G. Tilg, Lukas Ambühl, S. Batista, M. Menéndez, L. Leclercq, F. Busch
The design of network-wide traffic management schemes or transport policies for urban areas requires computationally efficient traffic models. The macroscopic fundamental diagram (MFD) is a promising tool for such applications. Unfortunately, empirical MFDs are not always available, and semi-analytical estimation methods require a reduction of the network to a corridor that introduces substantial inaccuracies. We propose a semi-analytical methodology to estimate the MFD for realistic urban networks without the information loss induced by the reduction of networks to corridors. The methodology is based on the method of cuts but applies to networks with irregular topologies, accounts for different spatial demand patterns, and determines the upper bound of network flow. Therefore, we consider both flow conservation and the effects of spillbacks at the network level. Our framework decomposes a given network into a set of corridors, creates a hypernetwork, including the impacts of source terms, and then treats the dependencies across corridors (e.g., because of turning flows and spillbacks). Based on this hypernetwork, we derive the free-flow and capacity branch of the MFD. The congested branch is estimated by considering gridlock characteristics and utilizing recent advancements in MFD research. We showcase the applicability of the proposed methodology in a case study with a realistic setting based on the Sioux Falls network. We then compare the results to the original method of cuts and a ground truth derived from the cell transmission model. This comparison reveals that our method is more than five times more accurate than the state of the art in estimating the network-wide capacity and jam density. Moreover, the results clearly indicate the MFD’s dependency on spatial demand patterns. Compared with simulation-based MFD estimation approaches, the potential of the proposed framework lies in the modeling flexibility, explanatory value, and reduced computational cost. Funding: G. Tilg acknowledges support from the German Federal Ministry for Digital and Transport (BMDV) for the funding of the project LSS (capacity increase of urban networks). S. F. A. Batista and M. Menéndez acknowledge support from the NYUAD Center for Interacting Urban Networks (CITIES), funded by Tamkeen under the NYUAD Research Institute Award [CG001]. L. Ambühl acknowledges support from the ETH Research Grant [ETH-27 16-1] under the project name SPEED. L. Leclercq acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program Grant [646592 - MAGnUMproject]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0402 .
为城市地区设计全网交通管理方案或交通政策需要计算效率高的交通模型。宏观基本图(MFD)是一种很有前途的工具。不幸的是，经验mfd并不总是可用的，半分析估计方法需要将网络减少到一个引入大量不准确性的走廊。我们提出了一种半解析的方法来估计现实城市网络的MFD，而不考虑网络减少到廊道所导致的信息损失。该方法基于切割方法，但适用于不规则拓扑的网络，考虑了不同的空间需求模式，并确定了网络流量的上限。因此，我们在网络层面同时考虑流量守恒和溢出效应。我们的框架将给定的网络分解为一组走廊，创建一个超网络，包括源项的影响，然后处理跨走廊的依赖关系(例如，由于转向流和溢出)。在此基础上，导出了MFD的自由流分支和容量分支。通过考虑交通阻塞特征和利用MFD研究的最新进展，对拥堵路段进行了估计。我们在一个基于苏福尔斯网络的现实设置的案例研究中展示了所提出方法的适用性。然后，我们将结果与原始切割方法和从细胞传输模型中导出的基础真值进行比较。这一比较表明，我们的方法在估计网络容量和堵塞密度方面比目前的技术水平精确五倍以上。此外，研究结果还清楚地表明，土地利用对空间需求格局的依赖性。与基于仿真的MFD估计方法相比，该框架的潜力在于建模灵活性、解释性和计算成本的降低。资金:G. Tilg感谢德国联邦数字和运输部(BMDV)对LSS(城市网络容量增加)项目的资金支持。S. F. A. Batista和M. menzendz感谢纽约大学城市网络互动中心的支持，该中心由塔姆肯根据纽约大学研究机构奖[CG001]资助。L. amb感谢联邦理工学院研究基金[ETH-27 16-1]在项目名称SPEED下的支持。L. Leclercq承认欧洲研究委员会(ERC)在欧盟地平线2020研究和创新计划拨款[646592 - MAGnUMproject]下的资助。补充材料:电子伴侣可在https://doi.org/10.1287/trsc.2022.0402上获得。
