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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
06 An Analysis of Highway-traffic Safety Based on Dynamic Risk Saturation
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2026, 44(1): 1-12.
doi: 10.3963/j.jssn.1674-4861.2026.01.001
Abstract:
As intelligent shipping systems continue to develop rapidly, efficient and reliable wireless communications become one of the key enabling technologies for supporting vessel state sensing, waterway environmental monitoring, and collaborative operations. Based on the existing body of research, this paper reviews recent advances in wireless channel characterization for intelligent inland shipping and highlights the unique challenges that distinguish inland waterways from terrestrial and near-shore environments, including corridor-like wide-area coverage induced by complex land-water mixed topologies, navigation environments and natural conditions, and dense man-made structures (e.g., bridges) and other obstructions. Representative inland waterways worldwide are examined, and by analyzing the adaptability bottlenecks of current communication technologies in inland scenarios, it is shown that conventional channel models cannot accurately capture the propagation mechanisms specific to inland waterways. The review focuses on two core aspects: channel measurement and channel modeling, while also summarizing differences across typical frequency bands and waterway segments, comparing the applicability boundaries of statistical and geometry-based modeling approaches, and outlining key procedures for parameter acquisition and validation to inform future research. On the measurement side, three major characteristics are identified, namely the sparsity of air-space-water links, the instability caused by vessel motion, and evaporation-duct effects; the limitations of existing measurement schemes are then discussed in terms of dynamic trajectory tracking, localized meteorological coupling, and high-resolution capture of non-line-of-sight paths. On the modeling side, large-scale and small-scale fading models applicable to inland environments are reviewed, and the limitations of existing models in finely characterizing the coupled dynamics of vessel-induced motion and time-varying water-surface reflections, as well as constrained scenarios such as bridge canyons, are emphasized. Finally, building a highly reliable communication network with full-coverage capability for inland waterways calls for breakthroughs in environment-driven high-fidelity channel modeling, multi-band propagation mechanism analysis, and the design of coordinated "shore-ship-cloud" architectures.
As intelligent shipping systems continue to develop rapidly, efficient and reliable wireless communications become one of the key enabling technologies for supporting vessel state sensing, waterway environmental monitoring, and collaborative operations. Based on the existing body of research, this paper reviews recent advances in wireless channel characterization for intelligent inland shipping and highlights the unique challenges that distinguish inland waterways from terrestrial and near-shore environments, including corridor-like wide-area coverage induced by complex land-water mixed topologies, navigation environments and natural conditions, and dense man-made structures (e.g., bridges) and other obstructions. Representative inland waterways worldwide are examined, and by analyzing the adaptability bottlenecks of current communication technologies in inland scenarios, it is shown that conventional channel models cannot accurately capture the propagation mechanisms specific to inland waterways. The review focuses on two core aspects: channel measurement and channel modeling, while also summarizing differences across typical frequency bands and waterway segments, comparing the applicability boundaries of statistical and geometry-based modeling approaches, and outlining key procedures for parameter acquisition and validation to inform future research. On the measurement side, three major characteristics are identified, namely the sparsity of air-space-water links, the instability caused by vessel motion, and evaporation-duct effects; the limitations of existing measurement schemes are then discussed in terms of dynamic trajectory tracking, localized meteorological coupling, and high-resolution capture of non-line-of-sight paths. On the modeling side, large-scale and small-scale fading models applicable to inland environments are reviewed, and the limitations of existing models in finely characterizing the coupled dynamics of vessel-induced motion and time-varying water-surface reflections, as well as constrained scenarios such as bridge canyons, are emphasized. Finally, building a highly reliable communication network with full-coverage capability for inland waterways calls for breakthroughs in environment-driven high-fidelity channel modeling, multi-band propagation mechanism analysis, and the design of coordinated "shore-ship-cloud" architectures.
