30 November 2025, Volume 38 Issue 11
    

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    Special Column on Long-term Performance Evolution Analysis and Evaluation of Bridge Structures
  • Research on Atmospheric Corrosion Environment of Steel Bridges
    LIU Yong-jian, CHEN Sha, WANG Zhuang, YE Ke-cheng, DUAN Hai, XU Bo
    China Journal of Highway and Transport. 2025, 38(11): 1-20. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.001
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Steel bridges exposed to the atmospheric environment inevitably suffer from corrosion damage under the combined effects of temperature, relative humidity, and pollutants, posing a global challenge. To deepen the understanding of atmospheric corrosion, this study summarizes existing research findings from three perspectives: the macro-environment near bridge sites, the local environment around components, and the micro-environment on component surfaces, while also exploring future research directions. Current research shows that the macro-environment has reached a stage where it can be characterized and classified, along with a classification method based on standard coupon exposure test results and climatic parameters. The local environment only reflects the influence of long-term exposure to corrosive media at different parts of steel bridges but lacks characterization parameters such as the intensity and duration of corrosive media effects, making corrosivity level determination reliant on engineering experience. The micro-environment focuses on the mechanism of steel atmospheric corrosion, dynamically characterizing the corrosivity of different points on steel bridges through parameters such as surface temperature, surface humidity, and surface pollutant deposition. The existing corrosion environment zoning map of China has limitations, including low data density and exposure test stations located far from bridge sites, making it difficult to accurately reflect the corrosivity level of the macro-environment at bridge locations. It is recommended to establish a gradient-based atmospheric corrosion exposure monitoring network along Chinese highway system to obtain multi-source atmospheric corrosion data and develop a corrosion environment zoning map tailored for steel bridges. Future research should aim to build a micro-environment research system that is characterizable, quantifiable, and applicable. Theoretical studies on micro-environment calculations should be conducted to clarify the interaction mechanisms of micro-environment parameters and establish quantitative analysis methods. A predictive model for atmospheric corrosion rates at the micro-environment level should be developed to provide scientific principles for precise detection of localized corrosion, corrosion-resistant structural design, and targeted maintenance strategies.
  • Fatigue Behavior of Steel Deck at Stud-to-deck Welded Connections in Steel- UHPC Composite Bridge Decks: Failure Mechanism and Life Prediction
    SU Qing-tian, LIU Feng-yao, SHI Zhan-chong, CUI Bing
    China Journal of Highway and Transport. 2025, 38(11): 21-33. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.002
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    Stud-to-deck welded connections play a crucial role in ensuring the composite effect between the ultra-high performance concrete (UHPC) overlay and orthotropic steel bridge deck, alleviating fatigue cracking in the steel bridge deck. The local stresses at these connections are complex, and the fatigue failure of the steel deck induced by cyclic loads, cannot be overlooked. To investigate the fatigue behavior of steel deck at these connections, this study analyzed the stress state of steel plate microelements based on the simplified mechanical model from push-out tests for welded studs, elucidating its fatigue failure mechanism. Based on the push-out fatigue test results for welded studs and finite element numerical simulations, hot-spot principal tensile stress S-N curves at 50% and 97.7% survival probabilities were developed. Subsequently, these curves were compared with the existing hot-spot stress S-N curve at 95% survival probability derived from tensile fatigue tests for steel plate with welded studs. By utilizing the established hot-spot stress S-N curves and linear damage accumulation criterion, the study quantified the fatigue damage of steel decks at the connections in full-scale composite bridge deck fatigue test, determined whether the steel decks failed or not, and predicted their fatigue lives. The research findings reveal that: fatigue crack initiation of steel deck is induced by the bending deformation of welded studs under reciprocating bending and shear loading, which consequently induces tensile deformation of the steel deck. The fatigue resistance at 2 million cycles based on the S-N curve at 95% survival probability from the tensile fatigue test for steel plate is 101 MPa, whereas that based on the S-N curve at 97.7% survival probability from push-out fatigue test is 34 MPa. The constant amplitude fatigue limit is 75 MPa for the former and 25 MPa for the latter. By referencing the failure modes distribution of steel decks in a full-scale composite bridge deck fatigue test, the study validated that the S-N curve at 50% survival probability provides the most accurate assessment of the fatigue failure of steel decks. The S-N curve at 97.7% survival probability yields overly conservative results. The predicted lifespans from S-N curves at 50% and 97.7% survival probabilities are recommended as the upper and lower limits, respectively, for decision-making purposes. This research provides theoretical foundations for anti-fatigue design, damage quantification, and life prediction of steel decks at these connections in steel-UHPC composite bridge decks, offering technical support for subsequent maintenance and management decisions.
  • Study on UHPFRC Reinforcement Method and Digital Fatigue Test for Distortion-induced Fatigue of Steel Plate Girder Bridges
    WANG Chun-sheng, WU Qing-lin, ZHANG Run-ze
    China Journal of Highway and Transport. 2025, 38(11): 34-50. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.003
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    Aiming at the distortion-induced fatigue cracking reinforcement of the horizontal gusset plate web gaps in steel plate girder bridges, a new reinforcement method of casting Ultra-High Performance Fiber Reinforced Cementitious Composite material (UHPFRC) on the detail cracking area was proposed. Considering the multi-field coupling effects of structural gravity, welding residual stress and fatigue stress, full-bridge digital fatigue test models of four types of steel plate girder bridges, including straight bridge, skew bridge, curved bridge and skew-curved bridge were established. The distortion-induced fatigue crack simulation and analysis of the horizontal web gap details in steel plate girder bridges, as well as the parameter analysis and effectiveness evaluation of the UHPFRC reinforcement of the distortion-induced fatigue details were realized, and the influence of the UHPFRC reinforcement size parameters on the reinforcement effect was clarified. Meanwhile, a cyclic cohesion analysis model considering the cumulative damage process of reinforcement interface stiffness degradation was established, and the damage evolution behavior of the UHPFRC composite reinforcement interface at the distortion-induced fatigue detail under cyclic loading was discussed. The research results indicate that the distortion-induced fatigue performance of steel plate girder bridges is closely related to their geometric configuration, the gap distortion-induced fatigue of straight bridge, skew bridge, curved bridge and skew-curved bridge increases in sequence. Under multi-field coupling loading, the distortion-induced fatigue cracks at gap details are Ⅰ-Ⅱ-Ⅲ mixed mode crack dominated by type Ⅰ, and the ratio of the cumulative strain energy release rate of type Ⅱ and type Ⅲ cracks gradually increases with the number of load cycles. The thickness of UHPFRC has a significant impact on reinforcement effect. When the thickness increases from 50 mm to 70 mm, the equivalent stress intensity factor amplitude at crack tip decreases by 13%. The established cyclic cohesion model can effectively describe the damage evolution behavior of the UHPFRC composite reinforcement interface under fatigue loading. The interface damage initiates at the edges of the composite interface and gradually extends towards the center as the number of loading cycles increases. Meanwhile, the presence of crack will accelerate the interface damage rate at the crack location. The research results of this paper can provide a technical reference for the design and evaluation of UHPFRC composite reinforcement of web gap distortion-induced fatigue details in steel plate girder bridges.
