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2025, Volume 43,  Issue 6

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In-vehicle Navigation Technology Considering Traffic Safety: A Systematic Literature Review
XU Chuan, HU Jialin, GONG Liting, JIANG Xinguo
2025, 43(6): 1-10. doi: 10.3963/j.jssn.1674-4861.2025.06.001
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With socioeconomic development, traffic safety receives increasing attention. This trend has driven traditional in-vehicle navigation technologies to shift from a single efficiency-oriented objective (minimizing travel time) toward a comprehensive optimization of traffic efficiency and safety. However, existing studies still suffer from limitations such as incomplete consideration of data dimensions, low adaptability to user needs, and difficulties in multi-objective trade-offs, which hinder their applicability in large-scale scenarios under future intelligent transportation systems. To address these gaps, this study adopts a systematic literature review approach and analyzes 51 core non-review publications. It focuses on four key research questions: data sources, traffic safety level measurement and prediction methods, safety-aware routing approaches, and technical validation strategies. The review synthesizes the current state of research and identifies core demands in the field, providing references and recommendations for the future development of navigation technologies in intelligent transportation environments. The findings indicate that, in terms of data sources, most existing studies rely on a single data source and fail to adequately capture real-time and microscopic traffic characteristics. Regarding safety level measurement and prediction, many approaches lack differentiation among various types of road units and do not sufficiently explore the evolutionary trends of traffic operational characteristics; moreover, due to challenges in data collection and the complexity of aggregation logic, achieving objective safety quantification remains difficult. For safety-aware routing methods, most studies simplify multi-objective problems into single-objective optimization, where the determination of objective weights lacks objective foundations and sufficient dynamic adaptability; although Pareto-front-based methods avoid explicit weight assignment, they face practical challenges such as reliance on preset parameters and relatively low computational efficiency. In terms of technical validation, real-world field tests provide the highest reliability but are constrained by high costs and limited scalability; simulations and data-driven scenario replay based on real data can effectively reduce testing costs, yet they often lack real-time interactive dynamics and may deviate from actual traffic conditions; subjective evaluation can supplement user perception and feedback, but trade-offs among sample size, participant characteristics, cost, and validity must be carefully balanced. Based on these insights, future research is recommended to focus on four major directions: ① establishing key navigation-related data framework under future traffic information environments. ②Integrating real-time traffic state inference into safety level prediction. ③Incorporating heterogeneous driving styles and personalized user needs to enhance routing techniques. ④Leveraging emerging technologies such as large language models to provide intelligent interaction and decision-support capabilities for next-generation navigation systems.
Scenario-based Testing and Evaluation Systems for Autonomous Vehicles: Research Status, Challenges, and Trends
FAN Bo, ZHOU Chongwei, ZHANG Sinan, YANG Jun, CHEN Yanyan, LI Tongfei
2025, 43(6): 11-20. doi: 10.3963/j.jssn.1674-4861.2025.06.002
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Autonomous driving technology is accelerating toward large-scale testing and commercial application. Consequently, constructing systematic scenario frameworks for testing and robust evaluation metrics is crucial for safe deployment. This paper reviews the research status, challenges, and future trends of these systems. The study analyzes complexities introduced by vehicle-road-cloud integration and dynamic mixed traffic. It finds that traditional"mileage-failure"statistical models are insufficient for end-to-end performance assessment. Regarding test scenarios, the paper outlines the evolution toward scenario-driven paradigms. It summarizes semantic description methods based on the ISO 34501 standard and the PEGASUS six-layer model. Mainstream scenario generation technologies are also reviewed. Current frameworks show insufficient coverage of long-tail and edge scenarios. Standards are highly fragmented. Furthermore, existing frameworks often under-represent vehicle-to-everything (V2X) collaborative elements due to an excessive focus on single-vehicle intelligence. Regarding evaluation metrics, existing methodologies are categorized into three dimensions, including competition-based, closed-track/simulation hybrid, and theory-oriented approaches. The review identifies several deficiencies in current systems. Specifically, current metrics insufficiently assess the use of V2X collaborative information. Evaluation dimensions and workflows are fragmented, and objective quantitative metrics for interactive experience are lacking. To address these challenges, next-generation testing systems should focus on four research paths. ①Unified scenario description languages and data-sharing frameworks are needed to establish benchmarks for measuring scenario risk criticality and realism. ② Hierarchical scenario systems should be built to cover nominal conditions as well as long-tail boundaries for full-domain coverage. ③Comprehensive metrics should integrate communication latency, system resilience, and social ethics. ④World models and generative AI, combined with causal inference, can simulate extreme conditions and explore unknown failure modes to validate the system's generalization capability.