2026, 44(1): 13-25.
doi: 10.3963/j.jssn.1674-4861.2026.01.002
Abstract:
Current maritime search and rescue (SAR) decision-making faces challenges in unified semantic modeling for multi-source heterogeneous information. This deficiency hinders joint reasoning across cross-modal data. Meanwhile, SAR missions involve multi-objective constraints with significant coupling between disparate decision goals. Traditional methods often rely on stage-wise or heuristic-driven processing. They lack the capacity for holistic reasoning and dynamic optimization within the multi-objective decision-making process.To address these challenges, this paper investigates a modeling methodology for the intelligent search and rescue agent (IS-RescueAgent), This agent utilizes a large language model (LLM) as the core reasoning engine. By implementing the model context protocol (MCP), the framework achieves semantic-level coordination of cross-modal information. It executes closed-loop reasoning and multi-objective optimization within a unified process. Through collaborative mechanisms like problem identification and information parsing, the agent achieves fusion of cross-modal data.This includes distress messages, environmental data, and historical records.It also enables dynamic optimization for vessel scheduling, search paths, and task priorities.Architecturally, the proposed agent comprises three stages: First, the information processing stage achieves unified representation of cross-modal features through semantic extraction and intent recognition.Second, The strategy generation stage constructs a multi-objective optimization framework for scenario-adaptive plan generation. Finally, the learning stage establishes an iterative mechanism through performance feedback and parameter updates.Experimental results show that IS-RescueAgent achieves a relevance of 93%, an accuracy of 92%, and a rationality of 92.3%. These metrics outperform traditional LLM and Retrieval-Augmented Generation (RAG) approaches. Furthermore, the system demonstrates strong stability under environmental disturbances. Under conditions of wind speeds below 10 m/s and resource availability above 70%, error rates remain within 5%. The system also maintains high response efficiency with positioning errors under 10 m and communication delays below 200 ms.
Current maritime search and rescue (SAR) decision-making faces challenges in unified semantic modeling for multi-source heterogeneous information. This deficiency hinders joint reasoning across cross-modal data. Meanwhile, SAR missions involve multi-objective constraints with significant coupling between disparate decision goals. Traditional methods often rely on stage-wise or heuristic-driven processing. They lack the capacity for holistic reasoning and dynamic optimization within the multi-objective decision-making process.To address these challenges, this paper investigates a modeling methodology for the intelligent search and rescue agent (IS-RescueAgent), This agent utilizes a large language model (LLM) as the core reasoning engine. By implementing the model context protocol (MCP), the framework achieves semantic-level coordination of cross-modal information. It executes closed-loop reasoning and multi-objective optimization within a unified process. Through collaborative mechanisms like problem identification and information parsing, the agent achieves fusion of cross-modal data.This includes distress messages, environmental data, and historical records.It also enables dynamic optimization for vessel scheduling, search paths, and task priorities.Architecturally, the proposed agent comprises three stages: First, the information processing stage achieves unified representation of cross-modal features through semantic extraction and intent recognition.Second, The strategy generation stage constructs a multi-objective optimization framework for scenario-adaptive plan generation. Finally, the learning stage establishes an iterative mechanism through performance feedback and parameter updates.Experimental results show that IS-RescueAgent achieves a relevance of 93%, an accuracy of 92%, and a rationality of 92.3%. These metrics outperform traditional LLM and Retrieval-Augmented Generation (RAG) approaches. Furthermore, the system demonstrates strong stability under environmental disturbances. Under conditions of wind speeds below 10 m/s and resource availability above 70%, error rates remain within 5%. The system also maintains high response efficiency with positioning errors under 10 m and communication delays below 200 ms.
2026, 44(1): 26-36.
doi: 10.3963/j.jssn.1674-4861.2026.01.003
Abstract:
To solve the path ambiguity and decision subjectivity in operational design domain (ODD) extension, a crash data-driven quantitative prioritization assessment framework is investigated. Data support and decision bases for safety capability iterations of autonomous driving systems in complex environments are provided by this framework. To address the long-tail distribution defect of severe crash samples, random forest and extreme gradient boosting (XGBoost) classification models are constructed. The synthetic minority over-sampling technique (SMOTE) is introduced to optimize the sample distribution. The dataset is rebalanced at a 1:20 classification weight ratio to ensure the identification capability for critical minority samples like severe crashes. To break the "black box" limitation of traditional models, the Shapley additive explanations (SHAP) method is employed to deconstruct internal decision-making mechanisms. Furthermore, the nonlinear effect thresholds of environmental feature variables on crash severity are quantified. A marginal effect model for ODD extension is established based on the control variates method and principal component analysis (PCA). By simulating the boundary expansion of a single ODD variable, the improvement in theoretical crash coverage under various extension strategies is calculated. The results indicate that the identification performance of classification models for critical severe crash samples is significantly enhanced after SMOTE processing. While an 83.4% overall accuracy is maintained, the sensitivity of the random forest model is increased from 17.8% to 90.5%. The area under the receiver operating characteristic (ROC) curve (AUC) is increased to 0.922, and high-risk features are captured more precisely. The highest marginal safety benefit among candidate dimensions is yielded by extending to unstructured road grades, as revealed by quantitative data. Theoretical coverage of ordinary and severe crashes is significantly increased by 15.7% and 13.0%, respectively, when expanding ODD from structured highways to state and county roads. Vehicle collision rates under emergency braking in extended scenarios like low road friction and low illumination are verified by CARLA simulation experiments. It is observed that collision rates are increased from 5.0% in the baseline group to 45.0% and 20.0%, respectively. Meanwhile, the average time to collision (TTC) is shortened by 1.3 seconds. The physical safety performance degradation trend is highly consistent with the high-risk feature ranking derived from the quantitative assessment model. It is proven by these quantitative indicators that the priority ODD extension direction with the greatest safety value is reliably identified by the proposed framework.
