With the continuous advancement of sensing, communication and computing technologies,and coupled with rapid deployment of intelligent transportation infrastructure, the cooperative autonomous driving is becoming a key development direction in intelligent connected vehicle (ICV) systems. This paper aims to systematically review the core technical architecture underpinning the integration of smart highways and cooperative autonomous driving, while summarizing the current progress and emerging trends in this field. First, it introduces the conceptual evolution of smart highways, their classification framework, and their functional role in supporting autonomous driving, along with representative development paths and engineering practices in major countries and regions. Second, it analyzes the development status and limitations of core single-vehicle autonomous driving technologies, highlighting their performance bottlenecks in complex scenarios. Third, the paper focuses on key enabling technologies for cooperative autonomous driving,e.g., vehicle-road-cloud integration, cooperative communication, sensor fusion, and joint decision-making and control;and discusses the challenges of multi-source data fusion and mixed traffic flow management. Finally, it reviews current testing and validation approaches for cooperative driving systems, and addresses the major issues in their engineering implementation, followed by an analysis on future development trends in smart highway-vehicle cooperative systems.

Smart highway refers to a highway system that comprehensively applies new-generation information technologies and intelligent technologies to achieve digital, networked, and intelligent upgrades of highway infrastructure, thereby significantly enhancing transportation efficiency, safety, and sustainability. Although the strategic importance is increasingly prominent, current studies predominantly focus on specific technologies or regional development analyses, lacking a systematic review and comparison of the global evolutionary trajectory of smart highways. By synthesizing the trajectories of representative countries and regions, e.g., the United States, Japan, Europe, and China, the evolution of smart highways can be understood as proceeding through several historical stages, ranging from the early period of emergence and exploration, to the rise of ITS, and further to stages characterized by cooperative vehicle-infrastructure systems and digital-intelligent development. The conceptual characteristics and technological connotations of various stages are investigated. The evolutionary process of system architectures across countries is analyzed, through which a development trend is revealed toward a physical hierarchy structured around the cloud-edge-end paradigm and a logical hierarchy centered on sensing-communication-computing-application. On this basis, key enabling technologies are summarized in the domains of sensing, control, safety, and vehicle-infrastructure cooperation. The findings are expected to contribute to a more comprehensive understanding of the concepts, architectures, and technological evolution of smart highways, while providing the theoretical foundations and decision-making references for technical roadmapping, standards development, and large-scale deployment.

The conventional traffic monitoring methods are struggling to meet the dynamic perception and management requirements in complex road network environments with the continuous expansion of China's road network scale and the rapid growth of traffic demand. Smart highways are the crucial component of national strategy for China's strong transportation network. To achieve this strategy, a comprehensive traffic information monitoring system, covering entire chain of perception, analysis and service, is progressively establishing. The system is driven by new-generation information technologies, e.g., Internet of Things (IoT), artificial intelligence (AI), and big data. Accordingly, this review conducted a systematic study on the core technologies and application scenarios of traffic information monitoring for smart highways. First, it analyzed the evolution status of multi-modal perception technologies from three aspects of roadside perception, vehicle-mounted perception, and collaborative perception, focusing on the key issues, i.e., multi-sensor fusion algorithm optimization, equipment deployment strategy innovation, and complex environment adaptability enhancement. Second, it analyzed the deep analysis framework based on cloud-edge-end collaboration, as well as studied the hierarchical processing mechanism for multi-source heterogeneous data. The intelligent traffic state recognition and prediction were achieved by integrating AI algorithms. Meanwhile, a hybrid solution combining encryption techniques and federated learning was investigated to address the challenges of privacy protection and data security. Third, considering the characteristics of urban and highway scenarios, it elaborated on the technical pathways and practical outcomes of service systems, e.g., traffic flow monitoring, incident response, and parking management. It revealed the application values of individual perception and new energy monitoring technologies in novel transportation paradigms. Finally, an integrated development strategy was proposed through the comparative analysis on differences between urban and highway systems in terms of information acquisition, data processing and service models. The strategy emphasized on standards and specifications coordination, data platforms integration, and service functions optimization. The study findings provide theoretical support and technical references for smart highway construction, facilitating the transition of transportation systems towards greater intelligence and efficiency.

With the rapid evolution of intelligent transportation systems, traditional highway systems are accelerating their evolution towards digitalization, networking, and intelligence, thereby giving rise to the concept of smart highways. A smart highway comprehensively applies new-generation information and intelligent technologies to upgrade highway infrastructure, significantly improving transportation efficiency, safety, and sustainability. As a key component of smart highway systems, traffic control technologies have become hotspots in current research and engineering practice. This review systematically reviews the research progress in ramp, mainline, special section, and coordinated control technologies for smart highways. It covers ramp metering methods including fixed-time, actuated, and adaptive strategies; mainline control approaches e.g., variable speed limits, part-time shoulder use, and high-occupancy vehicle lanes; control algorithms designed for special scenarios like construction work zones and tunnels; as well as coordinated control methods encompassing ramp-mainline and expressway-urban road interactions. This review identifies existing challenges in the adaptability of control algorithms, coordination of opposing traffic flows, and microscopic behavior modeling. Furthermore, the real-time performance and practical applicability of intelligent control systems require improvements. Finally, this review delivers future developments in smart highway traffic control from the perspectives of connected environment, intelligent algorithms, and multi-scenario coordination, emphasizing the trends of connectivity, intelligence, and collaboration. The study aims to provide theoretical guidance and research references for establishing efficient, safe, and sustainable smart highway traffic control systems.

Smart highways refer to the comprehensive perception and autonomous decision-making intelligent highway system constructed through the integration of new generation information technologies, e.g., big data, cloud computing, Internet of Things, and artificial intelligence. China’s highway mileage ranks the first in the world, but the traffic safety situation is still severe. The smart highway construction has become an inevitable choice to improve traffic safety, efficiency, and management capabilities. This study systematically reviews the research progress of theoretical methods and technological applications in the field of smart highway and traffic safety from multiple dimensions, e.g., human, vehicle, road, environment, and digital intelligence management. The focus is on exploring the research themes, i.e., road infrastructure and traffic safety, vehicle-infrastructure coordination and traffic safety, and digital intelligence management and traffic safety. The domestic and overseas comprehensive research and application practices indicate that smart highways can effectively reduce accident risks; however, there is an urgent need to break through issues, e.g., human-machine interaction complexity, data security, and technical standardization. By systematically summarizing the existing theoretical methods and technological application bottlenecks, it is proposed to deepen the research on mixed traffic flow theory, digital twin safety management system, and autonomous prevention and control technology in the future. It will provide the theoretical support and practical reference for the sustainable development of smart highway traffic safety.

[Objective] This study investigated the lane-changing intention and trajectory prediction of autonomous vehicles in mixed traffic flow scenarios in expressway interweaving zone. A novel Gra-Informer model with dynamic spatiotemporal fusion architecture was proposed. [Method] First, the model extracted high-dimensional intention features by using a graph neural network based on vehicle interaction topology. The intention-aware based spatiotemporal representations were established. Then, the encoder employed probabilistic sparse attention, combined with temporal distillation, to achieve the efficient long-sequence feature extraction and compression. Finally, the decoder used intention features as conditional priors to generate the vehicle trajectory predictions. [Result] The model was evaluated by using Savitzky-Golay filtered natural driving data, demonstrating 91.54% accuracy in lane-changing intention prediction. In the performance comparison, the model shows the prominent spatiotemporal features for modeling ability. During 3 seconds short-term prediction, the average displacement error reduced by 12.32%-30.06%; and the terminal error and the misjudgment rate both reduced by 4.00%-30.29%. During 5 seconds long-term prediction, the average displacement error reduced by 17.51%-33.33%; the terminal error reduced by 17.19%-33.47%; and the misjudgment rate reduced by 17.50%-33.50%. These outcomes demonstrate the advantage of modeling temporal coupling characteristics in interactive scenarios. [Conclusion] Gra-Informer model can effectively capture the dynamic interaction characteristics between autonomous vehicles and traffic vehicles through the spatiotemporal joint modeling and the intention-trajectory coordination optimization. The model introduces the prior intention information as conditional constraints for the trajectory prediction. It not only maintains the physical feasibility, but also significantly improves both short-term and long-term prediction accuracy in complex traffic scenarios.