  • Long-term Deformation Analysis Method of Prestressed Reinforced Concrete Bridges Based on Intelligent Prediction of Concrete Creep and Shrinkage
    WANG Yan-lei, ZHU Jin-song
    China Journal of Highway and Transport. 2025, 38(11): 51-63. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.004
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    A reliable long-term deformation analysis method for prestressed reinforced concrete (PRC) bridges is essential to ensure the long-term safety of such bridges. Limited by the performance of traditional concrete creep and shrinkage constitutive models, the long-term deformation prediction accuracy of existing PRC bridges is insufficient, and the latest concrete creep and shrinkage deep learning model cannot effectively mine the structured information between model parameters, which limits the further improvement of the prediction accuracy of bridge deformation. In this paper, the concrete creep flexibility model JGCN-CNN and the shrinkage strain model εsh,GCN-CNN, which are integrated with the graph convolutional neural network (GCN) and convolutional neural network (CNN), were developed respectively, and the time-varying constitutive differential expression of concrete based on the GCN-CNN model was derived. An analysis method of PRC bridge long-term deformation based on the intelligent prediction of concrete creep and shrinkage was proposed and the ABAQUS subroutine was compiled to realize the long-term deformation prediction of PRC bridge. The prediction performance of JGCN-CNN and εsh,GCN-CNN models and the long-term deformation analysis method proposed in this paper were verified by using the open database of Northwestern University and the measured long-term deflection data of a PRC bridge in China. The results show that the JGCN-CNN and εsh,GCN-CNN models are significantly better than the B4 model, and have better stability and faster convergence than the CNN model. The prediction effect of the proposed method is better than that of the B4 and CNN models, and the relative error of the prediction of mid-span deflection of the case bridge in 8 years of service is 10.9%. After about 20 years of service, the deflection of the bridge is basically unchanged, and the final deflection of the fourth span and the fifth span reach 147.4 mm and 177.3 mm respectively. This study can provide a novel reliable method for PRC bridge long-term deformation prediction.
  • Two-stage Bending Bearing Capacity Calculation Method and Deterioration Prediction of Concrete Beams in Operation Stage
    YUAN Yang-guang, LIU Xing, YI Ting-hua, HUANG Ping-ming, ZHENG Xu, WANG Xi-xi
    China Journal of Highway and Transport. 2025, 38(11): 64-80. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.005
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    To establish bending bearing capacity calculation method and deterioration prediction model of concrete beams, which totally face to the requirements of performance assessment in operation stage, the prestressed concrete (PC) beams in chloride environment are mainly focused. Firstly, the implication of two-stage bearing capacity deterioration process of PC beams in operation stage was explained, on basis of which, the key points of establishing the bending bearing capacity calculation method and deterioration prediction model were determined. The destructive load tests of PC beams were conducted to improve the database of destructive load test of concrete beams, and the parametrical models of typical bending cracks were extracted. Secondly, by COMSOL Multiphysics, the continuum simulation method for chloride diffusion that can incorporate the crack condition was proposed. Based on the concept of guarantee rate, a new method for steel corrosion initiation assessment was established. In the last, a corrosion time scale conversion method was proposed based on Coulomb criterion, and the Gamma process based bending bearing capacity deterioration model was improved. It is found that the bending crack of PC beam includes four typical types. The parametrical models of typical cracks were determined. The larger of crack size, the greater of influence of crack shape on chloride diffusion process. During the bearing capacity calculation of PC beams in operation stage, the partial factors of material strength of concrete, rebar and prestress tendons are suggested as 1.1, 1.0 and 1.1, respectively. Compared with the traditional design calculation method, the proposed bending bearing capacity calculation method for operation stage can improve the accuracy by 10%, which can tap the bearing potential in a reasonable way. The proposed method has a better applicability when the rebar mass loss percentage is not greater than 12.5%. When Gamma process is employed in the description of bearing capacity deterioration of PC beam in chloride environment, the initial values of the three major parameters, i.e., m, k, ξ, are suggested to be 0.31, 0.006 and 0.004 1, respectively. The deterioration model can be used in the evaluation of corrosion initiation moment and the rapid assessment of residual bearing capacity.
  • An Analytical Approach for Evaluating the Residual Shear Capacity of In-service Prestressed Concrete Box Girder Bridges After Cracking
    HE Zhi-qi, CHEN Jia-tong, LI Wen-jie, XU Tian, WANG Jing-quan
    China Journal of Highway and Transport. 2025, 38(11): 81-96. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.006
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    With the increase in service life, a large number of prestressed concrete box girder bridges have developed defects such as web cracking. To address the challenge of quantitatively evaluating the structural performance and residual shear capacity of in-service prestressed concrete box girder bridges after cracking, an inverse analytical evaluation method based on the Modified Compression Field Theory (MCFT) was proposed. First, using observable parameters such as the width and inclination angle of diagonal cracks in the web as inputs, and considering the effects of longitudinal and vertical prestress in the box girder, a mechanical correlation model was established between apparent cracks and the internal stress state of the structure. This model was developed based on mechanical equilibrium equations, deformation compatibility conditions, and material constitutive laws for cracked concrete. Next, the stress and strain fields of the cracked concrete were deduced to obtain the actual shear stress level at the interface of diagonal cracks under a given degree of cracking. Finally, based on the Critical Shear Crack Theory (CSCT), a calculation method for the ultimate shear capacity applicable to practical bridge structures was proposed. By comparing the actual shear stress level with the ultimate shear stress, the residual shear capacity ratio of the structure after cracking was determined, enabling the quantitative evaluation of the residual shear capacity for box girder bridges with web cracking. The research demonstrates that by establishing the correlation between apparent cracks and the internal stress state of the structure, the proposed method can effectively achieve the inverse identification of key parameters such as the residual shear capacity of concrete structures after cracking. The proposed method is applied to evaluate the performance of an in-service prestressed concrete continuous box girder bridge, yielding the residual shear capacity and safety reserve level of the box girder with diagonal cracks. This provides a scientific foundation for maintenance and management decisions regarding in-service prestressed concrete box girder bridges.
  • Mechanical Properties and Durability of Steel Fiber Graphite Concrete Under Freeze-thaw/Salt Corrosion Environment
    REN Xiang, YAN Hao-zhi, ZHANG Yu-jie, REN Long, SONG Fei, CHEN Shao-jie, SONG Chao-jie, XIN Yi-tao, WAN Wei-fu
    China Journal of Highway and Transport. 2025, 38(11): 97-111. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.007
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    Concrete bridges in Northwest China have long been subjected to severe environmental conditions, including significant temperature variations between day and night, and frequent freeze-thaw cycles. It has been demonstrated that, in particular, the coupling effect of freeze-thaw and salt corrosion has a deleterious effect on the integrity of concrete structures. This, in turn, has a significant impact on the service life of bridges. The present study aims to resolve the issue by enhancing the thermal conductivity, crack resistance and durability of concrete through the incorporation of steel fibers and flake graphite powder. The present study is based on the optimal ratio of steel fiber (1.5% by volume) and graphite powder (5% by mass) determined by the group's previous research on the thermal conductivity of steel fiber graphite concrete. The evolution of the mechanical properties and environmental durability of steel fiber graphite concrete were systematically studied. The findings indicate that the split tensile strength of steel fiber graphite concrete is considerably enhanced by 74.4% in comparison with conventional concrete; however, the augmentation in compressive strength is comparatively marginal. Following 300 cycles of freezing and thawing, salt erosion and their coupled cycles, respectively, a significant decrease in compressive strength was observed for both concrete types (14.63% to 32.23%). However, steel-fiber graphite concrete demonstrated superior durability, exhibiting a substantially lower mass loss rate and relative dynamic elastic modulus loss compared to standard concrete. Concurrently, the amalgamation of steel fibers and graphite materials enhances the concrete's impermeability grade, reduces the chloride ion migration coefficient, and leads to a comprehensive enhancement in durability performance. The microstructural analysis reveals that steel fibers effectively improve the concrete tensile ratio and toughness through the bridging effect, thereby delaying crack initiation and expansion. Graphite powder significantly reduces the porosity of the matrix by filling the pores, thereby reducing the width of micro-cracks and preventing the formation of through-cracks during damage. The synergistic effect of the two is reflected in the fact that the steel fiber inhibits the expansion of macroscopic cracks, and the graphite reduces the porosity and the temperature gradient stress, which together mitigate the degree of deterioration of the interfacial transition zone. This multiscale enhancement mechanism provides a novel concept for optimizing the performance of concrete structures in harsh environments.
