Pub Date : 2023-09-11DOI: 10.1080/21680566.2023.2254939
Jiyeon Lee, Ilkyeong Moon
The purpose of this study is to reschedule flights for an airline's profit to correspond to the airport’s changed capacity. In the event of a ground delay program (GDP), the number of flights the airport can accommodate is reduced. We formulated a mixed-integer linear programming (MILP) model to reschedule flights. The MILP models were divided into two versions to handle the uncertainty of the future. In scenarios in which the GDP is changed again, an optimal model obtains solutions for each scenario. The stochastic model solution obtains a minimizing expectation cost of all scenarios. All flights are connected to both the origin and destination airports, and one aircraft may be used for more than one flight. Therefore, we considered delay propagation not only within the same airport but from other airports by extending the setup to include several airports at once. Because the objective of this study is to minimize the operation cost of airline, we also considered costs associated with airline resources such as aircrafts and crews. Related experiments were conducted including comparison between two suggested versions.
{"title":"Flight rescheduling of an airline underground delay program considering delay propagation in multiple airports","authors":"Jiyeon Lee, Ilkyeong Moon","doi":"10.1080/21680566.2023.2254939","DOIUrl":"https://doi.org/10.1080/21680566.2023.2254939","url":null,"abstract":"The purpose of this study is to reschedule flights for an airline's profit to correspond to the airport’s changed capacity. In the event of a ground delay program (GDP), the number of flights the airport can accommodate is reduced. We formulated a mixed-integer linear programming (MILP) model to reschedule flights. The MILP models were divided into two versions to handle the uncertainty of the future. In scenarios in which the GDP is changed again, an optimal model obtains solutions for each scenario. The stochastic model solution obtains a minimizing expectation cost of all scenarios. All flights are connected to both the origin and destination airports, and one aircraft may be used for more than one flight. Therefore, we considered delay propagation not only within the same airport but from other airports by extending the setup to include several airports at once. Because the objective of this study is to minimize the operation cost of airline, we also considered costs associated with airline resources such as aircrafts and crews. Related experiments were conducted including comparison between two suggested versions.","PeriodicalId":48872,"journal":{"name":"Transportmetrica B-Transport Dynamics","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135939229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Road network maintenance scheduling mainly considers budget limits in the previous studies and largely ignores the capital constraints. This study proposes a bi-objective mixed integer programming model, in which the net present value (NPV) is maximized and the increased total system travel time (ITSTT) due to maintenance activities under the capital constraints is minimized. Since the road network flows cannot reach an equilibrium state overnight due to the variation of network capacity, a link-based day-to-day dynamics model is developed to simulate the transient fluctuation in the traffic flows and calculate the total system travel time of each day. The bi-objective model is solved by the nondominated sorting genetic algorithm-II (NSGA-II) that generates a set of optimal Pareto solutions. The TOPSIS method is then adopted to determine the best compromise solution. Finally, a case study is conducted to demonstrate the effects of key parameters on the values of the two objectives.
{"title":"Capital-constrained maintenance scheduling for road networks considering traffic dynamics","authors":"Xinhua Mao, Shin Dong, Jianwei Wang, Jibiao Zhou, Changwei Yuan, Tao Zheng","doi":"10.1080/21680566.2023.2250080","DOIUrl":"https://doi.org/10.1080/21680566.2023.2250080","url":null,"abstract":"Road network maintenance scheduling mainly considers budget limits in the previous studies and largely ignores the capital constraints. This study proposes a bi-objective mixed integer programming model, in which the net present value (NPV) is maximized and the increased total system travel time (ITSTT) due to maintenance activities under the capital constraints is minimized. Since the road network flows cannot reach an equilibrium state overnight due to the variation of network capacity, a link-based day-to-day dynamics model is developed to simulate the transient fluctuation in the traffic flows and calculate the total system travel time of each day. The bi-objective model is solved by the nondominated sorting genetic algorithm-II (NSGA-II) that generates a set of optimal Pareto solutions. The TOPSIS method is then adopted to determine the best compromise solution. Finally, a case study is conducted to demonstrate the effects of key parameters on the values of the two objectives.","PeriodicalId":48872,"journal":{"name":"Transportmetrica B-Transport Dynamics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44594478","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study investigates the flow distribution patterns at intersection approaches with variable approach lanes (VALs) and proposes a new lane utilization adjustment factor specifically for VALs. Unmanned aerial vehicles (UAVs) were used to collect naturalistic data, including traffic flow, approach geometry, and signal control-specific information, at selected intersections in Hangzhou, China. Machine learning techniques were employed to develop accurate regression models for estimating lane utilization, separately for the two VAL statuses. Preliminary analyses indicate different VAL utilization patterns across sites, suggesting the presence of external factors, beyond the VAL control itself, influencing the VAL utilization. The machine learning regression models provide insights into these factors by ranking them based on the importance and the impact magnitude. From a practical standpoint, this study recommends the implementation of uniform lane guiding signs, lane geometry improvements, and driver education to enhance the operational efficiency of variable approach lanes.