A Research Framework on Poor Driving Behavior and Regulation in Complex Geometric Sections of the Extra-long Urban Underwater Tunnels
DU Zhigang, ZHANG Kaiqing, HE Shiming, XIN Yuxuan, MAI Jing
2025, 43(6): 21-32. doi: 10.3963/j.jssn.1674-4861.2025.06.003
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Urban submerged extra-long tunnels typically exhibit three key characteristics: densely clustered ramp entrances and exits, complex and variable alignment profiles, and abrupt lighting transitions. Drivers' real-time cognitive load and driving task increase rapidly. They also create significant conflicts with the inertia of driving behavior in long-distance tunnel. Such conflicts easily induce poor driving behaviors, including speeding, insufficient following distance, and lane deviation. This further heightens accident risks. Based on a systematic review of existing studies on the complex environmental characteristics, driving behavior patterns, and regulatory methods of urban submerged extra-long tunnels, a clear logical chain is established: urban submerged extra-long tunnels—complex geometry and abrupt lighting transitions—escalating driving demands and behavioral inertia—poor driving behaviors— constancy regulation. The paper proposes rational zoning for critical zones, such as tunnel entrances, continuous gradient-change zones, and ramp exits. It analyzes the conflicts between shifting driving demands and behavioral inertia in these zones. Based on this analysis, the study constructs a research framework for investigating and regulating undesirable driving behaviors in urban submerged extra-long tunnels. The research puts forward an optimization approach for tunnel visual reference system. This approach involves enhancing visual reference system in complex alignment zones and moderating environmental changes in transition zones. It can be achieved through a constancy-based visual guidance system. The system features four core attributes: facility constancy, overall priority, redundancy, and long-range rhythmicity. Existing practice demonstrates that this constancy-based visual guidance system aligns with drivers' psychological expectations. It effectively decomposes driving tasks and improves safety in critical zones, such as tunnel entrances, continuous gradient-change zones, and ramp exits. Additionally, the system helps regulate undesirable driving behaviors on complex curved sections of urban submerged extra-long tunnels. Ultimately, it achieves a harmonious balance between tunnel's lighting energy saving and safe, comfortable driving conditions.
A Study on Aircraft Safety Target Levels Based on Lasso-Random Forest Model
LU Fei, ZHANG Xinyu, WANG Tian, ZHANG Zhaoning
2025, 43(6): 33-41. doi: 10.3963/j.jssn.1674-4861.2025.06.004
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As aviation safety continuously improves, transportation accidents exhibit small-sample and low-probability characteristics. Traditional prediction methods based on historical data struggle to characterize the evolution of aviation operational risks and refined safety management demands. To address prediction instability caused by insufficient accident samples, a method for calculating the target level of safety using a Lasso-random forest model is proposed. The method integrates Lasso regression and a random forest model to improve robustness under low-probability conditions. An influencing factor set for transportation incident precursors is constructed by considering transport scale, operational efficiency, resource input, and operational intensity. Lasso regression combined with time-series cross-validation is applied for feature selection to alleviate multicollinearity under small-sample conditions. This procedure improves the stability and rationality of selected features. A random forest model is employed to predict transportation incident precursors. Feature importance analysis is applied to improve prediction accuracy. An error-driven model simplification strategy is used to reduce model complexity and enhance practical applicability. Civil aviation operational data of China from 2003 to 2022 are used for validation. Results indicate that the Lasso-random forest model achieves the lowest SRMSE value of 45.2 and the highest R2 value of 0.834. The model significantly outperforms linear regression and support vector regression models. After simplification, the SRMSE is further reduced by 6.14%. Based on the simplified model, flight hours and incident precursor occurrences for 2023 are predicted. The resulting en-route aircraft collision safety target level is, which satisfies applicable safety standards. The proposed method provides a robust and operational framework for low-probability aviation risk assessment and safety target level formulation.