[Objective] The study investigated to accurately simulate the traffic flow characteristics in expressway curve sections in mountainous regions, and alleviate the traffic problems at curves by using autonomous driving technology. A two-lane cellular automaton model, considering the mixed traffic of manually driven and autonomous vehicles in curve sections, was established. [Method] The correctness of model was verified through curve radius and road friction coefficient, considering the influence of different curve conditions on traffic flow. The influence of curve section transition curves was proposed. The influence degree of different proportions of transition curves on traffic flow in curve sections was analyzed to improve the traffic flow simulation environment. On this basis, the scenarios with mixed connected and automated vehicles (CAVs) were introduced to explore the influence of different CAV penetration rates on traffic flow in curve sections. [Result] Different proportions of transition curves have the significant influence on traffic flow in curve sections. The traffic efficiency at curves is optimum when the ratio of three elements (i.e., curve section transition curve, circular curve, transition curve) is close to 1∶1∶1. The traffic congestion at curves is gradually alleviated when CAVs appearance on roads. When all vehicles are CAVs, the number of lane-changing drops to 0, and no congestion occurs anymore. In addition, a unique traffic flow plateau phenomenon is observed in the traffic flow in curve sections, i.e., the vehicles run stably for a long time in certain density conditions, and the traffic flow remains a fixed value without varying with density. [Conclusion] The proposed model effectively simulates the traffic flow characteristics in expressway curve sections in mountainous regions, and clarifies the optimal proportion of transition curves and the positive role of CAVs.

[Objective] The study investigated the influence of heavy-duty vehicles flow on the on-ramp system. The heterogeneous traffic flow model was developed for the on-ramp, aiming to examine how heterogeneous heavy-duty vehicles affect the traffic flow characteristics in merging areas. [Method] An improved two-lane cellular automaton model was employed in this study. By analyzing the characteristics of vehicle driving behaviors, the lane-changing rules were systematically optimized. The heterogeneous traffic flow model for the on-ramp system was developed with the aim of investigating how the traffic flows on main road and ramp affect the traffic characteristics in merging areas in heavy-duty truck operating conditions. [Result] As the proportion of heavy-duty trucks increases, the traffic congestion in merging areas of on-ramp system becomes more pronounced, accompanied by a corresponding reduction in the saturation flow rate. When the on-ramp entry probability and the proportion of heavy-duty trucks remain unchanged, the traffic flow in merging areas increases with the main road entry probability, which is below 0.4. The increase of heavy-duty trucks proportion on the main road negatively influences the performance of merging areas. Both the critical entry probability from main road and the saturation flow exhibit the decreasing trend as the proportion of heavy-duty trucks on main road increases. When the main road entry probability and the proportion of heavy-duty trucks remain unchanged, the speed in merging areas decreases as the on-ramp entry probability increases, which is below 0.3. The increase of heavy-duty trucks proportion on the ramp has a relatively small influence on the flow in merging areas, but has the significant effect on the speed. [Conclusion] The model, considering on-ramp heterogeneous traffic flow, can effectively reduce the probability of on-ramp traffic congestion.

[Objective] To improve the early warning capability for rockfall disasters on highway slopes and ensure road traffic safety, a risk assessment model for rockfall hazards was constructed. [Method] The study selected multiple analytical evaluation indicators from four aspects( i.e., unstable rock conditions, slope conditions, surface conditions, and other external factors, covering key geological, geomorphological, and environmental elements. The analytic hierarchy process), combined with the entropy-based disaster model, was employed to address the complex relations among these indicators. It integrated both expert subjective judgment and data objectivity for improving the scientific rigor and accuracy of the assessment system. Furthermore, referencing national standards and empirical data, the hazard classification standard for rockfall disasters was established. The risks were categorized into four levels, i.e., red (extremely high risk), orange (high risk), yellow (moderate risk), and blue (low risk). [Result] The model exhibits high accuracy in assessing rockfall hazards. The regions classified as red and orange closely align with the historically high-frequency rockfall disaster regions, while yellow and blue regions correspond to the regions with lower or negligible disaster occurrence. The systematic quantitative analysis on rockfall hazards along highways was achieved by using this model, providing a scientific basis for slope protection design and disaster early warning. [Conclusion] The rockfall hazards can be effectively evaluated with the proposed method. It offers the robust support for future monitoring and early warning systems for rockfall disasters on highway slopes, significantly contributing to the improvement of safety protection measures along roadways.

[Objective] Traditional expressway service areas have limited functions. Their integration with regional economies is lack of synergy. Transforming them from closed operations to opened-up development is widely recognized. In this context, the scientific evaluation on opened-up development potential of expressway service areas has become the key premise for the service areas development according to local conditions. [Method] First, the typical cases and literature on opened-up service areas were analyzed. The evaluation indicator system was established from four aspects, i.e., locational conditions, socio-economy, tourism resources, and project conditions. Next, the entropy weight method was used to determine the indicator weights. TOPSIS method was applied to calculate the comprehensive scores for opened-up development potential. K-means clustering algorithm was adopted to classify the results. Finally, 84 service areas located at 14 expressway sections in Hebei Province were selected. These service areas were chosen for high traffic, modern facilities, and abundant resources. Their opened-up development potentials were thoroughly evaluated. [Result] Based on the distributed locations and characteristics, 15 pilot service areas have been selected out. According to the principle of One Area, One Feature, various unique development themes have been defined for different service areas. [Conclusion] The service areas’ opened-up development relies not only on transportation infrastructure, but also closely links to the regional economies, tourism vitality, and cultural resources. 15 service areas with great development potential, distinctive features, and balanced overall distribution in Hebei Province can be prioritized as the pilot service areas

[Objective] Modular buses provide novel approaches to promoting the sustainable development of public transportation. This study aims to introduce the modular buses, i.e., a new form of public transport. It uses the advantages of flexible module coupling and decoupling, as well as the seamless transfer, to eliminate the transfer waiting time and optimize the bus route design. [Method] First, the main operational issues of conventional bus systems were examined. The study compared traditional bus in-station transfer with modular bus seamless transfer. It summarized the operational advantages of modular buses, and identified the key considerations for path design. Second, a line design model was developed with modular buses as the study object. With the dual objective of reducing operating costs and passengers’ generalized travel costs, a modular bus operation route design model considering passenger path assignment was constructed. Third, in the conditions of established set of running routes, set of path candidates and travel demand, the model was applied to obtain the modular bus running design and the corresponding passenger path allocation scheme. Finally, the example analysis was carried out by adopting a simple traffic network; and the sensitivity analysis on the model was carried out by changing the line length limit and adjusting the weight of each part of the objective function of model. [Result] The proposed model produces reasonable line designs and passenger path assignments. The computation time is within seconds. The passenger transfers are kept within two times per trip. [Conclusion] The operating lines design and passenger paths assignment obtained with the proposed model are helpful for modular bus to reduce the operation cost and generalized travel cost at the same time, which has the practical application values.

