Ezzat Elokda, Carlo Cenedese, Kenan Zhang, Andrea Censi, John Lygeros, Emilio Frazzoli, Florian Dörfler
This paper proposes a nonmonetary traffic demand management scheme, named CARMA, as a fair solution to the morning commute congestion. We consider heterogeneous commuters traveling through a single bottleneck that differ in both the desired arrival time and value of time (VOT). We consider a generalized notion of VOT by allowing it to vary dynamically on each day (e.g., according to trip purpose and urgency) rather than being a static characteristic of each individual. In our CARMA scheme, the bottleneck is divided into a fast lane that is kept in free flow and a slow lane that is subject to congestion. We introduce a nontradable mobility credit, named karma, that is used by commuters to bid for access to the fast lane. Commuters who get outbid or do not participate in the CARMA scheme instead use the slow lane. At the end of each day, karma collected from the bidders is redistributed, and the process repeats day by day. We model the collective commuter behaviors under CARMA as a dynamic population game (DPG), in which a stationary Nash equilibrium (SNE) is guaranteed to exist. Unlike existing monetary schemes, CARMA is demonstrated, both analytically and numerically, to achieve (a) an equitable traffic assignment with respect to heterogeneous income classes and (b) a strong Pareto improvement in the long-term average travel disutility with respect to no policy intervention. With extensive numerical analysis, we show that CARMA is able to retain the same congestion reduction as an optimal monetary tolling scheme under uniform karma redistribution and even outperform tolling under a well-designed redistribution scheme. We also highlight the privacy-preserving feature of CARMA, that is, its ability to tailor to the private preferences of commuters without centrally collecting the information.History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023.Funding: This work was supported by NCCR Automation, a National Centre of Competence in Research, funded by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Grant 180545].Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0323 .
{"title":"CARMA: Fair and Efficient Bottleneck Congestion Management via Nontradable Karma Credits","authors":"Ezzat Elokda, Carlo Cenedese, Kenan Zhang, Andrea Censi, John Lygeros, Emilio Frazzoli, Florian Dörfler","doi":"10.1287/trsc.2023.0323","DOIUrl":"https://doi.org/10.1287/trsc.2023.0323","url":null,"abstract":"This paper proposes a nonmonetary traffic demand management scheme, named CARMA, as a fair solution to the morning commute congestion. We consider heterogeneous commuters traveling through a single bottleneck that differ in both the desired arrival time and value of time (VOT). We consider a generalized notion of VOT by allowing it to vary dynamically on each day (e.g., according to trip purpose and urgency) rather than being a static characteristic of each individual. In our CARMA scheme, the bottleneck is divided into a fast lane that is kept in free flow and a slow lane that is subject to congestion. We introduce a nontradable mobility credit, named karma, that is used by commuters to bid for access to the fast lane. Commuters who get outbid or do not participate in the CARMA scheme instead use the slow lane. At the end of each day, karma collected from the bidders is redistributed, and the process repeats day by day. We model the collective commuter behaviors under CARMA as a dynamic population game (DPG), in which a stationary Nash equilibrium (SNE) is guaranteed to exist. Unlike existing monetary schemes, CARMA is demonstrated, both analytically and numerically, to achieve (a) an equitable traffic assignment with respect to heterogeneous income classes and (b) a strong Pareto improvement in the long-term average travel disutility with respect to no policy intervention. With extensive numerical analysis, we show that CARMA is able to retain the same congestion reduction as an optimal monetary tolling scheme under uniform karma redistribution and even outperform tolling under a well-designed redistribution scheme. We also highlight the privacy-preserving feature of CARMA, that is, its ability to tailor to the private preferences of commuters without centrally collecting the information.History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023.Funding: This work was supported by NCCR Automation, a National Centre of Competence in Research, funded by the Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Grant 180545].Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0323 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"2016 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178327","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}
Abhay Sobhanan, Junyoung Park, Jinkyoo Park, Changhyun Kwon
When vehicle routing decisions are intertwined with higher-level decisions, the resulting optimization problems pose significant challenges for computation. Examples are the multi-depot vehicle routing problem (MDVRP), where customers are assigned to depots before delivery, and the capacitated location routing problem (CLRP), where the locations of depots should be determined first. A simple and straightforward approach for such hierarchical problems would be to separate the higher-level decisions from the complicated vehicle routing decisions. For each higher-level decision candidate, we may evaluate the underlying vehicle routing problems to assess the candidate. As this approach requires solving vehicle routing problems multiple times, it has been regarded as impractical in most cases. We propose a novel deep learning-based approach called the genetic algorithm with neural cost predictor to tackle the challenge and simplify algorithm developments. For each higher-level decision candidate, we predict the objective function values of the underlying vehicle routing problems using a pretrained graph neural network without actually solving the routing problems. In particular, our proposed neural network learns the objective values of the HGS-CVRP open-source package that solves capacitated vehicle routing problems. Our numerical experiments show that this simplified approach is effective and efficient in generating high-quality solutions for both MDVRP and CLRP and that it has the potential to expedite algorithm developments for complicated hierarchical problems. We provide computational results evaluated in the standard benchmark instances used in the literature.History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023.Funding: This research was funded by the National Research Foundation of Korea [Grant RS-2023-00259550].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0369 .