{"title":"Estimating lane utilization for variable approach lanes using explainable machine learning","authors":"Adjé Jérémie Alagbé, Sheng Jin, Qianhan Bao, Wentong Guo","doi":"10.1080/21680566.2023.2250562","DOIUrl":"https://doi.org/10.1080/21680566.2023.2250562","url":null,"abstract":"This study investigates the flow distribution patterns at intersection approaches with variable approach lanes (VALs) and proposes a new lane utilization adjustment factor specifically for VALs. Unmanned aerial vehicles (UAVs) were used to collect naturalistic data, including traffic flow, approach geometry, and signal control-specific information, at selected intersections in Hangzhou, China. Machine learning techniques were employed to develop accurate regression models for estimating lane utilization, separately for the two VAL statuses. Preliminary analyses indicate different VAL utilization patterns across sites, suggesting the presence of external factors, beyond the VAL control itself, influencing the VAL utilization. The machine learning regression models provide insights into these factors by ranking them based on the importance and the impact magnitude. From a practical standpoint, this study recommends the implementation of uniform lane guiding signs, lane geometry improvements, and driver education to enhance the operational efficiency of variable approach lanes.","PeriodicalId":48872,"journal":{"name":"Transportmetrica B-Transport Dynamics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45206812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-24DOI: 10.1080/21680566.2023.2248400
Zohreh Fotouhi, H. Narimani, M. Hashemi
The charging behaviour of electric vehicle (EV) drivers significantly influences planning of the future deployment of public charging stations (CSs). Thus, identifying the EV drivers' charging behaviour plays a major role in CS management and development. In this regard, some parametric behavioural Markov models (BMMs) introduced in the literature have acceptable performance with tuned parameters. Enjoying the benefits of these BMMs needs accurate and feasible parameter tuning. To address this challenge, we propose a machine learning-based method to tune the parameters of such a BMM dynamically. A Deep Q-Network (DQN) algorithm is an appropriate solution in which the reward function is designed based on the statistical resemblance between the EV plug-in and charging times derived from CS simulation with their equivalents derived from the CS charging data. The evaluation results based on the real charging data demonstrate the convergence of the proposed algorithm and validate the accuracy of the adapted behavioural parameters. Accurately adapting the model parameters is an essential prerequisite for designing a system that identifies the EV drivers' behaviour. This novel system helps control the CS congestion and predict the CS requirements when the EV population grows in the future.
{"title":"Parameter tuning of EV drivers' charging behavioural model using machine learning techniques","authors":"Zohreh Fotouhi, H. Narimani, M. Hashemi","doi":"10.1080/21680566.2023.2248400","DOIUrl":"https://doi.org/10.1080/21680566.2023.2248400","url":null,"abstract":"The charging behaviour of electric vehicle (EV) drivers significantly influences planning of the future deployment of public charging stations (CSs). Thus, identifying the EV drivers' charging behaviour plays a major role in CS management and development. In this regard, some parametric behavioural Markov models (BMMs) introduced in the literature have acceptable performance with tuned parameters. Enjoying the benefits of these BMMs needs accurate and feasible parameter tuning. To address this challenge, we propose a machine learning-based method to tune the parameters of such a BMM dynamically. A Deep Q-Network (DQN) algorithm is an appropriate solution in which the reward function is designed based on the statistical resemblance between the EV plug-in and charging times derived from CS simulation with their equivalents derived from the CS charging data. The evaluation results based on the real charging data demonstrate the convergence of the proposed algorithm and validate the accuracy of the adapted behavioural parameters. Accurately adapting the model parameters is an essential prerequisite for designing a system that identifies the EV drivers' behaviour. This novel system helps control the CS congestion and predict the CS requirements when the EV population grows in the future.","PeriodicalId":48872,"journal":{"name":"Transportmetrica B-Transport Dynamics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49610465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ride-sourcing services have become increasingly vital in meeting on-demand mobility needs. However, safety issues and public health concerns are obstacles for passengers' trust in ride-sourcing service providers. In this paper, we propose a stochastic evolutionary dynamic game approach to modeling and managing the ride-sourcing market with limited platform reputation, and interpret some intriguing phenomena in terms of passengers' and drivers' travel behavior, e.g. survivor bias, reciprocity, and tragedy of the commons. A stochastic state dynamic game model is formulated to capture complicated interactions among three games/states, i.e. trust game, complaint game, and rating-based game. The stationary distribution of each state and transitional probabilities are deducted from the stochastic game theory. We analyze the ride-sourcing system's evolution and reveal its intrinsic mechanism based on the model. We found that, if the governmental support for complaints is inadequate and passengers bear the high complaint costs, drivers will be more inclined to compensate passengers privately (i.e. reciprocity) to escape punishment for unqualified service. As a result, the service quality will deteriorate in the long run. To prevent the deterioration of service quality, we explore adaptive pricing strategies for the ride-sourcing platform and suggest managerial strategies for the government. This paper bridges the gap between the dynamic evolution and the mechanism of trust in ride-sourcing services, which has been seldom studied in the literature. All of the analytical results are illustrated by numerical experiments.