[Objective] To address the initial support failure caused by large deformation during tunnel excavation through weak rock strata in rapid topographic change terrains, this study introduced the combined resistance-release concept of resistance-limiting and energy-dissipating support. Taking a tunnel on Zhenxiong-Hezhang expressway as engineering case, the study investigated the structural design and application of resistance-limiting and energy-dissipating support. [Method] A numerical model of rapid topographic change terrain was established to analyze the causes of large deformations. The steel-plate resistance-limiting and energy-dissipating support was simplified into the I-shaped structure. Numerical simulations and model tests were conducted to analyze the force-deformation characteristics of I-shaped steel resistance-limiting dampers. The key parameters were determined, e.g., peak resistance, constant resistance, and compressibility ratio. An initial support scheme was designed and validated through field applications. [Result] The primary cause of large deformation is identified as the downward transfer of mountain self-weight stress in rapid topographic change terrains. The I-shaped steel resistance-limiting dampers exhibit the staged compressive deformation, and provided the bearing capacity exceeding 0.91 MPa. The field tests demonstrate that after stress release via damper deformation, the maximum stresses on initial support steel frame and shotcrete are 23.1 MPa and 20.3 MPa respectively, both below the material strength limits. [Conclusion] The initial support structure integrated with I-shaped steel resistance-limiting dampers effectively releases surrounding rock stress through controlled deformation. It reduces the internal stress in support system, and ensures construction safety and quality.

[Objective] The study systematically investigated the influences of different activation methods on the structure of desulphurized rubber powder and the properties of modified asphalt. Three activation processes (i.e., microwave, chemical, and twin-screw extrusion) were applied to pre-treat the waste rubber powder, and prepare the corresponding modified asphalts. [Method] The chemical composition and microstructure of activated rubber powders were characterized by using Fourier transform infrared spectroscopy, scanning electron microscopy, and thermogravimetric analysis. Simultaneously, the dynamic shear rheometer and bending beam rheometer tests were performed to evaluate the high-and low-temperature performances of modified asphalts. [Result] While the microwave and chemical activations effectively break the unsaturatedCCbonds in the rubber powder, they have limited effects on the destruction of crosslinked network structure. In contrast, the twin-screw extrusion activation significantly disrupts the polysulfide crosslinks through shear forces, enhancing the solubility of rubber powder. The rheological tests reveal that the rubber powder with high solubility improves the low-temperature cracking resistance of asphalt, but the main chain fracture leads to 30% decrease in high-temperature rutting resistance. Moreover, the twin-screw extrusion activation promotes the refinement of rubber powder particles and the generation of surface functional groups, but the excessive desulphurization weakens its mechanical properties. [Conclusion] Different activation processes affect rubber powder structure and modified asphalt performance through distinct pathways. The microwave and chemical activation are suitable for scenarios requiring high-temperature performance, while the twin-screw extrusion activation has significant advantages in improving low-temperature crack resistance. These findings provide the theoretical guidance for selecting activation processes and precisely controlling the performance of rubber powder modified asphalt.

[Objective] This study proposed a multi-modal data fusion method for remaining useful life (RUL) prediction on expressway electromechanical equipment. The goal was to improve the equipment life prediction accuracy and robustness, and to provide the reliable support for proactive maintenance. [Method] First, a dataset named SW-RUL-DATAS was built, covering the transient voltage and current, temperature and humidity, maintenance records, and system logs. Second, the features were extracted separately from different modal data, including environmental data extraction with convolution, electrical signals extraction with frequency and time-domain analysis, and text data extraction with BERT. Third, an autoencoder was applied for feature fusion and dimensionality reduction,yielding the unified,high-quality feature representation. Finally, the fused sequence features were fed into the recurrent neural network. The hyperparameters were optimized by using AutoML to achieve the end-to-end RUL prediction. [Result] The tests on three sub-datasets indicate that the proposed method outperformed Kaplan-Meier, ARIMA, CNN+GRU and CNN-LSTM in both MSE and RMSE. The model can accurately predict potential faults of cameras, lighting systems, and other equipment 2-5 days in advance, with accuracy and recall improved by 5%-15% compared with baselines models. Moreover, the data sensitivity tests show that the transient electrical signals, maintenance logs, and environmental data all make significant contributions. The multi-modal fusion is the key factor for performance improvement. [Conclusion] The proposed multi-modal fusion prediction framework effectively integrates heterogeneous multi-modal data. It achieves higher accuracy and robustness in expressway electromechanical equipment RUL prediction. The study highlights the importance of transient electrical features and maintenance logs, as well as shows the effectiveness of deep learning with AutoML. The method provides the reliable data and technical support for the predictive and proactive maintenance of expressway electromechanical equipment.

[Objective] To accurately predict the unconfined compressive strength of cement stabilized recycled aggregate, so as to shorten the mix design period, the LSTM-based cement stabilized recycled aggregate strength prediction model was established. [Method] The particle swarm optimization (PSO) was used to perform a global search for the best parameters of LSTM model. The test data of unconfined compressive strength of cement stabilized recycled aggregate with different ages and mix proportions were used as the dataset. Eleven variables, e.g., cement content, moisture content, recycled aggregate replacement rate, and curing age, were used as the model input layers. The unconfined compressive strength was used as the output layers. The evaluation indicators were introduced to analyze the performance of established LSTM model and PSO-LSTM model. The eleven input layers were divided into material influences and non-material influences. The correlation coefficients were introduced to study the correlation between variables and unconfined compressive strength. [Result] The accuracy of LSTM model and PSO-LSTM model both reaches over 98%, and both of the models can accurately predict the unconfined compressive strength of cement stabilized recycled aggregate. MSE, RMSE, MAE, MAPE, R² of models indicate that PSO-LSTM model has higher accuracy, smaller error rate and better fitting effect. According to the model prediction results, the strength of cement stabilized recycled aggregate with different proportions was compared. The most suitable cement content, recycled aggregate replacement rate and skeleton type were obtained. [Conclusion] By introducing PSO, the accuracy and precision of LSTM model can be effectively improved for strength prediction on cement stabilized recycled aggregate.

[Objective] The study investigated to reduce energy losses caused by tire-pavement rolling resistance on asphalt pavement while meeting the performance requirements for road construction. The response surface methodology was used to incorporate PE/PP elastic modifiers, SBS modifiers, and rubber oil into base asphalt to prepare PPSE (polyethylene polypropylene SBS elastic) modified asphalt. [Method] The technical performance of PPSE modified asphalt was evaluated through high-temperature gel permeation chromatography tests, low-temperature bending beam rheometer tests, and dynamic shear rheometer tests. The road performance advantages of PPSE modified asphalt mixtures were investigated through rutting tests, low-temperature beam bending failure tests, pendulum-type skid resistance tests, and dynamic modulus tests. The tire-pavement contact tests were carried out by using pressure-sensitive film technology. The rolling resistance was evaluated in conjunction with contact mechanics theory. [Result] PPSE modified asphalt exhibits excellent technical performance, effectively functioning as a binder. PPSE modified asphalt mixtures demonstrate outstanding high-temperature stability and low-temperature crack resistance; the dynamic modulus performance contributes to reducing rolling resistance; and the skid resistance meets the road acceptance requirements. The contact stress distribution between PPSE modified asphalt pavement and tire is significantly non-uniform. The high shear modulus of PPSE modified asphalt within temperature-time domain enables stronger cohesion among aggregate particles under the same static load. That will form more stable mixture skeleton force chain, reduce adhesion by 21.12%, and significantly lower the rolling resistance (viscous force). [Conclusion] PPSE modified asphalt has excellent viscoelastic mechanics and cohesive characteristics, which improves the deformation coordination uniformity of its mixture, modifies the tire-pavement contact stress distribution, and reduces the tire rolling resistance.

