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01 An Overview of Traffic Management in "Automatic+Manual" Driving Environment
02 An Image Generation Method for Automated Driving Based on Improved GAN
03 An Analysis of Injury Severities in School Bus Accidents Based on Random Parameter Logit Models
04 A Visualization Analysis and Development Trend of Intelligent Ship Studies
07 An Analysis of Highway-traffic Safety Based on Dynamic Risk Saturation
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2025, 43(5): 1-11.
doi: 10.3963/j.jssn.1674-4861.2025.05.001
Abstract:
With the continuous growth of vehicle ownership and road network density, road markings face challenges such as rapid performance degradation and untimely maintenance during long-term service, severely compromising their continuous safety functionality. This study addresses the core issues of maintaining long-term retroreflective performance and improving prediction accuracy by conducting a systematic analysis of retroreflective performance degradation and enhancement pathways. Comparative analysis reveals that existing standard systems lack sufficient requirements for long-term performance maintenance. Furthermore, traditional degradation prediction models exhibit limitations in characterizing the coupled effects of multiple factors such as traffic volume, climate conditions, and material properties, while their poor scenario adaptability further restricts predictive effectiveness. To overcome these limitations, the development of explainable artificial intelligence technologies based on"mechanism-data fusion"represents a critical pathway. This approach enables accurate capture of the nonlinear degradation patterns of retroreflective luminance (RL) and provides reliable quantitative support for maintenance decision-making. In terms of performance enhancement, the study demonstrates the necessity of shifting from"minimum initial cost"to"whole-life-cycle cost optimization."It verifies the significant potential of combining high-quality glass beads with high-performance marking materials to achieve dual benefits in long-term performance and economic efficiency. Through innovative surface structure designs, the interaction pattern between markings and the environment can be modified, offering possibilities for synchronizing marking service life with pavement life. Future research should strengthen interdisciplinary collaboration to develop systematic solutions in predictive modeling, material system optimization, and management mechanism innovation, thereby providing solid support for enhancing the long-term service performance and safety assurance of road markings.
With the continuous growth of vehicle ownership and road network density, road markings face challenges such as rapid performance degradation and untimely maintenance during long-term service, severely compromising their continuous safety functionality. This study addresses the core issues of maintaining long-term retroreflective performance and improving prediction accuracy by conducting a systematic analysis of retroreflective performance degradation and enhancement pathways. Comparative analysis reveals that existing standard systems lack sufficient requirements for long-term performance maintenance. Furthermore, traditional degradation prediction models exhibit limitations in characterizing the coupled effects of multiple factors such as traffic volume, climate conditions, and material properties, while their poor scenario adaptability further restricts predictive effectiveness. To overcome these limitations, the development of explainable artificial intelligence technologies based on"mechanism-data fusion"represents a critical pathway. This approach enables accurate capture of the nonlinear degradation patterns of retroreflective luminance (RL) and provides reliable quantitative support for maintenance decision-making. In terms of performance enhancement, the study demonstrates the necessity of shifting from"minimum initial cost"to"whole-life-cycle cost optimization."It verifies the significant potential of combining high-quality glass beads with high-performance marking materials to achieve dual benefits in long-term performance and economic efficiency. Through innovative surface structure designs, the interaction pattern between markings and the environment can be modified, offering possibilities for synchronizing marking service life with pavement life. Future research should strengthen interdisciplinary collaboration to develop systematic solutions in predictive modeling, material system optimization, and management mechanism innovation, thereby providing solid support for enhancing the long-term service performance and safety assurance of road markings.
2025, 43(5): 12-23.