{"title":"Genetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems","authors":"Abhay Sobhanan, Junyoung Park, Jinkyoo Park, Changhyun Kwon","doi":"10.1287/trsc.2023.0369","DOIUrl":"https://doi.org/10.1287/trsc.2023.0369","url":null,"abstract":"When vehicle routing decisions are intertwined with higher-level decisions, the resulting optimization problems pose significant challenges for computation. Examples are the multi-depot vehicle routing problem (MDVRP), where customers are assigned to depots before delivery, and the capacitated location routing problem (CLRP), where the locations of depots should be determined first. A simple and straightforward approach for such hierarchical problems would be to separate the higher-level decisions from the complicated vehicle routing decisions. For each higher-level decision candidate, we may evaluate the underlying vehicle routing problems to assess the candidate. As this approach requires solving vehicle routing problems multiple times, it has been regarded as impractical in most cases. We propose a novel deep learning-based approach called the genetic algorithm with neural cost predictor to tackle the challenge and simplify algorithm developments. For each higher-level decision candidate, we predict the objective function values of the underlying vehicle routing problems using a pretrained graph neural network without actually solving the routing problems. In particular, our proposed neural network learns the objective values of the HGS-CVRP open-source package that solves capacitated vehicle routing problems. Our numerical experiments show that this simplified approach is effective and efficient in generating high-quality solutions for both MDVRP and CLRP and that it has the potential to expedite algorithm developments for complicated hierarchical problems. We provide computational results evaluated in the standard benchmark instances used in the literature.History: This paper has been accepted for the Transportation Science Special Issue on TSL Conference 2023.Funding: This research was funded by the National Research Foundation of Korea [Grant RS-2023-00259550].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0369 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"46 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178354","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}
Michael G. H. Bell, Dat Tien Le, Jyotirmoyee Bhattacharjya, Glenn Geers
On-demand meal delivery has become a feature of most cities around the world as a result of platforms and apps that facilitate it as well as the pandemic, which for a period, closed restaurants. Meals are delivered by couriers, typically on bikes, e-bikes, or scooters, who circulate collecting meals from kitchens and delivering them to customers, who usually order online. A Markov model for circulating couriers with n + 1 parameters, where [Formula: see text] is the number of kitchens plus customers, is derived by entropy maximization. There is one parameter for each kitchen and customer representing the demand for a courier, and there is one parameter representing the urgency of delivery. It is shown how the mean and variance of delivery time can be calculated once the parameters are known. The Markov model is irreducible. Two procedures are presented for calibrating model parameters on a data set of orders. Both procedures match known order frequencies with fitted visit probabilities; the first inputs an urgency parameter value and outputs mean delivery time, whereas the second inputs mean delivery time and outputs the corresponding urgency parameter value. Model calibration is demonstrated on a publicly available data set of meal orders from Grubhub. Grubhub data are also used to validate the calibrated model using a likelihood ratio. By changing the location of one kitchen, it is shown how the calibrated model can estimate the resulting change in demand for its meals and the corresponding mean delivery time. The Markov model could also be used for the assignment of courier trips to a street network.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT Conference.