[Objective] To address the issues of insufficient robustness and accuracy in vehicle state estimation by using the traditional Kalman filtering in non-Gaussian noise conditions, the unscented Kalman filtering based on the maximum correntropy criterion was proposed. This method aims to effectively suppress the influence of non-Gaussian noise, thereby significantly enhancing the reliability and accuracy of estimating key vehicle state parameters, e.g., yaw rate, longitudinal velocity, and lateral velocity. [Method] First, the nonlinear three-degree-of-freedom vehicle dynamics model was constructed. Then, based on the improved Dugoff tire model and integrating data collected by on-board sensors, a state observer capable of simultaneously observing yaw rate, longitudinal velocity, and lateral velocity was designed. Finally, through Simulink-CarSim co-simulation platform, the effectiveness of the proposed method was verified in double line and sine wave steering input conditions in non-Gaussian environment. [Result] In non-Gaussian noise conditions, the traditional unscented Kalman filtering suffers from poor convergence, weak tracking performance, and large following errors. In contrast, the proposed method effectively suppresses the non-Gaussian noise, significantly improving both convergence and tracking performance. This method can accurately and efficiently estimate the key vehicle state parameters, e.g., yaw rate, longitudinal velocity, and lateral velocity. [Conclusion] In non-Gaussian noise conditions, the proposed method demonstrates superior robustness, thereby providing more accurate and reliable state information for practical vehicle dynamics control systems, so as to effectively enhances the vehicle’s active safety and driving stability.

[Objective] The end of RC beam is prone to fatigue damage after being subjected to repeated actions of vehicles. It is necessary to study the fatigue shear behavior of beams with initial damage after strengthening, as well as clarify the mechanical behavior of damaged beam end after shear strengthening, thereby providing the technical support for reliable strengthening in the sheared area of beams. [Method] The side near surface mounted (SNSM) CFRP technique was adopted to strengthen RC beams. The shear fatigue mechanical behavior of beams by using this technique were studied. The material strain and the loading cross-section deflection of one comparative beam and two strengthened beams under fatigue loading were analyzed through experiments. Considering the initial damage of actual service beams before strengthening, in order to better simulate the actual situation, two strengthened beams (one strengthened only in the shear span area, and the other strengthened with longitudinal prestressed CFRP) were subjected to 2 million times of fatigue loads before undergoing shear strengthening; and then continued to withstand 500 000 times of fatigue loads. [Result] The residual shear bearing capacity of two strengthened beams increase by 5.6% and 9.8% respectively compared with the unstrengthened beams. The rapid decrease of test beam stiffness damage mainly occurs during the first 200 000 cycles of fatigue loads. The subsequent decrease of stiffness damage rate tends to be flat. In addition, due to the longitudinal prestressing of CFRP, the shear resistance of longitudinal bars is improved, resulting in better fatigue behavior of prestressed CFRP strengthened beams than ordinary CFRP strengthened beams. [Conclusion] The SNSM and longitudinal prestressed CFRP composite strengthening technique can improve the shear behavior of RC beams, and provide the technical support for shear fatigue reinforcement of beams with initial damage during service.

[Objective] To provide references for the composite reinforcement design and construction of RC beams, the prestressed carbon fiber-reinforced polymer (CFRP) bars embedded RC beams with textile reinforced concrete (TRC) layer were adopted. This study investigated the influences of prestress state and different reinforcement methods on the bending resistance of beams. [Method] The four-point bending tests were conducted on one conventional RC beam and three reinforced beams. The failure modes, load levels at different stages, crack distribution and width, as well as the strain of reinforcing materials of the reinforced beams were analyzed.A sectional analysis method was employed to develop the bearing capacity formula for RC beams reinforced with TRC and embedded CFRP bars. [Result] Compared with the unreinforced RC beams,the composite reinforced beams exhibit significant higher cracking, yield,and ultimate loads. The magnitudes of these improvements consistly supass those achieved by TRC-only reinforcement. For the composite-reinforced beams, the application of prestress notably enhances the cracking load compared with the non-prestressed counterparts, while having negligible influence on the yielding and ultimate loads. The flexural stiffness of composite-reinforced beams is significantly higher than that of both unreinforced and TRC-reinforced beams. The cracks in composite-reinforced beams are characterized by their fine and dense distribution. The number and distribution of cracks in composite-reinforced beams are less affected by whether prestress is applied or not. The synergistic interaction between fiber textile and CFRP bars cn effectively reduce the stress level of longitudinal reinforcement at beam bottom. The cross-sections remain planar during bending, thus verifying the validity of plane section assumption. The calculated ultimate bearing capacity deviated by less than 10% from the measured values, demonstrating the good applicability of the proposed model. [Conclusion] The composite reinforcement can significantly improve the bearing capacity of RC beams. The proposed calculation method for bearing capacity provides an effective basis for engineering reinforcement design.

[Objective] The water log at expressway superelevation transition section is a common hidden danger to traffic safety. The catchment path and water log distribution characteristics at superelevation transition sections are essential for improving road safety. [Method] A method was developed to calculate the maximum catchment path length based on the principle of maximum resultant gradient. The surface rainfall was simulated by using discrete phase model and Eulerian wall film model. The influences of various geometric parameters on catchment path and water log distribution characteristics at superelevation transition sections were systematically investigated. [Result] The maximum catchment path generally follows a parabolic shape. Based on different starting points of path, the flow directions are classified into two patterns, i.e., median strip-hard shoulder edge-median strip, and hard shoulder edge-median strip-hard shoulder edge. The maximum catchment path length increases with the longitudinal gradient. When originating from the median strip, the maximum catchment path length growth is fast with smaller longitudinal gradient. The maximum catchment path length increases as the superelevation runoff decreases, especially with steeper longitudinal gradients. The ratio of catchment path lengths across different road widths corresponds to their geometric proportions. Water log distributions along maximum catchment path show three patterns with longitudinal gradient variation, i.e., single peak, double peak, and continuous increase. The superelevation runoff and road width mainly affect the absolute thickness of water film, but have limited influence on the water log distribution trend. [Conclusion] This study quantitatively reveals the influence of geometric parameters on catchment path and water log distribution characteristics at superelevation transition sections. The results provide data support for the catchment design of superelevation transition sections.

[Objective] A peak particle vibration velocity calculation model considering medium interface effects was proposed to improve the accuracy of vibration response prediction on adjacent buildings during tunnel blasting. [Method] The study based on the physical mechanism of blasting stress wave propagation and reflection in rock and soil medium. The elastic wave theory was combined as well. The influences of incident angle and reflection angle on stress wave propagation path and amplitude were considered in different medium interface conversion conditions. The theoretical formula of blasting vibration peak particle velocity was proposed. The exit section of Guanlinzi tunnel on Baoji-Hanzhong expressway was taken for an engineering example. The theoretical prediction model was verified by using the monitoring data of blasting vibration velocity of in-situ monitoring points. [Result] The theoretical prediction values of peak vibration velocity were compared with the measured values of five monitoring points at monitoring section. First of all, the trend of prediction values and measured values was basically the same. Second, the peak vibration velocity decreased with the increase of distance from the blasting source. From the relative error result, the minimum error was 3.83%, the maximum error was 13.97%, and the average error was 8.21%. Therefore, the relative error was comparatively small. [Conclusion] The theoretical prediction formula of peak vibration velocity proposed in this study can accurately reflect the influence of blasting vibration on adjacent buildings. Besides, the relative error between predicted values and measured values is relatively small. The result indicates that the theoretical prediction formula considering interface effects can reasonably reflect the blasting stress wave propagation rule in different media, as well as accurately predict the peak vibration velocity.

Smart highways have been a focal point in China's road transportation sector in recent years, garnering significant attention from government departments, highway operators, industries, and academia. A series of demonstration projects have been implemented, yielding substantial results. Currently, the field of smart highways still faces numerous challenges. There is an urgent need to integrate interdisciplinary theories, fully apply cutting-edge technologies represented by new-generation digitalization and artificial intelligence, and align with China's social structure, governance models, and practical needs. This will facilitate the transition from "information guidance" to "collaborative optimization" and further to "autonomous operation," thereby supporting the national goal of building a country with a strong transportation network and contributing to China's transportation modernization.

