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Calculation for Carbon Emission Reduction Effect of Urban Rail Transit Based on Carbon Recovery Period Theory YANG Yang, WANG Xue-chun, YUAN Zhen-zhou, CHEN Jin-jie, NA Yan-ling 2023, 23(5): 1-11.  DOI: 10.16097/j.cnki.1009-6744.2023.05.001 Abstract ( )   PDF (2003KB) ( )   PDF(English version) (1142KB) ( 33 )   Reasonable quantification of the carbon emission reduction effect of urban rail transit has theoretical and practical significance to calculate the external cost of urban rail transit, enrich the theoretical system of carbon trading in the field of transportation, and even formulate subsidy policies for urban rail transit. This paper considered the passengers' travel behavior difference after the construction of urban rail transit, and established the carbon emission model of urban rail transit datum line and project activity from the perspective of life cycle. Furthermore, a theoretical model of carbon recovery period was established as a quantitative indicator of carbon emission reduction effect of urban rail transit. Carbon recovery period refers to the duration of carbon emission recovery toward the construction period through carbon emission reduction during the operation period, which is the time when the cumulative carbon footprint changes from positive to negative for the first time. Then, the urban rail transit data collection was completed in the datum line, project construction and activity period, and the model is calibrated. The Shijiazhuang Subway Line 3 was taken as a case study, the carbon emissions of its datum line, the project construction period and the project activity period were analyzed, and the carbon recovery period was calculated under the two models of future development. The results show that the carbon recovery period is respectively 27 years, 22 years and 29 years under normal, rapid, and slow growth scenarios. Under the scenario of normal development of energy structure and energy efficiency level, rapid development and slow development, the carbon recovery period is respectively 25 years, 24 years and 29 years. The conclusions indicate that large-scale passenger volume and efficient passenger transportation intensity are important elements for the positive impact of carbon emission reduction in urban rail transit. The systematic changes brought about by the adjustment of energy structure and the energy efficiency improvement can have a great positive impact on the carbon emission reduction of urban rail transit. References | Related Articles | Metrics

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Dynamic Optimization of Time-dependent Differential Fare for Urban Public Transport Network Considering Emission Reduction LI Xue-yan, LI Hai-yang, ZHANG Han-kun 2023, 23(5): 12-23.  DOI: 10.16097/j.cnki.1009-6744.2023.05.002 Abstract ( )   PDF (2158KB) ( )   To achieve low-carbon travel and reduce the social cost of public transport network systems, this paper considers the dynamic changes in travel demand between weekdays and weekends, differences in departure times, and travelers' bounded rational behavior. We construct a dynamic multi-objective optimization model with the objective of reducing carbon emissions and social costs. This model takes the time-dependent differential fare as the decision variable. To ensure that fare implementation can adapt to changes in demand, we use a population distribution prediction operator based on the BP neural network, an OD matrix equilibrium algorithm, and a static multi-objective particle swarm optimization algorithm with excellent genetic operators. By combining these approaches, we design a new cluster intelligent dynamic multi-objective optimization algorithm to solve the time-dependent differentiated fare. To evaluate the effectiveness of our approach, we provide calculation examples and conduct case studies. The results reveal the following findings: (1) When the overall travel demand level of the bus network is low, reducing bus ticket prices is more effective in reducing carbon emissions. (2) Compared to a fixed ticket system, the time-dependent differentiated fare can reduce social costs and carbon emissions while also making the passenger flow across different lines more balanced. References | Related Articles | Metrics

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Driver Fatigue Detection Based on Facial Inverted Pendulum Model and Information Entropy LI Tai-guo, ZHANG Tian-ce, LI Chao, ZHOU Xing-hong 2023, 23(5): 24-32.  DOI: 10.16097/j.cnki.1009-6744.2023.05.003 Abstract ( )   PDF (2035KB) ( )   Driver fatigue detection is helpful for reducing the traffic accidents related to driver fatigue. This paper proposes a fatigue detection method based on the facial inverted pendulum model and information entropy. First, the Practical Facial Landmark Detector (PFLD) model is used to detect the coordinates of key points on the driver's face and estimate the Pitch, Yaw and Roll angles used to represent the head posture. Then, a facial inverted pendulum model is developed with key point coordinates as input. The kinetic energy and potential energy of the linkage system in the model are calculated during the driver's driving process. The kinetic energy, potential energy and head attitude data of the inverted pendulum model are used as the indicators of driver fatigue state changes, and the information entropy of each fatigue feature is calculated based on the sliding window. The information entropy values are concatenated on the temporal axis by a Convolutional Neural Networks (CNN) to extract the effect of the fatigue state on the information entropy over time. Then, the output of the CNN at each time point is used as the input feature of the Long hort-term memory (LSTM), and the fatigue feature information entropy is used to classify and predict the CNN-LSTM model. The experimental results show that the predicted result of this method reaches 95.04% , which indicates the effectiveness of the proposed method. References | Related Articles | Metrics

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Intra-driver Heterogeneity Prediction and Modeling Based on Naturalistic Driving Experiment ZHANG Duo, RAO Hong-yu, LIU Jia-qi, WANG Jun-hua, SUN Jian 2023, 23(5): 33-44.  DOI: 10.16097/j.cnki.1009-6744.2023.05.004 Abstract ( )   PDF (2856KB) ( )   To provide reliable support for car-following behavior in traffic flow modeling research and advanced driving assistance systems, this study proposes a method for predicting and modeling the intrinsic heterogeneity of driver following behavior based on the Transformer deep learning model and considers individual driver's behavior changes in the car-following process. This study is based on a large-scale naturalistic driving experiment, which involved over 200000 kilometers of naturalistic driving records. First, the baseline models are developed using longterm behavioral observations of 41 drivers, and 3194 intra-driver heterogeneity events are identified and extracted according to the baseline model. Further, a deep learning predictor based on the Transformer multi-head self-attention mechanism is designed to accurately predict intra-driver heterogeneity events of drivers. The results show that the predictor performs better than the long short-term memory network in predicting the three-class time points of carfollowing intra-driver heterogeneity, with an F1 score of 87.13%. Based on prediction results, dynamic car-following parameter switching can reduce the driving behavior modeling error by 21.08%. The research results help to understand the behavioral response mechanism of drivers, and further improve the accuracy of traffic flow simulation and the design of personalized control strategies. References | Related Articles | Metrics

