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25 October 2024, Volume 24 Issue 5 Previous Issue   

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Hydrogen Fuel Cell Bus: A Literature Review and Prospects LIU Tao, GUO Jiaxin, HAN Ying, TANG Chunyan 2024, 24(5): 1-13.  DOI: 10.16097/j.cnki.1009-6744.2024.05.001 Abstract ( )   PDF (1708KB) ( )   The adoption of hydrogen fuel cell buses (HFCBs) contributes to reducing carbon emissions and promoting the sustainable development of transportation systems. This paper systematically reviews the research literature on HFCBs by searching relevant databases. The review covers five main areas: the feasibility and prospects of developing HFCBs, evaluation of HFCB systems and comparison with other transit modes, social acceptance of HFCBs, planning and operations management of HFCBs, and safety analysis of HFCBs. This study reveals that, as HFCBs are still in the exploratory development stage, there are relatively more studies on the feasibility, system evaluation, and social acceptance of HFCBs, whereas studies on the system planning, operations management, and safety analysis are relatively less. Although China's scientific research and practice in the field of HFCBs started later than other countries, it is currently among the world leaders. Driven by both policy support and market demand, HFCBs are rapidly developing in China. Based on the literature review, the paper further analyzes existing research limitations and proposes suggestions for future studies. The research indicates that further in-depth studies can be conducted in four areas: reducing the cost of HFCBs, enhancing infrastructure construction, increasing social acceptance, and strengthening safety management. Particularly, attention should be given to innovations in hydrogen fuel cell battery technology, supporting infrastructure development, and operational safety assurance. In the future, HFCBs have broad application prospects by providing transportation service in various transportation scenarios, such as in tourist attractions, large-scale sports events, urban transportation, or in intercity long-distance transport. Academia and industry should actively align with the relevant policy requirements and practical needs of the hydrogen energy industry and transportation development. Continuous in-depth research should be conducted on the key and challenging aspects of HFCBs development to jointly support its sustainable development. References | Related Articles | Metrics

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Prediction of Outbound Transportation Volume of Xinjiang Coal Railway by Integrating Sparrow Search with Long Short-Term Memory LI Haijun, ZHANG Xiaoyang , GAO Ruhu , WEI Dehua, CHEN Xiaoming 2024, 24(5): 14-23.  DOI: 10.16097/j.cnki.1009-6744.2024.05.002 Abstract ( )   PDF (2275KB) ( )   To enhance the precision of predicting the Xinjiang's coal railway outbound volume transportation, a prediction model integrating the sparrow search algorithm and the long and short-term memory network (SSA-LSTM) is proposed. The model introduces the sparrow search algorithm to optimize the hyper-parameters of the LSTM model in order to improve the model prediction performance. Based on the data of Xinjiang coal rail outbound transportation volume from 2015 to 2022, the gray correlation analysis is employed to comprehensively evaluate the impact of factors, including economic and transportation aspects, ensuring that the selected factors exhibit a strong correlation with the prediction targets. Among the influencing factors, the GDP data is adjusted for Consumer Price Index (CPI) effects, and the refined data are then fed into the model for prediction. Finally, the model is applied to predict the Xinjiang's coal rail outbound transportation volume across short, medium, and long time horizons. The results demonstrate that the SSA-LSTM model outperforms both the BP neural network and the conventional LSTM model, achieving a Mean Absolute Percentage Error (MAPE) of 0.88% and a Root Mean Square Error (RMSE) of 49.9. Furthermore, incorporating CPI processing into the prediction process significantly reduces the prediction error, with MAPE and RMSE decreasing by 75.8% and 56.2%, respectively, compared to non-CPI-processed predictions. This study provides an effective approach for predicting Xinjiang's coal rail outbound transportation volume, offering important data insights that inform the strategic design of coal transportation routes out of Xinjiang. References | Related Articles | Metrics

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Impact of "Star-Type" High-speed Railway Network on High-quality Development of Regional Social Economy YUE Guoyong, HU Hao 2024, 24(5): 24-36.  DOI: 10.16097/j.cnki.1009-6744.2024.05.003 Abstract ( )   PDF (2915KB) ( )   This paper focuses on the first "Star-type" high-speed railway network in China and analyzes its impact on regional high-quality development from four dimensions: spatial pattern, economic development, social development and ecological environment. The study constructs a comprehensive impact evaluation model and establishes a multidimensional indicator system to evaluate the effects. It culminates in a detailed quantitative analysis of the outcomes to provide a nuanced understanding of the impacts. The result indicates that the opening of the "Star-type" high-speed railway network has significantly reduced the weighted average travel time between and within Henan province to 4.90 hours and 1.62 hours, with improvements rate of 65.6% and 37.8% . The intensity of regional connections has been significantly enhanced, gradually forming a "center-periphery" development structure which focuses on intra-provincial connections and steadily expands to the energy consumption optimization and comprehensive operational emission reduction of railway northeast and southeast. The primacy index of Zhengzhou high-speed railway hub has increased from 1.90 to 2.83, further consolidated its position as a core hub. The differencein-differences model is used to verify that the "Star-type" high-speed railway network has a positive promotion on economic and social development indicators of Henan，such as social fixed assets investment, foreign capital utilization, per capita Gross Domestic Product (GDP), urbanization rate, employment upgrading index, etc. The amount of optimized comprehensive energy consumption and comprehensive operational emission reduction in railway passenger transport has steadily increased, and industrial SO2 emissions reduced, with significant ecological environment effects. References | Related Articles | Metrics