{"title":"From Corridor to Network Macroscopic Fundamental Diagrams: A Semi-Analytical Approximation Approach","authors":"G. Tilg, Lukas Ambühl, S. Batista, M. Menéndez, L. Leclercq, F. Busch","doi":"10.1287/trsc.2022.0402","DOIUrl":"https://doi.org/10.1287/trsc.2022.0402","url":null,"abstract":"The design of network-wide traffic management schemes or transport policies for urban areas requires computationally efficient traffic models. The macroscopic fundamental diagram (MFD) is a promising tool for such applications. Unfortunately, empirical MFDs are not always available, and semi-analytical estimation methods require a reduction of the network to a corridor that introduces substantial inaccuracies. We propose a semi-analytical methodology to estimate the MFD for realistic urban networks without the information loss induced by the reduction of networks to corridors. The methodology is based on the method of cuts but applies to networks with irregular topologies, accounts for different spatial demand patterns, and determines the upper bound of network flow. Therefore, we consider both flow conservation and the effects of spillbacks at the network level. Our framework decomposes a given network into a set of corridors, creates a hypernetwork, including the impacts of source terms, and then treats the dependencies across corridors (e.g., because of turning flows and spillbacks). Based on this hypernetwork, we derive the free-flow and capacity branch of the MFD. The congested branch is estimated by considering gridlock characteristics and utilizing recent advancements in MFD research. We showcase the applicability of the proposed methodology in a case study with a realistic setting based on the Sioux Falls network. We then compare the results to the original method of cuts and a ground truth derived from the cell transmission model. This comparison reveals that our method is more than five times more accurate than the state of the art in estimating the network-wide capacity and jam density. Moreover, the results clearly indicate the MFD’s dependency on spatial demand patterns. Compared with simulation-based MFD estimation approaches, the potential of the proposed framework lies in the modeling flexibility, explanatory value, and reduced computational cost. Funding: G. Tilg acknowledges support from the German Federal Ministry for Digital and Transport (BMDV) for the funding of the project LSS (capacity increase of urban networks). S. F. A. Batista and M. Menéndez acknowledge support from the NYUAD Center for Interacting Urban Networks (CITIES), funded by Tamkeen under the NYUAD Research Institute Award [CG001]. L. Ambühl acknowledges support from the ETH Research Grant [ETH-27 16-1] under the project name SPEED. L. Leclercq acknowledges funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program Grant [646592 - MAGnUMproject]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2022.0402 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42076122","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}
It is well-known that the cost of parcel delivery can be reduced by designing routes that take into account the uncertainty surrounding customers’ presences. Thus far, routing problems with stochastic customer presences have relied on the assumption that all customer presences are independent from each other. However, the notion that demographic factors retain predictive power for parcel-delivery efficiency suggests that shared characteristics can be exploited to map dependencies between customer presences. This paper introduces the correlated probabilistic traveling salesman problem (CPTSP). The CPTSP generalizes the traveling salesman problem with stochastic customer presences, also known as the probabilistic traveling salesman problem (PTSP), to account for potential correlations between customer presences. I propose a generic and flexible model formulation for the CPTSP using copulas that maintains computational and mathematical tractability in high-dimensional settings. I also present several adaptations of existing exact and heuristic frameworks to solve the CPTSP effectively. Computational experiments on real-world parcel-delivery data reveal that correlations between stochastic customer presences do not always affect route decisions, but could have a considerable impact on route cost estimates. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0005 .