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Inferring Individual Trip Chains from Smartphone-based GPS Data ZHOU Yang, YANG Chao, GUO Tang-yi 2023, 23(5): 45-54.  DOI: 10.16097/j.cnki.1009-6744.2023.05.005 Abstract ( )   PDF (2569KB) ( )   To address the limitations of current travel data, such as high-labor cost, inaccuracy in time and location, and missing trips, we develop a smartphone-based travel survey system and propose a method for inferring individual trip chains from smartphone GPS data with all-period, multimodal, and complete trips. Firstly, anchors are extracted from personal trajectories with a proposed spatiotemporal density-based clustering algorithm. The anchors are then classified into public transport transfer nodes and trip ends using a random forest model. Secondly, a spatial proximity matching method is used to identify residential and commuting activities. An XGBoost model is built with household and personal attributes, activity chain, and spatiotemporal characteristics. The model is employed to classify the types of non-home-non-work activities. Thirdly, we cut trip trajectory into trip slices and extract features referred to motion, trip, and GIS data, and infer travel mode by the XGBoost model. The proposed trip chain inference method is validated with an experimental dataset collected by travel survey system. Results show that the precision and recall of anchors extraction are 96.7% and 96.4%, respectively; the precision of identifying trip ends and public transport transfer nodes are 97.6% and 91.8%, respectively. For trip purpose inference, the model achieves accuracies of 100% for home, 89.8% for work/education, and 87.6% for non-home-non-work activities. For travel mode inference, each mode gets an accuracy of over 90%, and the comprehensive accuracy reaches 95.0%. This paper provides a method of mining trip chains from GPS trajectory to support the application of smartphone-based household travel surveys in the real world. References | Related Articles | Metrics

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Dynamic Configuration Method for Approach Lanes at Intersections in Connected Traffic Environment JIANG Xian-cai, CHENG Guo-zhu 2023, 23(5): 55-66.  DOI: 10.16097/j.cnki.1009-6744.2023.05.006 Abstract ( )   PDF (3358KB) ( )   The existing optimization control methods in the connected transportation environment have failed to achieve dynamic composite utilization of connected and automated vehicle (CAV) dedicated lanes. In view of this, considering the real-time changes in traffic demand and CAV penetration rate, this paper constructs a dynamic configuration method for CAV-shared lanes based on conversion index. This method can not only achieve equilibrium in the flow rate ratios of different functional lanes within the same lane group, but also minimize the sum of the flow rate ratios of all lane groups at the intersection and compress the signal cycle. On this basis, a control method for utilizing CAV-shared lanes between left-turn CAVs and through CAVs is proposed, and the optimized solution process is designed. The program is developed using COM module in VISSIM software to update vehicle trajectory and exchange information between CAV and connected human-driven vehicle (CHV), and the signal controllers. The simulation results show that the proposed configuration strategy enhances the dynamic adjustment ability of intersections to adapt to real-time traffic demand, which can improve average vehicle delay to a certain degree. Further analysis shows that the CAV penetration rate is closely related to whether there are CAV-shared lanes and how many CAV-shared lanes on the approach. When there are four approach lanes at an intersection and the CAV penetration rate is less than 0.35, the intersection shows best operation without a CAV-shared lane. When the CAV penetration rate is between 0.35 and 0.70, one CAV-shared lane provides operational benefit. When the CAV permeability exceeds 0.70, two or more CAV- shared lanes would provide operational benefit. The proposed optimization method is suitable for multi-lane intersections with medium to high CAV penetration rate. References | Related Articles | Metrics

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Intersection Traffic State Recognition and Correlation Degree Study Based on Temporal Graph Attention LI Peng-cheng, DONG Bao-tian, LI Si-xian 2023, 23(5): 67-74.  DOI: 10.16097/j.cnki.1009-6744.2023.05.007 Abstract ( )   PDF (2192KB) ( )   To analyze the intersection real-time traffic status and its correlation with adjacent intersections, this paper established the VISSIM simulation environment for the study based on actual traffic survey data. An intersection traffic characteristics matrix is proposed to describe traffic flow and physical characteristics. An intersection interaction time matrix is also derived by the traffic volume, traffic flow ratio, and effective green time of the traffic signals. This paper then proposes a temporal graph attention (TGAT) network based intersection traffic state recognition model. The matrix and initial labels of traffic data are inputted into the model, and the classification accuracy of the study intersection is obtained under "smooth", "stable", "congested", and "blocked" statuses. The weight matrix of neighbors can be calculated and used to represent the degree of association between intersections by computing the distances between neighbors. Additionally, the neighbor weight matrix can be obtained by computing the similarity between neighbors to describe the association between intersections. The TGAT, multilayer perceptron, long and short-term memory network, and support vector machine were selected for intersection traffic status recognition, and the accuracy rates were respectively 93.38%, 90.00%, 92.03%, and 82.84%. The precision, recall, and F1-score of TGAT were higher than the compared methods. At last, a weight judging factor based on correlation and traffic volume is proposed to quantitatively describe the reliability of the correlation, and data from 11 intersections on main roads are selected for validation. The results showed that for non-isolated intersections with relatively uniform traffic distribution, the correlation obtained in this paper was positively correlated with the corresponding traffic and was valid and interpretive. References | Related Articles | Metrics

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Short-term Traffic Flow Prediction Based on ASO-ELM Hybrid Optimization Model CAI Hao, LI Lin-feng, LI Han, LI Xin, ZHOU Teng 2023, 23(5): 75-82.  DOI: 10.16097/j.cnki.1009-6744.2023.05.008 Abstract ( )   PDF (2010KB) ( )   Due to the dynamic, uncertain and nonlinear characteristics of the short-term traffic flow, it is difficult to predict traffic flow accurately. In this paper, we build an ASO-ELM short-term traffic flow prediction hybrid optimization model based on Extreme Learning Machine (ELM) by embedding Atom Search Optimization (ASO). The hybrid optimization model is used to explore the prediction performance of the hybrid optimization model in the field of short-term traffic flow prediction by comparing the existing short-term traffic flow prediction models. The A10 ring road in Amsterdam, the Netherlands, is selected as the prototype of the road network, and the ASO-ELM hybrid model is used to compare with common traffic flow forecasting models for simulation forecasting experiments. The experimental results show that the mean absolute percentage error (MAPE) of the ASO-ELM hybrid model decreases by 4.3%, 3.5%, 6.9% and 5.4%, respectively, and the root mean squared error (RMSE) decreases by 4.8%、4.0%、2.0% and 5.2%, respectively. Secondly, MAPE decreased by 9.6%, 8.6%, 9.8% and 5.0%, respectively, and RMSE decreased by 4.5%, 5.9%, 2.6% and 1.7%, respectively, compared to the Artificial Neural Network (ANN). The research results reveal the potential of hybrid optimization models in the field of short-term traffic flow forecasting and provide an important basis for model exploration in the field of short-term traffic flow forecasting. References | Related Articles | Metrics