  • Research Advances on Diagnosis and Evaluation of Longitudinal Restraint Performance of Stiffening Girders for Long-span Suspension Bridges
    YANG Dong-hui, GU Hai-lun, YI Ting-hua, ZHAO Li-yan, WEI Ming-guang
    China Journal of Highway and Transport. 2025, 38(11): 112-134. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.008
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    Dampers, bearings, and expansion joints in long-span suspension bridges are key restraint devices for stiffening girders. They are vital for coordinating structural deformation, transmitting loads, dissipating energy, and controlling vibrations. However, excessive girder-end cumulative displacements (GECD) during long-term bridge service contribute primarily to the functional degradation of restraint devices. This degradation weakens the longitudinal restraint performance of the stiffening girders and severely threatens the operational safety of bridges. To ensure normal bridge operation, diagnosing the functional degradation of restraint devices and evaluating their service performance are important. Therefore, based on the logical framework of “motion mechanism traceability-performance degradation mechanism-diagnosis and evaluation system,” this study presents a systematic review in three key areas: ① the characteristics and underlying mechanisms of GECD in long-span suspension bridges; ② the degradation mechanisms and constitutive evolution of longitudinal restraint devices; and ③ the diagnostic and evaluative methodologies for girder-end restraint devices. First, the displacement components, response characteristics, and generation mechanisms of GECD were analyzed in detail. The effects of factors such as the structural form, bridge function, restraint systems, and load characterization on GECD was discussed. Second, the degradation mechanisms of static and dynamic restraint performances in restraint devices were elucidated, with a systematic summary of the evolution patterns of the mechanical constitutive characteristics during their deterioration processes. Finally, existing diagnostic and evaluation methods for static and dynamic restraint performance were systematically reviewed, with a critical assessment of their technical specifications, application scenarios, data requirements, and inherent limitations. Additionally, an outlook on the future development of diagnostic and evaluation methods for longitudinal restraint performance in the stiffening girders of long-span suspension bridges was presented.
  • Diffusion Behavior of Moisture Within Parallel Steel Wires of Large-span Bridges
    MENG Qing-ling, DUAN Hao-chen, YE Chao-fan, ZHU Jin-song, GUO Xiao-yu
    China Journal of Highway and Transport. 2025, 38(11): 135-150. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.009
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    Parallel steel wire strands are widely used in the construction of large-span suspension bridges in China. In recent years, corrosion fatigue-coupled fractures of cable systems have been frequent. Moisture is a critical factor in the corrosion of bridge cables because it can penetrate the cable system through damaged sheaths and create a corrosive environment inside the cable. To investigate the moisture diffusion behavior within parallel steel wire strands, experiments were conducted, simulating axial and radial moisture diffusion in humid environments. The influence of internal steel wire corrosion on diffusion behavior was studied, and the anisotropic moisture diffusion coefficients were calculated based on Fick's first law. The results indicate that the moisture diffusion behavior is closely related to the cable placement angle, type of sheath defect, temperature, corrosion level of the steel wires, and wire arrangement. The axial moisture diffusion coefficient Dax peaked when the cable placement angle was 60°, increasing by up to 15.37% compared to other angles. For sheath defects, the axial moisture diffusion coefficient Dax reached its highest value, increasing by up to 29.95% when the surface cracks of the sheath extended axially relative to radial cracks or hole-type defects. At an environmental temperature of 50 ℃, the axial diffusion coefficient Dax increased by up to 79% compared to that at 20 ℃. For corroded cables, severe corrosion at level Ⅱ may interrupt the moisture diffusion because of factors such as steel wire expansion that causes moisture to accumulate at sheath defects. Compared to non-corroded cables, the radial diffusion coefficient Drd and axial diffusion coefficient Dax for moderately corroded level Ⅰ cables decreased by up to 33.7% and 89.4%, respectively. Additionally, the axial diffusion coefficient Dax in the second and third layers of steel wires decreased by up to 45.73% and 25.81% in non-corroded cables and by up to 17.2% and 26.35% in level Ⅰ compared to that in the first layer.
  • Network-level Bridge Deterioration Model Based on Data Restoration of Defects
    XIA Ye, ZHANG Chen-hong, YANG Guo-qiang, LEI Xiao-ming
    China Journal of Highway and Transport. 2025, 38(11): 151-163. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.010
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    The network-level bridge deterioration model offers bridge managers a comprehensive understanding of deterioration trends. Existing models often rely on inspection report ratings, which are susceptible to incomplete and erroneous data, and fail to account for the effects of maintenance. This paper proposes a network-level bridge deterioration model based on multi-source incomplete and erroneous data. The method first cleans and corrects the faulty defect data from bridge inspection reports, ensuring the integrity of inspection data. Based on the corrected data, a new quantitative indicator is introduced to measure the deterioration of bridge components, and an empirical formula linking it to the ratings is established, addressing the issue of human bias in the rating system. The paper then uses multi-source data combined with an automated machine learning approach to construct a component-level bridge deterioration model, ensuring the model can adapt to grouped bridge condition assessments and predictions with minimal parameter adjustments. The model is validated using data from bridges along three highways in China, and the results show that the method improves the prediction accuracy of the deterioration model while correcting the data. The severity indicator enhances the model's ability to quantify maintenance actions, and the automated machine learning-based deterioration model increases its scalability and practicality, providing reliable decision support for network-level bridge management.
  • Accurate Positioning and Tracking of Vehicle Center Based on Multiple Projective Planes
    JIANG Xu, SUN Kai, QIANG Xu-hong
    China Journal of Highway and Transport. 2025, 38(11): 164-177. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.011
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    The vehicle load detection method based on computer vision can obtain spatiotemporal information of vehicle loads, which is of great significance for data-driven bridge health monitoring. During the recognition process, current vehicle load positioning is usually based on the overall feature of the vehicle. Although rich feature information is beneficial for vehicle tracking, there are shortcomings in the positioning accuracy of the vehicle center, which limits the further promotion and application of the technology. For fatigue monitoring of bridge, the stress at the fatigue structural details of orthotropic steel bridge decks belongs to the third system behavior, which requires higher accuracy on the positioning of vehicle loads. Currently, relevant methods that can meet the accuracy requirements of fatigue monitoring are still limited. In this context, this study proposes a vehicle center positioning method based on multiple projective planes. The feature of the whole vehicle is used for vehicle tracking while the detection box of the vehicle license plate is used for vehicle center positioning. Thus, the accuracy of vehicle center positioning can be improved without affecting the vehicle tracking effect. Further, a multiple projective plane system is proposed to perform projective transformation on the license plate center based on the vehicle category, through which accurate positioning of the vehicle center can be achieved. The results show that the vehicle license plate detection has advantages of high accuracy and strong stability. Besides, the proposed secondary detection framework can combine the advantages of vehicle detection and vehicle license plate detection, and the positioning accuracy can reach centimeter level without affecting vehicle tracking.