由于平台和应用程序的便利以及大流行病的影响，按需送餐已成为世界上大多数城市的一个特色。送餐员通常骑着自行车、电动自行车或滑板车，从厨房收集餐点并送到顾客手中，顾客通常在网上订餐。通过熵最大化，可以得出一个具有 n + 1 个参数的循环快递员马尔可夫模型，其中[公式：见正文]是厨房和顾客的数量。每个厨房和客户都有一个参数，代表对快递员的需求，还有一个参数代表送货的紧迫性。图中展示了已知参数后，如何计算配送时间的均值和方差。马尔可夫模型是不可还原的。本文介绍了在订单数据集上校准模型参数的两种程序。这两个程序都将已知的订单频率与拟合的访问概率相匹配；第一个程序输入紧急程度参数值并输出平均交货时间，而第二个程序输入平均交货时间并输出相应的紧急程度参数值。模型校准在 Grubhub 的公开餐单数据集上进行了演示。Grubhub 数据还用于使用似然比验证校准模型。通过改变一个厨房的位置，展示了校准后的模型如何估计由此产生的餐食需求变化以及相应的平均配送时间。马尔可夫模型还可用于将快递行程分配到街道网络：本文已被 ISTTT 会议交通科学专刊录用。
{"title":"On-Demand Meal Delivery: A Markov Model for Circulating Couriers","authors":"Michael G. H. Bell, Dat Tien Le, Jyotirmoyee Bhattacharjya, Glenn Geers","doi":"10.1287/trsc.2024.0513","DOIUrl":"https://doi.org/10.1287/trsc.2024.0513","url":null,"abstract":"On-demand meal delivery has become a feature of most cities around the world as a result of platforms and apps that facilitate it as well as the pandemic, which for a period, closed restaurants. Meals are delivered by couriers, typically on bikes, e-bikes, or scooters, who circulate collecting meals from kitchens and delivering them to customers, who usually order online. A Markov model for circulating couriers with n + 1 parameters, where [Formula: see text] is the number of kitchens plus customers, is derived by entropy maximization. There is one parameter for each kitchen and customer representing the demand for a courier, and there is one parameter representing the urgency of delivery. It is shown how the mean and variance of delivery time can be calculated once the parameters are known. The Markov model is irreducible. Two procedures are presented for calibrating model parameters on a data set of orders. Both procedures match known order frequencies with fitted visit probabilities; the first inputs an urgency parameter value and outputs mean delivery time, whereas the second inputs mean delivery time and outputs the corresponding urgency parameter value. Model calibration is demonstrated on a publicly available data set of meal orders from Grubhub. Grubhub data are also used to validate the calibrated model using a likelihood ratio. By changing the location of one kitchen, it is shown how the calibrated model can estimate the resulting change in demand for its meals and the corresponding mean delivery time. The Markov model could also be used for the assignment of courier trips to a street network.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT Conference.","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"11 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178356","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}
Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. Traditional calibration has been developed based on optimization methods. In this paper, we propose a novel physics-informed, learning-based calibration approach that achieves performances comparable to and even better than those of optimization-based methods. To this end, we combine the classical deep autoencoder, an unsupervised machine learning model consisting of one encoder and one decoder, with traffic flow models. Our approach informs the decoder of the physical traffic flow models and thus induces the encoder to yield reasonable traffic parameters given flow and speed measurements. We also introduce the denoising autoencoder into our method so that it can handle not only with normal data but also corrupted data with missing values. We verified our approach with a case study of Interstate 210 Eastbound in California. It turns out that our approach can achieve comparable performance to the-state-of-the-art calibration methods given normal data and outperform them given corrupted data with missing values.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: This study was supported by the National Science Foundation [Grant CMMI-1949710] and the C2SMART Research Center, a Tier 1 University Transportation Center.