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Collaborative Study of Speed Guidance and Path Planning for Responsive Feeder Transit with Heterogeneous Demands CHANG Yu-lin, CAI Yu-hang, SUN Chao, WANG Jian 2023, 23(5): 83-95.  DOI: 10.16097/j.cnki.1009-6744.2023.05.009 Abstract ( )   PDF (2211KB) ( )   To enhance the integration of "rail and bus" network and further improve passenger satisfaction and operational efficiency of bus services, this paper proposes a collaborative optimization model with responsive feeder bus speed guidance and path planning. First, the heterogeneous request information of passenger's transfer demand was incorporated into the model through a standardized processing. The interval speed was used to represent the road travel time in the actual situation, and the bus operation route was planned cooperatively through the speed guidance strategy to expand the high-quality solution space of the model. Then, according to the large-scale and high dynamic characteristics of the responsive feeder bus, the service time was divided into time domains by cycle driving. The static programming model is called at the beginning of each time domain, and the cultural gene algorithm is designed to solve the model. The dynamic requests are classified by event driven, and the immediate requests are inserted into the planned path using the nearest neighbor algorithm. At last, the effectiveness of the model and algorithm is verified using the Sioux Falls network example, which is applied to the case of Xuchang East Railway Station in China. The results show that the collaborative optimization model can optimize the responsive feeder bus route using the speed guidance strategy according to the heterogeneity of feeder travel demand. After the collaborative optimization, the load factor was increased by 10.8% , while operating costs and per capita time costs decreased by 12.85% and 10.79% ,respectively. References | Related Articles | Metrics

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Bus Network Planning Method Based on Improved Adaptive Large Neighborhood Search Algorithm LI Guang-chun, NIE Lei 2023, 23(5): 96-103.  DOI: 10.16097/j.cnki.1009-6744.2023.05.010 Abstract ( )   PDF (2671KB) ( )   This paper proposes a bus network planning method based on an improved adaptive large neighborhood search algorithm for the large scale network. The method is able to reduce the scale of problems, merge the OD(OriginDestination) of passengers, and select bus stops. The study develops the main line and branch line models considering the optimization objectives of passenger demand and bus station coverage, and the constraints of network length, nonlinear coefficient and transfer. Based on the results of the node merging algorithm, the improved adaptive large neighborhood algorithm is used to solve the problem, including eight improved operators and adaptive rules. The case analysis indicated that the two- stage algorithm performs well, and the improved adaptive large neighborhood algorithm performs better than existing meta-heuristic algorithms. The case analysis in Xingtai has shown good performance and can effectively solve large-scale network planning problems. References | Related Articles | Metrics

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Evaluation of Bus Station Layouts in Transportation Hub Based on Entropy Weight Method XU Shi-wei, SU Ye-hui, LI Hui-wen, WANG Shuai, JIANG Xin-guo, MA Jian 2023, 23(5): 104-112.  DOI: 10.16097/j.cnki.1009-6744.2023.05.011 Abstract ( )   PDF (2303KB) ( )   Bus stations in railway transportation hubs often operate under great pressure due to handling large passenger demand in short time. The layout of bus stations affects the operating efficiency and service quality of the entire transportation hub. In the early stage of planning, constructing layout evaluation system can help to identify effective bus station layout and improve the operation efficiency. This paper proposes a bus stop layout evaluation system which includes four performance measures for operation evaluation: transfer completion time, transfer volume per unit time, average transfer time of passengers, and average storage time of vehicles. The parameters of different bus station layout were obtained through the simulation method to calculate the value of the evaluation measures. To overcome the difference in operation efficiency caused by the different density of service facilities, the entropy weight method is used and a correction coefficient is introduced. The index data obtained through the simulation is weighted to get the comprehensive evaluation value of the layout. Finally, three typical bus station layouts were analyzed, including encirclement, corridor and passageway. The layout with the highest operating efficiency under different passenger demands and bus departure frequency were obtained. The wraparound layout is found to be the most efficient when the passenger demand and the bus departure frequency are low. When passenger demand and the bus departure frequency are high, the passageway layout is the most efficient. In other cases, the corridor layout is the most efficient. The evaluation system of bus stop and yard layout provides an evaluation method for the selection of bus stop and yard layout in railway transportation hubs. It provides reference for planners in organizing the transportation hubs with bus stations. References | Related Articles | Metrics

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A Segmentation of Bus Passenger Combining Repeatability and Periodicity of Individual Travel Pattern YAO Zhi-gang, LU Zhi-yuan 2023, 23(5): 113-119.  DOI: 10.16097/j.cnki.1009-6744.2023.05.012 Abstract ( )   PDF (2395KB) ( )   Existing literature on the regularity of bus passengers' travel behavior has primarily focused on measuring repeatability and periodicity separately. However, it is recognized that repeatability and periodicity coexist in the travel patterns of individuals. To address this, this paper proposes a method that combines repeatability and periodicity to measure the regularity of bus passengers. Daily activity sequential chains of bus passengers are utilized to identify individual travel patterns. And the information entropy model is improved to measure repeatability and the periodic detection function is improved to measure periodicity. By combining these measures, a K-means++ clustering method is developed for measuring the regularity of bus passengers. To evaluate the method, we obtain data from October to December 2019 in Haining City, Zhejiang Province, China. Four information entropy indices are presented to measure regularity, including temporal repeatability, spatial repeatability, temporal periodicity, and spatial periodicity. A total of 71080 bus passengers were classified into three groups featured with high repeatability and periodicity, low repeatability and high periodicity, low repeatability and periodicity. And we compare the frequency distribution of bus passengers with different departure times across the three groups. The results show that by combining repeatability and periodicity, an additional 21692 passengers, which account for 30.52% of all passengers, can be classified into an interpretable group. We also provide visualizations of travel patterns for the most regular passengers in each group, further illustrating the performance of the proposed measure. This paper demonstrates that combining repeatability and periodicity is a valuable approach for accurately identifying and understanding the individual travel demand of bus passengers. References | Related Articles | Metrics