To this end, the editorial office of the Journal of Highway and Transportation Research and Development has organized dozens of domestic professors, scholars, and experts to systematically summarize the research, development, and applications in the field of smart highways and related areas over recent decades. Their work covers the evolution of smart highways, traffic information monitoring, key technologies in traffic control, traffic safety, vehicle-road coordination, and autonomous driving, while also offering insights into future prospects. This is a highly significant undertaking for the digital and intelligent development of China's highway transportation, providing a wealth of resources for academic research and technological development in related fields.

[Objective] To comprehensively understand the development trends and research status of traffic flow prediction in the field of intelligent transport, 551 publications in the Web of Science core database from 2003 to 2023 were collected as research data sources. [Method] The bibliometric analysis method was adopted, and VOSviewer bibliometric tool was used to conduct a comprehensive review and analysis on machine learning based traffic flow prediction from multiple dimensions. Based on journal and collaboration network, the most influential journals, countries, institutions, and authors were identified. Simultaneously, the references co-citation and keyword co-occurrence analysis were utilized to explore the core research topics and evolutionary trends in traffic flow prediction. The keyword co-occurrence analysis was used to identify the main research clusters in intelligent transport. Starting from three research hotspots of statistics, traditional machine learning, and deep learning, the development status of traffic flow prediction technology in this field was discussed. [Result] ‘IEEE Transactions on Intelligent Transportation Systems’ publishes far more publications than other journals, and China ranks top 1 in the number of publications. However, the cross-team and cross-national collaboration still needs to be strengthened. From the core literature in recent years, it is found that technologies have become the main research topics in traffic flow prediction, e.g., deep learning and neural networks. [Conclusion] The traffic flow prediction technology faces three major challenges, i.e., data quality, computational complexity, and model generalization ability. Future research should focus on improving data quality, optimizing model structures, exploring lightweight models, and enhancing model generalization ability. Additionally, the utilization of big data, large models, and other technologies will propel the sustained development and interdisciplinary collaboration within the field of intelligent transport.

[Objective] The study investigated the applicability of linear delineators in expressway tunnel curved sections with varying radii, providing a basis for the setting of visual guiding system in curved sections and improving traffic safety. [Method] The simulated driving scenarios were constructed incorporating four types of delineators (traditional standard-defined, short-strip, medium-strip, and long-strip) and three curve radii (500, 1 500, 2 500 m). Vehicle speed data in tunnel curve areas, i.e., approach section, curved section and departure section, were collected at the frequency of 30 Hz. The analysis was carried out on average speed, speed profile trend, speed selection indicators (deceleration initiation point, entry speed, minimum speed, and exit speed), and speed control indicators (speed reduction amplitude and speed differential). [Result] Among curves with all three radii, the linear delineators demonstrate better applicability than traditional standard-defined delineators. Significant differences are observed in the effectiveness of different-length linear delineators in approach sections and curved sections. In approach sections, compared with short-strip delineators, the medium-strip and long-strip delineators facilitate earlier detection, earlier decision-making, and earlier action by drivers toward upcoming curve, resulting in earlier deceleration responses. Therefore, medium-strip or long-strip linear delineators are recommended for tunnel curved sections. Cautions are advised when installing linear delineators in curved sections with 500 m radius. For curved sections with 1 500 m radius, the length of linear delineators can be selected based on specific speed control requirements. In curved sections with 2 500 m radius, short-strip delineators may be installed from a cost-saving perspective. Medium-strip linear delineators have the broadest range of applicability. [Conclusion] It is recommended to set linear delineators in tunnel curved sections. Cautions are advised when installing linear delineators in curved sections with 500 m radius. For curved sections with 1 500 m radius, the length of linear delineators can be selected based on specific speed control requirements. In curved sections with 2 500 m radius, short-strip delineators may be installed from a cost-saving perspective. Medium-strip linear delineators are recommended as the most widely applicable scheme, because they can induce drivers to decelerate in advance, and the speed curve is under each radius.

[Objective] The mixed traffic flow comprising intelligent connected vehicles (ICV) and human-driven vehicles (HDV) will persist over a prolonged transition period during the deployment of ICV. HDV drivers differ in their acceptance attitudes toward ICV, resulting in heterogeneous car-following behaviors. The refined lane management strategies that accommodate this behavioral heterogeneity are therefore urgently needed. [Method] A questionnaire survey was conducted to collect car-following behavior and attitudinal data from 308 HDV drivers interacting with ICV. The K-means clustering algorithm was applied to classify drivers into positively and negatively inclined categories. The car-following model parameters were calibrated separately for each category. Four managed-lane schemes were then formulated. A basic unidirectional three-lane expressway segment was constructed using microscopic simulation platform SUMO. The capacity, space mean speed, and time-to-collision served as measures of effectiveness. The applicability of each scheme was quantitatively evaluated with varying ICV penetration rates and HDV attitudinal compositions. [Result] The statistically significant car-following behavioral differences were identified between the two driver categories. Positively inclined drivers exhibit car-following patterns comparable between following ICV and following HDV. Negatively inclined drivers, in contrast, maintain substantially enlarged headways and display abrupt speed adjustment profiles. The operational efficiency and safety of mixed traffic flow both improve with increasing ICV penetration rate and a growing share of positively inclined drivers. With ICV penetration rate of 0.3, one mixed managed lane shared by ICV and positively inclined HDV is recommended. The same scheme applies at a penetration rate of 0.6 when positively inclined drivers constitute less than 60% of all HDV drivers. When this share exceeds 60% at the same penetration level, one ICV-exclusive managed lane is recommended. With the penetration rate of 0.9, two mixed managed lanes are recommended if the positively inclined share remains below 40%. When this share exceeds 40%, two ICV-exclusive managed lanes are recommended. [Conclusion] The Managed-lane strategies for mixed traffic flow should jointly account for the ICV penetration rate and the attitudinal composition of HDV drivers toward ICV. The number of managed lanes and their access control rules should be dynamically adjusted accordingly, thereby achieving a coordinated optimization of operational efficiency and traffic safety.

[Objective] To analyze the safety of pedestrian crossing un-signalized intersection, the method for consecutive traffic conflicts discrimination and analysis was proposed. It based on the traffic conflict techniques, and considered the continuity of pedestrian crossing process. [Method] 9-hours video data were collected by unmanned aerial vehicle from 6 un-signalized intersections of a rotary interchange in Shenyang City. T-Analyst software was used to obtain the road users’ trajectories and conflict indicators. A unified conflict severity indicator was developed by integrating post-encroachment time, yaw rate ratio, and change of acceleration. The 85th and 15th quantiles of indicators were used as the critical values to distinguish the conflict severity. Finally, the negative binomial distribution model and the paired-samples T test were employed to investigate the occurrence and severity difference of consecutive conflicts respectively. [Result] A total of 925 consecutive conflicts were identified at 6 locations. 423 serious conflicts were identified by using the unified conflict severity indicator. 279, 398 and 596 serious conflicts were identified by using post-encroachment time, yaw rate ratio, and change of acceleration respectively. The occurrence of consecutive conflicts was closely related to traffic volumes of pedestrian, vehicles and non-motor vehicles. The frequency of consecutive conflicts was the highest when the pedestrian flow was over 10 persons per minute, the vehicle flow is 6-9 vehicles per minute, and the non-motor vehicle flow is 10-16 vehicles per minute. As the vehicle flow increased further, the consecutive conflict frequency decreased, likely due to the reduced acceptable gaps for pedestrian crossing. When pedestrian encountered successive conflicts when crossing, the severity difference between two conflicts mainly depended on the type of conflict objects, i.e., vehicles and non-motor vehicles. The conflict with non-motor vehicles exhibited higher severity. In addition, the risk of pedestrian crossing at merging areas of roundabout is significantly higher than that at diverging areas. [Conclusion] The study result will not only enrich the methodological system of traffic conflict analysis, but also provide a basis for improving pedestrian crossing safety.