    • Special Column on Road Transportation and Energy Integration
  • Transformative Development of Energy Use in Highway: Harvesting, Utilization, and Potential Assessment of Green Energy in the Road Area
    JIANG Wei, WANG Teng, SHA Ai-min, WANG Ya-qiong, ZHANG Shuo, ZHANG Yu-fei
    China Journal of Highway and Transport. 2025, 38(11): 178-197. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.012
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    Driven by global carbon neutrality goals, the clean energy supply for highways presents a critical pathway to decarbonize transportation. This study systematically reviews the characteristics, collection pathways, and utilization potential of green energy in the road area from the perspective of synergistic development between transportation and energy. First, green energy in the road area was categorized into two types based on energy sources: natural energy, such as solar energy, natural wind energy, geothermal energy, and hydro energy; and traffic-induced energy, including mechanical vibration energy, pavement thermal energy, and convective wind energy. Then, the study reviews various energy collection technologies, including photovoltaic cells, wind turbines, heat pumps, hydro and wave energy conversion devices, vibration energy harvesters, and thermoelectric generators, as well as their conversion efficiency and technical challenges. Finally, by establishing a potential assessment model under a unified scenario, the study conducted a comparative analysis of output power, economic viability, and carbon reduction benefits, and summarized typical application scenarios. The study noted that while the potential for green energy is significant, its development and utilization face multi-dimensional challenges, including precise energy assessment, core technology efficiency and durability, and system integration and economic viability. This study aims to provide a theoretical framework and decision-making reference for constructing a clean, low-carbon, efficiently integrated, transportation energy ecosystem.
  • Coordinated Low-carbon Dispatch of Electricity-HYDROGEN-heat Integrated Energy System Considering New Energy Vehicles
    SHI Rui-feng, MA Kai, LIN Yong-qi, ZHANG Ling-zhi, JIA Li-min
    China Journal of Highway and Transport. 2025, 38(11): 198-208. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.013
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    With the continuous advancement of the “dual-carbon” strategy, the green and low-carbon transition of power and transportation systems has become imperative. To address existing challenges in hydrogen utilization models, such as low energy efficiency and inflexibility in achieving coordinated electricity-hydrogen-heat supply, this paper proposes an integrated low-carbon scheduling model for electricity-hydrogen-heat integrated energy systems, incorporating new energy vehicles. First, based on traditional diversified hydrogen utilization methods, we developed an integrated hydrogen utilization scheme that integrates heat-hydrogen co-production from electrolyzers, fuel cell combined heat and power generation with variable thermoelectric ratios, and orderly hydrogen refueling for heavy-duty trucks. Second, we innovatively integrate new energy vehicles into a carbon-green certificate joint market mechanism. By employing the Minkowski sum method, a vehicle-grid collaborative scheduling model is established to fully exploit the transportation system's potential in carbon emission reduction. Furthermore, a joint carbon-green certificate trading constraint mechanism is introduced to synergistically optimize system carbon emission control and renewable energy accommodation. Multi-scenario case studies demonstrate that the comprehensive hydrogen utilization strategy and vehicle-grid coordination reduce total operational costs by 11.5% and 5.83%, respectively, while enhancing renewable energy utilization rates by 4.3% and 2.1%, respectively. The results demonstrate that the efficient hydrogen utilization scheme and vehicle-grid collaborative scheduling strategy not only reduce operational costs but also improve the renewable energy accommodation ratio, which can provide a reference for the low-carbon transition of transportation-energy coupled systems.
  • Assessment Methodology for Evaluating Solar Energy Potential in Highway Areas
    LIU Zhuang-zhuang, SUN Hao, WANG Zhen, SHA Ai-min
    China Journal of Highway and Transport. 2025, 38(11): 209-223. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.014
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    To improve the methodology system of transportation and energy integration engineering, as well as to promote the development and utilization of solar energy resources on and among roads, a general protocol for solar energy resource potential evaluation in highway areas is proposed. It includes data processing of solar energy resources, identification of suitable space for energilization, assessment of solar energy resource potential, and verification of consistency of assessment. According to the data of 6 highways surrounding Junggar Basin in Xinjiang and their infrastructures, solar energy data were obtained using inverse distance weighted (IDW) spatial interpolation method. Highway infrastructures are classified into five categories, including connective infrastructures, service infrastructures, operational infrastructures, maintenance infrastructures, and energy infrastructures. Based on the highway design parameters, remote sensing images and Mapflow classification tool, the suitable spaces of each category were calculated. This study evaluates the solar energy potential of the 6 highways based on the data of solar energy resources and infrastructure areas, and verifies the reliability of the evaluation method based on the example of Kelameili Service Area. Results show that the inverse distance weighted space interpolation method is suitable for the acquisition of solar energy resources data in highway areas, and the Mapflow method is suitable for the identification of the suitable space area. According to the evaluation, the Jing-Xin Expressway (Wu-Da section) of the Eastern Line in the Junggar Basin owns the highest solar energy resources whose solar abundance in the highway area is 1 585.74 kW·h·m-2, while its average performance of solar energy resources endowment is not as expected as 73.54 GW·h·km-1; the solar energy resources of the Kui-A expressway in Western Line are lower, whose solar abundance is 1 534.98 kW·h·m-2, while its average solar energy resources are considerable, which is 83.89 GW·h·km-1; the solar energy potentials of A-Wu Expressway in the Middle Line and Wu-Da expressway in the Eastern Line are higher, with 85.08 and 85.73 GW·h·km-1, respectively. Based on the example, it is proved that the consistency of solar potential evaluation of the Kalameli Service Area in A-Wu Expressway is 95.6%.
  • A Method for Road-domain Microgrid Operation Situation Warning Based on High-entropy Energy Availability Assessment
    LIU Bao-zhu, LIAO Qin-wei, HU Jun-jie, ZHU Xing-yi
    China Journal of Highway and Transport. 2025, 38(11): 224-240. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.015
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    The sloped regions of highways situated in mountainous areas are particularly susceptible to natural disasters, including landslides and structural failures. Consequently, the installation of roadside equipment is essential for the provision of traffic information services and the monitoring of such disasters. Given the challenges posed by the remoteness from the power grid and the economic impracticality of long-distance power supply, there is an urgent need for an energy solution that is contextually appropriate. The energy attributes of high-entropy energy are well-suited to meet the energy requirements of the equipment utilized in these settings. The implementation of integrated high-entropy-load-storage systems along highways has the potential to create a self-powered microgrid, thereby enhancing traffic safety and improving emergency response capabilities on these thoroughfares. Nonetheless, the process of harnessing high-entropy energy in intricate road environments presents several challenges, including equipment malfunctions and variability in energy output, which can negatively affect the functionality of the self-powered microgrid. To mitigate these challenges, this study introduces a methodology for monitoring the operational status of road-domain microgrid, predicated on the assessment of high-entropy energy availability. Initially, at the equipment level, a reliability model that accounts for competitive failure is developed. Furthermore, the study extracts group characteristics that are independent of installation conditions by conducting comparative analyses of multiple similar devices. Utilizing the identified group characteristics, a deep learning model is developed to facilitate real-time evaluations of anomalies in high-entropy energy capture equipment. Subsequently, at the cluster level, the output generated by the high-entropy energy cluster is analyzed to ascertain whether it meets the anticipated energy capture thresholds. Furthermore, at the microgrid level, an assessment is conducted regarding the capacity of high-entropy energy to fulfill microgrid load demands and storage requirements. In this context, a method for situation warning of self-powered microgrid operational conditions, grounded in Support Vector Data Description, is proposed. Finally, the efficacy of the suggested microgrid situation warning approach, predicated on availability assessment, is corroborated through case studies. The findings of the study indicate that the proposed methodology is capable of effectively addressing the uncertainties associated with the evaluation of high-entropy energy operational status, thereby improving the timeliness and objectivity of the evaluation outcomes. It enables the effective identification of abnormal operational trends within self-powered microgrid and achieves online, proactive condition monitoring. This capability significantly enhances the operational reliability assurance of the microgrid. Furthermore, it establishes a theoretical foundation for the management of operation and maintenance of high-entropy energy capture equipment, as well as for the monitoring of operational conditions in self-powered microgrid.