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Critical Stations Identification and Robustness Analysis of Weighted Metro-bus Composite Network ZHENG Yue, GAO Liang-peng, CHEN Xue-wu, SONG Bo, DING Lei 2023, 23(5): 120-129.  DOI: 10.16097/j.cnki.1009-6744.2023.05.013 Abstract ( )   PDF (3532KB) ( )   To identify the critical stations of metro-bus composite network and to improve network robustness, this paper considers the operational and transfer characteristics of metro and bus systems, and constructs a weighted metrobus composite network using travel time as edge weight. On this basis, three types of station importance evaluation indicators, namely accessibility importance, centrality importance, and path importance, are proposed for weighted composite networks. A more practical robustness evaluation model is designed to test the network performance under different attack modes and attack strategies. Taking the metro-bus composite network in Nanjing as an example, the network characteristics, critical stations, and network robustness are analyzed. The research results show that: (1) The subway network plays a key role in the composite network, which can greatly improve operational efficiency and reduce the average time spent by 13%. When key subway stations are attacked, it will cause a rapid increase in the detour rate and a rapid decrease in network efficiency; (2) When the composite network is subjected to the first attack mode, the attack strategy based on path importance has the greatest impact on the detour ratio, while the attack strategy based on accessibility importance has the greatest impact on the maximum network connectivity; (3) Compared to the first attack mode, composite networks exhibit stronger robustness under the second attack mode, and the attack strategy based on accessibility importance has the greatest impact on network performance. References | Related Articles | Metrics

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Strategies of Capacity Utilization Promotion in Low Load Direction of Transport Lines and Economic Benefit Evaluation of Lines ZENG Wei, JIA Jin-zhong 2023, 23(5): 130-135.  DOI: 10.16097/j.cnki.1009-6744.2023.05.014 Abstract ( )   PDF (1991KB) ( )   The phenomenon of bi-directional load imbalance commonly exists in passenger and cargo transportation channels of railway, highway and shipping in China. To improve the capacity utilization rate of transport lines in the low-load direction and to reasonably evaluate the two-way transport benefits, a model was established to evaluate the capacity utilization rate and benefits of transport lines, and an example was set to study the influence of concessionary tariff strategy on the capacity utilization rate in the low-load direction and the transport benefits of enterprises. The study shows that in the scenario of the existence of road and railway in the channel, considering the medium distance (600~650 km), the capacity utilization rate of a line increases by 28.63% when the concessionary tariff increases from 0 to 60%, and the increase increases gradually. The rate of increase in the degree of fare concessions from 0 to 15% increases the two-way transport efficiency by 0.92%, followed by a slight decrease; the rate of decrease in the two-way transport efficiency increases gradually when the degree of fare concessions exceeds 30%. Transport companies can control the degree of fare preference within the range reasonably, which is expected to achieve the protection of transport efficiency while improving the utilization of line capacity in the low-load direction. As the transport distance increases to around 1500 km, the maximum degree of fare concession can be increased to 35%. References | Related Articles | Metrics

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Spatial-temporal Heterogeneity Effects of Built Environment and Taxi Demand on Ride-hailing Demand MA Jian-xiao, ZHAO Fei-yan, YIN Chao-ying, TANG Wen-yun 2023, 23(5): 136-145.  DOI: 10.16097/j.cnki.1009-6744.2023.05.015 Abstract ( )   PDF (1950KB) ( )   To study the interaction between the built environment and the travel demand of ride-hailing under the influence of taxi travel, this paper proposes urban built environment indicators around the four dimensions of density, design, diversity and distance to transit based on the data of ride- hailing and taxi orders in Nanjing city of China. A semi-parametric geographically weighted regression model (SGWR) considering local changes and global fixed terms is developed for the three periods of morning peak, evening peak and off peak to describe the spatial-temporal heterogeneity of the built environment on the travel demand of ride-hailing. The results show that: compared with ordinary least squares regression (OLS) and traditional geographically weighted regression (GWR), the AICc values of SGWR model decreased by 2.44% and 0.15% during morning peak, decreased by 4.01% and 0.30% during evening peak, and decreased by 1.89% and 0.27% in the off peak. Adjustment R2 increased by 6.52% and 0.11% during the morning peak, 8.02% and 0.55% in the evening peak, and 2.75% and 0.11% in the off peak, indicating that the SGWR model has better explanatory power and goodness of fit. The regression results of local variables show that different built environment variables have different effects on the travel demand of ride-hailing, with spatial-temporal heterogeneity. The regression results of the global variables show that the land use mix has a significant negative impact on the demand for ride-hailing in the morning and evening peak hours. There is a cooperative relationship between taxis and ride-hailing, and the number of high-density companies and bus stops will promote the demand for ride-hailing. This study can provide a theoretical basis for the rational allocation of ride-hailing resources. References | Related Articles | Metrics

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A Doubly Dynamical Model of Multimodal Transportation Systems Considering Regional Transfer and Car-sharing Mode ZHOU Cheng-dong, SONG Fei, ZHAO Xiao-mei 2023, 23(5): 146-154.  DOI: 10.16097/j.cnki.1009-6744.2023.05.016 Abstract ( )   PDF (1741KB) ( )   In this paper, a doubly dynamical model is constructed in a multi-modal transportation system, including the day-to-day traffic dynamics and the within-day traffic dynamics. The day-to-day traffic dynamics update travelers' perceived travel costs of different modes over the days. According to travelers' perceived travel costs of different modes, the within-day traffic dynamics use the Logit model to split travel demand. Further, the macroscopic fundamental diagram (MFD) is adopted to calculate regional average travel speed and travel time. The effects of car-sharing and regional transfer on the time-dependent choices of travelers and the evolution of traffic conditions are investigated extensively. The results show that travelers are more likely to use car-sharing for short trips than for long trips. In the stabilization phase, car-sharing can replace 28.09% of private car trips and 8.52% of public bus trips and increase the total social cost (1.07%) and total travel time (16.53%) of the transportation system. Regional transfer is an important travel mode that reduces the total social cost, total travel time, and private car ownership in the transportation system. References | Related Articles | Metrics