[Objective] The frequency of traffic accidents involving heavy-duty trucks has been on a continuous rise. Crashes in heavy-duty truck transportation often result in more severe consequences. Identifying the factors influencing severity of such accidents is therefore essential for the prevention of heavy-duty truck transport accidents. [Method] 2 616 heavy-duty truck transport accident records were collected from the crash report sampling system database of United States from 2016 to 2020. The correlation analysis and recursive feature elimination algorithm were employed for feature selection. Fifteen potential factors influencing severity of heavy-duty truck transport accidents from seven dimensions (i.e., driver, vehicle, road, environment, time, space, and accident form) were extracted. Additionally, the class imbalance in training set was addressed by using adaptive synthetic sampling algorithm based on K-nearest neighbors. Four accident severity prediction models, i.e., LightGBM, XGBoost, random forest and SVM, were constructed based on data preprocessing. SHAP method was introduced to analyze the influencing mechanism of significant factors on accident severity. [Result] The proposed LightGBM model exhibits optimal overall performance. In terms of predictive accuracy, LightGBM model demonstrates superior performance with accuracy, F1 score, and AUC values of 0.872 1, 0.872 4, and 0.966 9 respectively. Regarding training speed, LightGBM model achieves the training speed of 7.65 s, which is more than 2.5 times faster than that with XGBoost, and notably faster than those with SVM and random forest, with speed advantages of 7 times and 16 times respectively. [Conclusion] The SHAP-based model interpretation indicates that collision manners, unsafe driving behaviors, time of day, month, days of week, and roadway attributes are critical factors influencing accident severity. Among them, driving with the influence of alcohol or drugs, disregarding traffic signs or signals, severe fatigue driving, speeding, and distracted driving violation, as well as head-on and angle collisions, significantly contribute to the occurrence of severe injuries and fatal accidents. Moreover, the probability of fatal accidents involving heavy-duty trucks is higher during the time period of 0:00-4:00. These findings provide a theoretical foundation for accident prevention and the safety management of heavy-duty truck transport.

[Objective] Suspension bridges exhibit significant geometric nonlinear effects. During the iterative calculation for finding main cable form in completed bridge state, the issues (e.g., calculation divergence and low computational efficiency) exist due to the influence of initial calculation values and iteration increments. To investigate the influence of frictional contact between main cable and main cable saddle on the form-finding of suspension bridge main cables during construction, this paper conducts research on a numerical analytic algorithm for cable form-finding. [Method] First, a recursive calculation formula for cable height was established based on the relation between the slope at cable end and the cable force slope. Second, the mechanical relational expressions for hanger points were established according to mechanical equilibrium conditions. Third, an analytical equation set for finding main cable form in the completed bridge state was formulated based on closure conditions for mid-span elevation and end-point elevation. Finally, the main cable form-finding for the completed bridge was achieved by solving the constructed nonlinear equation set. The analytical equations for main cable during construction were established based on the principle of unstressed length conservation, the frictional contact equations between main cable and saddle, and the compatibility conditions. [Result] The differences between cable alignment and unstressed length calculated with the proposed analytical algorithm for finding completed bridge main cable form and the finite element values are controlled within 3.0 mm. The analytical algorithm offers advantages, e.g., high computational efficiency and parametric modeling capability. [Conclusion] The frictional contact effect between main cable and saddle has different influences on mid-span and side-span main cable. As the girder erection proceeds, the difference of these influences becomes nonlinear, and the nonlinear effect becomes increasingly significant. The fundamental reason for the influence of frictional contact on main cable alignment calculation result is revealed to be the change in the tangent point position of cable on the main saddle. Attention should be paid to the frictional contact between main cable and saddle during the construction of suspension bridges.

[Objective] To accurately predict the lane changing decision behaviors of surrounding vehicles at signalized intersection areas, so as to enhance the driving safety and ride comfort of autonomous vehicles, the lane changing decision prediction method based on spatiotemporal graph convolutional neural networks was proposed. [Method] First, the spatial interaction relations, between lane changing vehicles and their surrounding vehicles at intersection area, were modeled by using graph theory. Second, GCN was then employed to extract the spatial features of multi-vehicle interactions. Subsequently, LSTM neural network was utilized to capture the temporal evolution patterns of the spatial relations. Finally, the classification result of lane changing decisions was output through the fully connected network combined with Softmax function. A total of 2 875 valid samples extracted from unmanned aerial vehicle trajectory data were used to train and validate the proposed model. [Result] The model achieves optimal prediction performance with a feature extraction time window of 4 s and a prediction time of ―2 s, attaining an overall accuracy of 91.3%. It represents an average improvement of 4.3% compared with four baseline models, i.e., LSTM, GCN, SVM and XGBoost. The ablation tests further confirm that the spatiotemporal information fusion modeling outperforms single-dimensional feature modeling in terms of prediction accuracy and balance among different lane changing types. [Conclusion] The findings provide an effective method for multi-vehicle interaction modeling and lane changing behavior prediction in connected environments, contributing to improving the safety decision-making capabilities of advanced driver assistance systems and autonomous driving systems.

[Objective] The processing and analysis on high-frequency response signal of mechanical sensor inside pavement is the research focus of the current field scientific observation work of subgrade and pavement. How to obtain the characteristics of real mechanical response signal inside pavement structure is an important basis for determining the mechanical response state of pavement structure and studying the evolution rule of the pavement structure service performance. [Method] In this study, based on the full-scale pavement test loop, the signal characteristics and time-frequency characteristics of dynamic response of pavement structure with two load modes were analyzed for the mechanical response signals inside pavement structure under real vehicle load and FWD drop hammer load. Three common filtering and noise reduction methods based on time domain, frequency domain and wavelet domain were introduced. The effects of running mean, low-pass filtering and wavelet transform on dynamic response signals of pavement were compared. The applicability of different filtering and noise reduction methods were analyzed. [Result] The pavement response signals under continuous real vehicle load and fixed-point FWD drop hammer load have different response characteristics. The signal under fixed-point FWD is more stable and the response waveform is clearer and simpler. Because the FWD load is more instantaneous than the real vehicle load, it is more sensitive to different filtering methods. By comparison, it is found that the selection of running mean steps and low-pass filtering cut-off frequency has great influences on the filtering effect. The wavelet transform filtering noise reduction method has a high degree of reduction and can better reflect the effective characteristics of original signal. [Conclusion] The findings can provide technical support for data processing and signal analysis on dynamic mechanical response signals in the field scientific observation of subgrade and pavement.

[Objective] In response to the problem of unsatisfactory prediction accuracy of existing models, an improved fully connected neural network model was proposed to further improve traffic flow prediction accuracy. [Method] First, defined a custom layer, where added a weight matrix and a bias term. Second, input a linear transformation, which multiplied the weight and added a bias term; and then performed nonlinear transformation through activation function for forward propagation. Third, customized a model function named Explainer_model, which was used to build, train and predict models. A flattening layer was adopted to expand input data into a one-dimensional array. Fourth, the Dropout layer between two fully connected layers was used to reduce the risk of overfitting. The fully connected neural networks were trained by using backpropagation algorithms. Nadam optimizer with the best optimization effect was used, in terms of optimizer selection after comparative verification. Finally, addd a fully connected layer with one output unit for outputting the results. [Result] The experiments were conducted using hourly traffic flow data at four collection points on a certain expressway in Shaanxi, China to verify the effectiveness of model. The improved neural network was used to model the time series and compared with four neural network models, i.e., GRU, LSTM, CNN-LSTM and CNN-GRU. The result indicates that the improved fully connected neural network model performs better than other models. [Conclusion] The proposed predictive model can be used to predict data with seasonality and trends.