{"title":"A stochastic evolutionary dynamic game model for analyzing the ride-sourcing market with limited platform reputation","authors":"Dong Mo, Xiqun (Michael) Chen, Zheng Zhu, Chaojie Liu, Na Xie","doi":"10.1080/21680566.2023.2248399","DOIUrl":"https://doi.org/10.1080/21680566.2023.2248399","url":null,"abstract":"Ride-sourcing services have become increasingly vital in meeting on-demand mobility needs. However, safety issues and public health concerns are obstacles for passengers' trust in ride-sourcing service providers. In this paper, we propose a stochastic evolutionary dynamic game approach to modeling and managing the ride-sourcing market with limited platform reputation, and interpret some intriguing phenomena in terms of passengers' and drivers' travel behavior, e.g. survivor bias, reciprocity, and tragedy of the commons. A stochastic state dynamic game model is formulated to capture complicated interactions among three games/states, i.e. trust game, complaint game, and rating-based game. The stationary distribution of each state and transitional probabilities are deducted from the stochastic game theory. We analyze the ride-sourcing system's evolution and reveal its intrinsic mechanism based on the model. We found that, if the governmental support for complaints is inadequate and passengers bear the high complaint costs, drivers will be more inclined to compensate passengers privately (i.e. reciprocity) to escape punishment for unqualified service. As a result, the service quality will deteriorate in the long run. To prevent the deterioration of service quality, we explore adaptive pricing strategies for the ride-sourcing platform and suggest managerial strategies for the government. This paper bridges the gap between the dynamic evolution and the mechanism of trust in ride-sourcing services, which has been seldom studied in the literature. All of the analytical results are illustrated by numerical experiments.","PeriodicalId":48872,"journal":{"name":"Transportmetrica B-Transport Dynamics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43234856","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-18DOI: 10.1080/21680566.2023.2243388
M. Ahmed, H. L. Khoo, O. Ng
This study introduces a control strategy based on intersection capacity. The optimisation technique is formulated from available space at discharge routes. The downstream policy utilises density and speed (k-v) measurements to guide a deep Q-learning agent (DQLA) in managing a signalised junction using a constrained local communication protocol. Testing of the DQLA k-v strategy against other control methods is carried out in a simulated micro-model of a real urban traffic network. Though the adaptive signal system design is decentralised, statistical analyses explicitly prove the effectiveness of the discharge-based controller in mitigating operation at a global scale. The DQLA k-v controller has achieved significant cost savings in waiting time (10%−36%) and travel time (5%−25%) and asserted the highest mean travel speed (3.4 m/s). Consequently, vehicular traffic experienced the least time loss when traversing routes and witnessed fewer stops leading to close to optimum network operation at a 0.80 clearance ratio.