To solve the path ambiguity and decision subjectivity in operational design domain (ODD) extension, a crash data-driven quantitative prioritization assessment framework is investigated. Data support and decision bases for safety capability iterations of autonomous driving systems in complex environments are provided by this framework. To address the long-tail distribution defect of severe crash samples, random forest and extreme gradient boosting (XGBoost) classification models are constructed. The synthetic minority over-sampling technique (SMOTE) is introduced to optimize the sample distribution. The dataset is rebalanced at a 1:20 classification weight ratio to ensure the identification capability for critical minority samples like severe crashes. To break the "black box" limitation of traditional models, the Shapley additive explanations (SHAP) method is employed to deconstruct internal decision-making mechanisms. Furthermore, the nonlinear effect thresholds of environmental feature variables on crash severity are quantified. A marginal effect model for ODD extension is established based on the control variates method and principal component analysis (PCA). By simulating the boundary expansion of a single ODD variable, the improvement in theoretical crash coverage under various extension strategies is calculated. The results indicate that the identification performance of classification models for critical severe crash samples is significantly enhanced after SMOTE processing. While an 83.4% overall accuracy is maintained, the sensitivity of the random forest model is increased from 17.8% to 90.5%. The area under the receiver operating characteristic (ROC) curve (AUC) is increased to 0.922, and high-risk features are captured more precisely. The highest marginal safety benefit among candidate dimensions is yielded by extending to unstructured road grades, as revealed by quantitative data. Theoretical coverage of ordinary and severe crashes is significantly increased by 15.7% and 13.0%, respectively, when expanding ODD from structured highways to state and county roads. Vehicle collision rates under emergency braking in extended scenarios like low road friction and low illumination are verified by CARLA simulation experiments. It is observed that collision rates are increased from 5.0% in the baseline group to 45.0% and 20.0%, respectively. Meanwhile, the average time to collision (TTC) is shortened by 1.3 seconds. The physical safety performance degradation trend is highly consistent with the high-risk feature ranking derived from the quantitative assessment model. It is proven by these quantitative indicators that the priority ODD extension direction with the greatest safety value is reliably identified by the proposed framework.
2026, 44(1): 37-51.
doi: 10.3963/j.jssn.1674-4861.2026.01.004
Abstract:
To address the complex pedestrian-vehicle interactions during left turns at intersections, an adaptive assessment framework based on a multi-dimensional fusion risk field is proposed. Limited observability, random motion states, and incomplete risk characterization are considered in the framework. The framework combines the YOLOv8n and DeepSORT to detect and track multiple road users from UAV aerial data. It extracts pedestrian crossing behaviors and vehicle motion features at intersections with high precision. Vehicle kinematic models and pedestrian stochastic motion models are established to describe pedestrian-vehicle motion states. A risk assessment model for left-turn scenarios is then constructed. The model integrates a blind spot field, an interaction potential field, and a field of possible future motion. An adaptive grid modeling method is further introduced to adjust spatial resolution according to risk complexity. This method improves computational efficiency while maintaining modeling accuracy in dynamic scenes. Simulation is conducted at the intersection of Jianshe Road and the East First Section of Yihuan Road in Chengdu. The model achieves a tracking accuracy of 97.30% and a localization accuracy of 0.71. Compared with existing tracking methods, it shows better stability and localization performance in dynamic pedestrian-vehicle interaction scenarios. Compared with fixed-grid methods, the adaptive grid method reduces computation time by 20.95% to 37.62%. The prediction error remains within ±2%. Compared with traditional risk assessment methods, the proposed multi-dimensional fusion risk field model performs best in conflict prediction accuracy and scenario adaptability. The proposed model enables high-precision, efficient, wide-area dynamic tracking and adaptive identification of pedestrian-vehicle interaction risks based on UAV aerial data.
