Journal of Chongqing Jiaotong University(Natural Science)

Calculation Method of Load Transverse Distribution Considering Guardrail Stiffness

KANG Ling1, XU Zhi2, CHEN Ming1, XU Jinhua2, MOU Tingmin1

2025, 44(8): 1-7. DOI: 10.3969/j.issn.1674-0696.2025.08.01

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Current load transverse distribution calculation methods, such as the rigid-jointed plate (beam) method and rigid crossbeam method, both neglect the influence of guardrail stiffness. However, for the low-height beam structures, the guardrail stiffness often significantly exceeds that of the main girders. Ignoring its influence will result in a deviation in the calculation of the load transverse distribution coefficient, making it difficult to accurately reflect the true stress state of the main beam. To address this, an improved load transverse distribution calculation method was developed by modifying the flexibility coefficient matrix to account for guardrail stiffness effects on the basis of the traditional rigid-jointed beam method and verified through on-site load tests. The research results demonstrate that compared to the traditional rigid-jointed beam method, the proposed method significantly improves calculation accuracy and aligns more closely with the actual mechanical response of bridges, which provides a more reliable theoretical basis for bridge load test, structural stress analysis and load-bearing capacity evaluation.

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Design of a New Precast Steel Inner Core-UHPC Outsourced Composite U-Shaped Beam

WANG Lei, LAI Yaping

2025, 44(8): 8-14. DOI: 10.3969/j.issn.1674-0696.2025.08.02

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Urban flyovers often utilize steel box girders due to under-bridge clearance constraints. However, conventional steel box girders are costly to fabricate and maintain. According to this, a steel inner core-UHPC outsourced composite U-beam was proposed, which was with low cost, high construction efficiency and maintenance-free in operation. Finite element analysis model was employed to calculate the stresses, displacements and natural vibration frequencies of the proposed composite beam. The effects of the jacking force applied to the side supports on the structural stresses were analyzed, and the assembly manufacturing and construction methods suitable for this type of structure were also proposed. The research results indicate that the stresses, displacements and natural vibration frequencies of the proposed composite U-shaped beam all meet design requirements. The maximum tensile stress of the structure can be effectively improved by jacking up the side supports. Additionally, the segmental prefabrication and on-site assembly method can shorten the construction period and reduce requirements of construction space.

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Vehicle Collision Force Reduction Effect of the Arched-Surface Composite Material Device

HE Peijian1,2， GENG Bo1,2， FENG Xia1,2， ZENG Haonan1,2

2025, 44(8): 15-24. DOI: 10.3969/j.issn.1674-0696.2025.08.03

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The arched-surface composite material device for protecting urban elevated piers against vehicle collision was proposed. In order to analyze the collision force reduction effect of the new device, the quasi-static compression tests of specimens with five kinds of collision surface shapes such as arched, wavy, semicircular, sawtooth and rectangular shapes were carried out, and the load-displacement curve and energy-absorbing capacity of the test specimens were compared. Compared with the traditional steel + concrete anti-collision device and the vertical face composite material device, the collision force reduction rate of the arched-surface composite material device under different collision conditions was analyzed through numerical simulation of vehicle-bridge collision, so as to verify the buffering performance of the new device. The research results show that the planar shape of the anti-collision device can adopt parallelograms to improve the protection of the collision-prone and damage-prone position of the elevated pier. The arched collision surface can significantly improve the energy-absorbing capacity of the anti-collision device, which is about 26% higher than that of the traditional vertical face. The arched-surface composite material device can effectively reduce the vehicle collision force by 33.3% for the collision of an 8-ton truck and by 51.5% for the collision of a 25-ton truck. The reduction effect of the vehicle collision force of the arched-surface composite material device is better than that of the steel + concrete device and the vertical facade device, which is at least 111.0% higher than that of the steel + concrete device and at least 45.5% higher than that of the vertical facade device.