doi: 10.3963/j.jssn.1674-4861.2025.05.002
Abstract:
Existing traffic guidance strategies are predominantly developed under homogeneous assumptions within simulation environments, lacking consideration of real-world driver behavioral response mechanisms. Consequently, they fail to meet the requirements of emergency traffic management in disaster-prone areas. To address this gap, this study takes the Tibet Autonomous Region as a case area, systematically analyzing driver behavioral characteristics and their responses to guidance information under disaster conditions. A dynamic traffic guidance framework is proposed based on event feature recognition and behavioral response characteristics across multi-hazard scenarios. Using geological hazard and traffic blockage data, the Gaussian mixture model (GMM) is employed to identify event-blockage characteristics and duration distributions. Categorizing en-route incidents into three types: complete blockage, conditional passage, and temporary control. Based on driver survey data, the Apriori association rule algorithm and structural equation model (SEM) are utilized to quantify drivers'responses to information cognition, types of guidance information, release locations, and waiting times. The results show that the path coefficients from information cognition to situational assessment and from situational assessment to behavioral response are 0.688 (p =0.07) and 0.635 (p =0.05), respectively, indicating that guidance information significantly and positively affects driver decision-making. Drivers'tolerance threshold for continuous interruptions is approximately 3 h, and their demand for guidance information exhibits a three-tier hierarchical structure, with primary attention given to event type, congestion distance, and optimal driving route. The combination of short messaging service (SMS) and quick response (QR) code distribution proves to be the most effective means of information delivery. On this basis, a"spatially hierarchical-temporally progressive"dynamic guidance strategy framework is established, consisting of three modes: route-level (long-distance diversion), segment-level (node control), and temporary-passage-level (short-term response), integrated with an emergency priority mechanism. Finally, validation using a traffic simulation platform for conditional passage events involving single-lane closures demonstrates that the proposed dynamic signal-control strategy reduces average waiting time by 21.5% compared to manual control, thereby significantly improving traffic operational efficiency under disaster conditions.
Existing traffic guidance strategies are predominantly developed under homogeneous assumptions within simulation environments, lacking consideration of real-world driver behavioral response mechanisms. Consequently, they fail to meet the requirements of emergency traffic management in disaster-prone areas. To address this gap, this study takes the Tibet Autonomous Region as a case area, systematically analyzing driver behavioral characteristics and their responses to guidance information under disaster conditions. A dynamic traffic guidance framework is proposed based on event feature recognition and behavioral response characteristics across multi-hazard scenarios. Using geological hazard and traffic blockage data, the Gaussian mixture model (GMM) is employed to identify event-blockage characteristics and duration distributions. Categorizing en-route incidents into three types: complete blockage, conditional passage, and temporary control. Based on driver survey data, the Apriori association rule algorithm and structural equation model (SEM) are utilized to quantify drivers'responses to information cognition, types of guidance information, release locations, and waiting times. The results show that the path coefficients from information cognition to situational assessment and from situational assessment to behavioral response are 0.688 (p =0.07) and 0.635 (p =0.05), respectively, indicating that guidance information significantly and positively affects driver decision-making. Drivers'tolerance threshold for continuous interruptions is approximately 3 h, and their demand for guidance information exhibits a three-tier hierarchical structure, with primary attention given to event type, congestion distance, and optimal driving route. The combination of short messaging service (SMS) and quick response (QR) code distribution proves to be the most effective means of information delivery. On this basis, a"spatially hierarchical-temporally progressive"dynamic guidance strategy framework is established, consisting of three modes: route-level (long-distance diversion), segment-level (node control), and temporary-passage-level (short-term response), integrated with an emergency priority mechanism. Finally, validation using a traffic simulation platform for conditional passage events involving single-lane closures demonstrates that the proposed dynamic signal-control strategy reduces average waiting time by 21.5% compared to manual control, thereby significantly improving traffic operational efficiency under disaster conditions.
2025, 43(5): 24-32.
doi: 10.3963/j.jssn.1674-4861.2025.05.003
Abstract:
Addressing frequent jaywalking, pedestrian-vehicle conflict risk, and heavy congestion at signalized intersections under mixed traffic, this study develops a cooperative control framework for traffic signals, connected autonomous vehicle (CAV), and pedestrians. In Stage 1, a protect/prohibit right-turning (PPRT) strategy is integrated into a pedestrian-oriented deep reinforcement learning (DRL) controller. The state encodes spatial-temporal conditions using matrices of vehicle and pedestrian positions and speeds. Actions split phases into pedestrian-through and vehicle left/right turns to achieve temporal-spatial separation of key conflicts. The reward is based on the difference in cumulative waiting time with passenger-load weighting to reflect social efficiency, and the optimal policy is learned with a dueling double deep Q-network. In Stage 2, coordinated speed planning for pedestrians and CAV is designed to further reduce interactions and delay. Pedestrian speeds are bounded by feasible ranges derived from crossing distance and remaining green time with acceleration and speed constraints and with compliance and stochastic perturbations considered. CAV adjust to safety-feasible speeds when high-risk situations are detected and receive speed guidance for left-turn and through movements to pass the intersection smoothly. Using a Changsha intersection and local traffic data, an intelligent connected intersection and mixed-traffic scenario and implement simulations are built in SUMO. Results show that the proposed method yields 897 pedestrian-vehicle conflicts and 272 jaywalking events at 50% CAV penetration, reductions of 43.37% and 53.7% compared with PPRT. Average per-capita delay is 11.61 s, which is 39.15%, 55.03%, and 13.62% lower than actuated control, PPRT, and DRL-based signal control, and the number of stops decreases to 3 279. Performance improves with higher CAV penetration, with conflicts reduced by 16.60% from 0% to 25%, and overall metrics reaching the best level at 100%.