  • A Study of Mobile Energy Storage Double Layer Power Dispatch Based on Hybrid Grid and Highway Operation Rules
    WU Hao, WANG Biao, NIU Ming-bo, HU Li-qun, JIANG Wei, LIU Zhuang-zhuang
    China Journal of Highway and Transport. 2025, 38(11): 241-256. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.016
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    With the ongoing energy transition and increasing penetration of electric vehicles, imbalances have emerged in highway energy supply. Establishing novel electricity supply models is crucial for ensuring energy security on motorways within transport engineering. To address the current shortage of charging points along the route, this proposal introduces additional charging zones between service areas. It establishes a hybrid dual-zone energy supply model based on a grid-connected structure, combining fixed and mobile components. Energy-consuming facilities are categorised into infrastructure and vehicle customer loads, each equipped with a dedicated Energy Storage System (ESS). Within charging zones, Mobile Energy Storage Systems (MESS) flexibly supplement vehicle demand loads while coordinating with fixed charging points. Network-based routing rules for MESS dispatch are designed through service area interconnectivity. To enhance dispatch intelligence and responsiveness, a MESS selection algorithm integrates distance and remaining energy considerations. Furthermore, a two-layer optimisation scheduling model is constructed to maximise economic benefits: the upper layer assigns tasks to MESS units via the scheduling algorithm to meet user demand; the lower layer enables dynamic energy trading with the main grid, based on ESS status and time-of-use pricing strategies, while ensuring basic load coverage and fixed charging point supply. Addressing the Whale Optimization Algorithm's (WOA) issues of low convergence accuracy and susceptibility to local optima, we enhance WOA by integrating particle swarm mechanism. This retains WOA's spiral search and encirclement strategy while introducing particle experience and optimal particle guidance capabilities, thereby strengthening global search performance. Ultimately, the improved whale algorithm was employed to solve the dual-layer optimisation model. Numerical simulations demonstrated that, compared to traditional genetic algorithms and particle swarm optimisation, performance improved by 2.08% and 1.24% respectively. Under the grid-connected structure, operator revenue reached 6.72 million yuan. This dual-layer MESS dispatch structure and algorithm possess considerable practical value.
    • Pavement Engineering
  • Normalized Characterization Model for Nonlinear Fatigue Damage of Asphalt Mixture Under Three-dimensional Stress State
    LYU Song-tao, WANG Shuang-shuang, LIU Chao-chao, ZHENG Jian-long
    China Journal of Highway and Transport. 2025, 38(11): 257-267. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.017
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    In order to objectively assess the fatigue damage characteristics of asphalt mixtures under complex service conditions, the strength, fatigue and residual strength tests of asphalt mixtures under different stress states, different test temperatures, and different loading frequencies (rates) have been carried out to reveal the limitations of the traditional fatigue damage model that characterizes fatigue damage with residual strength without taking into account the visco-elasticity characteristics of asphalt mixtures. Based on the three-dimensional strength yield model of asphalt mixtures characterized by effective stress, the fatigue stress ratio under a three-dimensional stress state is defined, the fatigue stress intensity ratio and fatigue life under a three-dimensional stress state are modeled, and the fatigue performance of different stress states, different temperatures, and frequencies is realized to characterize the fatigue performance in a normalized way. Furthermore, a nonlinear fatigue damage evolution model for asphalt mixtures under three-dimensional stress states was derived using the effective stress to characterize the residual strength under different stress states and the normalized fatigue equation to characterize the fatigue life. The results show that the traditional fatigue damage model characterizing fatigue damage by residual strength makes it difficult to objectively characterize the fatigue damage properties of asphalt mixtures under different test methods and conditions, with the model parameter γ1 fluctuating between 0.933--0.948, and the parameter γ2 fluctuating between 0.174--0.186. Three-dimensional stress state of asphalt mixture nonlinear fatigue damage evolution model to achieve the fatigue damage of the normalized characterization, not only intuitively verified the fatigue damage of asphalt mixtures of the time-temperature-stress state correlation and equivalence, but also to eliminate the impact of the test method and test conditions on the fatigue damage characterization, for quantitative analysis of asphalt mixtures of fatigue damage characteristics provide a theoretical basis.
  • AI-powered Smart Roads for Vehicle-road Collaboration
    ZHU Xing-yi, ZHAO Ren-jie, ZHANG Qi-fan, WANG Yi, WANG Yu-hong, PANG Ya-feng
    China Journal of Highway and Transport. 2025, 38(11): 268-282. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.018
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    As roads constitute a critical component of the “human-vehicle-road” system, the integration of artificial intelligence (AI) technologies has the potential to equip roads with enhanced perceptual accuracy, efficient decision-support capabilities, and diversified services, thereby fostering safer and more efficient urban transportation systems. However, systematic research on the realization of AI-enabled smart road upgrades remains limited, particularly in terms of the overall framework and key technologies. To address this gap, in this study, a comprehensive review of vehicle-road collaboration systems, smart road technologies, and AI developments was conducted. The study first examined the evolution of smart roads and summarized key research advancements across relevant domains. Subsequently, critical challenges hindering current vehicle-road collaboration efforts were identified, including perceptual limitations, positioning inaccuracies, and suboptimal traffic guidance. In response, an AI-driven framework for smart roads tailored to vehicle-road collaboration was proposed, outlining their essential functional requirements-including perception, positioning, and guidance capabilities. Furthermore, a corresponding technical roadmap for AI-enabled smart roads was developed, detailing specific implementations such as roadside-intersection collaborative sensing (enabled by Perception AI), vehicle-road joint high-precision positioning (facilitated by Positioning AI), and vehicle-road cooperative autonomous guidance (powered by Guidance AI). By leveraging AI to process and interpret large volumes of heterogeneous perceptual and positional data, this approach aims to improve traffic perception accuracy, positioning reliability, and guidance efficiency. The proposed approach contributes to the development of safer and more streamlined urban mobility systems.
    • Subgrade Engineering
  • Review of Soil Adsorptive Water Research and Its Applications Prospects in Subgrade Engineering
    ZHANG Rui, LI Lu, HU Shao-jie, GOU Ling-yun, ZHANG Chao
    China Journal of Highway and Transport. 2025, 38(11): 283-307. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.019
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    Understanding pore water in soils has long been a central and challenging topic in soil mechanics. Its physical properties are also a key scientific issue shared across the mechanical research of various special soils, such as high liquid limit soils and soft soils. Pore water can be classified into free water and adsorptive water, depending on its state. Adsorptive water exhibits unique physical properties, including high density and strong structural characteristics, resulting in its distinct flow and phase change behaviors from free water. However, the effects of the physical properties of adsorptive water on soil permeability, strength, and deformation remain unclear. Moreover, practical engineering generally overlooks the significance of adsorptive water, failing to fully utilize its physical properties to optimize engineering practices. This paper provided a comprehensive review of recent progress on adsorptive water in soil and its influence on soil properties, both domestically and internationally. It covered theoretical and experimental studies across microscopic, mesoscopic, and macroscopic scales. Specifically, it systematically summarizes the formation mechanisms of adsorptive water, analyzes the differences in physical properties between adsorptive water and capillary water, and clarifies the effects of adsorptive water on the hydraulic and mechanical properties of soil. It also reviews experimental research on the effects of adsorptive water on soil permeability, along with recent advancements in permeability coefficient models that consider adsorption. Additionally, the experimental research on the effects of adsorptive water on soil strength is reviewed, along with the development of strength models considering the effects of adsorptive water. Additionally, the paper summarizes the research progress on the role of adsorptive water in soil compression deformation, creep deformation, and subgrade soil resilience, with a focus on its role in the creep behavior of high liquid limit soils and soft soils. Finally, the paper discusses the potential applications of adsorptive water in high liquid limit soil embankments and soft soil foundation engineering, and future research priorities and directions are outlined to provide a reference for further studies.