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Detection and Analysis of Anomalous Values of Static Level Instrument Vertical Displacement Monitoring of Bridges Based on DBSCAN-LERP-LSTM

PAN Guobing1，YU Hongbing1，SU Lin2，ZHANG Shuntao3，WU Wei1

2025, 44(8): 25-32. DOI: 10.3969/j.issn.1674-0696.2025.08.04

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To address the issue of noise of bridge settlement data caused by environmental changes and sensor failures, a DBSCAN-LERP-LSTM-based analysis method was proposed to enhance data reliability and analysis accuracy. Taking the 2021—2023 static level instrument monitoring data of a cable-stayed bridge on a highway as an example, the DBSCAN algorithm (ε=40, M=20) was firstly used to remove 9.8% of the outliers and fill in the missing values through linear interpolation. Then, it was found that the settlement value reached approximately -0.4 mm at the end of 2022 by time series decomposition. Finally, an LSTM model was constructed, and the parameters were optimized by three methods such as PSO, SSA and ACO. The results show that the PSO-LSTM model performs the best, with an RMSE of 0.419, an MAE of 0.337, and an MAPE of only 0.142%, which provides an effective data processing procedure for static level instrument monitoring systems and is of great significance for the long-term safe operation of bridges.

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Coal Transportation Volume through the Three Gorges Dam Ship Locks Based on Industrial and Supply Chain

YE Wenjing1, DU Hongbo2, WAN Yu2

2025, 44(8): 33-41. DOI: 10.3969/j.issn.1674-0696.2025.08.05

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The efficient and smooth operation of the Three Gorges Dam ship locks is crucial for the economic development of the upstream region of the Yangtze River and the industrial layout along the river. The basic premise for solving the congestion status at the Three Gorges Dam ship locks is to make a reasonable judgment of the transportation demand for passing through the locks and development trends. Taking coal as an example, the analysis on its transportation demand through Three Gorges Dam ship locks was carried out. Firstly, the industrial and supply chain of coal in the upstream Yangtze River was investigated, the characteristics of coal production, supply and consumption through the Three Gorges Dam ship locks were revealed, and the transportation demand and flow direction in the upstream Yangtze River region were clarified. Secondly, the main influencing factors of coal freight volume passing through the Three Gorges Dam ship locks were analyzed. It was found that the selected factors based on the selection of the industrial chain and supply chain were strongly correlated with the volume passing through the ship locks, which explained the intrinsic relationship and interaction mechanism between the selected factors and the freight volume. Finally, the selected factors were respectively incorporated into BP, LSTM and Bi-LSTM models, all of which demonstrated that the back-testing results based on the industrial and supply chain were more accurate compared to those of traditional methods. And the transportation demand for coal passing through the Three Gorges Dam ship locks was further analyzed from the perspectives of macro-policy influence and industrial development. The research results provide new ideas for predicting regional freight volume and combining with the industrial chain and supply chain of transporting goods can more reasonably predict freight volume and development trends, providing support for the development of the transportation industry.

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Ship Signal Lights Detection and Identification Based on Optimized Background Subtraction Method

ZHAO Yuelin，GAO Xiangyu

2025, 44(8): 42-49. DOI: 10.3969/j.issn.1674-0696.2025.08.06

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Accurate detection and identification of ship signal lights is one of the effective ways to achieve situational awareness of marine vessels. A detection and identification method of ship signal lights in dynamic scenes based on background motion compensation and optimized background subtraction method was proposed. Firstly, based on the SURF feature point extraction algorithm, circular regions were used to replace rectangular regions to extract 32 dimensional descriptors, achieving dimensionality reduction of descriptors and improving the speed of the algorithm. Secondly, the refined SURF algorithm was utilized to extract and match feature points from video images, yielding linear parameters that reflected the mapping relationship between images. These parameters were then used for background estimation and completing background motion compensation. Finally, the background subtraction method was optimized by adopting a segmented updating strategy and adaptive difference threshold, and the geometry and color characteristics of the signal lights were combined to eliminate the influence of environmental factors such as interference lights and sea waves. The research results show that the proposed algorithm after completing background motion compensation has not only high detection and identification accuracy of ship signal lights but also strong robustness, which can better detect and identify ship signal lights in dynamic backgrounds.