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Research on Energy Consumption and Carbon Emissions in the Whole Life Cycle of Beijing-Xiong'an Intercity Railway CAO Meng, YUAN Zhenzhou, YANG Yang, NIE Yingjie, NA Yanling, SUN Yunchao, CHEN Jinjie 2024, 24(5): 37-44.  DOI: 10.16097/j.cnki.1009-6744.2024.05.004 Abstract ( )   PDF (1339KB) ( )   As the backbone of green transportation, intercity railways play a supporting role in reducing energy consumption and achieving the goal of "carbon peak and carbon neutrality". This paper analyzes the development trajectory and energy consumption patterns of intercity railways in China, with a particular focus on the entire lifecycle of planning, design, construction, and operation within the context of the "dual carbon" initiative. Utilizing the Beijing-Xiong'an intercity railway as a prototypical case, the paper examines energy consumption and carbon emissions across its lifecycle, incorporating the influence of energy-saving, emission-reduction strategies, and green carbon sink measures. The findings reveal that carbon emissions during the planning and design phases are negligible, whereas the energy consumption during the operation stage dominates, accounting for approximately 74.9% of the total annual lifecycle energy consumption. Additionally, the energy consumption attributed to building materials production (scaled to 100 years) constitutes roughly 22.4% of the total. Notably, the implementation of energy conservation, emission reduction, and green carbon sequestration measures has yielded substantial outcomes, achieving an average annual energy savings of approximately 12%. When compared with similar railway energy consumption indicators globally, the Beijing- Xiong'an intercity railway's unit transportation traction energy consumption of 6.42 tce per million person-kilometer aligns with expectations. Furthermore, its carbon reduction impact is significant in diverting highway passenger traffic, savings approximately 612.67 million yuan in carbon sink transactions and generating substantial societal benefits. This comprehensive analysis offers valuable insights and reference for the establishment of a green and low-carbon intercity railway carbon emission dual control indicator system. References | Related Articles | Metrics

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Energy Saving and Emission Reduction Potential of Road Traffic in Coastal Urban Agglomerations Under Background of Carbon Peak ZHANG Lanyi , XU Yinuo, WANG Shuo, XIE Zhengyi, WENG Dawei, WANG Zhenhao, HU Xisheng, ZHENG Pingting 2024, 24(5): 45-55.  DOI: 10.16097/j.cnki.1009-6744.2024.05.005 Abstract ( )   PDF (2617KB) ( )   In alignment with China's strategic "dual-carbon" goals, this paper aims to investigate the potential for energy saving and emission reduction in the road transportation system of coastal urban agglomerations. Taking the coastal urban agglomerations in Fujian Province as an example, a long-range energy alternatives planning system model (LEAP) has been constructed, three primary scenarios and four secondary sub-scenarios have been developed. The emission reduction potential and trend of regional road traffic were studied by adjusting the parameters of vehicle ownership and fuel economy. The study indicates that among all scenarios, the Modified Policy Scenario (MPS) demonstrates the most superior energy saving and emission reduction effects, with the greatest potential for energy conservation and emission reduction. Compared to the Business as Usual (BAU) scenario, the energy saving of the MPS is expected to be improved by 59.3% , by 2035. Under the MPS scenario, the trends in greenhouse gas and pollutant emissions both show a significant decline, with remarkable emission reduction effects. Looking specifically at the energy- saving and emission reduction potential of different vehicle types, under the MPS scenario, small- duty gasoline passenger vehicle (SGPV), heavy-duty diesel freight vehicle (HDFV), and light-duty gasoline freight vehicle (LGFV) have the greatest potential for energy conservation; carbon emissions from 8 types of vehicles can peak by 2025; and pollutant emissions can be effectively controlled, with the greatest potential for pollutant emission reduction found in HDFV. The research confirms that advancing the implementation of comprehensive policies, accelerating the phase-out of traditional fuel vehicles, and optimizing the structure of road vehicles will have a positive impact on the realization of the green and low-carbon goals. Through model simulation, it is anticipated that the road traffic system of the coastal urban agglomerations in Fujian Province will achieve significant energy-saving and emission-reduction effects before 2030. References | Related Articles | Metrics

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Dynamic Spatiotemporal Priority Control of Connected Vehicles Public Transport System LI Zhe, GOU Yangyang, LI Zhenyao, LI Ao, CEN Wei, GAO Jianping 2024, 24(5): 56-64.  DOI: 10.16097/j.cnki.1009-6744.2024.05.006 Abstract ( )   PDF (2303KB) ( )   To improve the utilization efficiency of bus lanes and reduce vehicle delays at intersections of continuous bus lanes, this paper investigates dynamic spatiotemporal priority control of connected public transport systems from spatial and temporal dimensions and analyzes the applicable traffic flow conditions. In the spatial dimension, intermittent bus entrance lanes are introduced and vehicle operation control strategies are formulated for four dynamic intervals, including clearance distance. In the temporal dimension, based on deep reinforcement learning, signal timing is dynamically adjusted through time extension of the green light and time interruption of the red light. A simulation verification platform is constructed using SUMO and Python, and comparative simulation experiments and three saturation scenarios are designed for four control schemes concluding the original scheme, spatial priority scheme, temporal scheme, and spatiotemporal collaborative priority scheme. The results show that at saturation levels of 0.2, 0.5, and 0.8, the spatiotemporal collaborative priority scheme reduces the average delay compared to the original scheme by respectively 40.96% , 39.93% , and 28.20% . At low saturation, the spatial priority effect is obvious; at medium saturation, the temporal effect is obvious. Using intermittent bus entrance lanes may lead to a slight increase in bus delays, but the average delay at the entire intersection is significantly reduced. The proposed dynamic spatiotemporal priority control method for connected vehicle bus systems can effectively improve intersection traffic efficiency while ensuring bus priority. References | Related Articles | Metrics

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Collaborative Lane Change Method for Autonomous Vehicles Based on Dynamic Trajectory Planning LIU Miaomiao, LIU Xiaochen, ZHU Mingyue, WEI Zeping, DENG Hui, YAO Mingkun, WU Silin, LI Ang, SHI Zan, GONG Xiaoyu 2024, 24(5): 65-78.  DOI: 10.16097/j.cnki.1009-6744.2024.05.007 Abstract ( )   PDF (2891KB) ( )   Traditional multi-vehicle coordination lacks effective utilization of information about target platoons and lane-changing vehicles. To address the impact of dynamic information changes on the lane-changing process, this paper proposes a collaborative lane-changing control method for autonomous vehicles based on dynamic trajectory planning. First, focusing on the scenario of a single vehicle merging into vehicle platoons in autonomous driving environments, a collaborative lane change control framework based on real-time dynamic information is proposed. Considering the cooperation between the lane-changing vehicle and the target platoon vehicles, and the impact of the lane-changing behavior on the target platoon, longitudinal collaborative control models are established for both non-lane changing and lane-changing periods. Second, after the lane-changing vehicle sends a lane-change request and satisfies the lane-change triggering conditions, a dynamic lane-change trajectory planning method using a sinusoidal curve is employed to derive a safe and reliable trajectory. Vertical coordination goals are considered. And based on the dynamic planning of longitudinal speed changes, a sine-curve-based dynamic lane change trajectory planning approach is introduced to derive safe and reliable trajectories. Then, a model predictive control-based trajectory tracking control algorithm is used to achieve real-time trajectory tracking. Finally, by constructing a joint simulation platform of Prescan and Simulink, several sets of simulation experiments under different speed conditions are designed. And traditional control algorithms based on vehicle tracking strategies are compared with the proposed control strategy by analyzing three key indicators: lane change trigger time, train stabilization time, and speed fluctuation amplitude. This comprehensive analysis validates the effectiveness and feasibility of the proposed control strategy. Simulation results show that, compared with traditional methods, the average stable time of the platoon is reduced by 34%, and the speed fluctuation amplitude of the platoon remains stable. In addition, safe and efficient lane changes can be achieved under different relative speed conditions. References | Related Articles | Metrics