{"title":"The Traveling Salesman Problem with Stochastic and Correlated Customers","authors":"Pascal L. J. Wissink","doi":"10.1287/trsc.2022.0005","DOIUrl":"https://doi.org/10.1287/trsc.2022.0005","url":null,"abstract":"It is well-known that the cost of parcel delivery can be reduced by designing routes that take into account the uncertainty surrounding customers’ presences. Thus far, routing problems with stochastic customer presences have relied on the assumption that all customer presences are independent from each other. However, the notion that demographic factors retain predictive power for parcel-delivery efficiency suggests that shared characteristics can be exploited to map dependencies between customer presences. This paper introduces the correlated probabilistic traveling salesman problem (CPTSP). The CPTSP generalizes the traveling salesman problem with stochastic customer presences, also known as the probabilistic traveling salesman problem (PTSP), to account for potential correlations between customer presences. I propose a generic and flexible model formulation for the CPTSP using copulas that maintains computational and mathematical tractability in high-dimensional settings. I also present several adaptations of existing exact and heuristic frameworks to solve the CPTSP effectively. Computational experiments on real-world parcel-delivery data reveal that correlations between stochastic customer presences do not always affect route decisions, but could have a considerable impact on route cost estimates. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0005 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43286040","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}
Tommaso Schettini, M. Gendreau, O. Jabali, F. Malucelli
In many major cities, metro lines constitute the backbone of urban public transport, providing an efficient and greener alternative to private mobility. An important feature that distinguishes metro lines from other public transport means, such as buses, is that metros are typically tightly resource constrained. The trains operating on a particular line are often specifically fitted for that line, making any capacity expansion extremely costly and time-consuming. Therefore, researchers and operators alike are seeking ways to make better use of existing resources. One possible way of doing so is by adapting timetables to forecasted demand while accounting for limited vehicle capacities. Thus, we consider a demand-driven nonperiodic timetabling problem for a two-directional metro line that minimizes the total passenger waiting time through the efficient scheduling of the available trains. Considering that passengers board trains using a well-mixed policy, we explicitly account for train capacities on a moment-to-moment basis. Last, we consider that trains are allowed to short turn. In this respect, we assume that trains must pass by a given station before short turning and are only allowed to idle after having short turned. We devise a polynomial time algorithm for assessing the total passenger waiting time generated by a given timetable and an effective lower bound that is evaluated in linear time. These are used in a variable neighborhood search algorithm, which is embedded in an iterated local search metaheuristic. Classical local search-based neighborhoods are not effective for our problem because they do not explicitly handle the vehicle scheduling decisions. To handle this challenge, we proposed three tailored neighborhoods. We validate our heuristic on the uncapacitated version of the problem. Considering a benchmark of 48 artificial instances with up to 20 stations, our heuristic achieved an average gap of 0.67% and found eight new best solutions. We also validated our heuristic on three sets of instances based on realistic lines from Milan, Madrid, and Beijing. Furthermore, we demonstrate the operational advantages of our optimized timetables in the capacitated version of the problem by comparing them with regular timetables and with exact solutions obtained for the uncapacitated case. Furthermore, we conduct a sensitivity analysis with respect to the capacity of the trains and investigate the impact of a priority boarding policy. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0271 .
{"title":"An Iterated Local Search Metaheuristic for the Capacitated Demand-Driven Timetabling Problem","authors":"Tommaso Schettini, M. Gendreau, O. Jabali, F. Malucelli","doi":"10.1287/trsc.2022.0271","DOIUrl":"https://doi.org/10.1287/trsc.2022.0271","url":null,"abstract":"In many major cities, metro lines constitute the backbone of urban public transport, providing an efficient and greener alternative to private mobility. An important feature that distinguishes metro lines from other public transport means, such as buses, is that metros are typically tightly resource constrained. The trains operating on a particular line are often specifically fitted for that line, making any capacity expansion extremely costly and time-consuming. Therefore, researchers and operators alike are seeking ways to make better use of existing resources. One possible way of doing so is by adapting timetables to forecasted demand while accounting for limited vehicle capacities. Thus, we consider a demand-driven nonperiodic timetabling problem for a two-directional metro line that minimizes the total passenger waiting time through the efficient scheduling of the available trains. Considering that passengers board trains using a well-mixed policy, we explicitly account for train capacities on a moment-to-moment basis. Last, we consider that trains are allowed to short turn. In this respect, we assume that trains must pass by a given station before short turning and are only allowed to idle after having short turned. We devise a polynomial time algorithm for assessing the total passenger waiting time generated by a given timetable and an effective lower bound that is evaluated in linear time. These are used in a variable neighborhood search algorithm, which is embedded in an iterated local search metaheuristic. Classical local search-based neighborhoods are not effective for our problem because they do not explicitly handle the vehicle scheduling decisions. To handle this challenge, we proposed three tailored neighborhoods. We validate our heuristic on the uncapacitated version of the problem. Considering a benchmark of 48 artificial instances with up to 20 stations, our heuristic achieved an average gap of 0.67% and found eight new best solutions. We also validated our heuristic on three sets of instances based on realistic lines from Milan, Madrid, and Beijing. Furthermore, we demonstrate the operational advantages of our optimized timetables in the capacitated version of the problem by comparing them with regular timetables and with exact solutions obtained for the uncapacitated case. Furthermore, we conduct a sensitivity analysis with respect to the capacity of the trains and investigate the impact of a priority boarding policy. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0271 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44927958","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 paper addresses an integrated biomass pricing and logistics network design problem. A bilevel design and pricing model is proposed to capture the dynamic decision process between a biofuel producer as a Stackelberg leader and farmers as Stackelberg followers. The bilevel optimization model is transformed into a tractable single-level formulation by using optimality constraints. Other unique characteristics of our problem at hand include the incorporation of the harvesting time and frequency decisions in the biomass supply chain network design problem for the first time and consideration of the uncertainty in switchgrass yield in a robust optimization setting to take into account the risk-averse behavior of the farmers (suppliers). To efficiently solve the model, we propose a Benders decomposition algorithm enhanced by surrogate constraints, strengthened Benders cuts, and in-out cut loop stabilization. The numerical experiments show that the proposed algorithm is significantly superior to the branch-and-cut approach of CPLEX in terms of run times and gaps. We conduct a case study with data from Texas to validate the capabilities of our mathematical model and solution approach. Based on extensive experiments, the benefits of modeling are analyzed, and significant insights are explored. Funding: This research was partially supported by the National Natural Science Foundation of China [Grants 71771135, 72171129]; and the scholarship from China Scholarship Council (CSC) [Grant CSC 201906210092]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0357 .