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Assembly Protection Structure of Dangerous Rock Based on Multi-level Shock Absorption and Strong Support Concept

WANG Xing1,2，HUANG Shuai1,2，NIE Yawei1,2，MEI Hua3，HU Zhaoxia3

2025, 44(8): 50-58. DOI: 10.3969/j.issn.1674-0696.2025.08.07

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To explore the dangerous rock protection structures with higher construction efficiency and safety factors, numerical simulation and indoor testing methods were jointly adopted to verify the reliability of numerical calculation software from both qualitative and quantitative aspects. A new type of assembly steel shed tunnel was constructed based on the concept of multi-level shock absorption and strong roof support. The composite cushion was composed of sand, EPE and foam rubber. The roof of shed tunnel was composed of hollow steel plate and foam concrete. Matrix steel supports were arranged at the belly of the roof for strong support, and shock-absorbing rubber bearings were installed between the columns and the roof. The research results indicate that when falling rocks impact steel shed tunnels at speeds of 16 m/s, 20 m/s and 24 m/s, the peak stresses at the center of the slab belly are 28.54 MPa, 34.71 MPa and 40.92 MPa, respectively, and the peak displacements are 15.83 mm, 15.92 mm and 16.04 mm, respectively. The protective structure is within a safe range. The stress of EPE and the central unit of the foam rubber cushion layer is maintained at about 0.3 MPa and 0.5 MPa, and the composite cushion layer has superior energy dissipation and shock absorption effects. The matrix steel support is subjected to significant stress, with peak stress reaching around 60 MPa during the impact process. The non-rigidity of rubber bearings results in a triangular variation trend of their overall stress curve, with stress inside the support of approximately 5 MPa.

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Influence of Built Environment of Residential Area and Workplace on Commuting Mode Choice Considering Cross-Classification Characteristics

YIN Chaoying1，CAO Yuze1，JIN Wei2，SHAO Chunfu3

2025, 44(8): 59-65. DOI: 10.3969/j.issn.1674-0696.2025.08.08

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In order to overcome the problem that the traditional multi-level model was difficult to model the spatial heterogeneity of residential area and workplace at the same time, a Bayesian cross-classification discrete choice model was constructed by use of survey data on residents travel in Changchun city, to explore the impact of the built environment on residents choice of commuting mode from a spatial perspective. The results show that compared to multi-level binomial Logistics models without considering the built environment of residential areas and binomial Logistics models without considering spatial heterogeneity, the Bayesian cross-classification discrete choice model with considering spatial heterogeneity has better fitting performance. The degree of variation in residents commuting mode choices caused by spatial heterogeneities in their residential area and workplace accounts for 22.5% and 11.6% of the total variation, respectively. On the basis of controlling individual socio-economic attributes, the built environment characteristics of residential area and workplace are mostly significantly correlated with the use of cars for commuting, and the land use mixing degree has a significant negative correlation with residents travel mode choices. The distance from the residential area and workplace to the CBD has a significant positive correlation with the impact of commuting by car. In terms of intersection density, the impact of workplace intersection density is not significant, while the impact of residential area intersection density is significantly negatively correlated. The research results can provide theoretical basis for the reasonable allocation of urban built environment and correct guidance of travel demand.

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Prediction of High-Speed Rail Express Transportation Volume Based on Gray-BP Neural Network

YANG Xiangfei, CHEN Yu

2025, 44(8): 66-74. DOI: 10.3969/j.issn.1674-0696.2025.08.09

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With the advancement of green logistics, the “high-speed rail + express delivery” freight model has demonstrated growing application value, and the accurate prediction of its transportation volume holds practical significance for optimizing railway transportation capacity. Taking the Gansu-Qinghai-Ningxia region as an example, the grey relational analysis method was employed to screen out key influencing factors from 15 initial indicators, and a combined prediction model based on grey-BP neural networks was constructed. The research results show that compared with GM (1,1) and ARIMA models, the combined model shows a significant improvement in prediction accuracy. An inverted U-shaped curve characterizing high-speed rail's sharing rate changing with transportation distances was revealed by establishing a Logit model incorporating seven dimensions such as economy, timeliness and environmental protection. And the advantageous transportation distance range is 400~1 000 km. Based on these models, the predicted values of high-speed rail express transportation volume for this region over the next three years are calculated out, providing data support for the allocation of transportation capacity for the railway department.

