Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) was originally founded in 1959. The publisher of the journal is Wuhan University of Technology. JWUT first got the scopus license in the year 2001. The journal generally publishes all aspect of engineering sciences like: physics, chemistry, mathematics, and all sorts of general engineering.
Scopus Indexed(2026)
Submission Deadline
Volume
50
, Issue
03
02 Apr 2026
Day
Hour
Min
Sec
Publish On
Volume
49
, Issue
06
31 Jul 2025
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Aim and Scopes
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) (ISSN:2095-3844) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are in the following fields but not limited to: :
Electrical Engineering and Telecommunication Section:
Computer Science Section :
Civil and architectural engineering :
Mechanical and Materials Engineering :
kinematics and dynamics of rigid bodies, theory of machines and mechanisms, vibration and balancing of machine parts, stability of mechanical systems, mechanics of continuum, strength of materials, fatigue of materials, hydromechanics, aerodynamics, thermodynamics, heat transfer, thermo fluids, nanofluids, energy systems, renewable and alternative energy, engine, fuels, nanomaterial, material synthesis and characterization, principles of the micro-macro transition, elastic behavior, plastic behavior, high-temperature creep, fatigue, fracture, metals, polymers, ceramics, intermetallics.Chemical Engineering :
Food Engineering :
Physics Section:
Mathematics Section:
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Research on Feature Extraction Method of Marine Diesel Engine Wear Information Based on Kernel Principal Component
Due to heavy work load of marine diesel engines, the failure in their mechanical components may result in serious accidents. Existing condition monitoring methods for marine diesel engines usually adopt warning after the failure occurred. In order to predict potential faults, this work has put forward a remote intelligent monitoring system for marine diesel engines. The global system for mobile communication mode was employed to construct the basis of data remote transmission, and a new multi-kernel extreme learning machine algorithm was proposed to diagnose the early faults in an intelligent method. Experimental tests were carried out in the marine diesel engine fault diagnosis set-up. The analysis results show that the proposed remote intelligent monitoring system can accurately, timely and reliably detect the potential failures. Meanwhile, the proposed multi-kernel extreme learning machine was compared with the existing methods. The comparison indicates that the multi-kernel extreme learning machine outperforms its rivals in term of fault detection rate by an increase of 3.4%. Therefore, the proposed remote intelligent monitoring system has good prospects for engineering applications.
Passenger Flow Prediction Method for Rail Transit Stations Based on Empirical Mode Decomposition and K-nearest Neighbors
It is difficult for a single model to simultaneously capture the nonlinear, correlation, and periodicity of data series in the passenger flow prediction of urban rail transit (URT). To better predict the short-term passenger flow of URT, based on the long short-term memory network (LSTM) model, a deep learning model prediction method combining the time convolution network (TCN) and the long short-term memory network (LSTM) based on machine learning is proposed. The model couples the external factors such as date attributes, weather conditions, and air quality, to improve the overall prediction performance and solve the difficulty of accurate prediction due to the large fluctuation and randomness of short-term passenger flow in rail transit. Using the swiping data and related weather information of some stations of Chongqing Rail Transit Line 3, the TCN-LSTM model is verified by an example, and the prediction results of the single LSTM model are given for comparison. The results show that the TCN-LSTM model can better predict the passenger flow characteristics of different stations at different times. Compared with the single LSTM model, the TCN-LSTM model has better prediction accuracy and data generalization ability.
Study on Equivalent Solution Method of Static and Dynamic Characteristics for Orthogonally Rib-stiffened Sandwich Plate
Free and forced vibrations of Z-reinforced sandwich plates stiffened by steel ribs are researched by experimental and mixed analytical–numerical techniques. A test sample of the plate is firstly manufactured and an underwater vibration test is conducted with a random white-noise force. Meanwhile, a mixed analytical–numerical method (MA-NM) is proposed, which uses an analytical homogenization for the Z-reinforced core and coupling finite element and boundary element (FE/BE) analyses for the plate. Natural frequencies given by the MA-NM are compared with ones from the detailed FE model. Forced vibration responses from the MA-NM are validated against the experimental data. Good agreements are obtained and the high accuracy of the MA-NM is demonstrated. Parameters influences are then analyzed through the MA-NM. The core material shows a larger effect on natural frequencies than the reinforcement material. Ribs at the orthotropic midlines greatly enhance the bend stiffness of the whole structure, and ribs at the surrounding sides mainly influence boundary conditions. When in-plane coordinates of the external force near the plate center, vibration responses have fewer resonance peaks and lower amplitudes, while the influence of the thickness direction coordinate could be ignored. The fluid loading reduces both natural frequencies and vibration responses.
Evaluation Method of Traffic Operation Status of Urban Road Intersections Based on Efficiency Coordination
Signal coordination is perceived by many agencies as an advantageous improvement to the community or corridor in consideration. In many cases, signal coordination techniques have proven to be successful in improving the quality of life and mobility through the area. This study determines the coordination system pattern of traffic signal for four consecutive intersections spaced at 780 m distance. Data for vehicles movement were collected using video camera during morning and evening peak hour with congested conditions. For evaluation of the possible coordination of signalized intersections a simulation model, TRANSYT7F, was used. The results show after coordinating, the amount of delay, travel time, and queue reduce.
Demand Forecast of Floating Bicycle Based on LSTM Network
As a convenient, economical, and eco-friendly travel mode, bike-sharing greatly improved urban mobility. However, it is often very difficult to achieve a balanced utilization of shared bikes due to the asymmetric spatio-temporal user demand distribution and the insufficient numbers of shared bikes, docks, or parking areas. If we can predict the short-run bike-sharing demand, it will help operating agencies rebalance bike-sharing systems in a timely and efficient way. Compared to the statistical methods, deep learning methods can automatically learn the relationship between the inputs and outputs, requiring less assumptions and achieving higher accuracy. This study proposes a Spatial-Temporal Graph Attentional Long Short-Term Memory (STGA-LSTM) neural network framework to predict short-run bike-sharing demand at a station level using multi-source data sets. These data sets include historical bike-sharing trip data, historical weather data, users’ personal information, and land-use data. The proposed model can extract spatio-temporal information of bike-sharing systems and predict the short-term bike-sharing rental and return demand. We use a Graph Convolutional Network (GCN) to mine spatial information and adopt a Long Short-Term Memory (LSTM) network to mine temporal information. The attention mechanism is focused on both temporal and spatial dimensions to enhance the ability of learning temporal information in LSTM and spatial information in GCN. Results indicate that the proposed model is the most accurate compared with several baseline models, the attention mechanism can help improve the model performance, and models that include exogenous variables perform better than the models that only consider historical trip data. The proposed short-term prediction model can be used to help bike-sharing users better choose routes and to help operators implement dynamic redistribution strategies.
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/Journal of Wuhan University of Technology (Transportation Science and Engineering)