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Disturbance Management of VRPSDP Based on Waiting Strategy XIN Yu-chen, LI Run-chao, YANG Hua-long 2023, 23(5): 155-161.  DOI: 10.16097/j.cnki.1009-6744.2023.05.017 Abstract ( )   PDF (1608KB) ( )   This study focused on the vehicle routing problem with simultaneous delivery and pickup requests (VRPSDP) in the context of frequent changes in customer requests. the impact of changes in customer delivery/pickup volume or time windows on the original vehicle delivery plan was analyzed, and interference events caused by these changes were identified. The study divided the decision period into equal time periods and determined the decision time for interference management by setting the cumulative threshold of the number of interference events at the end of each time period or the limitation parameter of the number of customer waiting periods. A VRPSDP interference management model based on a waiting strategy was established. And a two-stage heuristic algorithm combining an improved genetic algorithm and a Tabu search algorithm was designed. Multiple sets of data from the Solomon standard test case were taken for the case analysis. The example results show that the interference management method based on the waiting strategy saves more than 14% of the generalized total cost and more than 7.8% of the time deviate cost compared to real-time and time-phased interference management methods. The sensitivity analysis indicates that there is an optimal value for the cumulative threshold of the number of interference events and the limitation parameter of the number of customer waiting periods. References | Related Articles | Metrics

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Explainable Prediction of Inbound Flight Delays Based on Meteorological Factors and Feature Selection WANG Wei-li, WANG Yi-wen 2023, 23(5): 162-171.  DOI: 10.16097/j.cnki.1009-6744.2023.05.018 Abstract ( )   PDF (2209KB) ( )   Flight delay prediction is crucial for improving passenger satisfaction and optimizing resource allocation. However, the lack of visibility in predictive models hinders their further development. This paper aims to enhance the accuracy and interpretability of flight delay prediction. We focus on inbound flights of a specific airport route and develop a prediction model. We employ the max-Relevance and min-Redundancy (mRMR) algorithm to eliminate redundant features based on different meteorological factors at the airport. The optimal feature subset is then selected as input for the prediction model. The Catboost algorithm is chosen by comparing various machine learning algorithms. The Shapley Additive Explanation (SHAP) method is utilized for attribution analysis, which helps uncover the different influences of various factors on flight delay time through local and global explanations. By conducting a partial dependence analysis, the optimal threshold of key factors is extracted. The results demonstrate that the Catboost prediction model, after feature selection, performs better in capturing nonlinear features. Compared to a model without feature selection, the Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE) are reduced by 3.84%, 3.35%, and 4.22%, respectively. Statistical tests, such as the DM test, confirm the significance of the model's improvement. Moreover, the study reveals that flight delay time is influenced by various meteorological characteristics and previous delays. Specifically, airport wind speed and precipitation have a significant positive effect on delay time, while airport effective wind speed and visibility have a significant negative effect. References | Related Articles | Metrics

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Impact of Mountain Urban Roads on Vehicle Carbon Emissions Driven by Big Data ZHOU Tao, LI Yi-jun, SUN Qin-mei, REN Han-kun, LIU Yi, ZHANG Zhen-hao 2023, 23(5): 172-183.  DOI: 10.16097/j.cnki.1009-6744.2023.05.019 Abstract ( )   PDF (3529KB) ( )   PDF(English version) (1653KB) ( 28 )   Understanding the relationship between urban roads and vehicle carbon emissions is of great significance for the calculation of urban traffic carbon emissions, urban traffic construction, and urban traffic planning, design. Based on the OBD big data, this paper takes Chongqing as an example to analyze the influence of road type and road slope on vehicle carbon emissions, and explore the localized road carbon emission factors in Chongqing. First, introduce the OBD data and processing method, combine the gasoline fuel carbon emission factor to convert vehicle fuel consumption into vehicle carbon emissions. The characteristics divide the vehicles into three categories: non-operating, operating, and freight. Finally, the LM method is used to iteratively fit the relationship between the average vehicle speed and the carbon emission factor. The research shows that increasing the average speed of urban roads to over 25 km· h-1 has a significant effect on reducing vehicle carbon emissions; the order of road carbon emission factors is that secondary roads are greater than arterial roads than expressways, which is related to serious interchanges and openings. The vehicle carbon emission factor is most sensitive to steep slope roads, and the order of influence is that the road slope is greater than the vehicle type than the road type. References | Related Articles | Metrics

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Resilience Assessment of Yangtze River Delta and Guangdong-Hong Kong-Macao Shipping Networks Based on Complex Network WANG Zhi-huan, HU Wei-qin, WANG Yi-wen 2023, 23(5): 184-193.  DOI: 10.16097/j.cnki.1009-6744.2023.05.020 Abstract ( )   PDF (3370KB) ( )   Major global emergencies such as the pandemic have highlighted the importance of the resilience of shipping networks. This paper constructs a quantitative method to evaluate the resilience of port group shipping networks based on complex network and resilience triangle theory. In this method, the shipping network resilience assessment process is divided into four stages: initial, destruction, recovery, and stability, and four different attack and recovery modes are adopted in order of degree, betweenness, strength, and random, and the three characteristics of network independent paths, network efficiency, and network connectivity are used as the key indicators to measure the resilience of the shipping network of port groups. In this paper, the port groups of the Yangtze River Delta and Guangdong-Hong Kong-Macao port group are set as examples, and the shipping networks of the two port groups are constructed by ship port call data from January to June 2023 and then compares and analyzes the dynamic of resilience indicators of the two internal shipping network of two port groups under four different attack modes and recovery modes respectively. The results show that the scale of the Yangtze River Delta internal shipping network is much higher than that of GuangdongHong Kong-Macao, but it is more balanced, while it is more obvious that the role of some ports in Guangdong-Hong Kong-Macao internal shipping network as hub ports. The network independent path resilience and network efficiency resilience of the Yangtze River Delta shipping network are better than those of the Guangdong- Hong Kong-Macao shipping network, the former is 8.4 and 11.8 and the latter is 7.9 and 10.2 respectively. While the network connectivity resilience is 8.3 which is inferior to the Guangdong-Hong Kong-Macao shipping network with 9.5, and the resilience of the two was the worst in the degree attack mode. The shipping network in the Yangtze River Delta should focus on strengthening the connectivity between ports and the transit capacity of high betweenness ports such as Rugao Port, and the port group of Guangdong-Hong Kong-Macao should strengthen the hub capacity and channel redundancy of high strength ports such as Victoria Port to improve the resilience of the shipping network of the port group. References | Related Articles | Metrics