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Automatic Driving Risk Prediction Model Based on Improved Vision Algorithm ZHAO Hongzhuan, ZHANG Jikang, PAN Jiawen, YUAN Quan, XU Enyong, WEI Jinzhan, ZHOU Dan, LIU Chengkun 2024, 24(5): 79-90.  DOI: 10.16097/j.cnki.1009-6744.2024.05.008 Abstract ( )   PDF (3045KB) ( )   In order to deal with the problem of traditional vehicles cutting too close to each other resulting in disengagement of automatic driving, this paper proposes an automatic driving risk prediction model with improved YOLOV7-Tiny and SS-LSTM. The model improves the visual target detection model YOLOV7-Tiny(You Only Look Once Version 7 Tiny), adds a small target detection layer, introduces the SimAM (A Simple, Parameter-Free Attention Module for Convolutional Neural Networks) attention mechanism module, optimizes the training loss function, and performs trajectory tracking and prediction of its target vehicle. The short-term prediction of Strong SORT (Strong Simple Online and Realtime Tracking) is utilized to continuously correct the long-term prediction of LSTM (Long Short Term Memory) to establish the SS-LSTM model. And the predicted overtaking trajectory is fitted with the trajectory of the intelligent networked vehicle itself at the same time latitude, so as to obtain the risk prediction model when the traditional vehicle cuts in. The experimental results show that the automatic driving risk prediction method in this paper effectively predicts the risk of traditional vehicles when cutting in, and the simulation experiments show that the improved YOLOV7-Tiny improves the prediction accuracy by 2.3% compared with the original algorithm mAP (mean Average Precision). The FPS (Frames Per Second) is 61.35 Hz. The model size is 12.6 MB, and the model meets the lightweight demand of the vehicle end. The real-vehicle experiments show that the accuracy of risk prediction based on the SS-LSTM model is 90.3%. References | Related Articles | Metrics

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Urban Road Traffic Accidents Prediction Based on Image Sequence Analysis HU Zhenghua, ZHOU Jibiao, MAO Xinhua, ZHANG Minjie 2024, 24(5): 91-102.  DOI: 10.16097/j.cnki.1009-6744.2024.05.009 Abstract ( )   PDF (3453KB) ( )   To further improve the accuracy of traffic accident prediction in road networks, a short-term traffic accident prediction method based on sequential image analysis is proposed. First, an oversampling technique is applied to interpolate traffic accident data collected from a WeChat mini-program to mitigate the impact of extensive zero values within the data on model training accuracy. These data are then integrated with road network traffic flow and accidentrelated attributes to generate stable time series as input for the model. A Bidirectional ConvLSTM U-Net with densely connected convolutions (BCDU-Net) is constructed. In this model, bidirectional ConvLSTM structures are used to integrate the features from the encoder and decoder layers, comprehensively capturing spatiotemporal correlations in the sequential data. Additionally, densely connected convolutions are employed to concatenate feature maps in the depth dimension, ensuring that each layer can directly access gradients from the loss function. Finally, the performance of the proposed model is evaluated by comparing the predicted results with actual traffic accident data. The results show that, compared to the Fully Connected Long Short-Term Memory (FC-LSTM) model, the Convolutional LSTM (ConvLSTM) model, and the U-Net model, the proposed model achieves reductions in cross-entropy loss of 65.96%, 15.70%, and 3.47%, reductions in root mean square error of 21.48%, 3.13%, and 1.71%, and increases in precision of 75.06%, 11.82%, and 3.08%, respectively. It is demonstrated that the proposed method offers superior performance in predicting urban road traffic accidents. References | Related Articles | Metrics

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Eco-driving Strategy for Electric Bus Entering and Leaving Stops Considering Velocity Mode ZHANG Yali, FU Rui, WEI Wenhui, YUAN Wei, GUO Yingshi 2024, 24(5): 103-115.  DOI: 10.16097/j.cnki.1009-6744.2024.05.010 Abstract ( )   PDF (3064KB) ( )   The promotion of electric vehicles brings new opportunities to energy conservation and emission reduction. However, due to the differences in their dynamic systems, it also highlights a problem of high energy consumption of electric vehicles if the operation mode and driving habits of traditional fuel vehicles continue to be used. To increase inbound energy recovery and reduce outbound energy consumption, two eco- driving strategies were established by considering the actual inbound and outbound velocity mode. Firstly, natural driving data of electric bus rapid transit (E-BRT) was collected and the differences in energy consumption between electric and gasoline buses were analyzed. Secondly, the actual inbound and outbound velocity mode was deeply analyzed, and five driving strategies that consider driving mode in the process of entering and leaving stops were established separately. By comparing the energy consumption rate, the inbound and outbound eco-driving strategies were determined. Thirdly, an eco-driving strategy based on a NSGA-II was established based on the driving mode. Finally, the energy-saving benefits of the two strategies were verified using the actual inbound and outbound data under the three driving styles. The energy saving rate of the eco-driving strategies based on driving mode and NSGA-II is 17.04%/23.58%, 14.76%/21.48%, and 5.78%/ 13.21% for energy-consuming, general, and energy-saving driving styles, respectively. The proposed strategy demonstrated the highest energy-saving rate for energy-consuming driving styles, followed by general styles, and the lowest for energy-efficient driving styles. Compared to the eco-driving strategy based on driving mode, the strategy based on NSGA-II exhibited a 7.89% reduction in energy consumption. References | Related Articles | Metrics