{"title":"Physics-Informed Machine Learning for Calibrating Macroscopic Traffic Flow Models","authors":"Yu Tang, Li Jin, Kaan Ozbay","doi":"10.1287/trsc.2024.0526","DOIUrl":"https://doi.org/10.1287/trsc.2024.0526","url":null,"abstract":"Well-calibrated traffic flow models are fundamental to understanding traffic phenomena and designing control strategies. Traditional calibration has been developed based on optimization methods. In this paper, we propose a novel physics-informed, learning-based calibration approach that achieves performances comparable to and even better than those of optimization-based methods. To this end, we combine the classical deep autoencoder, an unsupervised machine learning model consisting of one encoder and one decoder, with traffic flow models. Our approach informs the decoder of the physical traffic flow models and thus induces the encoder to yield reasonable traffic parameters given flow and speed measurements. We also introduce the denoising autoencoder into our method so that it can handle not only with normal data but also corrupted data with missing values. We verified our approach with a case study of Interstate 210 Eastbound in California. It turns out that our approach can achieve comparable performance to the-state-of-the-art calibration methods given normal data and outperform them given corrupted data with missing values.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: This study was supported by the National Science Foundation [Grant CMMI-1949710] and the C2SMART Research Center, a Tier 1 University Transportation Center.","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"26 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178355","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 proposes the use of heatmaps as a control lever to manage the probabilistic repositioning of independent drivers in crowdsourced delivery platforms. The platform aims to maximize order fulfillment by dynamically matching drivers and orders and selecting heatmaps that trigger the probabilistic flow of unmatched drivers to balance driver supply and delivery requests across the service region. We develop a Markov decision process (MDP) model to sequentially select matching and heatmap decisions in which the repositioning behavior of drivers is captured by a multinomial logit discrete choice model. Because of the curse of dimensionality and the endogenous uncertainty of driver repositioning, the MDP model is solved using a rolling-horizon stochastic lookahead policy. This policy decomposes matching and heatmap decisions into two optimization problems: a two-stage stochastic programming upper bounding problem for matching decisions and a mixed-integer programming problem for heatmap decisions. We also propose a simple policy for efficiently solving large-scale problems. An extensive computational study on instances derived from the Chicago ride-hailing data set is conducted. Computational experiments demonstrate the value of heatmaps in improving order fulfillment beyond the level achieved by matching alone (up to 25%) and identify conditions that affect the benefit of using heatmaps to guide driver repositioning.Funding: The authors gratefully acknowledge the support of the Natural Sciences and Engineering Research Council of Canada through Discovery Grants [Grants RGPIN-2024-04881, RGPIN-2020-04498, and RGPIN-2019-06207] awarded to the first, second, and third authors, respectively.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0418 .
{"title":"Heatmap Design for Probabilistic Driver Repositioning in Crowdsourced Delivery","authors":"Aliaa Alnaggar, Fatma Gzara, James Bookbinder","doi":"10.1287/trsc.2022.0418","DOIUrl":"https://doi.org/10.1287/trsc.2022.0418","url":null,"abstract":"This paper proposes the use of heatmaps as a control lever to manage the probabilistic repositioning of independent drivers in crowdsourced delivery platforms. The platform aims to maximize order fulfillment by dynamically matching drivers and orders and selecting heatmaps that trigger the probabilistic flow of unmatched drivers to balance driver supply and delivery requests across the service region. We develop a Markov decision process (MDP) model to sequentially select matching and heatmap decisions in which the repositioning behavior of drivers is captured by a multinomial logit discrete choice model. Because of the curse of dimensionality and the endogenous uncertainty of driver repositioning, the MDP model is solved using a rolling-horizon stochastic lookahead policy. This policy decomposes matching and heatmap decisions into two optimization problems: a two-stage stochastic programming upper bounding problem for matching decisions and a mixed-integer programming problem for heatmap decisions. We also propose a simple policy for efficiently solving large-scale problems. An extensive computational study on instances derived from the Chicago ride-hailing data set is conducted. Computational experiments demonstrate the value of heatmaps in improving order fulfillment beyond the level achieved by matching alone (up to 25%) and identify conditions that affect the benefit of using heatmaps to guide driver repositioning.Funding: The authors gratefully acknowledge the support of the Natural Sciences and Engineering Research Council of Canada through Discovery Grants [Grants RGPIN-2024-04881, RGPIN-2020-04498, and RGPIN-2019-06207] awarded to the first, second, and third authors, respectively.Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.0418 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"11 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178363","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}
Dana Hassani, Anna Nagurney, Oleg Nivievskyi, Pavlo Martyshev
The world is facing immense challenges because of increasing strife and the impacts of climate change with accompanying disasters, both sudden-onset as well as slow-onset ones, which have affected the trade of agricultural commodities needed for food security. In this paper, a multiperiod, multicommodity, international, agricultural trade network equilibrium model is constructed with capacity constraints on the production, transportation, and storage of agricultural commodities. The model allows for multiple routes between supply and demand country markets, different modes of transport, and storage in the producing and consuming countries as well as in the intermediate countries. The generality of the underlying functions, coupled with the capacity constraints, allow for the modeling of competition among agricultural commodities for production, transportation, and storage. The capacity constraints also enable the quantification of various disaster-related disruptions to production, transportation, and storage on the volumes of commodity flows as well as on the prices. A series of numerical examples inspired by the effects of Russia’s full-scale invasion of Ukraine on agricultural trade is presented, and the results are analyzed to provide insights into food insecurity issues caused by the war.