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Cooperative Optimization Model for Emergency Vehicles Using Reverse Left-turn Lanes and Traffic Signals LONG Ke-jun, ZHANG Zhong-gen, LIU Yang, GAO Zhi-bo 2023, 23(5): 194-201.  DOI: 10.16097/j.cnki.1009-6744.2023.05.021 Abstract ( )   PDF (2413KB) ( )   This study presents a cooperative optimization model that enables emergency vehicles to use reverse left-turn lanes along with intersection signals because a single signal priority method cannot ensure the uninterrupted passage of emergency vehicles through junctions. First, a cooperative control method is used on road segments. This strategy involves timing pre-signals on reverse left-turn lanes and synchronizing emergency vehicle speed advice. As a result, emergency vehicles can more efficiently use the reverse left-turn lane for passage on road stretches where their travel speed is optimized. Secondly, an emergency phase is added to ensure the priority passage of emergency vehicles through the intersection. To minimize the total travel time for regular vehicles, the phase start time and green light duration are considered decision variables, taking into account constraints such as phase conflicts, pre-signal start, and duration. A mixed-integer linear programming model is built and solved by CPLEX with the A Mathematical Programming Language (AMPL) coding language. Finally, the proposed model is validated using an intersection example where a main road intersects with a secondary road. The results show that compared to the green light extension model, the coordinated optimization model can significantly reduce the travel time of emergency vehicles in the intersection area and decrease the total travel time for regular vehicles. Sensitivity analysis of parameters such as traffic volume and lane length reveal that the cooperative optimization model exhibits better optimization performance as traffic volume and lane length increase. References | Related Articles | Metrics

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Demand Responsive Feeder Transit Scheduling Considering Candidate Stops and Full-service Process REN Jing-xuan, CHANG Xiao-ting, WU Wei-tiao, JIN Wen-zhou 2023, 23(5): 202-214.  DOI: 10.16097/j.cnki.1009-6744.2023.05.022 Abstract ( )   PDF (1913KB) ( )   In the context of demand-responsive feeder transit, it is important to consider the passenger's requirements for the entire journey, including the start and end points, as well as the mobility of people and the characteristics of urban roads. Passengers have the earliest departure time and the latest arrival time to ensure that passengers are delivered to their destination before the service schedule of the following leg. Furthermore, when passengers specify their pickup stops, they often have multiple walkable stops to choose from. Considering these aspects of passenger requirements, this study focuses on designing a demand-responsive feeder transit service model that considers candidate stops and full-service time windows and optimizes the scheduling of the system. Firstly, this paper establishes a mapping between dummy stops and realistic demand and stop sets based on candidate stops for requests. An impedance matrix is constructed, which takes into account node relationships and spatial distances. With the goal of minimizing the start cost and route cost of vehicles, the problem is transformed into a joint optimization problem of fleet size configuration and vehicle routing with a full-service time window and capacity constraint. A mixed integer linear programming model that can be solved using a commercial solver is established. To improve the efficiency of the solution process, it introduces the vehicle number symmetry elimination constraint and the uniqueness constraint for demand sequences at the same stop. These constraints prevent the same solution from being obtained by only exchanging vehicle numbers or sequencing requirements differently at the same stop. To handle larger-scale instances, multiple neighborhood structures and an adaptive neighborhood simulation annealing algorithm based on problem characteristics are designed. Finally, taking the sub-network of transit in Guangzhou University Town as an example, numerical experiments under different influencing factors were designed. The experimental results show that compared with the requests specifying exactly one pick-up stop, the introduction of candidate stops reduces the fleet size and vehicle travel distance required for service, reducing fixed costs and variable costs by 25% and 22.19%, respectively, thereby reducing the total cost by 23.32%. In addition, the full-service time window constraints are stricter than single-point time window and tour time constraints. However, the introduction of candidate stops can compensate for the increased effect of the time windows' tight temporal requirements on route costs and passenger travel time. The results indicate that the full-service time window can ensure the quality of service of passengers, and the introduction of candidate stops can effectively reduce operating costs. References | Related Articles | Metrics

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Optimization of Urban Rail Train Operation Plan Based on Waiting Time and Benefits Loss ZHANG Zheng-kun, ZHU Chang-feng, JING Yun, XING Jin 2023, 23(5): 215-226.  DOI: 10.16097/j.cnki.1009-6744.2023.05.023 Abstract ( )   PDF (2995KB) ( )   In an urban rail transit line, the conflicting interests of passengers and enterprise are the major focus of the determination of a train operation plan. Given a line with two depots at the terminals, the concept of benefit loss is proposed by considering the operational effectiveness impacted by the passenger loading state of trains as well as its duration. Considering the individual difference of benefit loss caused by various passenger flow demand at each section, a control factor reflecting this difference is introduced in the optimization objective to optimize the waiting time and the supremum of benefit loss. The optimization model considers operational constraints, such as the number of train cars, the turning-back track's capacity, train connection, as well as the process of train entry and exit depots. To facilitate the model solution, the model is linearized, and a GA (Genetic Algorithm)-Gurobi hybrid solution strategy is designed based on the reconstruction principle. Finally, the effectiveness of the model and solution method is verified by an example analysis. The result analysis shows that: the obtained train operation plan can save passenger waiting time by 30.1% , decrease the benefit loss by 17.9% , and reduce train cars by 7.4% . The performance of the train operation plan can be improved by the model and solution method. References | Related Articles | Metrics

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Guidance Information Release Strategy During Operation Emergencies in Urban Rail Transit YU Ding-yuan, YAO En-jian, LIU Sha-sha, LI Si-hui, GUO Dong-bo, LIU Wei-yi 2023, 23(5): 227-237.  DOI: 10.16097/j.cnki.1009-6744.2023.05.024 Abstract ( )   PDF (2471KB) ( )   During operation emergencies in urban rail transit, providing targeted guidance information to passengers is essential for efficient evacuation and ensuring safety. This study considers the heterogeneity of passengers and investigates the strategy of issuing guidance information during operation emergencies. A route choice model is constructed using the Latent Class Model (LCM), taking into account both operational emergencies and guidance information. Passenger acceptance of guidance information and route preferences are analyzed. An information-induced optimization model based on route choice behavior is formulated. The optimization objective is to minimize total travel time and the Gini coefficient of passenger flow distribution. The Non-dominated Sorting Genetic Algorithm II (NSGA- II) is used to solve the mode for the optimized recommendations for effective routes between Origin and Destination (OD) pairs. A case study is conducted on the regional line network of Beijing's urban rail transit during weekday morning peak hours. The results reveal three groups of passengers with different levels of acceptance of guidance information: "induced obedience", "induced neutrality", and "induced ignorance". Providing guidance information reduces the total travel time by 3.906%, the Gini coefficient by 4.063%, and the number of high fullness intervals. Continuing the release of guidance information after the emergency reduces the full load rate of affected sections by 7.08% and prevents the re-gathering of passengers as sections resume normal operation. References | Related Articles | Metrics