[Objective] Load stability is a critical factor for ensuring vehicle driving safety. This study focuses on container semi-trailer trucks and aims to investigate the container loading optimization that balances load stability and load efficiency. [Method] First, a multi-container loading problem considering load stability was proposed and decomposed into two sub-models, i.e., a wall-unit sub-model aimed at maximizing space utilization, and a wall-unit assignment sub-model designed to minimize both the number of vehicles used and the deviation of center of gravity. Second, a hybrid heuristic algorithm, based on wall-building approach and improved genetic algorithm (HHA-WBIGA), was developed for two-stage solution. In the first stage, a wall-building approach was employed to combine the cargo to be loaded into a set of candidate wall-unit. In the second stage, an improved genetic algorithm was designed to load these wall units onto vehicles. By adjusting the positions of wall units within each vehicle, the load efficiency was maximized while satisfying the load stability requirements. Finally, the effectiveness of the proposed model and algorithm was validated through computational experiments using benchmark instance from the literature and real-world enterprise case. [Result] Compared with the benchmark algorithm, HHA-WBIGA reduces the longitudinal deviation of center of gravity by 15.11%, thereby achieving better load stability. Compared with the enterprise's current loading scheme, HHA-WBIGA loads an average of 1 067.5 kg of more cargo per vehicle. It reduces the average longitudinal deviation of center of gravity by 38.58%, increases the average vehicle volume utilization rate by 1.21%, and improves the average vehicle weight utilization rate by 3.15%. [Conclusion] The findings achieve high vehicle utilization while ensuring load stability, providing a methodological reference for solving the loading optimization problem of container semi-trailer trucks considering load stability.

[Objective] Taking the double-arch section of a highway tunnel as the study object, this study focuses on the significant increase in stress on primary support and secondary lining of pilot-tunnel, as well as the lining cracking caused by subsequent tunnel construction method for double-arch tunnel without middle drift. [Method] First, the field monitoring tests were carried out to determine the distribution characteristics of internal forces on primary support during construction of pilot-tunnel. Then, 3D numerical simulation was used to compare the variations in internal forces on primary support and secondary lining of pilot-tunnel caused by using different construction methods for the subsequent tunnel. The reasons for the excessive tensile stress in the secondary lining on the right side of inverted arch of pilot-tunnel were analyzed. Finally, the grouting reinforcement was applied to the central wall basement. This measure effectively improved the stress state of the secondary lining structure of pilot-tunnel. [Result] With the excavation of subsequent tunnel, the stress on primary support structure of pilot-tunnel increased significantly. The structure was also obviously in an unsymmetrical loading state. The maximum tensile stress, reaching 142.4 MPa, occurred on the outer side of steel frame at the crown. The maximum compressive stress, reaching 201.4 MPa, occurred on the outer side of steel frame at the right arch shoulder. The excavation of subsequent tunnel had a limited effect on the primary support of pilot-tunnel. However, it had a greater effect on the secondary lining of pilot-tunnel. When the subsequent tunnel was excavated by using CD method, the maximum tensile stress on the secondary lining of inverted arch of pilot-tunnel was significantly lower than that using the three-bench method. After excavation of subsequent tunnel, the plastic zone area in the upper part of central wall and the surrounding rock at basement increased sharply. As a result, the bearing capacity of pilot-tunnel basement decreased significantly. It led to the tensile stress concentration on the secondary lining on the right side of inverted arch. The reinforcement of basement surrounding rock can significantly improve the stress state of pilot-tunnel. It can also markedly reduce the maximum principal stress on the secondary lining of inverted arch. Therefore, the grouting reinforcement is recommended in a symmetrical zone on both sides of the central wall basement. The recommended zone is with 12-m-wide and 3-m-deep. [Conclusion] This study optimized the construction method for subsequent tunnel. It significantly reduced its influence on the pilot-tunnel, as well as improved the stress characteristics of the secondary lining of pilot-tunnel by reinforcing the basement of central wall. The findings can provide a reference for the design and construction of similar double-arch tunnel projects.

[Objective] This study investigated to accurately predict short-term traffic flow, tap the potential value of traffic flow data, and provide a scientific decision-making basis for traffic management departments and travelers. A short-term traffic flow prediction model, IDBO-SVM, was proposed based on the improved dung beetle optimization (IDBO) algorithm and support vector machine (SVM). [Method] First, the uneven initial population distribution with traditional DBO algorithm was solved by introducing Bernoulli chaotic mapping, avoiding the algorithm falling into local optimum. Second, the adaptive weight factor was added to improve the position update formula of stealing dung beetles; and the global search and local development capabilities of algorithm were balanced. Third, the differential evolution (DE) strategy was integrated to enhance the later convergence ability of algorithm and improve the model accuracy. Finally, eight benchmark functions were selected to simulate and verify IDBO algorithm. Based on the real traffic flow data of M6 motorway in UK, the prediction performance of IDBO-SVM model was compared with PSO-SVM, DE-SVM and DBO-SVM models to verify the effectiveness of the proposed model. [Result] The simulation results indicate that IDBO algorithm has excellent optimization performance in single-peak, multi-peak and fixed-dimensional multi-peak test functions. The optimization speed and accuracy are significantly improved compared with the traditional algorithms. The prediction results show that the MAE of IDBO-SVM model is 23.14 with improvement of 0.11-3.53, the RMSE is 30.79 with improvement of 0.21-4.57, and the MAPE is 3.77 with improvement of 0.005-0.87. [Conclusion] IDBO-SVM model optimizes SVM parameters through IDBO algorithm in multiple dimensions, effectively overcomes the performance defects of traditional model, and accurately completes the prediction on short-term traffic flow. It has certain application prospects.

[Objective] In response to the current limitations in understanding the skeleton structure and the skeleton composition of open-graded friction course (OGFC) asphalt mixture, this study utilizes an analysis on the skeleton by using IPAS-2 image analysis software. [Method] First, the threshold segmentation was applied to categorize images into two target regions, i.e., skeletons (target object) and voids. Second, the coarse aggregates with particle size over 2.36 mm and contact points with contact distance less than 0.54 mm were identified through pixels; so as to identify and make statistics on the meso-skeleton structure and parameters of OGFC asphalt mixture, e.g., contact points quantity, average contact line length, average coordination number, contact direction angle and internal structure index. Finally, the statistical result was subjected to Pearson correlation analysis and linear regression analysis with the experimentally measured macro-mechanical properties of asphalt mixture, including Marshall stability, split tensile strength, and rutting dynamic stability. [Result] Pearson correlation coefficients between Marshall stability, representing the deformation resistance of asphalt mixtures, and the number of contact points and average coordination number are 0.87 and 0.85, respectively. For the indirect tensile strength represented by split tensile strength, the Pearson correlation coefficients with the number of contact points and average coordination number are 0.94 each. Additionally, the linear regression coefficient between internal structure index and rutting dynamic stability is 0.934 4. [Conclusion] The proposed meso-skeleton indicators exhibit the strong correlation with measured macro-mechanical properties. These indicators can be effectively used to characterize the macro-mechanical performance of OGFC asphalt mixture.