An Extraction Method of Waterlogged Areas and Analysis of Road Network Traffic Capacity Based on Multi-source Remote Sensing Data Fusion
GU Xin, JIANG Haotian, AI Qi, XU Chengcheng
2025, 43(6): 42-53. doi: 10.3963/j.jssn.1674-4861.2025.06.005
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To address the challenge of accurately quantifying urban traffic risks during the dynamic progression of flood disasters, this study proposes a deep learning-based method for high-precision flood area extraction and analyzes the traffic capacity of urban roads during flooding through dynamic hydrodynamic simulations. A dynamic road network traffic assessment framework, encompassing flood detection, water depth calculation, and traffic capacity analysis, is established. This approach integrates synthetic aperture radar (SAR) imagery, optical imagery, and high-resolution digital elevation model (DEM) data, employing a U-Net deep learning model to precisely extract flood areas. By combining remote sensing data and geographic information, along with terrain factors such as slope and curvature, a flood water level boundary model is developed. The system utilizes real-time precipitation and land use information to drive dynamic water flow simulations, and integrates water depth change grids with vectorized road network data to estimate the inundation depth of different road types. A road topology structure and traffic capacity update mechanism are then established, including water depth and vehicle speed attenuation models for different road classes, quantifying the impact of water depth on traffic speed. Based on this model, multi-temporal traffic capacity change maps are generated, and complex network indicators are used to quantitatively assess road network connectivity. Results show that the method effectively addresses challenges such as shadow interference and building occlusion, significantly improving the accuracy of flood area segmentation. Specifically, the Intersection over Union (IoU) and F1 scores for flood area recognition achieved 97.56% and 97.79%, respectively, outperforming the support vector regression (SVR) model, with a 5% improvement across all metrics. The analysis indicates that, with a rainfall of 270.76 mm, the average water depth of internal roads and branches is significantly higher than that of arterial roads and highways. As water depth increases, the traffic speed of roads decreases by approximately 13.2%, with urban roads'traffic capacity reduced by an average of 13.2%. However, arterial roads maintain 83.3% of their traffic capacity, indicating their potential for emergency use. Furthermore, post-flood road network structure deteriorates significantly, with overall network connectivity decreasing by 58.2% compared to pre-flood conditions.
An Analysis and Prediction Model of Aircraft Landing States on Wet Runways with Crosswind Based on Taxiing Dynamics Model
CAI Jing, LI Jianping, NIU Yufa, LI Yue, DAI Xuan
2025, 43(6): 54-66. doi: 10.3963/j.jssn.1674-4861.2025.06.006
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To address the frequent occurrence of runway excursion accidents in aviation safety, this study conducts a quantitative analysis of the factors influencing aircraft landing taxiing states and establishes a corresponding prediction model. A human-aircraft-environment coupled dynamics model for aircraft landing taxiing is developed in Simulink, focusing on the Airbus A320-214. This model incorporates a dynamic engine thrust module and integrates pilot operations, aircraft dynamics, crosswind, and wet runway surface conditions. Closed-loop simulations yield 3, 191 sets of data for analysis. The influence of various factors, such as water film thickness, pilot reaction speed, and touchdown ground speed, on runway excursions is quantified using multiple linear regression. The mechanism of thrust reverser imbalance affecting deviation distance is analyzed, leading to the establishment of predictive models for landing taxiing distance and deviation distance. The findings indicate that during landing taxiing, touchdown ground speed has a greater impact on taxiing distance than on deviation distance. Environmental factors like water film thickness, friction imbalance, and crosswind velocity are more likely to cause runway deviations. Among these, friction imbalance has the most pronounced effect on yaw direction, exceeding the impact of thrust reverser imbalance by a factor of 14.5, which ranks as the second most influential factor. Under specified conditions, a thrust reverser imbalance exceeding 0.4 pushes the deviation distance close to the safety threshold, representing a substantial risk. The multiple linear regression model for taxiing distance prediction demonstrates a coefficient of determination (R2) of 0.88, a mean absolute error (MAE) of 48.32 m, and a mean absolute percentage error (MAPE) of 7.75%. Prediction deviations for actual cases remain within 5%, indicating superior accuracy of the model for predicting aircraft landing taxiing distance.