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Cross-line Train Service Plan Optimization in Urban Rail Transit Network JIAN Min, CHEN Shaokuan, WANG Zhuo, LI Hao 2024, 24(5): 116-127.  DOI: 10.16097/j.cnki.1009-6744.2024.05.011 Abstract ( )   PDF (3725KB) ( )   In order to improve the service quality of urban rail transit by reducing transfer times, a method for generating cross-line train routes and an optimization model for planning train routes with cross-line operation are proposed based on the characteristics of passenger flow in the network. First, with the developed inference method of passenger travel routes, the various passenger flows and proportions in the network are calculated to obtain the crossline times, thereby generating the set of alternative long cross-line train routes. Then, with the goal of minimizing passenger transfer times, an optimization model for the operation of long cross-line train routes in the network is constructed, which satisfies the constraints of basic operating conditions and cross-line capacity. An improved genetic algorithm with a frequency-based passenger flow assignment method is used to solve the problem to obtain the operating frequency of the mainline and cross-line train routes in the network. Finally, the effect of long cross-line train routes is analyzed based on an urban rail network. The results show that the optimized train service plan reduces the transfer times of all transfer passengers by 2.02% to 5.97% . Thus, the passenger transfer time and network transfer coefficient are reduced, and the direct passenger flow is increased by 1.58% to 4.58% with the operated long cross-line train routes. The operated long cross-line train routes play the role of short train routes in the connected line to supplement the sectional transportation capacity, thus reducing the total number of running trains on the line and the train kilometers on the main line. In addition, when the transfer passenger volume at the transfer station is high, the cross-line train route cannot be operated due to the high operating frequency of the main-line train on the connected line, and the effect of improving the operational services gradually decreases as the operating frequency of the crossline train route reaches the upper limit of the cross-line capacity References | Related Articles | Metrics

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Energy-efficient Train Timetable Optimization Model for Urban Rail Transit Line with Asymmetric Passenger Demand SUN Yuanguang, DENG Chengyuan, PENG Lei, CHEN Hongbing, LI Zongran, BAI Yun 2024, 24(5): 128-139.  DOI: 10.16097/j.cnki.1009-6744.2024.05.012 Abstract ( )   PDF (2500KB) ( )   To relieve the asymmetric phenomenon of urban rail transit line, such as the stranding of passengers in the heavy-demand direction, the wastage of capacity and high energy consumption in the low-demand direction, this paper proposes an asymmetric transportation strategy combining skip-stop tactics and flexible train composition technology. In this strategy, more train services will be arranged in the heavy-demand direction of the transit line, and the service frequencies will be reduced in the low-demand direction. The flexible train composition technology is also utilized to improve the flexibility of capacity supply and reduce energy consumption waste. In addition, several express trains are operated in the low- demand direction to accelerate the turnover speed of rolling stocks. Based on this, an integrated optimization model of operation plan, train timetable and rolling stock circulation plan were constructed to determine the bidirectional service frequency, train composition, stopping plan, timetable and circulation plan of rolling stocks, to minimize the total passenger travel time and total traction energy cost of the entire corridor. A customized variable neighborhood search algorithm is designed to solve the mixed integer nonlinear programming model. The case study in Guangzhou Metro Line 14 showed that: compared with the actual symmetric transportation strategy, the proposed method can reduce the total passenger travel time by 6.52 %, the total traction energy consumption by 34.20 %, and the total objective function by 11.40 %. Express trains can accelerate the turnover speed of the rolling stocks, reducing six on-line rolling stocks, which can facilitate the asymmetric strategy. Flexible train composition technology can further improve the flexibility of the capacity supply, and significantly reduce the number of on-line rolling stocks, where the average load factor of trains is increased by approximately 20%, and the optimization rate of the total traction energy consumption is increased by approximately 33%. The asymmetric strategy combined with skip-stop trains and flexible train composition can save train operating costs, and greatly improve the capacity matching degree under the asymmetric passenger demand. References | Related Articles | Metrics

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An Optimization Method for Train Unit Flexible Scheduling Using Virtual Coupling Technology ZHOU Housheng, QI Jianguo, YANG Lixing, SHI Jungang, ZHANG Huimin 2024, 24(5): 140-147.  DOI: 10.16097/j.cnki.1009-6744.2024.05.013 Abstract ( )   PDF (3031KB) ( )   This study deals with the optimization problem of flexible scheduling of train units using virtual coupling technology. It investigates the intricate relationship between the temporal and spatial characteristics of passenger flow and the real-time dynamic marshalling of train units using virtual coupling technology. It also aims to coordinate the virtual coupling and virtual decoupling operations between the train operating in different directions. The model incorporates constraints related to train unit allocation using virtual coupling technology, train unit circulation, and passenger assignment. To address this optimization problem, a mixed integer linear programming model is formulated, which can be solved directly by the commercial solver (such as CPLEX). Finally, several numerical experiments are conducted based on real data from a specific city to validate the efficiency of the proposed method. The experimental results show that compared to a fixed train composition model with 6 carriages, the proposed method exhibits a significant reduction of 32.8% in total passenger waiting time and a simultaneous reduction of 20.9% in system operating cost. In addition, compared to a fixed train composition model with 8 carriages, the method shows a slight increase in total passenger waiting time (i.e., 0.3%) while achieving a significant reduction in system operating cost (i.e., 40.7% ). These numerical experiments demonstrate the potential benefits of virtual coupling technology in improving the efficiency of urban rail transit, which have tangible practical implications. References | Related Articles | Metrics

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Optimized Rolling Stock Circulation Planning for Intercity Railways with Train Diagram Adjustment by Routing DU Peng, ZHANG Luyao 2024, 24(5): 148-159.  DOI: 10.16097/j.cnki.1009-6744.2024.05.014 Abstract ( )   PDF (2759KB) ( )   This paper proposes a method for formulating rolling stock circulation plans that takes into account routing adjustments, in response to the issue that existing intercity railway rolling stock circulation planning methods do not consider the distribution of seat occupancy rates on operating lines which makes it challenging to effectively suspend low-occupancy train services and retain high-occupancy ones during routing adjustments. The proposed method aims to minimize the number of Electric Multiple Units (EMUs) in service and reduce the disparity in seat occupancy rates along operating lines within each circulation route. A nonlinear integer programming model for rolling stock circulation plan formulation is developed with the constraints of primary maintenance. The model is solved through linearization and the Hierholzer's algorithm is used to search for feasible solutions. The case analysis demonstrates that, compared to traditional methods that only consider the number of EMUs used, the proposed model significantly reduces the disparity in load factors of different routings without increasing the number of EMUs. The optimized plan improves the extreme value of seat occupancy rates along optimized circulation routes by 68.67% and reduces the sum of the variances of seat occupancy rates along operational lines in circulation routes to 66.27% of the comparison plan. During routing adjustments, the optimized solutions accurately prioritize the cancellation of low-occupancy train services. When the number of adjusted routes is set to 1, 2, or 3, the proportion of the operating lines with seat occupancy rates less than 60% in the optimized plan remains above 77% , whereas in the comparison plan, this proportion does not exceed 45% . In addition, the average seat occupancy rates of canceled train services in the optimized solution are reduced by 4.48%, 5.88%, and 6.01% contrary to comparison plans. References | Related Articles | Metrics