Addressing frequent jaywalking, pedestrian-vehicle conflict risk, and heavy congestion at signalized intersections under mixed traffic, this study develops a cooperative control framework for traffic signals, connected autonomous vehicle (CAV), and pedestrians. In Stage 1, a protect/prohibit right-turning (PPRT) strategy is integrated into a pedestrian-oriented deep reinforcement learning (DRL) controller. The state encodes spatial-temporal conditions using matrices of vehicle and pedestrian positions and speeds. Actions split phases into pedestrian-through and vehicle left/right turns to achieve temporal-spatial separation of key conflicts. The reward is based on the difference in cumulative waiting time with passenger-load weighting to reflect social efficiency, and the optimal policy is learned with a dueling double deep Q-network. In Stage 2, coordinated speed planning for pedestrians and CAV is designed to further reduce interactions and delay. Pedestrian speeds are bounded by feasible ranges derived from crossing distance and remaining green time with acceleration and speed constraints and with compliance and stochastic perturbations considered. CAV adjust to safety-feasible speeds when high-risk situations are detected and receive speed guidance for left-turn and through movements to pass the intersection smoothly. Using a Changsha intersection and local traffic data, an intelligent connected intersection and mixed-traffic scenario and implement simulations are built in SUMO. Results show that the proposed method yields 897 pedestrian-vehicle conflicts and 272 jaywalking events at 50% CAV penetration, reductions of 43.37% and 53.7% compared with PPRT. Average per-capita delay is 11.61 s, which is 39.15%, 55.03%, and 13.62% lower than actuated control, PPRT, and DRL-based signal control, and the number of stops decreases to 3 279. Performance improves with higher CAV penetration, with conflicts reduced by 16.60% from 0% to 25%, and overall metrics reaching the best level at 100%.
2025, 43(5): 33-43.
doi: 10.3963/j.jssn.1674-4861.2025.05.004
Abstract:
The complex traffic behaviors in urban expressway weaving areas significantly impact traffic efficiency and safety. To deeply investigate the interaction characteristics between vehicles merging from mainlines and auxiliary roads, this study, based on large-scale vehicle trajectory data from the Luoshi Road Elevated Expressway in Wuhan, systematically proposes and defines four interactive lane-changing patterns: competitive, cooperative-competitive, cooperative, and competitive-cooperative. This classification framework, by innovatively introducing surrogate safety measures (SSMs) for quantitative constraints, comprehensively covering key parameters such as time-to-collision (TTC), following distance (GAP), vehicle speed differential, and lateral offset of vehicles. To achieve accurate and automated identification of these lane-changing patterns, the study constructs and optimizes a behavior identification model based on the eXtreme Gradient Boosting (XGBoost) algorithm. During model construction, rigorous data filtering addressed the ambiguity of data constraints found in traditional research by removing confounding data such as intersecting merges, ultimately resulting in 1 049 typical, stable merging cases for modeling. The experimental results show that the model achieves an accuracy of 91.83%. Further analysis of traffic flow impacts reveals that the competitive-cooperative lane-changing pattern demonstrates the best overall benefits in enhancing lane-changing efficiency and ensuring driving safety. It achieves this by optimizing the cooperative and competitive relationships between vehicles, which effectively reduces the risk of traffic conflicts.
The complex traffic behaviors in urban expressway weaving areas significantly impact traffic efficiency and safety. To deeply investigate the interaction characteristics between vehicles merging from mainlines and auxiliary roads, this study, based on large-scale vehicle trajectory data from the Luoshi Road Elevated Expressway in Wuhan, systematically proposes and defines four interactive lane-changing patterns: competitive, cooperative-competitive, cooperative, and competitive-cooperative. This classification framework, by innovatively introducing surrogate safety measures (SSMs) for quantitative constraints, comprehensively covering key parameters such as time-to-collision (TTC), following distance (GAP), vehicle speed differential, and lateral offset of vehicles. To achieve accurate and automated identification of these lane-changing patterns, the study constructs and optimizes a behavior identification model based on the eXtreme Gradient Boosting (XGBoost) algorithm. During model construction, rigorous data filtering addressed the ambiguity of data constraints found in traditional research by removing confounding data such as intersecting merges, ultimately resulting in 1 049 typical, stable merging cases for modeling. The experimental results show that the model achieves an accuracy of 91.83%. Further analysis of traffic flow impacts reveals that the competitive-cooperative lane-changing pattern demonstrates the best overall benefits in enhancing lane-changing efficiency and ensuring driving safety. It achieves this by optimizing the cooperative and competitive relationships between vehicles, which effectively reduces the risk of traffic conflicts.