A Causal Correlations Analysis with Multi-sample Controlled Flight Into Terrain Incidents
LIU Junjie, YI Wenzheng, LEI Li, TIAN Pengcheng, ZHANG Aihua
2025, 43(6): 67-75. doi: 10.3963/j.jssn.1674-4861.2025.06.007
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To address the research gap regarding the causal correlations among incidents with varying consequence severities, this study adopts a Safety-Ⅱ perspective and takes controlled flight into terrain (CFIT), a high-risk aviation accident category, as the research object to analyze the causal factors and the interrelationships across samples with distinct outcome levels. A total of 128 CFIT accident investigation reports from the Aviation Safety Network (ASN) and 354 voluntary reports from the Aviation Safety Reporting System (ASRS) are selected as the analytical sample. Guided by the threat and error management (TEM) model, a systematic analysis is conducted to identify latent conditions, threats, flight crew errors, undesired aircraft states, and countermeasures inherent in the sampled accidents, which resulted in the identification of 3 367 causal factors and 2 169 causal-temporal relationships. Following semantic analysis and integration of these relationships, a Bayesian network (BN) model is constructed, resulting in an accident evolution network model comprising 46 BN nodes. Association rule mining is employed to compute conditional probabilities and inter-node correlations, so as to delineate the high-probability causal chains of the samples. Results demonstrate that: ①the primary high-risk pathway leading to CFIT accidents is: operational pressure → flight crew fatigue → loss of situational awareness → incorrect altimeter setting → aircraft deviation from the intended altitude profile. ②When negative factors including flight crew fatigue (accounting for 57% of the identified negative factors) and inadequate management by airlines/regulatory authorities (17%) are present, the probability of CFIT accidents increases by 24%. ③From the Safety-Ⅱ perspective, when positive factors, such as aviation system response measures (with an effectiveness rate of 78%) and the execution of recovery actions (84%) take effect, the probability of a stable approach is enhanced by 34%, thereby significantly mitigating the risk of CFIT occurrences.
A Stability Analysis of Mixed Traffic Flow in Connected Environment
LI Shuai, WANG Jiawei, XU Qing, WANG Jianqiang, LI Keqiang
2025, 43(6): 76-85. doi: 10.3963/j.jssn.1674-4861.2025.06.008
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This study investigates how intelligent connected vehicles (ICVs) affect the stability of mixed traffic flow consisting of ICVs and human-driven vehicles (HDVs). In mixed traffic, HDVs, degraded ICVs, and ICVs are modeled using an optimal velocity model, an adaptive cruise control (ACC) model, and a cooperative adaptive cruise control (CACC) model, respectively. Based on these car-following models, traffic stability is numerically compared using transfer-function infinity norm, with explicit consideration of whether HDVs have connectivity. In addition, the predecessor-acceleration gain in the CACC model is further examined through a systematic frequency-domain sensitivity analysis. Subsequently, microscopic traffic simulations are conducted under different ICV market penetration rates. Results show that, when HDVs have connectivity, a larger predecessor-acceleration gain in CACC model significantly improves overall stability. When the predecessor-acceleration gain increases from 0 to 1, the critical ICV penetration rate for stability decreases from 62% to 33% at any speed. In contrast, when HDVs lack connectivity, the critical penetration rate only decreases from 62% to 54%, indicating a limited stability improvement. These findings demonstrate that connectivity of HDVs is a key factor that amplifies the stability benefits of cooperative control in mixed traffic flow.