{"title":"A Multiperiod, Multicommodity, Capacitated International Agricultural Trade Network Equilibrium Model with Applications to Ukraine in Wartime","authors":"Dana Hassani, Anna Nagurney, Oleg Nivievskyi, Pavlo Martyshev","doi":"10.1287/trsc.2023.0294","DOIUrl":"https://doi.org/10.1287/trsc.2023.0294","url":null,"abstract":"The world is facing immense challenges because of increasing strife and the impacts of climate change with accompanying disasters, both sudden-onset as well as slow-onset ones, which have affected the trade of agricultural commodities needed for food security. In this paper, a multiperiod, multicommodity, international, agricultural trade network equilibrium model is constructed with capacity constraints on the production, transportation, and storage of agricultural commodities. The model allows for multiple routes between supply and demand country markets, different modes of transport, and storage in the producing and consuming countries as well as in the intermediate countries. The generality of the underlying functions, coupled with the capacity constraints, allow for the modeling of competition among agricultural commodities for production, transportation, and storage. The capacity constraints also enable the quantification of various disaster-related disruptions to production, transportation, and storage on the volumes of commodity flows as well as on the prices. A series of numerical examples inspired by the effects of Russia’s full-scale invasion of Ukraine on agricultural trade is presented, and the results are analyzed to provide insights into food insecurity issues caused by the war.","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"8 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178357","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}
Longitudinal vehicle trajectories suffer from errors and noise because of detection and extraction techniques, challenging their applications. Existing smoothing methods either lack physical meaning or cannot ensure solution existence and uniqueness. To address this, we propose a two-step quadratic programming method that aligns smoothed speeds and higher-order derivatives with physical laws, drivers’ behaviors, and vehicle characteristics. Unlike the well-known smoothing splines method, which minimizes a weighted sum of discrepancy and roughness in a single quadratic programming problem, our method incorporates prior knowledge of position errors into two sequential quadratic programming problems. Step 1 solves half-smoothed positions by minimizing the discrepancy between them and raw positions, subject to physically meaningful bounds on speeds and higher-order derivatives of half-smoothed positions. Step 2 solves smoothed positions by minimizing the roughness while maintaining physically meaningful bounds and allowing the deviations from raw data of smoothed positions by at most those of the half-smoothed positions and prior position errors. The second step’s coefficient matrix is not positive definite, necessitating the matching of the first few smoothed positions with corresponding half-smoothed ones, with equality constraints equaling the highest order of derivatives. We establish the solution existence and uniqueness for both problems, ensuring their well-defined nature. Numerical experiments using Next Generation Simulation (NGSIM) data demonstrate that a third-order derivative constraint yields an efficient method and produces smoothed trajectories comparable with manually re-extracted ones, consistent with the minimum jerk principle for human movements. Comparisons with an existing approach and application to the Highway Drone data set further validate our method’s efficacy. Notably, our method is a postprocessing smoothing technique based on trajectory data and is not intended for systematic errors. Future work will extend this method to lateral vehicle trajectories and trajectory prediction and planning for both human-driven and automated vehicles. This approach also holds potential for broader smoothing problems with known average error in raw data.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: The authors extend their gratitude to the Pacific Southwest Region University Transportation Center and the University of California Institute of Transportation Studies (UC ITS) Statewide Transportation Research Program (STRP) for their valuable financial support.