2025, 43(5): 44-56.
doi: 10.3963/j.jssn.1674-4861.2025.05.005
Abstract:
In response to the difficulties in identifying the causes of truck traffic accidents and the unclear influence of factors, a method for identifying the causes of truck traffic accidents and mining the relationships between factors in mountainous and hilly areas is studied. Data from 1, 839 truck accidents on a freight expressway in a mountainous and hilly area of Guangdong Province are collected. Through mathematical statistical methods, the spatiotemporal distribution of truck traffic accidents in mountainous and hilly areas is analyzed. Employing an improved Apriori algorithm, the study mined association rules to uncover factors influencing truck traffic accidents, resulting in 571 rules across comprehensive, self-correlated, specific dimensions (time, road elements), and accident dimensions. The model evaluation results indicate that the accuracy of the improved Apriori algorithm is 86.4% higher than that of the traditional Apriori algorithm. The results of association rule mining reveal: Clear weather conditions and longitudinal slopes less than 2% are significantly associated with minor accidents (lifted confidence>1.0), indicating that minor accidents primarily occur under these road conditions; Improper operation and insufficient safe distance are strongly associated with rollover and rear-end accidents (lifted confidence>1.8), suggesting that these accidents are predominantly caused by these factors; Slopes between -2% to -3% and radii greater than 1 000 m are significantly associated with major accidents (lifted confidence>1.6), indicating that major and severe truck accidents mainly occur on downhill sections with these slope and radius characteristics; Accidents causing injuries are significantly associated with the hours between 01:00 to 03:00 am (lifted confidence>1.3), highlighting a concentration of injury accidents during the early morning hours. The research results have revealed the causes of truck traffic accidents in mountainous and hilly areas and discovered the correlations among the elements of truck traffic accidents.
In response to the difficulties in identifying the causes of truck traffic accidents and the unclear influence of factors, a method for identifying the causes of truck traffic accidents and mining the relationships between factors in mountainous and hilly areas is studied. Data from 1, 839 truck accidents on a freight expressway in a mountainous and hilly area of Guangdong Province are collected. Through mathematical statistical methods, the spatiotemporal distribution of truck traffic accidents in mountainous and hilly areas is analyzed. Employing an improved Apriori algorithm, the study mined association rules to uncover factors influencing truck traffic accidents, resulting in 571 rules across comprehensive, self-correlated, specific dimensions (time, road elements), and accident dimensions. The model evaluation results indicate that the accuracy of the improved Apriori algorithm is 86.4% higher than that of the traditional Apriori algorithm. The results of association rule mining reveal: Clear weather conditions and longitudinal slopes less than 2% are significantly associated with minor accidents (lifted confidence>1.0), indicating that minor accidents primarily occur under these road conditions; Improper operation and insufficient safe distance are strongly associated with rollover and rear-end accidents (lifted confidence>1.8), suggesting that these accidents are predominantly caused by these factors; Slopes between -2% to -3% and radii greater than 1 000 m are significantly associated with major accidents (lifted confidence>1.6), indicating that major and severe truck accidents mainly occur on downhill sections with these slope and radius characteristics; Accidents causing injuries are significantly associated with the hours between 01:00 to 03:00 am (lifted confidence>1.3), highlighting a concentration of injury accidents during the early morning hours. The research results have revealed the causes of truck traffic accidents in mountainous and hilly areas and discovered the correlations among the elements of truck traffic accidents.