  • Isolation of Surface Waves by Isolation Barrier of Periodic Open Trench-soil Mound
    ZHOU Feng-xi, DU Chao, LIANG Yu-wang, MA Qiang, BA Zhen-ning
    China Journal of Highway and Transport. 2025, 38(11): 308-319. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.020
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    To address the environmental vibration pollution caused by surface waves and reduce the damage caused by vibration, a temporary periodic open trench-soil mound joint isolation barrier is proposed, and its isolation performance is theoretically studied. First, based on the theory of periodic structures, the unit cell of the joint isolation barrier of the open trench-soil mound was selected, and the formation mechanism of the joint barrier bandgap was analyzed. The influence of the geometric size of the barrier on bandgap characteristics was also studied. Second, a dynamic response analysis model was established for a new type of isolation barrier with a periodic open trench-soil mound under harmonic loading. The influence of geometric parameters such as trench width, trench depth, and soil height on the isolation effect was numerically analyzed using the finite element method. Finally, considering the degradation of the joint barrier in relation to the isolation effect of a single barrier, the isolation performance of the joint isolation barrier was compared with that of a single trench and a single soil mound barrier. The results show that the joint barrier of the open trench and soil mound exhibits both a local resonance bandgap and Bragg scattering bandgap, thereby expanding the bandgap range of the barrier. The influence of the open trench size change on the bandgap boundary and width was greater than that of the soil mound size. When the structural size is too large, it leads to a decrease in the bandgap width, which is not conducive to barrier isolation. Notably, for the isolation effect, high-frequency isolation is better than low-frequency isolation. The number of periods and trench depth had the most significant impact on the isolation effect. The larger the number of periods and depth, the stronger was the isolation effect, and the smaller was the impact of the soil pile width. In addition, compared with a single trench and single soil barrier, the periodic trench soil barrier had a better vibration isolation effect, with a difference of more than 10 dB, indicating the rationality of the joint barrier. Therefore, the joint barrier can effectively reduce the excavation depth of the trench and lower construction costs.
    • Tunnel Engineering
  • Experimental Investigation on Mechanical Characteristics of Lining Structures Under Blockage Conditions of Longitudinal Drainage Pipe of Tunnel in Karst Area
    QIAN Wang-ping, WANG Bo, XIONG Wen-wei, LUO Ding-wei, LI Shu-chen
    China Journal of Highway and Transport. 2025, 38(11): 320-332. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.021
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    To reveal the evolutionary law of the mechanical properties of lining structures under the blockage of the longitudinal drainage pipe in karst tunnels, a test device system was independently developed to simulate the blockage of longitudinal drainage pipes and karst channels. The complex tunnel drainage system was equivalent to the circumferential blind pipe and the longitudinal drainage pipe characterized by the cross-sectional area and longitudinal length, and the physical model experiment of tunnel seepage under different blockage degrees of longitudinal drainage pipe was conducted. The results reveal that the groundwater reduction speed in the karst channel and the tunnel drainage volume decrease rapidly with the increase of the blockage of the longitudinal drainage pipe, which significantly reduces the dissipation capacity of the tunnel drainage system. The groundwater in the karst channel directly exerts localized high-water pressure on the tunnel lining, significantly increasing the stress response of the tunnel lining structure. When the longitudinal drainage pipe is completely blocked, the decline rate of groundwater reduction speed and tunnel drainage are as high as 82.5% and 95.9%, respectively. The water pressure at the arch bottom position and the structural stress at the left waist position are the most sensitive, and the growth rates are 30.1% and 37.6%, respectively. Compared with the two blockage indices of the longitudinal drainage pipe, the longitudinal length blockage index directly influences the flow path length, and the cross-sectional area blockage index directly affects the equivalent permeability coefficient, which jointly determine the drainage performance of the tunnel drainage pipe. Furthermore, due to the nonlinear evolution trend, there are noticeable differences in the relative influence weights of two blockage indices during the blockage process, that is, the longitudinal length blockage index is the primary influencing factor under low blockage conditions, whereas the cross-sectional area blockage index becomes the dominant factor under high blockage conditions. The research results can provide a theoretical basis for the safety assessments and maintenance measures of lining structure affected by drainage system blockages during the operational phase of karst tunnels.
  • Research on the Calculation Method of Pipe Jacking Face Resistance Considering Soil Arching Effect
    ZHANG Yong-jie, LI Jia-bing, OU Xue-feng, LUO Zhi-min, ZHENG Shuang, CAO Wen-gui
    China Journal of Highway and Transport. 2025, 38(11): 333-341. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.022
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    The pipe jacking face resistance is one key indicator to evaluate the excavation face stability. It is also can be used to guide the selection of pipe jacking machine and the evaluation of cutter head wear. A simple and effective calculation method of pipe jacking face resistance is the basic guarantee for the fast excavation. So the stress model of the soil in front of the cutter head was first analyzed during the excavation. Meanwhile the influence of the major principal stress arch of the soil was considered for the analysis on the basis of traditional passive earth pressure calculation theory. Then the analysis model and its calculation method of pipe jacking face resistance considering soil arching effect were proposed. And the influence rules of different factors to pipe jacking face resistance were obtained through the sensitivity analysis. When the soil arching effect is fully realized, the pipe jacking face resistance generally exhibits a linear increase with the rise of soil self-weight, soil thickness and soil cohesion. Furthermore, it nonlinearly increases with the rise of cutter head outer diameter or soil internal friction angle. The most significant influencing factors to the pipe jacking face resistance are cutter head outer diameter and soil internal friction angle. The monitored values and calculated results with different methods of pipe jacking face resistance of six typical cross-sections in four actual engineering projects were compared. It can be known that the calculated results with the method proposed in this paper are consistently bigger than the monitored values, and smaller than the calculated values obtained with method in standard. The average errors between the monitored values and calculated results obtained with the proposed method were approximately 23.7%. The robust result of the pipe jacking face resistance can be obtained with the proposed method, which is more suitable for the practical engineering. Therefore, the proposed method can effectively guide the selection of pipe jacking machine, and prevent the problems such as pipe stuck due to insufficient thrust force, low jacking efficiency, stratigraphic instability and so on. And it can provide valuable reference for similar projects in the future.
    • Traffic Engineering
  • Research on the Comprehensive Coordination of Freeway Network Guide Signs Based on Sign Information, Format, and Driver Cognitive Load
    YANG Yan-qun, WANG Lin-wei, ZHAO Xiao-hua, LIU Qi-qi
    China Journal of Highway and Transport. 2025, 38(11): 342-361. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.023
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    Guide signs serve as essential tools for providing drivers with route guidance and decision-making support. However, existing studies on freeway guide signs largely concentrate on the effectiveness of signage at critical interchanges or exits, with limited attention to the overall coordination of signage systems across entire road networks. To address this gap, the concept of comprehensive coordination of network guide signs is introduced. A quantitative evaluation method based on a multidimensional collaborative cloud model is developed, incorporating three key dimensions: information continuity, format consistency, and stability of driver cognitive load. Taking the Shijiazhuang freeway network as a case study, sign information continuity at interchange nodes is determined using graph theory and Monte Carlo simulation. Format consistency is assessed via the Euclidean distance of factors such as information density, the number of signs, and advance placement distance. Driver cognitive load stability is measured through EEG and eye-tracking data collected from on-road driving experiments. These indicators are synthesized to evaluate the overall coordination level of guide signs within the network. Results indicate that the network exhibits a moderate level of coordination, with only 1 out of 13 interchanges rated as highly coordinated. While the average information continuity across interchanges reaches 0.93, one node shows a significantly low unidirectional continuity of 0.16, impairing effective navigation. Additionally, 53.85% of the interchanges score below the network average in format consistency, cognitive load stability, and overall coordination. The findings support the validity of the proposed coordination assessment model and reveal that information density is positively correlated with cognitive load. More critically, discontinuity in signage information exerts a stronger impact on cognitive load than density, indicating that cognitive load stability can serve as an effective indirect measure of guide sign coordination across freeway networks.
