretable driving decision results. The proposed autonomous driving decision-making, control framework, and intelligent speed planning approach can support online ride comfort improvement of autonomous vehicles and promote the development of autonomous driving mobility service.

To address the practical need for enhancing the refinement level of urban traffic demand management measures, this study explores the impact of introducing a subsidy and penalty mechanism into urban road travel reservation strategy (TRS) on heterogeneous users and the road network. Existing studies on TRS predominantly focus on static evaluations and lack the theoretical foundation for determining optimal reservation volume. Thus, they exhibit limitations in travel service methods and homogeneous travel choices. They also do not sufficiently consider the multidimensional decision variables of individual travel and the synergetic effects of urban multimode transportation. First, the road capacity distribution function was estimated using censored data models and the product limit method, introducing the sustained flow index to determine the optimal reservation volume for reserved roads. Second, a subsidy incentive and default penalty mechanism was introduced for users, to build an agent-based multi-objective optimization model, with the objective of designing travel incentive schemes aimed at maximizing social benefits, considering the interests of both managers and travelers. Within the context of multidimensional decision variables in individual travel, an agent-based multimode dynamic traffic simulation model was constructed based on users' travel reservation information, real-time traffic conditions, and the current state of road supply and demand matching multiple traffic information sources. The results indicate that: the optimal reservation volume ranged from approximately 0.79 to 0.89 of the actual road capacity. After implementing TRS, traffic performance and spatial-temporal distribution of congestion in the road network significantly improved. Specifically, with the introduction of the reward and punishment mechanism, the motor vehicle flow and average saturation of the road network improved by 4.8% and 13.3%, respectively, compared to without the strategy implementation. After introducing the reward and punishment mechanism, the proportion of users participating in travel reservation increased by 48.5%, and the willingness of travelers to adjust their travel times significantly increased. Heterogeneous travelers with different time values exhibited significant differences in their responses to TRS. These findings reveal the specific benefits of TRS for shifting user travel patterns and enhancing system performance, thereby providing new insights and methods for urban traffic congestion management, to support the sustainable development of urban traffic systems.

To address the issues of high testing costs and poor accuracy caused by individual differences in subjective perceptions and objective physiological information of passengers in traditional motion sickness comfort evaluations for intelligent electric vehicles, a motion sickness evaluation model based on vehicle motion parameters is proposed. First, based on the mechanism of motion sickness, real-vehicle tests were conducted to collect passengers' subjective perceptions of motion sickness along with corresponding physiological data, such as galvanic skin response (GSR), respiratory rate, and pupil diameter. Simultaneously, vehicle motion parameters, including longitudinal, lateral, and vertical accelerations, were collected. Second, passengers' subjective assessments of motion sickness comfort combined with objective physiological data to analyze significant differences and degrees of association using the Kruskal-Wallis (K-W) non-parametric test and partial effect equivalence methods. A motion sickness evaluation model was established based on multiple objective physiological signals such as GSR and pupil diameter variations. Furthermore, Pearson correlation analysis method was employed to construct a correlation matrix linking subjective motion sickness perceptions, objective physiological signals, and vehicle motion parameters. The relationships and sensitivities of vehicle motion parameters, including longitudinal, lateral, and vertical accelerations, and their rates of change with passenger motion sickness on real-world open roads were explored. Using a ridge regression analysis, weights for the impacts of different vehicle motion parameters and cumulative time on motion sickness comfort were determined, facilitating the development of a motion sickness evaluation model based on vehicle motion parameters. Finally, experimental validation was conducted to compare the motion sickness evaluation model developed based on multiple physiological signals with that based on vehicle motion parameters. The results showed that the overall prediction accuracy of the motion sickness evaluation model developed based on the vehicle motion parameters was 88.7%. The proposed evaluation model effectively mitigates the impact of individual physiological differences in traditional testing and achieves accurate comfort evaluations for intelligent electric vehicles. This study provides theoretical support and a practical foundation for the design and optimization of decision-making algorithms and control execution strategies for future intelligent electric vehicles.

Vehicle trajectory prediction is a core function of autonomous driving systems and serves as a critical foundation for downstream decision-making and planning modules, enabling safe and effective driving behaviors. To achieve accurate long-term trajectory prediction of surrounding vehicles in structured road scenarios, a hierarchical, interactive vehicle multi-modal trajectory prediction method, S-VectorNet, was proposed based on the classical VectorNet model. First, gated recurrent unit (GRU) was introduced to encode historical trajectory data and map information, thereby enhancing the temporal representation capability of the encoded features. Second, a two-layer interaction model incorporating attention blocks and graph neural network (GNN) was constructed to model interactions between traffic agents (target vehicles and surrounding agents) and the map. This approach improves the model's ability to capture long-range dynamic interactions. Next, dynamic scene representation module, which is updated over time, was proposed to capture the temporal correlations of individual motion states and interactions using multi-head attention mechanisms and time-series models. This allows the model to learn rich scene memory information. Finally, a two-stage trajectory generation method was employed to generate multi-modal trajectories, enhancing the model's ability to predict trajectory endpoint. Experiments conducted on the Argoverse dataset show that S-VectorNet reduces the minimum average displacement error by 12% and the minimum final displacement error by 22% compared to the baseline model on the validation set. On the test set, the minimum average displacement error is 0.83 m, and the minimum final displacement error is 1.23 m, demonstrating significant comprehensive performance advantages over other existing trajectory prediction models.

The purpose of accelerated editing of automotive durability load spectra is to improve the efficiency of fatigue analysis. To address the low editing efficiency of the multiaxial load spectrum and insufficient characterization of the damage properties of reference signals, a multi-parameter index-constrained editing method, combining load equivalent synthesis and the Wigner-Ville transform, was proposed. Equivalent synthesis of the multiaxial load based on the maximum damage parameter was performed using the multiaxial rain-flow projection technique. The synthesized load was then used as a reference signal to obtain the instantaneous energy spectrum (IES) using the Wigner-Ville transform, and optimal editing thresholds were determined by optimizing the dual-threshold model using a genetic algorithm. Therefore, time segments with little or no damage can be located to obtain the accelerated multi-axis load spectrum. The measured loads of the wheel-center six-component forces were edited as an example, and a modified down-resampling method was proposed during pre-processing to eliminate the damage discrepancies caused by the omission of peaks/valleys in traditional resampling. The proposed method was compared with a strain reference signal editing method in terms of damage characterization and editing effect. Furthermore, the editing effects of the proposed method were compared with those of time-domain editing and short-time Fourier transform editing methods across various aspects. The results showed that compared with the strain signal, the equivalent load synthesized by the proposed method was more effective in characterizing the damage properties of a multi-axis load, and the editing effect was better when used as a reference signal. Moreover, the IES derived from the Wigner-Ville transform accurately identified instances of no damage or minimal damage within in the original load data. The compression effect revealed that, under the same pseudo-damage retention ratio, the proposed method compresses the original load spectrum to 76.50%. Furthermore, the relative errors of the corresponding statistical parameters, such as the root mean square and kurtosis of the accelerated spectra, were within 15%. Power spectral density and level-crossing counting analyses showed that the proposed method effectively reduced the cyclic frequency only at small amplitudes, without significantly altering the amplitude/frequency-domain energy properties of the load. All characteristics outperformed those of the above two methods. Fatigue simulation results showed that the proposed method ensured the analysis accuracy and improved the computational efficiency by approximately 26% compared with the original loading, indicating a more significant acceleration effect. This method can effectively improve the efficiency of fatigue durability analysis for automotive components.