2025, 43(5): 57-69.
doi: 10.3963/j.jssn.1674-4861.2025.05.006
Abstract:
This study proposes a coupled causal analysis model integrating decision making trial and evaluation laboratory, interpretative structural modeling, and Bayesian network to address challenges in clarifying risk evolution paths and enabling dynamic prediction for airport flight area safety. A causal factor system for safety incidents is first constructed, covering human, equipment, environment, and management dimensions, based on aircraft incident reports, literature, and expert knowledge. The improved decision-making trial and evaluation laboratory method integrates objective accident causation chains with expert judgments to enhance relationship identification accuracy. The interpretative structural modeling then establishes a multi-level hierarchical structure, explicitly preserving critical cross-level relationships to better characterize system complexity. This optimized topology is mapped into a Bayesian network. Through improved methods for determining prior and conditional probabilities, the Bayesian parameter estimation enables the forward prediction of incident probabilities and the backward diagnosis of critical causation chains. A case study of an airport in North China demonstrates the model's effectiveness. The prior probability of a flight area safety incident is 4.26%. Under different evidence inputs, the probability dynamically ranges between 4.38% and 14.00%. Backward diagnosis identifies the key causation chain as: "imperfect departmental coordination mechanism → communication and coordination failure → inadequate emergency response → flight area safety incident". Comparisons with existing studies, such as questionnaire-based structural equation modeling, show the proposed model provides clearer path logic and advances from static correlation analysis to dynamic probabilistic inference. The case analysis results align closely with actual accident findings, validating the model's practical utility and reliability in proactive safety management for airport flight areas.
This study proposes a coupled causal analysis model integrating decision making trial and evaluation laboratory, interpretative structural modeling, and Bayesian network to address challenges in clarifying risk evolution paths and enabling dynamic prediction for airport flight area safety. A causal factor system for safety incidents is first constructed, covering human, equipment, environment, and management dimensions, based on aircraft incident reports, literature, and expert knowledge. The improved decision-making trial and evaluation laboratory method integrates objective accident causation chains with expert judgments to enhance relationship identification accuracy. The interpretative structural modeling then establishes a multi-level hierarchical structure, explicitly preserving critical cross-level relationships to better characterize system complexity. This optimized topology is mapped into a Bayesian network. Through improved methods for determining prior and conditional probabilities, the Bayesian parameter estimation enables the forward prediction of incident probabilities and the backward diagnosis of critical causation chains. A case study of an airport in North China demonstrates the model's effectiveness. The prior probability of a flight area safety incident is 4.26%. Under different evidence inputs, the probability dynamically ranges between 4.38% and 14.00%. Backward diagnosis identifies the key causation chain as: "imperfect departmental coordination mechanism → communication and coordination failure → inadequate emergency response → flight area safety incident". Comparisons with existing studies, such as questionnaire-based structural equation modeling, show the proposed model provides clearer path logic and advances from static correlation analysis to dynamic probabilistic inference. The case analysis results align closely with actual accident findings, validating the model's practical utility and reliability in proactive safety management for airport flight areas.
2025, 43(5): 70-78.
doi: 10.3963/j.jssn.1674-4861.2025.05.007
Abstract:
Road traffic accidents occur frequently, yet the data distribution based on traditional accident severity classification is often imbalanced. To explore the coupling effects of multidimensional factors on severe traffic accidents under sample imbalance conditions, this study proposes an analytical framework integrating the Adaptive Synthetic Sampling (ADASYN) algorithm, a Stacking ensemble learning model, and the Apriori algorithm. Utilizing data from the U.S. Department of Transportation's Fatality Analysis Reporting System (FARS) from 2017 to 2021, fifteen potential feature variables are selected across four dimensions—human, vehicle, road, and environment—to analyze the effects of multidimensional factor coupling on the occurrence of severe accidents. The ADASYN algorithm was employed to address sample imbalance. Four classical machine learning models including random forest (RF), categorical boosting (CatBoost), extreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT), are selected as base learners. Five types of meta-learners, namely logistic regression, Gaussian Na?ve Bayes, support vector machine (SVM), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP), are compared to identify the optimal Stacking ensemble model with the strongest generalization performance. Based on the optimal model, feature importance ranking is obtained to determine key influencing factors, followed by the application of the Apriori algorithm for multidimensional coupling analysis, which explored the impact of five-dimensional factor coupling on the rate of severe accidents. The results indicate that: ①The Stacking ensemble model composed of Logistic Regression as the meta-learner and RF, CatBoost, XGBoost, and GBDT as base learners achieved the best overall performance, with a recall of 0.80; ②The five factors of road type, season, collision type, lighting conditions at the time of the collision, and driver alcohol consumption, accounted for 53.2% of the total importance of all factors, which is substantially higher than that of the other variables. Among them, collisions involving"impact with trees or other pole-like objects"exhibited the highest severe accident rate at 86.2%, and the severe accident rate under illuminated conditions is 13.5% higher than under non-illuminated conditions; ③ Multidimensional factor coupling analysis reveals that the probability of severe crashes is highest when multiple factors coexist: municipal roads, sober drivers, transitions between unlit and lit lighting conditions at the time of the collision, and the autumn season. Under this coupled condition, the confidence level reaches 89.0%, challenging the conventional perception that non-drinking is a low-risk factor.