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Passenger Flow Assignment Method for Multi-routing Operation of Urban Rail Transit Based on Train Plan XU De-jie, PAN Xing, GONG Liang, HU Chen-hao, WANG Xue-xin 2023, 23(5): 238-246.  DOI: 10.16097/j.cnki.1009-6744.2023.05.025 Abstract ( )   PDF (1763KB) ( )   The research on the passenger flow assignment based on the train plan with multi-routing operation is beneficial to analyze and evaluate the quality of train plan, assess the load equalization of train transportation resources. This paper analyzes the typical train plan with multi-routing common-line operation of urban rail transit and develops a common-line operation service network based on the typical train plans. The paper proposes a mathematic model of common-line problem considering the passengers' route choice behavior in the common-line sections. A frequency-based assignment model of hyperpath is developed by transforming the solution of effective paths loading the passenger flow into the shortest hyperpath problem of the service network. Furthermore, an equilibrium passenger flow assignment model for common-line operation mode with multi-routing is developed, along with a successive averages mixed algorithm to solve the model. The results of the numerical example show that the maximum relative difference of load factor is 4.54% , which indicates the passenger volume is well matched with train capacity. Under the same service frequencies, the maximum section load factor of the multi-formation mode is 15.64%, which is lower than that of the uniform formation, and the passenger flow adaptability of multi-formation train plan performs better than that of uniform formation. The proposed method can provide the basis for complex routing design and train plan optimization for the urban rail transit. References | Related Articles | Metrics

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Peak Hour Stopping Scheme Optimization of Suburban Line Considering Train Capacity Constraints ZHANG Hui-ru, DOU Fei, WEI Yun, LIU Jie, NING Yao 2023, 23(5): 247-257.  DOI: 10.16097/j.cnki.1009-6744.2023.05.026 Abstract ( )   PDF (2869KB) ( )   The passenger flow of suburban rail transit lines in metropolises often have long travel distance and uneven temporal and spatial distributions. Especially during peak hours, there are stranded passengers on the platform due to limited train capacities. Train operations with differentiated stopping scheme is an effective way to improve the peak hour operations. This paper proposes an optimization model that minimizes the total passenger travel cost including waiting time and travel time based on the calculation of boarding/alighting, transferring/arriving, and remaining onboard passenger numbers. With the uniform encoding of 0-1 variables that indicate whether trains stop at a station, an improved genetic algorithm with catastrophe theory is developed by setting the maximum number of catastrophes triggered. The mean deviation and deviation range of the departure intervals between subsequent trains at stations are used as the indicators to evaluate the equilibrium of the timetable, and the train stopping patterns are then optimized in consideration of the train capacities. The case study in Beijing Subway Line 15 shows that the improved genetic algorithm can effectively escape from local optima through catastrophe operations. The algorithm can obtain the optimal stopping scheme under three designed scenarios, particularly, the objective value of scenarios 2 and 3 was respectively improved by 12.20% and 4.28% compared to scenario 1. The skip-stop strategy can effectively reduce the train's running time, for example, the train running time in scenarios 2 and 3 was respectively saved by 17.49 s and 17.97 s in the downstream direction compared to scenario 1. References | Related Articles | Metrics

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Preference-oriented Task-type-mixed Crew Rostering Optimization Model for Urban Railway Transit PAN Han-chuan, QI Bo-yang, HU Hua, KANG Lei, SHA Yue, LIU Zhi-gang 2023, 23(5): 258-267.  DOI: 10.16097/j.cnki.1009-6744.2023.05.027 Abstract ( )   PDF (2174KB) ( )   This paper proposes a modeling method for the mixed crew rotation mode based on an acyclic rotation scheme. It incorporates the driver's demand for the crew plan as their preference and provides a quantitative calculation method for this preference. Additionally, the crew rotation plan is included as a solution goal, creating a mixed crew rotation optimization model for urban rail transit that considers the driver's preference. The paper also designs a simulated annealing algorithm based on large-scale domain search to solve the model. The case study is conducted using actual operation data from a line in the Shanghai subway. The results demonstrate that the calculated results are improved when compare to those obtained from the traditional model. The optimized rotation plan achieves an actual attendance point preference satisfaction rate of approximately 90% , and the task type preference distribution rate exceeds 65%. These figures are considerably higher than those achieved by traditional rotation schemes. In addition, the model significantly reduces the average working time variance. The calculation results validated the effectiveness and practicality of the proposed model. References | Related Articles | Metrics

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An Integrated Optimization Method of Urban Rail Line Service Frequency and Ticket Price with Passenger Flow Control XU Guang-ming, LU Chun-yu, ZHONG Lin-huan, DENG Lian-b 2023, 23(5): 268-278.  DOI: 10.16097/j.cnki.1009-6744.2023.05.028 Abstract ( )   PDF (1915KB) ( )   This paper proposes a multi-period integrated optimization method for passenger flow control, service frequency, and ticket price for an urban rail transit (URT) line to alleviate the unbalanced distribution of passenger flow in URT lines, and thus improve the passenger service level and operation benefit of enterprises. A bi-level optimization model is constructed, with the upper model aiming to maximize social welfare through the integrated optimization of multi-period service frequency, fare rate, and passenger flow control scheme. The lower model describes the travel choice behavior of passengers under the integrated optimization scheme and constructs a multi-period stochastic passenger assignment model based on elastic demand. A genetic algorithm with a nested Logit method of passenger assignment is designed to solve the model. A numerical example is carried out based on an actual URT line to verify the effectiveness of the model and algorithm. The results demonstrate that: the proposed method can reduce peak-hour demand, balance travel demand in different periods, alleviate uneven distribution of passenger flow in time and space; the optimization of ticket price can attract more potential travel demand, and increases the fare revenue of enterprises by 11.81% and the consumer surplus by 5.94% ; optimize service frequency can improve the utilization level of transportation capacity and reduce operating costs by 8.11%. At the same time, the implementation of the passenger flow control scheme can further improve the service level and enterprise revenue. Therefore, the proposed method can provide a theoretical basis for the formulation of the URT train operation plan, ticket price, and passenger flow control scheme. References | Related Articles | Metrics