  • Optimization Methods for Multi-source Sensor Deployment on Intelligent Highways with Perception Continuity Considerations
    XU Zhi-hang, GAO Ying, XU Zhi-gang, ZHANG Yu-qin, QU Xiao-bo
    China Journal of Highway and Transport. 2025, 38(11): 362-378. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.024
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    A foundational function of intelligent highways is the deployment of roadside, multi-source, heterogeneous sensing systems that accurately identify driving-safety risks and abnormal traffic events and promptly disseminate diverse warnings and driver-assistance information to safeguard travel. However, the industry currently lacks unified, operational guidelines for efficiently deploying multiple types of roadside sensing devices. To address this gap, this study systematically reviews existing research on sensor deployment and develops an improved bi-level integer programming optimization model aimed at maximizing abnormal-event detection benefits. The model integrates an anomaly detection algorithm with an active sensing mechanism, thereby mitigating the high computational complexity associated with conventional assumptions about closely spaced adjacent sensors. Given the stochastic nature of abnormal events, we further tailor the model to ensure continuous and stable event perception across the entire corridor. Building on the Lighthill-Whitham-Richards (LWR) traffic-flow model, we derive reasonable inter-sensor spacings for different sensor types from shockwave propagation speeds and use these spacings as references to partition the roadway into cells; we then quantify traffic conditions using an information-entropy-based multi-criteria evaluation, enabling the alignment of sensor types with segment-specific traffic-flow characteristics. Simulation experiments are conducted to analyze optimal deployment schemes under varying investment budgets. Finally, we benchmark the model-derived, detection-benefit-optimal deployment against two baselines: a traditional loop-detector deployment and a single-sensor-type, equal-spacing deployment under the same capital investment. Under a common detection algorithm, the proposed intelligent-highway roadside sensing deployment reduces average response time by approximately 67% relative to the traditional loop-detector scheme, and by about 5.68% and 24.55% relative to single-type millimeter-wave radar and LiDAR schemes, respectively. We also examine the impacts of maintenance costs and weather conditions on deployment performance, confirming the robustness and adaptability of the proposed model. The findings provide theoretical support and methodological guidance for the scientifically grounded deployment of roadside perception systems on intelligent highways.
  • Vehicle Merging Distance Calculation and Trajectory Planning for Accompanying Merging Scenarios in Ramp Merging Areas
    YUAN Hua-zhi, ZHANG Yu, YAN Ying, ZHANG Ze-yi, WANG Wen-xuan, YANG Yang, WANG Jun-hua
    China Journal of Highway and Transport. 2025, 38(11): 379-390. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.025
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    To optimize the merging process at highway ramps based on real vehicle trajectory data from the German eXID dataset, four highway merging areas were selected and divided into five sub-areas, with 183 vehicles screened out in the accompanying merging scenarios, where the relative position of vehicles related to the rightmost lane of the mainline changes during the merging process. The concept of “merging distance ratio” was introduced and the factors influencing the merging distance in the accompanying merging scenarios were analyzed. By fully integrating spatial and temporal features from trajectory data, a vehicle merging distance calculation model (MChebNet-AMTCN) was developed through the fusion of Chebyshev graph convolution (ChebNet) and an attention-mechanism temporal convolutional network (AM-TCN). Comparative experiments with other models validate the performance of the proposed model. Considering both safety and passenger comfort during merging, recommended merging time intervals were determined, and quintic polynomial curves were employed for trajectory planning. This study concludes that the parallel time of merging vehicles with accompanying vehicles and the remaining length of the acceleration lane are critical factors that determine the merging distance. The MChebNet-AMTCN model, which integrates spatiotemporal feature recognition, outperforms the comparative models. The recommended merging time is 2.0-4.5 s when the parallel time is in the restricted merging area, and 2.5-6.0 s when in the unrestricted merging area. The conclusions of this study provide insights for intelligent vehicles in cooperative environments during ramp merging, with the aim of enhancing the overall safety and optimizing the driving experience of passengers and drivers.
  • Ontology-enhanced Systems Theoretic Process Analysis Method of Scenario Safety Constraint Identification for Autonomous Driving
    ZHAO Ming-yang, LIANG Ci, XU Zhi-gang, ZHENG Wei
    China Journal of Highway and Transport. 2025, 38(11): 391-402. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.026
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    Potential hazards in complex operational scenarios pose critical challenges to the operational safety of Autonomous Driving (AD) systems, necessitating a systematic safety analysis method with semantic reasoning and interaction modeling capabilities. To address this need, an External Operational Scenario-Systems Theoretic Process Analysis (EOS-STPA) method was proposed that extends the traditional STPA framework beyond internal technical systems to external operational scenarios. A layered control modeling framework enhanced by ontology was established, consisting of three core components: an Interactive Conceptual Model (ICM) describing the output-feedback relationships between the host vehicle and scenario elements; a Scenario Refinement Model (SRM) enabling multidimensional semantic decomposition of traffic infrastructure, roads, environments, and objects; and a Concrete Scenario Model (CSM) performing safety analysis on specific interactions to identify unsafe control actions (UCAs) and generate formalized scenario safety constraints (SCs). This process constructs a closed-loop structure that integrates control logic, ontological semantics, and structured scenario decomposition. Considering the car-following task as a case study, the EOS-STPA method was applied to a typical AD scenario, in which 12 UCAs and 11 scenario SCs were identified. A simulation environment based on the CARLA platform was built, and EOS-STPA-derived SCs were incorporated into the reinforcement learning training. Compared to a traditional STPA-based reward strategy, the EOS-STPA-based model achieves reductions of 63.6%, 62.8%, 36.5%, and 10.4% in red-light violations, dangerous car-following events, emergency braking, and lane departures, respectively. The results demonstrate the effectiveness of the method in structured safety modeling and behavioral optimization under dynamic and complex scenarios. EOS-STPA shows strong generalizability and extensibility and delivers robust performance in formalized scenario safety analysis.
  • 3D Object Detection with Deformable Attention and Adaptive Fusion
    FAN Xing, LIU Zhan-wen, XUE Zhi-biao, ZHAO Xiang-mo, FAN Hai-wei
    China Journal of Highway and Transport. 2025, 38(11): 403-415. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.027
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    To enhance the robustness of 3D object detection for intelligent vehicles in open-road environments, a multi-modal feature fusion model named Deformable Shared Attention Mechanism and Density Threshold-Guided (DSAM-DTG) was proposed. Firstly, a deformable shared attention mechanism was designed, which utilized coarse-grained fused features to guide the internal feature aggregation of LiDAR and camera modalities, enhancing key regional information, compensating for missing features, and suppressing redundant noise, thereby providing high-quality single-modal feature representations for subsequent fusion. Secondly, a density threshold-guided multi-modal fusion strategy was proposed, which evaluated the reliability of each modality based on point cloud density distribution and scene context information, adaptively adjusting fusion weights and strategies to adapt to different scenarios. Finally, 3D objects detection was achieved based on the fused multi-modal features. Experimental the results on the nuScenes public dataset show that the DSAM-DTG model achieved 75.6% in mean Average Precision (mAP) and 77.9% in nuScenes Detection Score (NDS), respectively，representing improvements of 7.6% and 7.8% compared to the baseline model. Further tests under various scenarios, including perception-limited scenarios, environmental changes, distance variations, and cross-dataset evaluations, demonstrated that the model's mAP and NDS only decreased by 8.3% and 4.2% in perception-limited scenarios, with a significantly smaller performance drop compared to other methods. In rainy and nighttime, the mAP reached 74.4% and 47.9%, respectively. For distant vehicles and pedestrians, the accuracy improved by 22.1% and 19.2% compared to the baseline. Meanwhile, the model'achieves an inference speed of 21.7 frame·s-1 , meeting the deployment requirements for intelligent connected edge computing. The above research indicates that the DSAM-DTG model has good detection performance and generalization ability in different environments, providing important technical support for improving the driving safety of intelligent vehicles in open road environments.