A Method of Deep Reinforcement Learning-based Ramp Metering for Mainline-ramp Coordination
ZHANG Yujie, TANG Haotong, XU Qian, YAO Jinqiang, XIONG Hui, XU Zhigang
2025, 43(6): 86-97. doi: 10.3963/j.jssn.1674-4861.2025.06.009
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The merging area of expressway ramps is prone to traffic congestion and frequent accidents. To improve the performance of traditional ramp metering algorithms in terms of response speed and control accuracy, a ramp metering method based on reinforcement learning is studied. The ramp metering problem is formulated as a Markov decision process. The action space is designed using discrete signal phases to improve training efficiency. A state space and a multi-dimensional reward function are constructed to represent the operating states of the mainline and ramps. At the state perception level, a real time traffic detection mechanism is incorporated. To avoid high frequency phase switching, a minimum phase duration constraint is imposed on action outputs. Meanwhile, prioritized experience replay is used during the training process to enhance the model performance. Furthermore, the deep network structure is optimized to improve convergence speed and generalization in complex traffic environments. Residual connections and layer normalization are introduced to construct a lightweight and efficient multi-layer perception network. A microscopic simulation platform is used to conduct systematic experiments to verify the control effect of the proposed method. The results show that compared with the no-control scenario, the system throughput increased by 52.67% under the proposed mainline-ramp coordinated control. Meanwhile, the average travel time decreases by 58.21% under the proposed method. Moreover, traffic efficiency on the mainline and ramps improves significantly under the proposed method. The proposed method is deployed in the entrance traffic limiting project of the section from Hangzhou West to Yuqian Interchange on the Hangzhou-Huizhou Expressway. The road network structure and traffic flow characteristics of this section are accurately reproduced. The results indicate that network vehicle numbers and mainline average speed increase, while speed fluctuation is more moderate. These improvements demonstrate that the proposed method has high potential for engineering deployment.
A Method of Parking Path Planning for Underground Autonomous Mining Trucks Based on Wd-RRT* Algorithm
SONG Chunhui, LENG Yao, CHEN Zhijun, QIAN Chuang
2025, 43(6): 98-107. doi: 10.3963/j.jssn.1674-4861.2025.06.010
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Autonomous mining trucks provide an effective solution for safe production and efficient transportation in underground mining areas. The low-speed maneuvering process of underground mining trucks in loading zones can be regarded as parking behavior. Due to the characteristics of underground mining areas, such as confined spaces, steep slopes, and sharp turns, traditional ground-based parking path planning methods suffer from insufficient real-time performance and low collision detection accuracy when applied in underground environments. Therefore, based on the traditional RRT* algorithm, the Wd-RRT* algorithm suitable for underground mining scenarios is developed. Using the Wd-RRT* algorithm, an automatic underground parking path planning framework is constructed, which includes three processes: node generation integrated with an artificial potential field, path generation and smoothing incorporating Reeds-Shepp (RS) curves, and swept area generation along with collision detection. To better adapt to the underground mining environment, unlike traditional rectangular bounding box collision detection strategies, this paper combines the wheelbase difference with the planned path to generate the vehicle's swept area during movement, and performs collision detection based on this swept area to ensure safety and improve efficiency. The study conducted numerical simulation experiments, 1∶1 simulated tunnel field tests, and actual underground tunnel field tests, collecting a total of 180 experimental datasets. Simulation results show that compared to the Informed-RRT* algorithm, the Wd-RRT* algorithm reduces the average planning time by 28.67%, shortens the average path length by 5.76%, and decreases the average number of nodes by 3.95%, thereby better meeting the real-time requirements of underground parking path planning. Field test results indicate that the mining truck's lateral tracking error does not exceed 40 cm, the heading angle tracking error remains within 0.2 rad, and the minimum distance to obstacles is 65.32 cm. The paths planned by the Wd-RRT* algorithm exhibit smooth curvature and good trackability while satisfying the safety requirements for underground parking.