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Lightweight Driving Behavior Detection Network Based on Attitude Assistance

LAN Zhangli1，FAN Liang1，ZHANG Hong2，TANG Ruohan1，XU Yuantong1，HUANG Darong1,3

2025, 44(8): 75-82. DOI: 10.3969/j.issn.1674-0696.2025.08.10

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Distracted driving is one of the primary causes of traffic accidents. To improve the speed and accuracy of detecting distracted driving behaviors, a lightweight driving behavior detection network based on pose assistance was proposed. To address the issue of poor feature extraction quality, an efficient large-kernel self-attention mechanism was designed to enhance the ability to capture both local and global features, thereby extracting rich low-level features. Meanwhile, grouped convolution was combined with capsule network to extract semantic features of driving behavior, which reduced the amount of model parameters while ensuring high accuracy. Furthermore, the detection accuracy of network in complex backgrounds was further improved by introducing pose estimation as an auxiliary. Experiment results show that the proposed method achieves accuracy of 99.71% and 95.38% on the SFD and AUC benchmark datasets, respectively; compared to current advanced models, while maintaining the same accuracy, the parameter count is only 0.29M (a reduction of 61.8%), and the inference speed in a server with a throughput of 801 images/s is about 2.57ms. The proposed lightweight driving behavior detection network based on attitude assistance achieves relatively high accuracy and its parameter amount satisfies the demands of embedded devices, providing support for safe driving.

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Task Assignment and Trajectory Planning of Pharmaceutical Logistics UAV with A* Algorithm

LI Hang， ZHANG Xiaona

2025, 44(8): 83-89. DOI: 10.3969/j.issn.1674-0696.2025.08.11

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Aiming at the problem of task assignment and trajectory planning for using UAVs for pharmaceutical transportation in urban environments, a UAV task assignment model under multiple constraints based on the coordinates of demand points and daily demand volume was established, with the objective function of minimizing cost. K-means clustering analysis was embedded into the genetic algorithm for solving UAV takeoff and landing site selection and task allocation. A task line was selected on the basis of task assignment, and the grid method was used to perform spatial 3D modeling of the terrain and obstacle environment around the task line. And then, the A* algorithm was introduced to search for the optimal trajectory for this task. The simulation of trajectory planning based on A* algorithm, ACO algorithm and RRT algorithm were carried out respectively on 3D grid map. The simulation results show that in the process of trajectory planning, the A* algorithm has shorter trajectories, higher search return rate and smoother trajectories compared to the other two algorithms, which verifies the feasibility and effectiveness of the A* algorithm.

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Container Throughput Forecasting of Wenzhou Port Based on CNN-BiLSTM Model with Hybrid Attention Mechanism

DING Tianming，GAO Lingjia

2025, 44(8): 90-98. DOI: 10.3969/j.issn.1674-0696.2025.08.12

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In order to predict port container throughput more accurately, a prediction model that integrated convolutional neural networks (CNN) with bidirectional long short-term memory (BiLSTM) networks was proposed, and multiple attention mechanisms were also introduced to comprehensively capture the global features of the data. In the proposed model, the influencing indicators and historical container throughput data were combined as multivariate inputs for prediction. The results indicate that, compared to traditional LSTM prediction models and CNN-LSTM hybrid models, both the mean absolute percentage error (MAPE) and root mean square error (RMSE) of the proposed model are reduced, and the model fitting degree (R2) is significantly improved. Notably, in instances of significant data fluctuations, the proposed model achieves more accurate prediction results, which helps port and shipping enterprises adjust their planning decisions and business strategies in a timely manner.