  • Granular Vehicle-flow Coordination: Generalization-enhanced Reinforcement Learning Method for Ramp Merging Control in Cooperative Vehicle-infrastructure System
    HE Zi-liang, SU Zi-cheng, LIN Yun-qing, LIU Jia-mei, WANG Ling, HOU Jin-quan, LIU Jia-qi, MA Wan-jing
    China Journal of Highway and Transport. 2025, 38(11): 416-435. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.028
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    Connected and Automated Vehicles (CAVs) provide a more efficient and accurate control solution for ramp merging control on expressways, and can be regarded as “granular vehicle in the traffic flow”. However, in the mixed environment of Human Vehicles and CAVs, the merging control method based on CAV routes still faces challenges related to limited sensing range and difficult scalability. To address these challenges, this paper proposed a generalization-enhanced reinforcement learning method for ramp merging control based on the Cooperative Vehicle-Infrastructure System (CVIS). Firstly, we utilized roadside units in the CVIS to provide surrounding vehicles and traffic flow information to CAVs, expanding the perception range and ensuring that CAVs can obtain unified state features to support the model's generalization. Secondly, a universal state representation method was designed, which is applicable to merging scenarios with different lanes, CAV penetration rates, and vehicle numbers. This state design facilitates the reinforcement learning (RL) agent to achieve parameter sharing, significantly enhancing the model's generalization. To optimize both individual CAV states and the overall efficiency of the merging area, the reward function consisted of vehicle safety, efficiency, execution effectiveness, and the impact on the speeds of surrounding vehicles. The Double Deep Q-Network (DDQN) algorithm was employed within a distributed control framework to train the RL agents, aiming to reduce computational complexity and enhance generalization. Additionally, the training speed and stability were further enhanced through prioritized experience replay and soft update strategies. This paper compared the DDQN model under the CVIS scenario (DDQN-CVIS) with the Considering Intentions Decision Tree CAV model (CIDT-CAV) and the DDQN-based CAV model (DDQN-CAV) under the CAV environment. The results demonstrate that under the DDQN-CVIS model: ① the acquisition of comprehensive information reduces vehicle blind spots, and most of the ramp vehicles change lanes earlier, resulting in more efficient utilization of the merging area; ② mainline vehicles can actively create gaps for ramp vehicles to merge, fostering cooperative merging; and ③ the proposed method outperforms the benchmarks under various CAV penetration rates, dynamic CAV penetration rates, and different traffic demand scenarios, particularly in the high-demand scenario, where the average speed increases by 11.13% and the average flow rises by 3.98%. These results validate the feasibility of this method in balancing and optimizing traffic flow and individual vehicle benefits in complex merging scenarios, further realizing the new “granular vehicle-flow coordination” traffic management and control paradigm.
    • Automotive Engineering
  • Active Control of In-car Road Noise Based on Virtual Reference Signals
    ZHANG Zhi-fei, FU Xiao-yu, XIA Zi-heng, HE Yan-song, LI Shu, YAN Hui, LIANG Tao
    China Journal of Highway and Transport. 2025, 38(11): 436-446. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.029
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    In order to achieve effective noise reduction performance, the vehicle road noise control system require numerous reference signals, increasing computation costs of hardware. The virtual reference signal can effectively balances computational efficiency and noise reduction performance in the vehicle road noise control system. However, the balance of computational complexity and practicality for various road conditions is still one challenge of the virtual reference signal method. To address that issue, a new time-domain virtual reference signal method is proposed using the multi-condition conversion matrix. The multi-condition conversion matrix was synthesized based on the vibration data characteristics under multiple conditions. First, the original reference signals were selected based on the multiple coherence method. For each condition, the covariance matrix of the reference signals was analyzed using singular value decomposition to obtain eigenvalues and eigenvectors. Next, the extracted eigenvectors matrices from each condition were sorted in descending order of their corresponding eigenvalues. Then, eigenvectors associated with smaller eigenvalues were truncated. The truncated eigenvector matrices were subsequently partitioned and reorganized in sequential order. The Pearson correlation of each eigenvector was analyzed within each eigenvector group. Finally, the eigenvectors with the highest average correlation coefficient in each group were selected to form the multi-condition conversion matrix. Virtual reference signals could be constructed under various conditions based on this matrix. To verify the feasibility and applicability of the proposed method, simulation and real-vehicle experiments were conducted on a hybrid vehicle under various conditions. Twelve original reference signals collected under various operating conditions were used to construct a multi-condition conversion matrix. Based on this matrix, seven virtual reference signals were generated. Real-vehicle experiments were conducted to validate the noise reduction performance of the method in practical engineering applications. The results indicate that, compared to existing method, the proposed method effectively reduces the computational complexity by approximately 11.5%. Furthermore, the testing and analysis across various typical road conditions demonstrated that the proposed method achieved superior noise reduction performance under all operating conditions. Compared to existing methods, the noise reduction of the total sound pressure level in the 50-500 Hz band is increased by 0.7-1.8 dB(A). This study demonstrates a promising approach for enhancing the application of virtual reference signals in engineering practice.
  • Research on Vehicle Trajectory Prediction Method Based on Spatio-temporal Interaction Feature Fusion
    XU Dong-wei, CHENG Qian-bing, GU Tong-cheng, GUO Hai-feng
    China Journal of Highway and Transport. 2025, 38(11): 447-458. https://doi.org/10.19721/j.cnki.1001-7372.2025.11.030
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    With the development of autonomous driving technology, vehicle trajectory prediction has become one of the critical tasks in intelligent transportation systems. To address the problem that existing methods are unable to deeply explore the spatio-temporal interaction relationships between vehicles and suffer from low long-term prediction accuracy in complex dynamic environments, this paper proposed a trajectory prediction network based on spatio-temporal interaction feature fusion, which was mainly composed of three modules: spatio-temporal interaction, deep fusion, and trajectory prediction. Firstly, the spatio-temporal interaction module employed a multi-head cross-attention mechanism to quantify the strength of influence between the vehicles based on attention weights, thereby extracting spatial interaction features between the target vehicle and surrounding vehicles at each time step. A stacked Temporal Convolutional Network (TCN) based on causal convolutions was utilized to capture local temporal dependencies between time steps. Furthermore, a multi-head self-attention mechanism was adopted to model the logical relationships between arbitrary time steps, enabling the exploration of global temporal correlations within the time series. Secondly, the deep fusion module generated an adaptive weight matrix based on the Attentional Feature Fusion (AFF), dynamically adjusting the importance of different features to effectively integrate historical trajectory features and interaction features, thereby establishing complementarity and correlation between the features. Finally, based on the Gated Recurrent Unit (GRU), the decoder generated the parameters of a bivariate Gaussian distribution to output the predicted trajectory distribution, enabling multi-modal trajectory prediction for the vehicles. The NGSIM and HighD datasets were used for training and testing. The experimental results demonstrate that, compared with existing methods, the Root Mean Square Error (RMSE) at 5 seconds is reduced by 10.9% and 7.9%. The proposed trajectory prediction network based on spatio-temporal interaction feature fusion effectively improves the accuracy of vehicle trajectory prediction in complex dynamic environments, providing more reliable technical support for the safe decision-making of autonomous driving systems.
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