Performance Test and Evaluation of Vehicle IMU Installation Angle Calculation Considering Road Surface State
WU Xixi, MA Xiaofeng, QIAN Chuang
2025, 43(6): 108-116. doi: 10.3963/j.jssn.1674-4861.2025.06.011
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Wheel speed odometry and nonholonomic constraints are two commonly used methods to suppress error divergence in Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation systems during prolonged GNSS signal outages. Accurate in-vehicle inertial measurement unit (IMU) mounting attitude is a prerequisite for applying wheel speed odometry and nonholonomic constraints. Traditional mounting angle calibration methods perform well on ideal road surfaces, but the validity of their core kinematic constraints heavily relies on ideal tire-ground contact conditions. In actual complex driving environments, different road surface conditions can disrupt the fundamental assumptions of these constraints by inducing abnormal vehicle motions, leading to degraded performance or even failure of online mounting angle estimation algorithms. To investigate the impact of different road conditions and driving states on IMU mounting attitude estimation algorithms, this study conducts simulation analyses and in-vehicle experiments focusing on three scenarios: road bumps, prolonged small-angle turns, and short-duration large-angle turns. By comparing the performance of velocity observation models and position observation models across these scenarios, the influence of different road surface conditions on the accuracy and robustness of IMU mounting angle estimation is analyzed. Experimental results show that in road bump scenarios, the position observation model achieves higher estimation accuracy than the velocity observation model, improving pitch mounting angle estimation by 76% and heading mounting angle estimation by 67%. In prolonged small-angle curve driving scenarios, the velocity observation model performs better, enhancing pitch mounting angle estimation by 32% and heading mounting angle estimation by 57%. However, in large-angle sharp turn scenarios, rapid changes in vehicle heading generate significant lateral velocity and displacement, which violate the constraint conditions. Therefore, in such high-dynamic scenarios, enhanced dynamic constraints and error compensation are required to achieve stable and accurate mounting angle estimation.
An EEG-based Workload Recognition Method for Civil Aviation Student Pilots
LIU Lingbo, SI Haiqing, SHANG Lei, WANG Haibo, LI Tianhao, LI Xiaojun
2025, 43(6): 117-127. doi: 10.3963/j.jssn.1674-4861.2025.06.012
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The workload of civil aviation student pilots directly impacts flight safety. To address the limitations of existing electroencephalogram (EEG) based pilot workload recognition methods, such as poor model generalization and insufficient utilization of cross-band and spatial features. This study investigates and develops an EEG-based approach for workload recognition in civil aviation student pilots. An integrated subjective-objective evaluation framework is established. EEG signals and NASA Task Load Index (NASA-TLX) data are collected from student pilots under different task scenarios in a simulated flight environment to concurrently acquire both objective physiological measurements and subjective workload assessments. To overcome the limitation of traditional studies that isolate individual frequency bands and neglect inter-band interactions, an independent samples t-test is applied to identify EEG features with significant differences (P < 0.05). Furthermore, by incorporating whole-brain power spectral density activation maps, the neural response mechanisms, and spatial distribution patterns of the θ, δ, α, and β bands, as well as cross-band power ratios, are analyzed under varying workload levels. Third, the extracted EEG features from the full frequency band and each sub-frequency band are used for model training, and a hybrid model based on convolutional neural network (CNN) and long short-term memory (LSTM) for workload recognition is developed to achieve accurate recognition of workload states. Experimental results showed that the selected features could distinguish the neural regulation modes under different workloads. At high workload, the spectral power of the α, θ, and β bands increased in civil aviation student pilots, while the spectral power of the δ band decreased. Specifically, the θ rhythm facilitated the priority allocation of resources through a frontal-parietal and right temporal circuit, while α rhythms exhibited enhanced interference suppression along the left temporal-parietal-prefrontal pathway. The constructed model successfully captured both the spatial and temporal dynamics of EEG signals. Moreover, the hybrid model achieved a test accuracy of 94.5%, outperforming traditional single models such as CNN, LSTM, and Transformer. Notably, using α-band features alone yielded a test accuracy of 95.5%, confirming the efficacy of the proposed method in identifying pilot workload states.