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Optimization of Bus Unit Dynamic Formation Plan in Modular Public Transport System YUE Hao, DONG Xianlong, WANG Li, QU Qiushi, ZHANG Xu 2024, 24(5): 160-172.  DOI: 10.16097/j.cnki.1009-6744.2024.05.015 Abstract ( )   PDF (2005KB) ( )   This paper investigates the optimization of dynamic formation plan for bus unit road operation based on modular public transport system. A two-stage joint optimization model for the direction assignment and formation permutation of platoon was proposed. In the first stage, an integer linear programming model was developed with the objective of minimizing the number of passengers in-motion transfer. The model enables the direction assignment of bus units and the calculation of replenishment bus units. Based on this, a second-stage bi-objective optimization mixed integer nonlinear programming model was constructed, with the objectives of minimizing formation permutation time and in-motion transfer time, to optimize the efficiency of dynamic formation of bus units. Furthermore, the algorithms was designed to solve the proposed models. The CPLEX solver was used to solve the first-stage direction assignment model and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithm was used to solve the second-stage formation permutation model. At last, the study verified the effectiveness of the proposed model and its solution algorithms. It also included an analysis of the optimization of bus unit formation efficiency and change in bus occupancy rate in a modular public transport system under different passenger demands and bus unit capacities. The results indicate that within a certain increase in modular bus unit capacity, the formation efficiency of modular bus units improves with the increase in bus unit capacity. When the increase in bus unit capacity is too big, the dynamic formation efficiency cannot be improved effectively, and the bus occupancy rate will be reduced, which would lead to overcapacity of modular bus platoon. References | Related Articles | Metrics

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Single-line Bus Operations Dynamic Holding Control Strategy Based on Deep Reinforcement Learning LIU Dong, ZHANG Dapeng, WAN Yun, XIAO Feng 2024, 24(5): 173-184.  DOI: 10.16097/j.cnki.1009-6744.2024.05.016 Abstract ( )   PDF (2411KB) ( )   Large headway and fluctuations in bus operations can lead to instability of the bus operation system, such as the bus bunching phenomena. This paper proposes a dynamic holding control strategy based on deep reinforcement learning to improve the stability of bus system operations and avoid bus bunching. A linear bus system is established, and the operating rules for vehicles and passenger behavior are defined. Then, a dynamic control method is introduced based on deep reinforcement learning, the elements of the reinforcement learning framework are defined, and an eventdriven simulator environment is developed to train and test the agents. Extensive simulation experiments are conducted to compare the proposed method with traditional methods. Various evaluation metrics are selected for comparative analysis, and the sensitivity analysis is also performed. The experimental results show that the proposed method achieves the most stable vehicle trajectories and the smallest passenger occupancy dispersion. The headway variation was reduced respectively by 61.90%, 60.98%, and 37.98% compared to the no control strategy, the schedule-based control strategy, and the headway-based control strategy. The average waiting time was reduced by 28.36%, 26.53%, and 23.61% compared to the aforementioned strategies. The proposed method also demonstrates strong robustness under varying travel time variability and headway conditions. References | Related Articles | Metrics

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Single-file Pedestrian Flow Simulation Considering Pedestrian Stepping Characteristics in Microscopic Perspective HU Zuoan , ZENG Tian, DU Jun, WEI Yidong, WANG Shibo, MA Yi 2024, 24(5): 185-196.  DOI: 10.16097/j.cnki.1009-6744.2024.05.017 Abstract ( )   PDF (3922KB) ( )   To reveal the pedestrians movement characteristics from the microscopic perspective of pedestrian bipedal movement, this paper analyzes the movement mechanism of pedestrians' continuous alternating stepping behavior, establishes a single-file pedestrian flow model simulating stepping motion, and analyzes the nonlinear change process of pedestrian position in the stepping period. The pedestrian stepping process is categorized into a single support stage and a double support stage based on the relative position of the pedestrian's center of gravity and bipedal center. The position coordinates at any given moment in each stepping cycle are determined according to the wave-shaped pattern observed in the instantaneous velocity of pedestrian feet. Based on empirical data, the model considers the adjustment of pedestrians' step length and duration in response to headway to avoid trampling with pedestrians in front. The simulation results indicate that the proposed model effectively captures the micro-stepping characteristics of pedestrians and can replicate fundamental diagrams and space-time diagrams. In addition, this study calculates the stepping dynamic characteristics based on the wave-shaped pattern of pedestrian feet, revealing that pedestrians tend to adopt a low step length and frequency when navigating crowded spaces. Furthermore, it is observed that synchronization phenomena occur most frequently when the global density reaches 1.9 ped · m- 1 . The results indicate that the effective establishment of pedestrian stepping model can provide reference for the evaluation and prediction of pedestrian movement state. References | Related Articles | Metrics

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Bi-level Programming Method for Differentiated Toll Rate Optimization of Expressway Empty Freight Vehicle DUAN Lizhen, HE Mingwei, HE Min, PU Ronghui 2024, 24(5): 197-204.  DOI: 10.16097/j.cnki.1009-6744.2024.05.018 Abstract ( )   PDF (1806KB) ( )   This paper proposes a toll rate optimization method to improve the empty freight vehicle diversion due to adjusting the expressway freight vehicle charging from weight-based to vehicle-type-based charging. The upper level planning model is developed to maximize the revenue of the operating entity, considering the improvement of expressway traffic efficiency and the management and maintenance expenditure of incremental empty freight vehicle. Then, the lower-level planning model is developed to maximize the travel utility of road users. The empty freight vehicle travel utility function is developed based on the logical regression model and stochastic utility maximization theory, to analyze the impact of empty freight vehicle property variables on travel utility. The incremental expressway empty freight vehicle is predicted under different preferential rates using the multi- layer neural network model and Hermite interpolation method, as the connection variable of upper and lower levels. In the experimental analysis, the optimal rate of empty freight vehicle is calculated using the Limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm (L-BFGS-B). The results show that the rate discount of expressway is the key factor influencing the choice of empty freight vehicle. The location of vehicles registration has a great impact on the choice of empty freight vehicle of class I and class IV, and the transportation mode has a great impact on the choice of empty freight vehicle of class II,class III and class VI. Compared to the current rate scheme, the optimized rate scheme can increase the annual toll revenue by 39.41 million yuan, improve the efficiency of expressway by 4.17%, and increase the travel utility of empty freight vehicle by 3.51 per vehicle. References | Related Articles | Metrics