To address the complex pedestrian-vehicle interactions during left turns at intersections, an adaptive assessment framework based on a multi-dimensional fusion risk field is proposed. Limited observability, random motion states, and incomplete risk characterization are considered in the framework. The framework combines the YOLOv8n and DeepSORT to detect and track multiple road users from UAV aerial data. It extracts pedestrian crossing behaviors and vehicle motion features at intersections with high precision. Vehicle kinematic models and pedestrian stochastic motion models are established to describe pedestrian-vehicle motion states. A risk assessment model for left-turn scenarios is then constructed. The model integrates a blind spot field, an interaction potential field, and a field of possible future motion. An adaptive grid modeling method is further introduced to adjust spatial resolution according to risk complexity. This method improves computational efficiency while maintaining modeling accuracy in dynamic scenes. Simulation is conducted at the intersection of Jianshe Road and the East First Section of Yihuan Road in Chengdu. The model achieves a tracking accuracy of 97.30% and a localization accuracy of 0.71. Compared with existing tracking methods, it shows better stability and localization performance in dynamic pedestrian-vehicle interaction scenarios. Compared with fixed-grid methods, the adaptive grid method reduces computation time by 20.95% to 37.62%. The prediction error remains within ±2%. Compared with traditional risk assessment methods, the proposed multi-dimensional fusion risk field model performs best in conflict prediction accuracy and scenario adaptability. The proposed model enables high-precision, efficient, wide-area dynamic tracking and adaptive identification of pedestrian-vehicle interaction risks based on UAV aerial data.
2026, 44(1): 52-61.
doi: 10.3963/j.jssn.1674-4861.2026.01.005
Abstract:
The development of vehicle-to-infrastructure cooperation (V2I) technology enables real-time dynamic information interaction between humans, vehicles, and roads, providing technical support for real-time vehicle collision risk prediction. Current models for predicting vehicle collision risk have limitations in capturing dynamic features and performing uncertainty inference. Thus, a two-layer progressive model based on long short-term memory-Bayesian network (LSTM-BN) is proposed. The LSTM-BN model utilizes LSTM at the bottom layer to capture the temporal evolution characteristics of vehicle states by setting a 0.1 s sampling window. At the top layer, the model performs probabilistic inference of collision risk through the BN. Based on data from naturalistic driving experiments in Wuhan, the time-to-collision (TTC) index is used to define and quantify the collision risk of vehicles (cars). This process yields 25, 826, 19, 344, and 4, 051 frames of no-risk, low-risk, and high-risk data, respectively. After preliminarily determining the variables affecting collision risk, the decision tree method is employed to extract key causal features from the dimensions of driver-vehicle-road. Interaction indicators, such as driver proficiency, speed difference, road type, and headway, are further excavated and characterized to reflect the non-linear coupling relationships among multi-dimensional factors. A Bayesian network reflecting these coupling relationship indicators is then constructed to evaluate the current vehicle collision risk. On this basis, to compensate for the deficiency of static probabilistic models in sequential prediction, LSTM is used to predict vehicle state variables. These variables are then mapped into the BN to infer the collision probability at the next time step. Through the above steps, the model achieves a precise prediction of real-time vehicle collision risk under dynamic evolution. To verify the effectiveness of the proposed model, its prediction performance is evaluated from four aspects: robustness, accuracy, timeliness, and complexity. Results from comparative modeling indicate that the LSTM-BN model achieves the highest accuracy at 91%. This performance is 7% and 12% higher than the accuracies of the support vector machine (SVM) and random forest (RF) models, respectively. Furthermore, its index fluctuations across different sampling periods are all below 0.1, demonstrating strong robustness.