{"title":"A Bilevel Model for Robust Network Design and Biomass Pricing Under Farmers’ Risk Attitudes and Supply Uncertainty","authors":"Qiaofeng Li, H. Üster, Zhi-Hai Zhang","doi":"10.1287/trsc.2021.0357","DOIUrl":"https://doi.org/10.1287/trsc.2021.0357","url":null,"abstract":"This paper addresses an integrated biomass pricing and logistics network design problem. A bilevel design and pricing model is proposed to capture the dynamic decision process between a biofuel producer as a Stackelberg leader and farmers as Stackelberg followers. The bilevel optimization model is transformed into a tractable single-level formulation by using optimality constraints. Other unique characteristics of our problem at hand include the incorporation of the harvesting time and frequency decisions in the biomass supply chain network design problem for the first time and consideration of the uncertainty in switchgrass yield in a robust optimization setting to take into account the risk-averse behavior of the farmers (suppliers). To efficiently solve the model, we propose a Benders decomposition algorithm enhanced by surrogate constraints, strengthened Benders cuts, and in-out cut loop stabilization. The numerical experiments show that the proposed algorithm is significantly superior to the branch-and-cut approach of CPLEX in terms of run times and gaps. We conduct a case study with data from Texas to validate the capabilities of our mathematical model and solution approach. Based on extensive experiments, the benefits of modeling are analyzed, and significant insights are explored. Funding: This research was partially supported by the National Natural Science Foundation of China [Grants 71771135, 72171129]; and the scholarship from China Scholarship Council (CSC) [Grant CSC 201906210092]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0357 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44981356","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}
Emerging autonomous vehicles (AVs) are expected to bring about a revolution in both the automotive industry and transportation systems. Introducing AVs into the existing mobility system with human-driven vehicles (HVs) yields mixed traffic with the following new features: in-vehicle compensation on value of time for AV users, distinct road capacities for pure AV and HV flows, and stochastic road capacity for the inseparable AV-HV traffic pattern. In this paper, we aim to investigate equilibrium traffic dynamics for the morning commuting problem where AVs and HVs coexist in a transportation corridor by considering these new features, and also explore several novel mixed AV-HV traffic management strategies. The AV-HV traffic pattern could be either separable (i.e., pure AV flow and pure HV flow depart from home in different periods) or inseparable, depending on the user profile condition. In addition to deriving departure time equilibriums for scenarios with separable traffic flows, significant effort is put into the scenario with an inseparable AV-HV traffic pattern, where stochastic road capacity is taken into account. Based on these equilibrium traffic analyses, we propose and explore some new traffic management strategies, including AV certificate of entitlement management scheme for scenarios with separable traffic flows and departure-period management (DPM) scheme and lane management policies for the scenario with an inseparable AV-HV traffic pattern. Eligibilities for applying these strategies are analytically derived and extensively discussed, and numerical experiments are conducted to demonstrate our theoretical findings and reveal the underlying impacts of road capacity randomness. Some lessons learned from the numerical experiments are (i) overlooking the impact of road capacity uncertainty will lead to an overestimation of system performance and even yield biased policymaking, (ii) the full dedicated-lane policy is the preferred option for the medium-level AV situation and partial dedicated-lane policies are more attractive choices for the early AV era or a market with a high AV share, and (iii) the DPM scheme could be a better substitute for partially dedicated-lane policies. Funding: This study was supported by the Ministry of Education of Singapore [Project T2EP40222-0002 under the MOE Tier 2 Grant] and the National Natural Science Foundation Council of China [Grant 72001133 and the Excellent Young Scientists Fund Program (Overseas)]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0469 .