Road traffic accidents occur frequently, yet the data distribution based on traditional accident severity classification is often imbalanced. To explore the coupling effects of multidimensional factors on severe traffic accidents under sample imbalance conditions, this study proposes an analytical framework integrating the Adaptive Synthetic Sampling (ADASYN) algorithm, a Stacking ensemble learning model, and the Apriori algorithm. Utilizing data from the U.S. Department of Transportation's Fatality Analysis Reporting System (FARS) from 2017 to 2021, fifteen potential feature variables are selected across four dimensions—human, vehicle, road, and environment—to analyze the effects of multidimensional factor coupling on the occurrence of severe accidents. The ADASYN algorithm was employed to address sample imbalance. Four classical machine learning models including random forest (RF), categorical boosting (CatBoost), extreme gradient boosting (XGBoost), and gradient boosting decision tree (GBDT), are selected as base learners. Five types of meta-learners, namely logistic regression, Gaussian Na?ve Bayes, support vector machine (SVM), light gradient boosting machine (LightGBM), and multilayer perceptron (MLP), are compared to identify the optimal Stacking ensemble model with the strongest generalization performance. Based on the optimal model, feature importance ranking is obtained to determine key influencing factors, followed by the application of the Apriori algorithm for multidimensional coupling analysis, which explored the impact of five-dimensional factor coupling on the rate of severe accidents. The results indicate that: ①The Stacking ensemble model composed of Logistic Regression as the meta-learner and RF, CatBoost, XGBoost, and GBDT as base learners achieved the best overall performance, with a recall of 0.80; ②The five factors of road type, season, collision type, lighting conditions at the time of the collision, and driver alcohol consumption, accounted for 53.2% of the total importance of all factors, which is substantially higher than that of the other variables. Among them, collisions involving"impact with trees or other pole-like objects"exhibited the highest severe accident rate at 86.2%, and the severe accident rate under illuminated conditions is 13.5% higher than under non-illuminated conditions; ③ Multidimensional factor coupling analysis reveals that the probability of severe crashes is highest when multiple factors coexist: municipal roads, sober drivers, transitions between unlit and lit lighting conditions at the time of the collision, and the autumn season. Under this coupled condition, the confidence level reaches 89.0%, challenging the conventional perception that non-drinking is a low-risk factor.
2022, 40(4): 1-12.
doi: 10.3963/j.jssn.1674-4861.2022.04.001
Abstract:
2022, 40(4): 13-25.
doi: 10.3963/j.jssn.1674-4861.2022.04.002
Abstract:
Journal of Transport Information and Safety
(Founded in 1983 bimonthly )
Former Name：Computer and Communications
Supervised by：Ministry of Education of P. R. CHINA
Sponsored by：Wuhan University of Technology
Network of Computer Application Information in Transportation
In Association With：Intelligent Transportation Committee of China Association of Artificial Intelligence
Editor-in-Chief：ZHONG Ming
Edited and Published by：Editorial Office of Transport Information and Safety
Address：No. 1178，Heping Avenue, Wuchang, Wuhan, CHINA
Postcode：430063
Tel：027-86580355
E-mail：jtjsj@vip.163.com
Website：http://www.jtxa.net/
Postal Code：38-94
Domestic Issue：
CN 42-1781/U
Publication No.：ISSN 1674-4861
Indexed In
- Chinese Core Journal in “Integrated Transportation” category
- Chinese Science Citation Database (CSCD)
- Core Science and Technology Journals
- Chinese Scientific and Technological Papers and Citations (CSTPCD)
- Class A of Research Center for Chinese Science Evaluation (RCCSE)
- Chinese Academic Journal Comprehensive Evaluation Database (CAJ-CED)
- Chinese Core Journals (Selection) Database
- Chinese Scientific and Technological Periodicals Database
- China National Knowledge Infrastructure (CNKI)
- Chinese Academic Journals (CAJ-CD)
- Chinese Lifelong Education Academic Research Database
- Japan Science and Technology Agency (JST)
- World Journal Clout Index Report (2020 STM)