{"title":"Discharge control policy based on density and speed for deep Q-learning adaptive traffic signal","authors":"M. Ahmed, H. L. Khoo, O. Ng","doi":"10.1080/21680566.2023.2243388","DOIUrl":"https://doi.org/10.1080/21680566.2023.2243388","url":null,"abstract":"This study introduces a control strategy based on intersection capacity. The optimisation technique is formulated from available space at discharge routes. The downstream policy utilises density and speed (k-v) measurements to guide a deep Q-learning agent (DQLA) in managing a signalised junction using a constrained local communication protocol. Testing of the DQLA k-v strategy against other control methods is carried out in a simulated micro-model of a real urban traffic network. Though the adaptive signal system design is decentralised, statistical analyses explicitly prove the effectiveness of the discharge-based controller in mitigating operation at a global scale. The DQLA k-v controller has achieved significant cost savings in waiting time (10%−36%) and travel time (5%−25%) and asserted the highest mean travel speed (3.4 m/s). Consequently, vehicular traffic experienced the least time loss when traversing routes and witnessed fewer stops leading to close to optimum network operation at a 0.80 clearance ratio.","PeriodicalId":48872,"journal":{"name":"Transportmetrica B-Transport Dynamics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42989486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-18DOI: 10.1080/21680566.2023.2240961
Hossein Moradi, Sara Sasaninejad, S. Wittevrongel, J. Walraevens
Information of Connected Vehicles (CVs) could describe vehicular dynamics in much greater detail, enhancing the effectiveness of traffic control systems. One important such system is perimeter control, which can achieve better performance by incorporating the evolution of congestion into the identification of protected regions through a dynamic approach. However, little attention has been given to identifying such dynamic regions by developing CV-based network partitioning models in a spatiotemporal dimension. To address this gap, this paper proposes a three-module framework that (1) collects the relevant information of CVs, (2) performs initial partitioning based on some rational considerations, and (3) identifies the optimal protected regions through a partitioning evaluation, improvement, and iteration algorithm. The carried-out comparisons between perimeter control systems employing the resulting protected regions and those using static regions confirm that the proposed framework enhances the efficiency of perimeter control, even for CVs' penetration rates that are as low as 15%.
{"title":"Dynamic and heterogeneity-sensitive urban network partitioning: a data-driven technique","authors":"Hossein Moradi, Sara Sasaninejad, S. Wittevrongel, J. Walraevens","doi":"10.1080/21680566.2023.2240961","DOIUrl":"https://doi.org/10.1080/21680566.2023.2240961","url":null,"abstract":"Information of Connected Vehicles (CVs) could describe vehicular dynamics in much greater detail, enhancing the effectiveness of traffic control systems. One important such system is perimeter control, which can achieve better performance by incorporating the evolution of congestion into the identification of protected regions through a dynamic approach. However, little attention has been given to identifying such dynamic regions by developing CV-based network partitioning models in a spatiotemporal dimension. To address this gap, this paper proposes a three-module framework that (1) collects the relevant information of CVs, (2) performs initial partitioning based on some rational considerations, and (3) identifies the optimal protected regions through a partitioning evaluation, improvement, and iteration algorithm. The carried-out comparisons between perimeter control systems employing the resulting protected regions and those using static regions confirm that the proposed framework enhances the efficiency of perimeter control, even for CVs' penetration rates that are as low as 15%.","PeriodicalId":48872,"journal":{"name":"Transportmetrica B-Transport Dynamics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44072825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-07DOI: 10.1080/21680566.2023.2241643
Yongfu Li, Sen Zhang, Longwang Huang, Gang Huang
This article designs a novel controller for heterogeneous connected vehicles (CVs) platoon subject to communication delay. Firstly, third-order vehicle dynamics is used to capture the heterogeneity of vehicles. A new nonlinear controller for the CV platoon is proposed in the presence of car-following interactions, the acceleration difference and communication delays. Then, the internal stability of the CV platoon and the upper bound of communication delay are deduced by using the Lyapunov theorem. Also, the string stability of the linearized CV platoon system is proved by using the infinite-norm method. Additionally, a hierarchical control strategy suitable for co-simulation is designed to overcome the nonlinearity of vehicles and achieve consistency between the desired and actual acceleration. Finally, the superiority and effectiveness of the developed controller are verified by extensive simulation and cosimulation.
{"title":"Nonlinear longitudinal cooperative control of heterogeneous connected vehicle platoon considering car-following interactions and communication delay","authors":"Yongfu Li, Sen Zhang, Longwang Huang, Gang Huang","doi":"10.1080/21680566.2023.2241643","DOIUrl":"https://doi.org/10.1080/21680566.2023.2241643","url":null,"abstract":"This article designs a novel controller for heterogeneous connected vehicles (CVs) platoon subject to communication delay. Firstly, third-order vehicle dynamics is used to capture the heterogeneity of vehicles. A new nonlinear controller for the CV platoon is proposed in the presence of car-following interactions, the acceleration difference and communication delays. Then, the internal stability of the CV platoon and the upper bound of communication delay are deduced by using the Lyapunov theorem. Also, the string stability of the linearized CV platoon system is proved by using the infinite-norm method. Additionally, a hierarchical control strategy suitable for co-simulation is designed to overcome the nonlinearity of vehicles and achieve consistency between the desired and actual acceleration. Finally, the superiority and effectiveness of the developed controller are verified by extensive simulation and cosimulation.","PeriodicalId":48872,"journal":{"name":"Transportmetrica B-Transport Dynamics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43316113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-31DOI: 10.1080/21680566.2023.2240533
Lin Yu, Fangce Guo, A. Sivakumar, Sisi Jian
Short-term traffic prediction has been widely studied in the community of Intelligent Transport Systems for decades. Despite the advances in machine learning-based prediction techniques, a challenging problem that affects the applications of such methods in practice is the prevalence of insufficient data across an entire road network. To address this few-shot traffic prediction problem at a local network scale, we develop a hybrid framework in conjunction with the prior knowledge transferring algorithm and two widely used models, i.e. Long-short Term Memory and Spatial–Temporal Graph Convolutional Neural Network. The proposed modelling framework is trained and tested using five-minute interval traffic flow data collected from London under different few-shot learning scenarios. Results show that transferring local network prior knowledge can improve the accuracy of both one-step prediction and multi-step prediction under inadequate data conditions, regardless of the deep-learning tool used.