{"title":"Equilibrium Traffic Dynamics with Mixed Autonomous and Human-Driven Vehicles and Novel Traffic Management Policies: The Effects of Value-of-Time Compensation and Random Road Capacity","authors":"Hua Wang, Jing Wang, Shukai Chen, Q. Meng","doi":"10.1287/trsc.2021.0469","DOIUrl":"https://doi.org/10.1287/trsc.2021.0469","url":null,"abstract":"Emerging autonomous vehicles (AVs) are expected to bring about a revolution in both the automotive industry and transportation systems. Introducing AVs into the existing mobility system with human-driven vehicles (HVs) yields mixed traffic with the following new features: in-vehicle compensation on value of time for AV users, distinct road capacities for pure AV and HV flows, and stochastic road capacity for the inseparable AV-HV traffic pattern. In this paper, we aim to investigate equilibrium traffic dynamics for the morning commuting problem where AVs and HVs coexist in a transportation corridor by considering these new features, and also explore several novel mixed AV-HV traffic management strategies. The AV-HV traffic pattern could be either separable (i.e., pure AV flow and pure HV flow depart from home in different periods) or inseparable, depending on the user profile condition. In addition to deriving departure time equilibriums for scenarios with separable traffic flows, significant effort is put into the scenario with an inseparable AV-HV traffic pattern, where stochastic road capacity is taken into account. Based on these equilibrium traffic analyses, we propose and explore some new traffic management strategies, including AV certificate of entitlement management scheme for scenarios with separable traffic flows and departure-period management (DPM) scheme and lane management policies for the scenario with an inseparable AV-HV traffic pattern. Eligibilities for applying these strategies are analytically derived and extensively discussed, and numerical experiments are conducted to demonstrate our theoretical findings and reveal the underlying impacts of road capacity randomness. Some lessons learned from the numerical experiments are (i) overlooking the impact of road capacity uncertainty will lead to an overestimation of system performance and even yield biased policymaking, (ii) the full dedicated-lane policy is the preferred option for the medium-level AV situation and partial dedicated-lane policies are more attractive choices for the early AV era or a market with a high AV share, and (iii) the DPM scheme could be a better substitute for partially dedicated-lane policies. Funding: This study was supported by the Ministry of Education of Singapore [Project T2EP40222-0002 under the MOE Tier 2 Grant] and the National Natural Science Foundation Council of China [Grant 72001133 and the Excellent Young Scientists Fund Program (Overseas)]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0469 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46754350","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}
Jan-Rasmus Künnen, Arne K. Strauss, Nikola Ivanov, Radosav Jovanović, Frank Fichert, Stefano Starita
In European air traffic management (ATM), it is an important decision how much capacity to provide for each airspace, and it has to be made weeks or even months in advance of the day of operation. Given the uncertainty in demand that may materialize until then along with variability in capacity provision (e.g., due to weather), Airspace Users could face high costs of displacements (i.e., delays and reroutings) if capacity is not provided where and when needed. We propose a new capacity sharing scheme in which some proportion of overall capacities can be flexibly deployed in any of the airspaces of the same alliance (at an increased unit cost). This allows us to hedge against the risk of capacity underprovision. Given this scheme, we seek to determine the optimum budget for capacities provided both locally and in cross-border sharing that results in the lowest expected network costs (i.e., capacity and displacement costs). To determine optimum capacity levels, we need to solve a two-stage newsvendor problem: We first decide on capacities to be provided for each airspace, and after uncertain demand and capacity provision disruptions have materialized, we need to decide on the routings of flights (including delays) as well as the sector opening scheme of each airspace to minimize costs. We propose a simulation optimization approach for searching the most cost-efficient capacity levels (in the first stage), and a heuristic to solve the routing and sector opening problem (in the second stage), which is [Formula: see text]-hard. We test our approach in a large-sized simulation study based on real data covering around 3,000 flights across Western European airspace. We find that our stochastic approach significantly reduces network costs against a deterministic benchmark while using less computational resources. Experiments on different setups for capacity sharing show that total variable costs can be reduced by more than 8% if capacity is shared across borders: even though we require that no airspace can operate lower capacities under capacity sharing than without (this is to avoid substitution of expensive air traffic controllers with those in countries with a lower wage level). We also find that the use of different technology providers is a major obstacle to reap the benefits from capacity sharing and that sharing capacities across airspaces of the same country may instead be preferred. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the Horizon 2020 Framework Programme [893380]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1210 .