An Optimization of Arrival and Departure Aircraft Scheduling in Multi-Airport Terminal Areas Considering Complex Air Traffic Control Decisions
XIA Chaoyu, Hu Minghua, HOU Changbo, HUANG Zihuan, XIE Yanyi, LIANG Di
2025, 43(6): 128-147. doi: 10.3963/j.jssn.1674-4861.2025.06.013
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From a tactical flow management perspective, this study focuses on optimizing the temporal and spatial distribution of flight traffic at the microscopic operational level within multi-airport terminal areas, to enable dynamic management of arrival and departure flows and achieve orderly and efficient operations. This paper analyzes the unique airspace structure of multi-airport terminal areas and constructs a spatiotemporal node-link graph to represent arrival and departure operations. Then, based on actual air traffic control requirements, it incorporates multiple decision factors, including route selection, waypoint sequencing, speed adjustments, holding procedures, and dynamic time-domain separation, at critical resource nodes such as entry points, terminal airspace, approach airspace, and runways. Meanwhile, two mixed-integer linear programming scheduling models are proposed: one based on nominal paths and another incorporating multiple route options. The objective is to minimize cumulative scheduling deviations for all flights while also reducing air holding times for arriving aircraft. Finally, this study uses the Chengdu terminal area as a case study and conducts simulation verification under three typical operational scenarios: normal operations, departure peak operations, and arrival peak operations. The simulation result demonstrates that both proposed models are capable of generating conflict-free flight schedules. In both normal operation and departure peak scenarios, the two models exhibit similar performance, reducing the average scheduling deviation per aircraft by up to approximately 15.8 s compared to other scheduling models. In the arrival peak scenario, the model incorporating multiple paths shows superior performance, reducing the average air holding time per arriving aircraft by up to 42.3 s compared to other models.
A Coordinated Optimization Method for Peak-Shaving Operation of Key Port Crane Equipment
WANG Xiao, ZHAO Shanfeng, ZHANG Qianneng, FAN Rui, YUAN Chengqing, REN Haidong
2025, 43(6): 148-158. doi: 10.3963/j.jssn.1674-4861.2025.06.014
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Intelligent and green development is an inevitable trend for future port development. After electrification upgrades, key port equipment is supplied by the port microgrid, and a safe and stable power supply is the foundation for efficient port transportation operations. Focusing on critical large-scale port cranes in automated container terminals, this study investigates a coordinated operational scheduling strategy for peak shaving and valley filling in port microgrids. A unified load-calculation model for ship-to-shore cranes (STS) and rail-mounted gantry cranes (RMG) is established. By dividing the stages of the equipment's dual-cycle operating process, a multi-stage, full-time-horizon load profile under standard working conditions is developed. Building on the load modeling and considering the charge-discharge capability of centralized port energy storage, an exact optimization model is proposed for the coor-dinated operation between the operating cycles of crane clusters and the energy storage system. Logical variables are introduced to characterize the operating stage and power level of each machine, and constraints and an objective function are formulated to account for aggregated power impacts from equipment clusters. Solving the optimization model yields the optimal start times for multiple machines, thereby smoothing load fluctuations of port equipment clusters and improving energy utilization efficiency. Simulation analyses are conducted for equipment operation and energy storage capacity configuration, verifying that the multi-equipment coordinated scheduling method can effectively reduce the peak-valley difference, achieving more than a 20% reduction in peak power in the considered scenarios. Comparative analyses between exact optimization and heuristic optimization validate the optimality and scalability of the exact approach. In addition, the feasible region of start times under stochastic equipment operation is provided, identifying safe start-time combinations under non-ideal conditions. An extension of the proposed optimization model to non-standard operating conditions is also presented, demonstrating strong engineering practicality.