[Objective] To improve the pioneer road quality and traffic safety, the study investigated the climbing performance of typical heavy-duty vehicles on pioneer roads. The key indexes of pioneer road longitudinal grade were systematically studied. [Method] A survey was carried out on the construction conditions of pioneer roads for nine national expressways and one provincial road project in China. The common types of construction transport vehicles were identified. Dump trucks and semi-trailers were selected as the representative models to obtain the critical design parameters. Based on vehicle dynamic characteristics theory, the driving equilibrium equation and power factor model were established. The vehicle equilibrium speed with different gear shifts and slopes was studied. The speed decay patterns and driving behaviors during climbing were analyzed. The vehicle speed on typical longitudinal grade sections was measured through actual vehicle test, which was compared with the vehicle motion state simulated with TruckSim, thereby verifying the reliability of theoretical calculations. [Result] The longitudinal grade calculated with the vehicle dynamic characteristics meets the actual requirements for pioneer roads. There is a significant difference in climbing performances between dump trucks and semi-trailers. Semi-trailers have lower equilibrium speed and more frequent gear shifts at the same slope. The actual vehicle test indicates that the driving behaviors on grades are consistent with the theoretical analysis result. The simulation result shows that the greater the slope, the more pronounced the speed decay during gear shifting. The difference between theoretical calculated equilibrium speed and simulated value is less than 0.5 km/h, validating the model’s effectiveness. [Conclusion] The longitudinal grade design indexes for representative vehicle models at different design speeds are recommended, due to the comprehensive consideration of theoretical calculation, experimental verification and engineering safety. The findings provide the directly applicable quantitative indexes for the pioneer road longitudinal grade design, which is of great significance to ensure the safe of construction vehicles.

[Objective] This study investigates to clarify the temperature gradient patterns for concrete solid slab bridge, and to accurately evaluate the temperature effects in insolation conditions. [Method] First, the finite element analysis with ABAQUS was conducted on the temperature field of concrete slab bridge. The simplified temperature distribution characteristics yielded the vertical temperature gradient patterns. Second, the long-term simulations of concrete slab temperatures were conducted by using observational data for a decade from a meteorological station at the bridge site. A super-threshold extreme value model based on GP distribution yielded representative temperature difference values for a 100-year-return period event. Finally, the proposed temperature gradient pattern was compared with existing specifications. The influence of different asphalt pavement thicknesses on temperature distribution was investigated. [Result] The outer surface of slab beam rapidly heated up under insolation and ambient temperature effects, while the core concrete exhibited slow heat transfer and gradual warming. The cross-section displayed the gradient pattern characterized by high temperatures at the top and bottom surfaces, and low temperatures internally. The nonlinear temperature distribution along the height of concrete slab beam could be described using two exponential curves, i.e., one for the top surface heating segment due to direct radiation, and the other for the bottom surface heating segment due to reflected radiation. The asphalt pavement significantly influenced the top temperature difference in vertical thermal gradient of concrete slab bridges. As the pavement thickness increased from 0 to 150 mm, the top temperature difference decreased from 21.59 ℃ to 10.86 ℃, exhibiting a clear linear relation with the R² of 0.989 5. This excellent fit provided a reference for accurately determining temperature gradient of concrete slab bridges with different pavement thicknesses. [Conclusion] The proposed vertical temperature gradient pattern for concrete slab bridges eliminates the isothermal segments found in China's specifications and European specifications, achieving better temperature continuity. The derived temperature difference values align more closely with the requirements of the limit state design method.

[Objective] The study developed a hybrid architecture named RC-YOLO by fusing RT-DETR and YOLOv11 to address inefficiency in manual inspection, high computational complexity for mobile deployment and insufficient feature representation in traditional pavement defect detection. [Method] The proposed method employed the structural reparameterization technology to embed RepConv structure into HG Block, forming the RC-HG Block module. A multi-branch heterogeneous architecture was enabled during training, and fused into a single convolution kernel during inference, balancing detection accuracy and computational efficiency. RC-HGNetV2 feature network was constructed based on RC-HG Block. The multi-scale feature representation was strengthened via a four-stage progressive feature pyramid and cross-layer feature enhancement strategy to improve the recognition capability of minor defects. A spatial edge-aware enhancement attention mechanism (SEAM) was proposed, which utilized the dual-path complementary framework of edge extraction and standard convolution to enhance the model’s sensitivity to linear structures and texture features of road defects. SEAM was integrated into C3K2 module, and inserted into the high-level semantic layer of feature pyramid, thereby enhancing the model’s interaction ability between local edge details and global semantic information. [Result] RC-YOLO achieved the optimal performance on both self-built and public datasets. The mAP50 reached 81.3% on the self-built dataset, improving 5.4% and 8.3% compared with YOLOv11 and RT-DETR respectively. Regarding computational efficiency, the model parameters were reduced by 19.4%, and the computational cost decreased by 9.5% compared with YOLOv11. [Conclusion] Through coordinated optimization of computational efficiency and detection accuracy, the hybrid architecture significantly enhances the precision and robustness in road defect detection in complex scenarios, while maintaining lightweight characteristics, and providing a feasible technical solution for intelligent transformation of highway maintenance models.

[Objective] The study evaluated the influences of different types of guardrails on the collision behavior of passenger vehicles. It revealed the mechanisms of crash energy distribution and damage evolution, thereby providing experimental and theoretical basis for guardrail structure optimization and standards revision. [Method] First, a multi-dimensional evaluation system integrating standard indicators was constructed, e.g., vehicle kinetic energy loss rate, contact length, heading angle, intrusion distance, and peak acceleration. Second, a collaborative evaluation framework for assessing both guardrail safety performance and vehicle damage was established by using a quantitative methodology combining vehicle external damage and internal deformation. Finally, the full-scale crash tests were conducted to compare the dynamic responses and damage characteristics of passenger vehicles impact with W-beam guardrails, post-and-beam guardrails, and concrete barriers. [Result] W-beam guardrails exhibit the best energy absorption performance. The vehicle kinetic energy loss rate varies from 66.6% to 83.1%, which is 3 to 6 times that of concrete barriers and twice that of post-and-beam guardrails. The contact length varies from 774.8 cm to 1 225.5 cm, representing 42% to 63% increase compared with concrete barriers and 236% to 431% increase compared with post-and-beam guardrails. The peak longitudinal acceleration is reduced by 63%-80% compared with concrete barriers, and by 35%-54% compared with post-and-beam guardrails, demonstrating the significant buffering effectiveness. Furthermore, when the compaction decreases to 81%, the intrusion distance of W-beam guardrails increases by 9.5%. Due to rigid connections, the post-and-beam guardrails cause intensified local damage, with the maximum deformation depth of 674 mm. [Conclusion] It is suggested to incorporate kinetic energy loss rate into evaluation systems, and take internal deformation as a key indicator for battery protection in new energy vehicles. Future work should expand the test sample size, standardize vehicle parameters, and validate applicability in multiple scenarios to enhance engineering practicality.

[Objective] To improve the efficiency of resource allocation on network freight platforms and reduce transportation costs, a vehicle-cargo matching strategy is proposed for intra-city short haul. This strategy is based on a comprehensive consideration of dynamic vehicle-cargo demands and the matching success rate. [Method] To achieve effective cargo combination and meet the real-time requirement of first-come-first-matched cargo, the cargo-pooling was adopted to design a cargo combination scheme. A bilateral matching evaluation index for vehicle-cargo combinations was then established. The matching dimensions included vehicle type, loading, time, routing, and transportation cost. The bilateral comprehensive matching degree was obtained through weighting. On this basis, a greedy-based dynamic vehicle-cargo matching algorithm was designed; and a dynamic vehicle-cargo matching model with sliding time window was established. Finally, a freight scenario analysis was carried out in Beijing to evaluate the model's effectiveness in optimizing resource allocation. The applicability of matching results with different time window widths and vehicle-to-cargo resource ratios was also analyzed. [Result] Compared with traditional non-combination matching models, the proposed strategy improves the loading factor by approximately 20% in various transportation scenarios. The matching success rate is improved by approximately 10%. It saves nearly half of vehicle resources. Meanwhile, the significant advantages are achieved in freight profit for both vehicle providers and cargo owners. The proposed strategy is versatile and adaptable to various situations. Both loading factors and resource utilization rate hold steady with varying vehicle-to-cargo resource ratios. The matching success rate consistently exceeds 95% with flexible time window adjustment, demonstrating good dynamic demand response. [Conclusion] To achieve system optimization in a practical application, the proper time window can be identified based on various vehicle-cargo resource ratios.