{"title":"Cross-Border Capacity Planning in Air Traffic Management Under Uncertainty","authors":"Jan-Rasmus Künnen, Arne K. Strauss, Nikola Ivanov, Radosav Jovanović, Frank Fichert, Stefano Starita","doi":"10.1287/trsc.2023.1210","DOIUrl":"https://doi.org/10.1287/trsc.2023.1210","url":null,"abstract":"In European air traffic management (ATM), it is an important decision how much capacity to provide for each airspace, and it has to be made weeks or even months in advance of the day of operation. Given the uncertainty in demand that may materialize until then along with variability in capacity provision (e.g., due to weather), Airspace Users could face high costs of displacements (i.e., delays and reroutings) if capacity is not provided where and when needed. We propose a new capacity sharing scheme in which some proportion of overall capacities can be flexibly deployed in any of the airspaces of the same alliance (at an increased unit cost). This allows us to hedge against the risk of capacity underprovision. Given this scheme, we seek to determine the optimum budget for capacities provided both locally and in cross-border sharing that results in the lowest expected network costs (i.e., capacity and displacement costs). To determine optimum capacity levels, we need to solve a two-stage newsvendor problem: We first decide on capacities to be provided for each airspace, and after uncertain demand and capacity provision disruptions have materialized, we need to decide on the routings of flights (including delays) as well as the sector opening scheme of each airspace to minimize costs. We propose a simulation optimization approach for searching the most cost-efficient capacity levels (in the first stage), and a heuristic to solve the routing and sector opening problem (in the second stage), which is [Formula: see text]-hard. We test our approach in a large-sized simulation study based on real data covering around 3,000 flights across Western European airspace. We find that our stochastic approach significantly reduces network costs against a deterministic benchmark while using less computational resources. Experiments on different setups for capacity sharing show that total variable costs can be reduced by more than 8% if capacity is shared across borders: even though we require that no airspace can operate lower capacities under capacity sharing than without (this is to avoid substitution of expensive air traffic controllers with those in countries with a lower wage level). We also find that the use of different technology providers is a major obstacle to reap the benefits from capacity sharing and that sharing capacities across airspaces of the same country may instead be preferred. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the Horizon 2020 Framework Programme [893380]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1210 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136185297","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}
Autonomous vehicles (AVs) are expected to operate on mobility-on-demand (MoD) platforms because AV technology enables flexible self-relocation and system-optimal coordination. Unlike the existing studies, which focus on MoD with pure AV fleet or conventional vehicles (CVs) fleet, we aim to optimize the real-time fleet management of an MoD system with a mixed autonomy of CVs and AVs. We consider a realistic case that heterogeneous boundedly rational drivers may determine and learn their relocation strategies to improve their own compensation. In contrast, AVs are fully compliant with the platform’s operational decisions. To achieve a high level of service provided by a mixed fleet, we propose that the platform prioritizes human drivers in the matching decisions when on-demand requests arrive and dynamically determines the AV relocation tasks and the optimal commission fee to influence drivers’ behavior. However, it is challenging to make efficient real-time fleet management decisions when spatiotemporal uncertainty in demand and complex interactions among human drivers and operators are anticipated and considered in the operator’s decision making. To tackle the challenges, we develop a two-sided multiagent deep reinforcement learning (DRL) approach in which the operator acts as a supervisor agent on one side and makes centralized decisions on the mixed fleet, and each CV driver acts as an individual agent on the other side and learns to make decentralized decisions noncooperatively. We establish a two-sided multiagent advantage actor-critic algorithm to simultaneously train different agents on the two sides. For the first time, a scalable algorithm is developed here for mixed fleet management. Furthermore, we formulate a two-head policy network to enable the supervisor agent to efficiently make multitask decisions based on one policy network, which greatly reduces the computational time. The two-sided multiagent DRL approach is demonstrated using a case study in New York City using real taxi trip data. Results show that our algorithm can make high-quality decisions quickly and outperform benchmark policies. The efficiency of the two-head policy network is demonstrated by comparing it with the case using two separate policy networks. Our fleet management strategy makes both the platform and the drivers better off, especially in scenarios with high demand volume. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the Singapore Ministry of Education Academic Research [Grant MOE2019-T2-2-165] and the Singapore Ministry of Education [Grant R-266-000-135-114].