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Ship Schedule Recovery Model in Container Liner Shipping Network ZHU Xuebin, LV Jing 2024, 24(5): 205-216.  DOI: 10.16097/j.cnki.1009-6744.2024.05.019 Abstract ( )   PDF (3599KB) ( )   To reduce the negative effects of schedule delays on the operational costs of liner shipping networks, this paper investigates the schedule recovery problem considering container routing replanning within the network. Three strategies are employed for schedule recovery, namely, increasing the ship speed, shortening the berthing time at the port and cancelling the call of port. These strategies underpin a mixed-integer nonlinear programming model, constructed to minimize the overall operational cost of the liner shipping network. Considering the complexity of the solution structure in the ship schedule recovery problem, a parallel constrained genetic algorithm is developed. The spatio-temporal network of liner shipping is constructed with 22 publicly available schedule data on four routes of OCEAN Alliance, and 150 examples are randomly generated to verify the effectiveness of the model and the algorithm. The results show that the proposed parallel constrained genetic algorithm exhibits robust stability and superior problemsolving capabilities in the context of ship schedule recovery. When compared to a single-ship schedule perspective, recovering disrupted schedules from a network-wide vantage point results in a lower total operational cost, with savings of approximately $37 million in certain scenarios. The initial transport plan is an important criterion for liner shipping network operations, and delays in vessel schedules can disrupt the intricate network dynamics. By adopting a network perspective approach to schedule recovery, not only are the adverse impacts of schedule changes on overall operational costs reduced, but also the continuity of the original transport plan within the liner shipping network is safeguarded. References | Related Articles | Metrics

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On Invulnerability of Container Transport Network Considering Zero-carbon Route LI Junjun, MIAO Quanli, XU Bowei, CUI Qinke, LIU Congyue, ZHU Jiangwen 2024, 24(5): 217-225.  DOI: 10.16097/j.cnki.1009-6744.2024.05.020 Abstract ( )   PDF (2152KB) ( )   As countries around the world pay more attention to the carbon emissions in container transportation, zerocarbon routes have attracted many attentions as an innovative way to reduce carbon emissions. The container transportation process can be analyzed in the form of complex network topology. Although the addition of the zerocarbon route cannot change the structure of the shipping network, it has a certain impact on the container transport network due to the unique nature and the role of the port played on the zero-carbon route. The container transport network model including zero carbon routes is developed based on the actual data of ports. Three indicators: network efficiency, maximum connection subgraph scale and green port invulnerability index are proposed to measure the invulnerability of the network. Deliberate attack is chosen to trigger network cascade effect by simulation experiment. The impact of capacity parameters, green port number and load distribution coefficient on invulnerability of container transport network is analyzed. The results show that the addition of zero-carbon routes has a great impact on the invulnerability of the container transport network. When 25% of ports are green, the three aspects of network invulnerability can be balanced, including global network efficiency, green port resilience index, and maximum connectivity subgraph size. When the load distribution coefficient is 4, the invulnerability of the container transport network can be improved, and the impact caused by the addition of zero-carbon routes is reduced. After the important nodes are attacked, the network efficiency and the maximum connection subgraph scale are both reduced greatly References | Related Articles | Metrics

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Resilience Assessment of Urban Road Networks to Flooding Under Heavy Rainfall and Flooding JIN Xi, MU Yan 2024, 24(5): 226-236.  DOI: 10.16097/j.cnki.1009-6744.2024.05.021 Abstract ( )   PDF (2953KB) ( )   Extreme rainfall brings great challenges to the normal operation of urban road networks. To assess the resilience of road networks against flooding when coping with extreme rainfall, this paper adopts a scenario simulation method to analyze the impacts of heavy rainfall flooding disasters on road networks. To evaluate the connectivity of the road by whether the water depth of the road exceeds the water depth threshold, a comprehensive evaluation framework is developed with the node strength and the attenuation function of the road speed under different water depths as the calculation indexes. Based on the performance change curve of the road system under rainfall- waterlogging process and 4R feature theory, the resilience of the road network against flooding is analyzed under four heavy rainfall recurrence periods. An enhancement strategy is proposed to improve the resilience of the road network to flooding by retrofitting flood-prone transportation hubs. The results show that: the impact of rainfall on the service performance of the road network is greater than that on the structural performance of the road network. The flood resilience of the road network decreases with the increase of the rainfall return period. The degree of inundation of the road is related to both the road class and the intensity of the rainfall. The flood-prone transportation hub has a greater impact on the flood resilience of the road network and the smaller the rainfall return period is, the greater the impact is. With the implementation of the upgrading strategy, the robustness and rapidity of the road network can be improved significantly, thus enhancing the resilience of the road network against flooding. References | Related Articles | Metrics

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Nonlinear Relationship Model of Factors Influencing Carbon Emissions in Mountainous Urban Road Transportation CHEN Jian, CHEN Jiaguo, CHEN Qi, LIU Keliang, DAI Xueyang 2024, 24(5): 237-245.  DOI: 10.16097/j.cnki.1009-6744.2024.05.022 Abstract ( )   PDF (2576KB) ( )   To quantitatively measure the carbon emissions of road transportation in mountainous cities and its influencing factors, this paper uses portable emission measurement system to collect the carbon emissions of cars in different time and space, and selects eight variables in three dimensions, including vehicle engine characteristics, driving characteristics and road traffic environment, which are explanatory variables in combination with the road traffic characteristics of mountain cities. Utilizing the random forest regression model, this paper developed an analytical model to understand the factors influencing carbon emissions from road transportation in mountainous cities. The partial dependence function was used to quantitatively analyze the non-linear and interactive effects of these factors on carbon emissions. Additionally, a comparative analysis was conducted across multiple models. The results indicate: (1) The random forest regression model has better performance and prediction accuracy than the multiple linear regression, support vector machine and Long Short-Term Memory Network (LSTM) models, with a goodness-of-fit of 74.3% . (2) From the perspective of impact contribution, speed (25.97% ) and exhaust cylinder temperature (23.73% ) are the most important factors affecting carbon emission factors. (3) The nonlinear effect of velocity and elevation change on carbon emission factor is significant, and the threshold of speed is 58 km⋅h-1 and the threshold of elevation change is 0.10 m. Complex traffic structures such as overpasses and steep slopes in mountainous cities should be optimized to reduce the range of vehicle speed fluctuations. (4) There is a positive interaction between low-speed driving and other factors. The research results can be combined with real-time calculation of road traffic carbon emissions based on road traffic volume and road condition information, providing decision-making basis for traffic management strategy design. References | Related Articles | Metrics