{"title":"Two-Step Quadratic Programming for Physically Meaningful Smoothing of Longitudinal Vehicle Trajectories","authors":"Ximeng Fan, Wen-Long Jin, Penghang Yin","doi":"10.1287/trsc.2024.0524","DOIUrl":"https://doi.org/10.1287/trsc.2024.0524","url":null,"abstract":"Longitudinal vehicle trajectories suffer from errors and noise because of detection and extraction techniques, challenging their applications. Existing smoothing methods either lack physical meaning or cannot ensure solution existence and uniqueness. To address this, we propose a two-step quadratic programming method that aligns smoothed speeds and higher-order derivatives with physical laws, drivers’ behaviors, and vehicle characteristics. Unlike the well-known smoothing splines method, which minimizes a weighted sum of discrepancy and roughness in a single quadratic programming problem, our method incorporates prior knowledge of position errors into two sequential quadratic programming problems. Step 1 solves half-smoothed positions by minimizing the discrepancy between them and raw positions, subject to physically meaningful bounds on speeds and higher-order derivatives of half-smoothed positions. Step 2 solves smoothed positions by minimizing the roughness while maintaining physically meaningful bounds and allowing the deviations from raw data of smoothed positions by at most those of the half-smoothed positions and prior position errors. The second step’s coefficient matrix is not positive definite, necessitating the matching of the first few smoothed positions with corresponding half-smoothed ones, with equality constraints equaling the highest order of derivatives. We establish the solution existence and uniqueness for both problems, ensuring their well-defined nature. Numerical experiments using Next Generation Simulation (NGSIM) data demonstrate that a third-order derivative constraint yields an efficient method and produces smoothed trajectories comparable with manually re-extracted ones, consistent with the minimum jerk principle for human movements. Comparisons with an existing approach and application to the Highway Drone data set further validate our method’s efficacy. Notably, our method is a postprocessing smoothing technique based on trajectory data and is not intended for systematic errors. Future work will extend this method to lateral vehicle trajectories and trajectory prediction and planning for both human-driven and automated vehicles. This approach also holds potential for broader smoothing problems with known average error in raw data.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: The authors extend their gratitude to the Pacific Southwest Region University Transportation Center and the University of California Institute of Transportation Studies (UC ITS) Statewide Transportation Research Program (STRP) for their valuable financial support.","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"10 1","pages":""},"PeriodicalIF":4.6,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178358","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}
Martina Cerulli, Claudia Archetti, Elena Fernández, Ivana Ljubić
In last-mile delivery logistics, peer-to-peer logistic platforms play an important role in connecting senders, customers, and independent carriers to fulfill delivery requests. As the carriers are not under the platform’s control, the platform has to anticipate their reactions while deciding how to allocate the delivery operations. Indeed, carriers’ decisions largely affect the platform’s revenue. In this paper, we model this problem using bilevel programming. At the upper level, the platform decides how to assign the orders to the carriers; at the lower level, each carrier solves a profitable tour problem to determine which offered requests to accept, based on her own profit maximization. Possibly, the platform can influence carriers’ decisions by determining also the compensation paid for each accepted request. The two considered settings result in two different formulations: the bilevel profitable tour problem with fixed compensation margins and with margin decisions, respectively. For each of them, we propose single-level reformulations and alternative formulations where the lower-level routing variables are projected out. A branch-and-cut algorithm is proposed to solve the bilevel models, with a tailored warm-start heuristic used to speed up the solution process. Extensive computational tests are performed to compare the proposed formulations and analyze solution characteristics.Funding: The research of E. Fernandez was partially funded through the Spanish Ministerio de Ciencia y Tecnología and European Regional Development Funds (ERDF) [Grant MTM2019-105824GB-I00]. The research of C. Archetti, M. Cerulli, and I. Ljubić was partially funded by CY Initiative of Excellence, France [Grant “Investissements d’Avenir ANR-16-IDEX-0008”].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0129 .