{"title":"Two-Sided Deep Reinforcement Learning for Dynamic Mobility-on-Demand Management with Mixed Autonomy","authors":"Jiaohong Xie, Yang Liu, Nan Chen","doi":"10.1287/trsc.2022.1188","DOIUrl":"https://doi.org/10.1287/trsc.2022.1188","url":null,"abstract":"Autonomous vehicles (AVs) are expected to operate on mobility-on-demand (MoD) platforms because AV technology enables flexible self-relocation and system-optimal coordination. Unlike the existing studies, which focus on MoD with pure AV fleet or conventional vehicles (CVs) fleet, we aim to optimize the real-time fleet management of an MoD system with a mixed autonomy of CVs and AVs. We consider a realistic case that heterogeneous boundedly rational drivers may determine and learn their relocation strategies to improve their own compensation. In contrast, AVs are fully compliant with the platform’s operational decisions. To achieve a high level of service provided by a mixed fleet, we propose that the platform prioritizes human drivers in the matching decisions when on-demand requests arrive and dynamically determines the AV relocation tasks and the optimal commission fee to influence drivers’ behavior. However, it is challenging to make efficient real-time fleet management decisions when spatiotemporal uncertainty in demand and complex interactions among human drivers and operators are anticipated and considered in the operator’s decision making. To tackle the challenges, we develop a two-sided multiagent deep reinforcement learning (DRL) approach in which the operator acts as a supervisor agent on one side and makes centralized decisions on the mixed fleet, and each CV driver acts as an individual agent on the other side and learns to make decentralized decisions noncooperatively. We establish a two-sided multiagent advantage actor-critic algorithm to simultaneously train different agents on the two sides. For the first time, a scalable algorithm is developed here for mixed fleet management. Furthermore, we formulate a two-head policy network to enable the supervisor agent to efficiently make multitask decisions based on one policy network, which greatly reduces the computational time. The two-sided multiagent DRL approach is demonstrated using a case study in New York City using real taxi trip data. Results show that our algorithm can make high-quality decisions quickly and outperform benchmark policies. The efficiency of the two-head policy network is demonstrated by comparing it with the case using two separate policy networks. Our fleet management strategy makes both the platform and the drivers better off, especially in scenarios with high demand volume. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the Singapore Ministry of Education Academic Research [Grant MOE2019-T2-2-165] and the Singapore Ministry of Education [Grant R-266-000-135-114].","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135845535","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 : 2023-07-01DOI: 10.1287/trsc.2023.intro.v57.n4
K. Smilowitz, J. Cordeau, Barrett W. Thomas, David Pisinger, Yafeng Yin, A. Campbell
{"title":"Special Issue on Emerging Topics in Transportation Science and Logistics","authors":"K. Smilowitz, J. Cordeau, Barrett W. Thomas, David Pisinger, Yafeng Yin, A. Campbell","doi":"10.1287/trsc.2023.intro.v57.n4","DOIUrl":"https://doi.org/10.1287/trsc.2023.intro.v57.n4","url":null,"abstract":"","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43764980","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 paper studies the distributionally robust fair transit resource allocation model (DrFRAM) under the Wasserstein ambiguity set to optimize the public transit resource allocation during a pandemic. We show that the proposed DrFRAM is highly nonconvex and nonlinear, and it is NP-hard in general. Fortunately, we show that DrFRAM can be reformulated as a mixed integer linear programming (MILP) by leveraging the equivalent representation of distributionally robust optimization and monotonicity properties, binarizing integer variables, and linearizing nonconvex terms. To improve the proposed MILP formulation, we derive stronger ones and develop valid inequalities by exploiting the model structures. Additionally, we develop scenario decomposition methods using different MILP formulations to solve the scenario subproblems and introduce a simple yet effective no one left-based approximation algorithm with a provable approximation guarantee to solve the model to near optimality. Finally, we numerically demonstrate the effectiveness of the proposed approaches and apply them to real-world data provided by the Blacksburg Transit. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the Division of Computing and Communication Foundations [Grant 2153607] and the Division of Civil, Mechanical and Manufacturing Innovation [Grant 2046426]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1159 .