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Conflict Features and Impact Factors of Severity in Weaving Area of Cloverleaf Interchange HE Huiyu, DING Rui, YING Dan, ZHANG Yuhao, ZHANG Heshan, XU Jin 2024, 24(5): 246-258.  DOI: 10.16097/j.cnki.1009-6744.2024.05.023 Abstract ( )   PDF (3441KB) ( )   To examine the conflict features and the key impact factors of conflict severity in the weaving area of the cloverleaf interchange of the urban expressway, this paper uses the North Ring Interchange of Chongqing city as the research object, and collected vehicles natural driving videos by UAV (Unmanned Aerial Vehicle). A vehicle target detection and tracking framework based on the YOLOX and Deep-SORT was established, and a total of 10483 vehicle trajectory data were obtained from aerial video. The longitudinal and lateral conflicts were extracted based on the relative positions of the interacting vehicles. The conflict events were categorized as minor, general and severe based on the two- dimensional expansion of the collision time metrics. The assessment of indicators of the multivariate Logistic regression model, Random Forest model and CatBoost model were calculated in consideration of the factors of macro 17 explanatory variables including traffic flow parameters and vehicle micro- motion parameters. The models with better performance for longitudinal and lateral conflict identification were selected, and the key factors affecting the severity of conflicts within the weaving area were further analyzed. The results indicate that lateral conflict is the main type of conflict in the cloverleaf interchange weaving area, and the duration of conflict is longer and the risk of collision is higher than that of longitudinal conflict. The general conflict has the widest distribution in the intersection area, followed by serious conflict, and minor conflict has the most concentrated distribution, which is mainly distributed in the vicinity of merging triangles. There are some differences in the degree of influence of different explanatory variables on the severity of the conflict, longitudinal serious conflict is significantly related to six indicators such as relative speed difference and target vehicle length. For the lateral conflict, the top five variables in terms of importance are the relative speed difference, the average headway spacing, the target vehicle speed, and the target vehicle transverse longitudinal position. The risk of collision increases dramatically when the relative speed is more than 20 km·h-1 , the speed of vehicle is more than 60 km·h-1 , and the average headway spacing is below 12 meters. References | Related Articles | Metrics

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Joint Optimization of Dynamic Pricing and Pre-sale Period Division for High-speed Trains XU Jing, DENG Lianbo, LIU Huaru, HU Xinlei 2024, 24(5): 259-267.  DOI: 10.16097/j.cnki.1009-6744.2024.05.024 Abstract ( )   PDF (1828KB) ( )   Based on the need to enhance high-speed rail revenue and implement a flexible market ticket pricing system, this paper focuses on the joint optimization of dynamic pricing and pre-sale period division considering the demand fluctuations and differences on each day during the booking horizon, as well as the impact of the pre-sale period division on railway revenue. Separate elastic demand functions are constructed for each day. A large-scale nonlinear model is developed to optimize the dynamic pricing and pre-sale period division for high-speed trains in consideration of the train capacity constraints, demand constraints, and price-related constraints. To solve the optimization problem, a bi-level genetic-simulated annealing algorithm is designed according to the model's properties. The optimization problem is divided into an outer-level pre-sale period division problem and an inner-level dynamic pricing and seat allocation problem, which are solved by genetic algorithm and simulated annealing algorithm, respectively. At last, a numerical instance is provided to evaluate the effectiveness of the optimization model and solution algorithm, and the results for different numbers of pre-sale period are discussed. The results indicate that as the number of period increases, the division of the booking horizon primarily concentrates on the latter half. For a case with five periods, the optimized revenue increased by approximately 1.21%. References | Related Articles | Metrics

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Location-inventory-routing Optimization of Maritime Logistics Network in Remote Islands Under Demand Uncertainty WU Di, HAN Xinli, SHI Shuaijie, JI Xuejun, ZHENG Jianfeng, LIU Baoli 2024, 24(5): 268-282.  DOI: 10.16097/j.cnki.1009-6744.2024.05.025 Abstract ( )   PDF (2962KB) ( )   To reduce the effects of uncertain material demands on the stability of maritime logistics network in remote islands, this paper investigates the design problem of a three-level hub-and-spoke material distribution network consisting of a mainland supply port, central islands, and satellite islands. The problem is formulated as a locationinventory-routing model that includes decisions on the number of central island locations, aiming to minimize system costs. The model takes into account some practical factors such as heterogeneous fleets, transportation mode diversity, and inventory capacity constraints. An Integrated Genetic-Annealing Optimization Algorithm Embedded with Monte Carlo Simulation-Based Neighborhood Traversal Operators (GAAEMCNT) is developed to decompose the original problem into several sub-problems, including location and assignment, route grouping, and optimization of route and inventory. The integrated optimization of the problem is realized through the interaction and iteration of inner and outer layer of the GAAEMCNT algorithm. Experiments on islands in the South China Sea are conducted to analyze the effects of changes in the number of islands, density distributions and demand on the maritime network system. The results show that: (i) when the distribution of material demand on islands is unchanged and the number of islands is the same, the unit cost of logistics network in the aggregation distribution is lower than that in the discrete distribution; (ii) when the distribution of island material demand is unchanged and the distribution of island is the same, the change of island number has minimum influence on the unit cost of logistics network; (iii) the change of the mean value of the material demands in the islands has a significant impact on the cost of each part of the system, and the total cost is positively correlated with the mean value; (iv) the fluctuation of the demand has a more obvious impact on the cost of the storage system, but a smaller impact on the cost of the transportation system. These findings validate the applicability of the algorithm proposed in this study across various island scenarios, providing decision-making support for the construction and optimization of maritime logistics network in remote islands under demand uncertainty. References | Related Articles | Metrics