A Traffic Assignment Model Considering Heterogeneous Speed and Route Choice Behaviors under Mixed Driving Environments
HAN Fei, WANG Jian, LI Yan, ZHANG Rui, SUN Chao, KONG Yiheng
2025, 43(6): 159-170. doi: 10.3963/j.jssn.1674-4861.2025.06.015
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This study aims at quantitatively evaluating the impacts of travelers'heterogeneous behaviors of connected autonomous vehicle (CAV) and human-driven vehicle (HDV) on road network performance. A traffic equilibrium assignment model is proposed under mixed driving environments, which considers the heterogeneity in joint speed-route choice behaviors of CAV and HDV. Specifically, the quantitative relationship is established between driving speed and perceived crash risk, travel time and speeding ticket risk. By using the quantitative relationship, a speed choice behavior model is then formulated based on utility theory. The model could analytically characterize the behavior heterogeneity in optimal speed selection and speed limit (SL) obeying decisions of CAV and HDV. The concept of path time surplus (PTS) is introduced to characterize travelers'non-compensatory decision rules when weighing path travel time and monetary cost. The user equilibrium (UE) and Logit-based stochastic user equilibrium (SUE) principles with PTS maximization are used to describe the heterogeneous route choice behaviors of CAV and HDV, respectively. The speed choice behavior would intertwine with the path choice behavior via the PTS. By utilizing variational inequality (VI) theory, an equivalent mixed traffic equilibrium assignment model is finally proposed. A heuristic double-layer loop iterative algorithm is also developed to solve the model. Nguyen-Dupuis network and Huainan road network are used to validate the proposed model and algorithm. The total travel time (TTT) and total accident risk (TAR) are compared and analyzed in a road network under different traffic regulation conditions. The results indicate that with the market penetration rate (MPR) of CAVs increasing from 20% to 80%, the TTT shows an increasing trend in both 40 km/h and 50 km/h SL scenarios. Meanwhile, the TAR would increase first and then decrease across all SL scenarios. Under a high SL scheme (80 km/h), the TTT would decrease as the CAV traffic conversion factor reduces from 0.9 to 0.1, while the TAR exhibits a rising trend. Under a low SL scheme (40 km/h), however, the influences of CAV traffic conversion factor would become very insignificant. As the SL value increases from 40 km/h to 80 km/h, both the TTT and TAR decrease initially, and then stabilize or increase. The numerical results demonstrate that an appropriate speed limit scheme can reduce both TTT and TAR simultaneously.
Optimal Siting of Electric Ship Battery Swap Stations and Economic Speed Optimization Considering Time-of-Use Electricity Pricing
PAN Pengcheng, XING Tianwei
2025, 43(6): 171-182. doi: 10.3963/j.jssn.1674-4861.2025.06.016
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High energy replenishment cost and low navigation efficiency in inland electric vessels are caused by decoupled decisions on battery swap station siting and cruising speed selection. To address this issue, a coordinated optimization method for swap station layout and sailing speed under time-of-use electricity pricing is investigated. A bi-level multi-objective optimization model is established to describe the coupling between infrastructure planning and vessel operation. At the upper level, the construction and operation costs of battery swap stations are minimized while maximizing the proportion of energy replenishment completed during off-peak electricity periods, using binary siting variables and inter-station distance constraints. At the lower level, given the siting results, cruising speeds on each route segment are continuously optimized to minimize total sailing time and energy replenishment cost. Considering the practical cost transmission characteristics of time-of-use pricing under containerized battery swapping, a charging time-window conversion mechanism is introduced. Electricity prices are converted from vessel arrival times to equivalent settlement prices based on station-side historical charging behavior, thereby revising the conventional energy-cost relationship in speed optimization. Constraints on battery capacity updating, maximum depth of discharge, swap triggering conditions, and segmented speed limits are incorporated to ensure engineering feasibility and navigational compliance. A case study on the Yangtze River involving three representative pure electric vessels is conducted. Results show that, while satisfying endurance and safety speed constraints, the coordinated scheme reduces one swap station and decreases single-voyage replenishment cost by 4000 to 7000 CNY, with an average reduction of 14%, while total sailing time is shortened by 15 to 23 hours. Compared with fixed-speed operation, the optimized speed strategy further reduces replenishment cost by 25 000 to 80 000 CNY, with an average reduction of 45%, and shortens sailing time by 16 to 21.5 hours. Across different vessel types and departure times, the system-level average sailing time is reduced by 19.5 hours and replenishment cost is reduced by approximately 55%. The results confirm that siting-speed coordinated optimization under time-of-use electricity pricing effectively improves the economic performance and operational efficiency of inland electric vessel battery swapping systems.

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