{"title":"Distributionally Robust Fair Transit Resource Allocation During a Pandemic","authors":"Luying Sun, Weijun Xie, Tim Witten","doi":"10.1287/trsc.2022.1159","DOIUrl":"https://doi.org/10.1287/trsc.2022.1159","url":null,"abstract":"This paper studies the distributionally robust fair transit resource allocation model (DrFRAM) under the Wasserstein ambiguity set to optimize the public transit resource allocation during a pandemic. We show that the proposed DrFRAM is highly nonconvex and nonlinear, and it is NP-hard in general. Fortunately, we show that DrFRAM can be reformulated as a mixed integer linear programming (MILP) by leveraging the equivalent representation of distributionally robust optimization and monotonicity properties, binarizing integer variables, and linearizing nonconvex terms. To improve the proposed MILP formulation, we derive stronger ones and develop valid inequalities by exploiting the model structures. Additionally, we develop scenario decomposition methods using different MILP formulations to solve the scenario subproblems and introduce a simple yet effective no one left-based approximation algorithm with a provable approximation guarantee to solve the model to near optimality. Finally, we numerically demonstrate the effectiveness of the proposed approaches and apply them to real-world data provided by the Blacksburg Transit. History: This paper has been accepted for the Transportation Science Special Issue on Emerging Topics in Transportation Science and Logistics. Funding: This work was supported by the Division of Computing and Communication Foundations [Grant 2153607] and the Division of Civil, Mechanical and Manufacturing Innovation [Grant 2046426]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1159 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136260268","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 paper studies the problem of determining the strategic location of charging stations and their capacity levels under stochastic electric vehicle flows and charging times taking into account the route choice response of users. The problem is modeled using bilevel optimization, where the network planner or leader minimizes the total infrastructure cost of locating and sizing charging stations while ensuring a probabilistic service requirement on the waiting time to charge. Electric vehicle users or followers, on the other hand, minimize route length and may be cooperative or noncooperative. Their choice of route in turn determines the charging demand and waiting times at the charging stations and hence, the need to account for their decisions by the leader. The bilevel problem reduces to a single-level mixed-integer model using the optimality conditions of the follower’s problem when the charging stations operate as M/M/c queues and the followers are cooperative. To solve the bilevel model, a decomposition-based solution methodology is developed that uses a new logic-based Benders algorithm for the location-only problem. Computational experiments are performed on benchmark and real-life highway networks, including a new eastern U.S. network. The impact of route choice response, service requirements, and deviation tolerance on the location and sizing decisions are analyzed. The analysis demonstrates that stringent service requirements increase the capacity levels at open charging stations rather than their number and that solutions allowing higher deviations are less costly. Moreover, the difference between solutions under cooperative and uncooperative route choices is more significant when the deviation tolerance is lower. History: This paper has been accepted for the Transportation Science Special Issue on 2021 TSL Workshop: Supply and Demand Interplay in Transport and Logistics. Funding: This research was supported by the Ontario Graduate Scholarship when Ö. B. Kınay was a PhD candidate at the University of Waterloo, and this support is acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0494 .
{"title":"Charging Station Location and Sizing for Electric Vehicles Under Congestion","authors":"Ömer Burak Kinay, Fatma Gzara, Sibel A. Alumur","doi":"10.1287/trsc.2021.0494","DOIUrl":"https://doi.org/10.1287/trsc.2021.0494","url":null,"abstract":"This paper studies the problem of determining the strategic location of charging stations and their capacity levels under stochastic electric vehicle flows and charging times taking into account the route choice response of users. The problem is modeled using bilevel optimization, where the network planner or leader minimizes the total infrastructure cost of locating and sizing charging stations while ensuring a probabilistic service requirement on the waiting time to charge. Electric vehicle users or followers, on the other hand, minimize route length and may be cooperative or noncooperative. Their choice of route in turn determines the charging demand and waiting times at the charging stations and hence, the need to account for their decisions by the leader. The bilevel problem reduces to a single-level mixed-integer model using the optimality conditions of the follower’s problem when the charging stations operate as M/M/c queues and the followers are cooperative. To solve the bilevel model, a decomposition-based solution methodology is developed that uses a new logic-based Benders algorithm for the location-only problem. Computational experiments are performed on benchmark and real-life highway networks, including a new eastern U.S. network. The impact of route choice response, service requirements, and deviation tolerance on the location and sizing decisions are analyzed. The analysis demonstrates that stringent service requirements increase the capacity levels at open charging stations rather than their number and that solutions allowing higher deviations are less costly. Moreover, the difference between solutions under cooperative and uncooperative route choices is more significant when the deviation tolerance is lower. History: This paper has been accepted for the Transportation Science Special Issue on 2021 TSL Workshop: Supply and Demand Interplay in Transport and Logistics. Funding: This research was supported by the Ontario Graduate Scholarship when Ö. B. Kınay was a PhD candidate at the University of Waterloo, and this support is acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0494 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43378202","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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