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Long-Distance and Dual-Scale Transformer Short-Term Traffic Flow Prediction Model

ZHANG Jianhua, WEN Zhenglong

2025, 44(8): 99-107. DOI: 10.3969/j.issn.1674-0696.2025.08.13

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Traffic flow prediction, as a core technology of intelligent transportation systems (ITS), faces the fundamental challenge of effectively modeling complex spatio-temporal dependencies in traffic data. Current mainstream models (based on graph neural networks and attention mechanisms) have two key limitations. Firstly, node similarity computation is affected by temporal misalignment in traffic fluctuations, causing misjudgment of similar nodes with delayed propagation characteristics. Secondly, spatial feature extraction fails to jointly capture macro-level patterns (e.g., periodic travel patterns) and micro-level dynamics (e.g., sudden congestion, traffic accidents) in traffic flows. To address these issues, LDFormer model was proposed, which introduced dynamic time warping (DTW) algorithm to reconstruct node similarity measurement, eliminating spatio-temporal deviations caused by propagation delays. Meanwhile, a dual-path spatial modeling mechanism was designed. Macro-micro characteristics of the attention-generated spatial dependencies were respectively captured by learnable mask matrices (Mglo and Mmic). Experiments on three benchmark datasets demonstrate that LDFormer model significantly outperforms existing spatio-temporal prediction methods.

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Port Cargo Throughput Forecasting Based on CPSO Algorithm Improved GM-Markov Model

CHEN Danyong1, WANG Yuliang1, ZENG Fenghong1, WU Chengxi2

2025, 44(8): 108-115. DOI: 10.3969/j.issn.1674-0696.2025.08.14

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To address the nonlinear dynamic prediction requirements for cargo throughput in Huilai Port area, Jieyang Port in Guangdong Province, a GM-Markov combined prediction model based on chaotic particle swarm optimization was proposed. By integrating the advantages of the grey GM (1,1) model and Markov chain, Logistic mapping was employed to achieve chaotic initialization of particle swarm parameters and state intervals, and a prediction framework with dynamic adaptability was established. The improved model effectively verified the random fluctuation characteristics of throughput data in the port area from 2007 to 2022 through state space partitioning and independent probability transition matrix calculation. The research results demonstrate that the optimized model reduces the mean absolute percentage error (MAPE) to 8.06%, which significantly enhances the prediction accuracy and stability, compared to traditional methods. The engineering applicability of the proposed model in dynamic system forecasting is also verified.

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Evaluation Method for Operational Safety of Provincial Road Freight Enterprises Based on Entropy Weight-TOPSIS

YAN Shengyu1，ZHAO Jiaqi2，LIU Yang1，HAO Shijie1，MA Zhenxiang2

2025, 44(8): 116-122. DOI: 10.3969/j.issn.1674-0696.2025.08.15

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Investigating method for the operational safety of road freight enterprises was proposed by using stratified sampling theory. Taking the number of trucks owned by the enterprise as the standard, the number of safety education, safety inspections, traffic violations, and traffic accidents were set as evaluation indicators, and the entropy weight method was used to determine the weight of evaluation indicators. Based on the TOPSIS theory, the evaluation method for the operational safety of road freight enterprises was proposed, and the operational safety evaluation values of road freight enterprises were calculated. By analyzing sample data from 469 road freight enterprises in 5 key cities of a certain province, the feasibility of the proposed method was verified. The results show that the coefficient of variation for the relative proximity of the proposed model is 16.45%, indicating good dispersion. The stratified sampling proportion of enterprises under regulations in the sample province is 6.04%, which can effectively reduce the workload of evaluation. The weights of the two evaluation indicators, that is, the number of traffic violations and the number of traffic accidents of enterprises are 24.49% and 34.93%, respectively, which are higher than the positive evaluation indicators by an average of 9.42%. The proposed method can provide technical support for the safety management of the provincial road freight industry.

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