{"title":"Few-Shot traffic prediction based on transferring prior knowledge from local network","authors":"Lin Yu, Fangce Guo, A. Sivakumar, Sisi Jian","doi":"10.1080/21680566.2023.2240533","DOIUrl":"https://doi.org/10.1080/21680566.2023.2240533","url":null,"abstract":"Short-term traffic prediction has been widely studied in the community of Intelligent Transport Systems for decades. Despite the advances in machine learning-based prediction techniques, a challenging problem that affects the applications of such methods in practice is the prevalence of insufficient data across an entire road network. To address this few-shot traffic prediction problem at a local network scale, we develop a hybrid framework in conjunction with the prior knowledge transferring algorithm and two widely used models, i.e. Long-short Term Memory and Spatial–Temporal Graph Convolutional Neural Network. The proposed modelling framework is trained and tested using five-minute interval traffic flow data collected from London under different few-shot learning scenarios. Results show that transferring local network prior knowledge can improve the accuracy of both one-step prediction and multi-step prediction under inadequate data conditions, regardless of the deep-learning tool used.","PeriodicalId":48872,"journal":{"name":"Transportmetrica B-Transport Dynamics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49553435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-07-12DOI: 10.1080/21680566.2023.2234087
Xiongwen Qian, Jianfeng Mao, Yuan Wang, Meng Qiu
A column generation-based framework is proposed for air traffic flow management (ATFM) incorporating a user-driven prioritization process (UDPP). Airspace Users' (AUs') preferences and priorities can be explicitly reflected in the framework. AUs are allowed to propose their preferred routes as initial options and AUs' delay cost structure can be customized and not revealed to other AUs. Major UDPP features, including Fleet Delay Reordering, Selective Flight Protection, and Margins, are fully incorporated in the proposed framework. In the master problem, the model selects 4D-routes for flights subject to sector capacity constraints. In the subproblems, new 4D-routes are generated. Numerical experiments demonstrate that the proposed framework can efficiently solve the ATFM problem considering UDPP features with often zero or small optimality gaps. The effectiveness of UDPP is also verified using case studies. Parallelism of subproblems considerably further reduces the runtime and the large-scale scenario computational time can be reduced to around 30 min.
{"title":"A column generation-based framework for ATFM incorporating a user-driven prioritization process","authors":"Xiongwen Qian, Jianfeng Mao, Yuan Wang, Meng Qiu","doi":"10.1080/21680566.2023.2234087","DOIUrl":"https://doi.org/10.1080/21680566.2023.2234087","url":null,"abstract":"A column generation-based framework is proposed for air traffic flow management (ATFM) incorporating a user-driven prioritization process (UDPP). Airspace Users' (AUs') preferences and priorities can be explicitly reflected in the framework. AUs are allowed to propose their preferred routes as initial options and AUs' delay cost structure can be customized and not revealed to other AUs. Major UDPP features, including Fleet Delay Reordering, Selective Flight Protection, and Margins, are fully incorporated in the proposed framework. In the master problem, the model selects 4D-routes for flights subject to sector capacity constraints. In the subproblems, new 4D-routes are generated. Numerical experiments demonstrate that the proposed framework can efficiently solve the ATFM problem considering UDPP features with often zero or small optimality gaps. The effectiveness of UDPP is also verified using case studies. Parallelism of subproblems considerably further reduces the runtime and the large-scale scenario computational time can be reduced to around 30 min.","PeriodicalId":48872,"journal":{"name":"Transportmetrica B-Transport Dynamics","volume":" ","pages":""},"PeriodicalIF":2.8,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48697379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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