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High-speed Railway Timetable Rescheduling Under Random Interruptions Based on Reinforcement Learning PANG Zi-shuai, WANG Li-wen, PENG Qi-yuan 2023, 23(5): 279-289.  DOI: 10.16097/j.cnki.1009-6744.2023.05.029 Abstract ( )   PDF (2368KB) ( )   Research on high-speed train timetable rescheduling under interruption conditions is of significant importance for enhancing the real-time dispatching capabilities of railways and optimizing train operation efficiency. This study employs a data-driven optimization approach, specifically deep reinforcement learning, to explore methods for reconstructing train operation trajectories under interruptions. Using the Proximal Policy Optimization (PPO) model while considering train operation constraints, we propose a train rescheduling approach to minimize train delays. We establish a train operation simulation environment where the PPO intelligent agent continuously interacts with the environment, seeking the optimal strategy with minimal delay. To evaluate the PPO model's performance and efficiency, we conduct tests using scenarios involving random interruptions and actual data from the Wuhan-Guangzhou high-speed railway in China. The verification results demonstrate that the train rescheduling scheme derived from the PPO model outperforms those obtained from other common reinforcement learning models and even the decisions made by on-site dispatchers. It can reduce train delays by about 13%. PPO exhibits significantly faster convergence compared to other commonly used reinforcement learning models. Although the solution quality obtained by PPO is about 2% less than the optimal solution, the PPO model has a significant improvement in the computation speed of obtaining the near-optimal solution. This makes it a more practical choice for real-time decision-making. References | Related Articles | Metrics

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Optimization of Multiple Tractor-and-semitrailer Scheduling for Highway Port with Unmatched Supply-demand and Network Conditions GUO Hong-xia, NI Shao-quan, HE Yu-yan 2023, 23(5): 290-297.  DOI: 10.16097/j.cnki.1009-6744.2023.05.030 Abstract ( )   PDF (1413KB) ( )   Multiple tractor- and- semitrailer scheduling is commonly used in the highway port network and plays an important role when the supply and demand are unbalanced between highway ports and helps to reduce the network operating costs. To analyze the advantages of multiple tractor-and-semitrailer scheduling, this paper proposes the optimization model for multiple tractor-and-semitrailer scheduling with multi depots, multi demands, and unbalanced supply and demand. The optimization goal of the model is the minimum network operation cost. A two-stage heuristic algorithm is designed based on the dynamic programming, and the mileage saving method and the improved simulated annealing algorithm are used to solve the model. The model was compared with the improved particle swarm optimization algorithm and the improved ant colony algorithm to verify the effectiveness of the model. The results show that compared with traditional transportation mode, the reduction rate of the number of tractors under network condition is 7.30% , and the cost savings rate is 2.32% . The reasonable value of the suspension ratio is 1.00∶1.08, reflecting the superiority of multiple vehicles models in suspension. Under different working hours of the tractor, the reduction rate of the number of common semitrailers increased from 7.30% to 53.68%, the cost savings rate increased from 2.32% to 15.86%, and the reasonable value of the drag and drop ratio improved from 1.00∶1.08 to 1.00∶2.16, and the advantages of multiple tractor-and-semitrailer scheduling were prominent. The reasonable value of the tractor and semitrailer ratio and the scheduling scheme under different working hours provide certain reference for the enterprise operational decision-making process. References | Related Articles | Metrics

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Optimization of Terminal Allocation of Airlines Considering Passengers' Cross-terminal Transfer LI Yan-hua, YANG Jie, LIU Zhi-shuo, DENG Jie 2023, 23(5): 298-311.  DOI: 10.16097/j.cnki.1009-6744.2023.05.031 Abstract ( )   PDF (3460KB) ( )   To improve the efficiency of cross-terminal transfer in multi-terminal hub airports, this study investigates an optimization model of terminal allocation that determines the airline's distribution layout in each terminal. In order to improve the convenience of airlines, enhance the travel experience of transit passengers, and reduce the operating cost of airports, a multi-objective integer programming model for airline allocation optimization with multiple terminals was established. The model minimizes the annual number of cross-terminal passengers, the number of cross-terminal combinations between airlines of the same airline alliance, the total annual cross-terminal passengers' transfer time, and the annual number of international/Hong Kong/Macau/Taiwan cross-terminal passengers, considering the terminal capacity limit, airline passenger processing capacity limit and other factors. A hybrid optimization algorithm of Simulated Annealing and Adaptive Particle Swarm Optimization (SA-APSO) was designed to solve the model. A hub airport in southwest China was selected for example analysis. Firstly, the SA-APSO algorithm was used to solve small-scale examples. The results showed that the optimized targets were reduced by 14.51%~50.00% respectively, and the objective function values change slightly at different runs, which verifies the effectiveness of the model and algorithm, and the stability of the algorithm. Then, the SA-APSO algorithm was used to solve large-scale examples, and the optimal schemes corresponding to different target weight combinations were obtained. The results showed that the optimized objectives were reduced by 26.86%, 28.33%, 89.91%, and 28.84%, respectively. The results show that: the proposed model and algorithm can fully take into account the interests of airlines, transit passengers, and airports. While meeting the limitations of terminal capacity and passenger handling capacity of airlines, it can improve the efficiency of cross-terminal passenger transfer, reduce airport operating costs, and meet the convenience needs of airlines, which can enhance the attraction of hub airport transit and provide theoretical guidance for the optimization of airline terminal allocation in multi-terminal hub airports. References | Related Articles | Metrics

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Collaborative Sequencing of Arrival and Departure Aircraft Considering Potential Conflicts WANG Jian-zhong, DING Xiao-qin, WANG Shu-wei 2023, 23(5): 312-320.  DOI: 10.16097/j.cnki.1009-6744.2023.05.032 Abstract ( )   PDF (1447KB) ( )   This paper proposes an arrival and departure flight sequencing method based on a multi-objective time-indexed model to address the increase in controller workload caused by potential conflicts and improve the enforceability of sequencing results. The length of potential conflict time is used to quantify the controller's workload in resolving conflicts. Taking the minimum delay time and conflict resolution load as the objective function, a time-indexed model is introduced to discretize the time period into time slots, allowing for the allocation of landing and take-off time slots for each flight instead of fixed time points, thereby enhancing the enforceability of the sorting results. A genetic algorithm is designed to optimize the model, and the effectiveness of the model is verified using data from Tianjin Binhai International Airport. Experimental results demonstrate that the model effectively reduces total flight delay time and the controller's workload in solving potential conflicts under different weight combinations. Compared to actual operation scenarios and the first-come-first-served strategy, the optimization rate for flight delays is 11.25% and 11.87% for a weight combination of 0.2-0.8, and 9.70% and 10.34% for a weight combination of 0.1-0.9. The optimization rates for the controller's conflict resolution load are 28.70% and 37.90% , and 45.37% and 52.42% respectively. Therefore, in addition to reducing flight delays, the model significantly reduces potential conflict time, alleviates controller workload, and improves operational efficiency and safety in the terminal area. References | Related Articles | Metrics