The development of vehicle-to-infrastructure cooperation (V2I) technology enables real-time dynamic information interaction between humans, vehicles, and roads, providing technical support for real-time vehicle collision risk prediction. Current models for predicting vehicle collision risk have limitations in capturing dynamic features and performing uncertainty inference. Thus, a two-layer progressive model based on long short-term memory-Bayesian network (LSTM-BN) is proposed. The LSTM-BN model utilizes LSTM at the bottom layer to capture the temporal evolution characteristics of vehicle states by setting a 0.1 s sampling window. At the top layer, the model performs probabilistic inference of collision risk through the BN. Based on data from naturalistic driving experiments in Wuhan, the time-to-collision (TTC) index is used to define and quantify the collision risk of vehicles (cars). This process yields 25, 826, 19, 344, and 4, 051 frames of no-risk, low-risk, and high-risk data, respectively. After preliminarily determining the variables affecting collision risk, the decision tree method is employed to extract key causal features from the dimensions of driver-vehicle-road. Interaction indicators, such as driver proficiency, speed difference, road type, and headway, are further excavated and characterized to reflect the non-linear coupling relationships among multi-dimensional factors. A Bayesian network reflecting these coupling relationship indicators is then constructed to evaluate the current vehicle collision risk. On this basis, to compensate for the deficiency of static probabilistic models in sequential prediction, LSTM is used to predict vehicle state variables. These variables are then mapped into the BN to infer the collision probability at the next time step. Through the above steps, the model achieves a precise prediction of real-time vehicle collision risk under dynamic evolution. To verify the effectiveness of the proposed model, its prediction performance is evaluated from four aspects: robustness, accuracy, timeliness, and complexity. Results from comparative modeling indicate that the LSTM-BN model achieves the highest accuracy at 91%. This performance is 7% and 12% higher than the accuracies of the support vector machine (SVM) and random forest (RF) models, respectively. Furthermore, its index fluctuations across different sampling periods are all below 0.1, demonstrating strong robustness.
2026, 44(1): 62-74.
doi: 10.3963/j.jssn.1674-4861.2026.01.006
Abstract:
Existing research on the deployment of safety interventions primarily focuses on urban roads and highways. However, methods for evaluating the effectiveness of safety interventions at sharp bends on mountain roads remain limited. To clarify the effectiveness of different safety interventions and the basis for placement, a driving simulation scenario is established based on the S545 section in Yongchuan, Chongqing. Twenty-eight drivers are recruited to participate in the driving simulation tests. Variable message signs (VMS) in four colors: red, yellow, blue, and green—are selected as visual interventions. On the basis of the steady-state noise from the driving simulator, on-board warning sounds with increases of 8, 11.5, and 15 dB are added as auditory interventions. Single-modality and audiovisual combination experiments are designed, and a model for calculating the distance for placing interventions is proposed. The effectiveness of different interventions is comprehensively evaluated by analyzing the average speed, peak acceleration, and lateral deviation rate. The results indicate that, at sharp bends on mountain roads, the distances for placing visual and auditory interventions are 31.4 m and 41.7 m, respectively. The yellow VMS is the optimal single visual intervention, with reductions in average speed, peak acceleration, and lateral deviation rate of 22.3%, 34.7%, and 58.4%, respectively. The 11.5 dB increase condition is the optimal single auditory intervention, with reductions in average speed, peak acceleration, and lateral deviation rate of 17.3%, 38.5%, and 43.7%, respectively. Both the yellow VMS single visual intervention and the 11.5 dB auditory intervention yield good results. The yellow VMS and 11.5 dB combination intervention achieves the best effect, with reductions in average speed, peak acceleration, and lateral deviation rate of 26.0%, 40.3%, and 66.9%, respectively. This combination intervention is superior to single interventions and is recommended as the preferred deployment scheme for sharp bends on mountain roads.