在最后一英里配送物流中，点对点物流平台在连接发件人、客户和独立承运商以满足配送要求方面发挥着重要作用。由于承运商不受平台控制，平台在决定如何分配配送业务时必须预测承运商的反应。事实上，承运商的决策在很大程度上会影响平台的收入。在本文中，我们使用双层编程来模拟这一问题。在上层，平台决定如何将订单分配给承运商；在下层，每个承运商根据自身的利润最大化，解决盈利巡回问题，决定接受哪些提供的请求。或许，平台还可以通过决定为每个已接受请求支付的报酬来影响承运商的决策。考虑到这两种情况，我们提出了两种不同的方案：分别是具有固定补偿金的双层盈利巡回问题和具有补偿金决策的双层盈利巡回问题。针对每种情况，我们都提出了单层次的重构公式和将低层次路由变量投影出来的替代公式。我们还提出了一种分支-切割算法来求解双层模型，并使用一种量身定制的热启动启发式来加速求解过程。进行了大量的计算测试，以比较所提出的公式并分析求解特征：费尔南德斯（E. Fernandez）的研究部分由西班牙科技部和欧洲区域发展基金（ERDF）资助[资助金 MTM2019-105824GB-I00]。C. Archetti、M. Cerulli 和 I. Ljubić 的研究得到了法国 CY Initiative of Excellence [Grant "Investissements d'Avenir ANR-16-IDEX-0008"] 的部分资助：在线附录见 https://doi.org/10.1287/trsc.2023.0129 。
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This paper examines the effects of hypercongestion mitigation by perimeter control and the introduction of autonomous vehicles on the spatial structures of cities. By incorporating a bathtub model, we develop a land use model where hypercongestion occurs in the downtown area and interacts with land use. We show that hypercongestion mitigation by perimeter control decreases the commuting cost in the short run and results in a less dense urban spatial structure in the long run. Furthermore, we reveal that the impact of autonomous vehicles depends on the presence of hypercongestion. The introduction of autonomous vehicles may increase the commuting cost in the presence of hypercongestion and may cause a decrease in the suburban population; however, it may make cities spatially expand outward. This result contradicts that of the standard bottleneck model. When perimeter control is implemented, the introduction of autonomous vehicles decreases the commuting cost and results in a less dense urban spatial structure. These results show that hypercongestion is a key factor that can change urban spatial structures.History: This paper has been accepted for the Transportation Science Special Issue on ISTTT25 Conference.Funding: This work was supported by ACT-X [Grant JPMJAX21AE], Fusion Oriented REsearch for disruptive Science and Technology [Grant JPMJFR215M], the Japan Society for the Promotion of Science [Grants 23K13422 and 22H01610], and the Council for Science, Technology and Innovation [Grant JPJ012187].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2024.0519 .
本文探讨了通过周边控制和引入自动驾驶汽车缓解过度拥堵对城市空间结构的影响。通过结合浴缸模型，我们建立了一个土地利用模型，在该模型中，过度拥堵发生在市中心区域，并与土地利用相互作用。我们的研究表明，通过周边控制缓解过度拥堵会在短期内降低通勤成本，并在长期内降低城市空间结构的密度。此外，我们还揭示了自动驾驶汽车的影响取决于是否存在过度拥堵。在过度拥堵的情况下，自动驾驶汽车的引入可能会增加通勤成本，并可能导致郊区人口减少；但是，自动驾驶汽车的引入可能会使城市在空间上向外扩张。这一结果与标准瓶颈模型相矛盾。当实施周边控制时，自动驾驶汽车的引入会降低通勤成本，并导致城市空间结构密度降低。这些结果表明，过度拥堵是改变城市空间结构的一个关键因素：本文已被 ISTTT25 会议交通科学专刊接受：本研究得到了 ACT-X [JPMJAX21AE], Fusion Oriented REsearch for disruptive Science and Technology [JPMJFR215M], the Japan Society for the Promotion of Science [Grants 23K13422 and 22H01610], and the Council for Science, Technology and Innovation [Grants JPJ012187] 的支持：在线附录见 https://doi.org/10.1287/trsc.2024.0519 。
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Marjolein Aerts-Veenstra, Marilène Cherkesly, Timo Gschwind
In this paper, we study the pickup and delivery problem with time windows and multiple compartments (PDPTWMC). The PDPTWMC generalizes the pickup and delivery problem with time windows to vehicles with multiple compartments. In particular, we consider three compartment-related attributes: (1) compartment capacity flexibility that allows the capacities of the compartments to be fixed or flexible, (2) item-to-compartment flexibility that specifies which items are compatible with which compartments, and (3) item-to-item compatibility that considers that incompatible items cannot be simultaneously in the same compartment. To solve the PDPTWMC, we propose an exact branch-price-and-cut algorithm in which the pricing problem is solved by means of a unified bidirectional labeling algorithm. The labeling algorithm can tackle all possible combinations of the studied compartment-related attributes of the PDPTWMC. Furthermore, we implement several acceleration techniques that allow to, among others, reduce the symmetry in the label extensions with empty compartments, the symmetry in the dominance between compartments with similar attributes, and the complexity of the algorithm with fixed compartment capacity. Finally, we introduce benchmark instances for the PDPTWMC and conduct an extensive computational campaign to test the limits of our algorithm and to derive relevant managerial insights in order to highlight the applicability of considering the studied compartment-related attributes.Funding: This work was supported by Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant 439.18.459] and the Natural Sciences and Engineering Research Council of Canada [Grant 2017-06106].Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.0252 .
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