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Joint Optimization of Block Allocation and Transfer for Shared Yards at Sea-rail Intermodal Container Terminals WANG Xiaohan, JIN Zhihong 2024, 24(5): 283-294.  DOI: 10.16097/j.cnki.1009-6744.2024.05.026 Abstract ( )   PDF (2682KB) ( )   Sea-rail intermodal container terminals facilitate shared yards, allowing sea-rail intermodal containers to be stored in blocks at port and railway container terminals, and containers to be transferred between different yard blocks. There are significant differences in the operation of equipment and block utilization between different storage systems. To effectively utilize space resources at sea-rail intermodal container terminals, the joint optimization of block allocation and transfer is determined by considering import and export container flows based on shared yards. An integer nonlinear programming model is developed to minimize the total transshipment costs. Based on the characteristics of the problem, a co-evolutionary genetic algorithm is designed to adaptively balance population diversity and global convergence by incorporating Q-learning principles to obtain solutions. Various numerical experiments are conducted to verify the effectiveness of the model and the algorithm. The results show that the proposed algorithm has better convergence accuracy. Compared with the other three traditional storage forms, the proposed "shared and transferred" form can effectively balance the transfer cost and handling efficiency, resulting in an average reduction of 15.17%, 11.96%, and 15.09% in the total handling cost. Finally, a sensitivity analysis to multiple unit costs is performed to verify the universality of the algorithm. References | Related Articles | Metrics

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Path Optimization for Vertical Take-off and Landing Aircraft in Dynamic Urban Airspaces for Urban Air Mobility ZHOU Hang, ZHAO Fengyang, HU Xiaobing 2024, 24(5): 295-308.  DOI: 10.16097/j.cnki.1009-6744.2024.05.027 Abstract ( )   PDF (4788KB) ( )   To address the current challenges of achieving optimality and computational efficiency in dynamic airspace route optimization for urban air mobility, as well as the inadequacy in addressing mixed urban and suburban operational scenarios, an innovative approach to constructing a combined urban-suburban network is initially proposed to support both urban and suburban operations seamlessly. Based on the flight dynamics model of electric vertical takeoff and landing (eVTOL) aircraft, an accurate eVTOL power consumption model is developed to optimize flight paths. A Dynamically Weighted Routing Network (RSA-DWRN) algorithm for dynamic airspace is introduced by leveraging the Ripple Spreading Algorithm. With a combined urban-suburban network framework that incorporates time-varying airflow patterns and obstacle zones, the optimization performance of the RSA-DWRN's is compared against the traditional DPO-A* algorithm across five scenarios through 600 experiments, considering path power consumption, flight time, computation time, and matching degree as key metrics. Simulation results show that RSA-DWRN algorithm performs best under the four indexes, especially as the complexity of dynamic airspace environmental factors increases. In scenarios with moving obstacles, the DPO- A* algorithm fails to predict their trajectories and requires frequent updates to the network state, significantly increasing the computational cost of path planning. In contrast, the RSA-DWRN algorithm co-evolves with changes in the dynamic environment, finally obtaining optimal solutions that simultaneously ensure optimization results and computational efficiency. References | Related Articles | Metrics

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Modeling and Analysis of Vehicle-passenger Behavior in Airport Drop-off Areas Based on Extended Decision Fields Theory TANG Tieqiao, ZHONG Jingran, YUAN Xiaoting, QIN Mengxin 2024, 24(5): 309-317.  DOI: 10.16097/j.cnki.1009-6744.2024.05.028 Abstract ( )   PDF (2255KB) ( )   In recent years, the rapid growth of air passenger transportation has progressively constrained landside traffic resources at airports. As a crucial interface between internal and external airport traffic, the passenger drop-off areas experiences significant conflicts between pedestrians and vehicles, leading to inefficiencies in traffic operations. This study focuses on a domestic airport, utilizing field investigation video data to develop a vehicle yield model based on Logistic regression. In addition, a survey was conducted to gather passengers' preferences in the drop-off area, focusing on three attributes: efficiency, safety, and comfort. These data were then used to develop a simulation model of pedestrian-vehicle conflicts in the drop-off environment. The simulation model was employed to investigate various conflict scenarios within the drop-off area. The simulation results reveal that in drop-off areas with multiple road structures, the likelihood of jaywalking is influenced by traffic density and the frequency of required crossings. Higher traffic density and more frequent crossings result in a lower rate of jaywalking. The highest rate of jaywalking recorded for passengers in the middle lane was 6.8%, compared to a rate of 2.1% for passengers in the outer lane. When the distance between the sidewalk and entrance is doubled, the average jaywalking rate of passengers dropped off in the outer lane increases by 1.6%, while the average jaywalking rate of passengers dropped off in the middle lane increases by 0.9%. Furthermore, restricting passenger jaywalking effectively reduces the number of vehicle concessions during off-peak periods, thereby improving traffic efficiency in the drop-off areas. References | Related Articles | Metrics

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Fine Modeling of Aviation Emissions and Green Trajectory Optimization of Complex En-route Networks CHEN Dan, TANG Cheng, ZHANG Hao, MA Yuanyuan, XU Feng 2024, 24(5): 318-326.  DOI: 10.16097/j.cnki.1009-6744.2024.05.029 Abstract ( )   PDF (2330KB) ( )   In order to accurately measure aviation emissions and their environmental impacts, and thus mitigate the air pollution problem of the aviation networks, this paper proposes a method for fine modeling of aviation emissions and green trajectory optimization of large-scale aircraft fleets based on four-dimensional flight trajectories and threedimensional spatial meteorological data analysis. For different flight states of aircrafts, a CO2 emission model and a contrail generation model are established, and the atmospheric environmental impact of aviation emissions is evaluated by the global absolute temperature potential (AGTP). Further, with the objective of minimizing the AGTP, a green trajectory optimization model for the large-scale aircraft fleet (GTO-LSA) is proposed, and an environmental impact threshold based green trajectory optimization (GTO-EIT) method is proposed by mining the environmental impact distribution characteristics of a large amount of historical operation data. Case study shows that the proposed method can effectively alleviate the atmospheric environmental impact of aviation emissions. And GTO-LSA can reduce the environmental impacts by 11.51% , 4.82% , and 4.20% , while GTO- EIT can reduce the environmental impacts by 10.87%, 4.05%, and 3.67% at the time levels of 25, 50, and 100 years. The operation efficiency is increased by 27.58% on average, and the aircraft regulation sorties and regulation frequency decreased by 50.22% and 48.37% on average. References | Related Articles | Metrics