Existing research on the deployment of safety interventions primarily focuses on urban roads and highways. However, methods for evaluating the effectiveness of safety interventions at sharp bends on mountain roads remain limited. To clarify the effectiveness of different safety interventions and the basis for placement, a driving simulation scenario is established based on the S545 section in Yongchuan, Chongqing. Twenty-eight drivers are recruited to participate in the driving simulation tests. Variable message signs (VMS) in four colors: red, yellow, blue, and green—are selected as visual interventions. On the basis of the steady-state noise from the driving simulator, on-board warning sounds with increases of 8, 11.5, and 15 dB are added as auditory interventions. Single-modality and audiovisual combination experiments are designed, and a model for calculating the distance for placing interventions is proposed. The effectiveness of different interventions is comprehensively evaluated by analyzing the average speed, peak acceleration, and lateral deviation rate. The results indicate that, at sharp bends on mountain roads, the distances for placing visual and auditory interventions are 31.4 m and 41.7 m, respectively. The yellow VMS is the optimal single visual intervention, with reductions in average speed, peak acceleration, and lateral deviation rate of 22.3%, 34.7%, and 58.4%, respectively. The 11.5 dB increase condition is the optimal single auditory intervention, with reductions in average speed, peak acceleration, and lateral deviation rate of 17.3%, 38.5%, and 43.7%, respectively. Both the yellow VMS single visual intervention and the 11.5 dB auditory intervention yield good results. The yellow VMS and 11.5 dB combination intervention achieves the best effect, with reductions in average speed, peak acceleration, and lateral deviation rate of 26.0%, 40.3%, and 66.9%, respectively. This combination intervention is superior to single interventions and is recommended as the preferred deployment scheme for sharp bends on mountain roads.
2026, 44(1): 75-87.
doi: 10.3963/j.jssn.1674-4861.2026.01.007
Abstract:
Privacy protection methods based on pseudo-trajectory generation face vanishing gradients in large-scale sequence modeling and trajectory distortion due to insufficient modeling of geographic semantics. To address these problems, this paper proposes a map-conditioned trajectory generation framework (MCTG) based on generative adversarial networks. SN-GAN is employed to learn the global spatial distribution of trajectories. Constraints on the spectral norm of the discriminator stabilize training over long sequences and suppress mode collapse. A conditional GAN incorporating geographic semantics is further constructed, with an encoder-decoder architecture for extracting features of road networks. A symmetric bidirectional long short-term memory (LSTM) combined with a teacher-forcing mechanism jointly models temporal dependencies and boundary constraints of trajectory sequences. Experiments on the geolife and portugal datasets show that the cosine similarity between synthetic and real trajectories reaches up to 0.98, remaining stable within 0.90~0.95 under complex multi-modal conditions. The Jensen-Shannon divergence decreases from 0.22 to 0.105, indicating good distributional consistency. The Hausdorff distance of the synthetic trajectories is consistently lower than those of k-anonymity and differential privacy, reflecting superior preservation of spatial morphology. In simulated privacy attacks, the recognition rate for synthetic trajectories falls below 0.01 and the discriminator accuracy approaches 0.519, indicating that real and synthetic trajectories are nearly indistinguishable. MCTG enables effective privacy protection for vehicle trajectory data by generating synthetic trajectories with high geographic plausibility while preserving statistical distribution consistency and road-network semantic constraints.
Privacy protection methods based on pseudo-trajectory generation face vanishing gradients in large-scale sequence modeling and trajectory distortion due to insufficient modeling of geographic semantics. To address these problems, this paper proposes a map-conditioned trajectory generation framework (MCTG) based on generative adversarial networks. SN-GAN is employed to learn the global spatial distribution of trajectories. Constraints on the spectral norm of the discriminator stabilize training over long sequences and suppress mode collapse. A conditional GAN incorporating geographic semantics is further constructed, with an encoder-decoder architecture for extracting features of road networks. A symmetric bidirectional long short-term memory (LSTM) combined with a teacher-forcing mechanism jointly models temporal dependencies and boundary constraints of trajectory sequences. Experiments on the geolife and portugal datasets show that the cosine similarity between synthetic and real trajectories reaches up to 0.98, remaining stable within 0.90~0.95 under complex multi-modal conditions. The Jensen-Shannon divergence decreases from 0.22 to 0.105, indicating good distributional consistency. The Hausdorff distance of the synthetic trajectories is consistently lower than those of k-anonymity and differential privacy, reflecting superior preservation of spatial morphology. In simulated privacy attacks, the recognition rate for synthetic trajectories falls below 0.01 and the discriminator accuracy approaches 0.519, indicating that real and synthetic trajectories are nearly indistinguishable. MCTG enables effective privacy protection for vehicle trajectory data by generating synthetic trajectories with high geographic plausibility while preserving statistical distribution consistency and road-network semantic constraints.
2022, 40(4): 13-25.
doi: 10.3963/j.jssn.1674-4861.2022.04.002
Abstract:
2022, 40(4): 1-12.
doi: 10.3963/j.jssn.1674-4861.2022.04.001
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)
