This paper proposes a method to recover an unknown probability distribution given a censored or truncated sample from that distribution. The proposed method is a novel and conceptually simple detruncation method based on sampling the observed data according to weights learned by solving a simulation-based optimization problem; this method is especially appropriate in cases where little analytic information is available but the truncation process can be simulated. The proposed method is compared with the ubiquitous maximum likelihood estimation (MLE) method in a variety of synthetic validation experiments, where it is found that the proposed method performs slightly worse than perfectly specified MLE and competitively with slightly misspecified MLE. The practical application of this method is then demonstrated via a pair of case studies in which the proposed detruncation method is used alongside a car-sharing service simulator to estimate demand for round-trip car-sharing services in the Boston and New York metropolitan areas.
本文提出了一种从未知概率分布中恢复截短样本的方法。该方法是一种新颖且概念简单的去截断方法，该方法基于求解基于仿真的优化问题所获得的权重对观测数据进行采样;这种方法特别适用于分析信息很少但可以模拟截断过程的情况。在各种综合验证实验中，将所提方法与泛在极大似然估计(ubiquitous maximum likelihood estimation, MLE)方法进行了比较，发现所提方法的性能略差于完全指定极大似然估计，并与轻微错指定极大似然估计竞争。然后通过一对案例研究证明了该方法的实际应用，其中所提出的去截断方法与汽车共享服务模拟器一起使用，以估计波士顿和纽约大都市地区往返汽车共享服务的需求。
{"title":"A Data-Driven Method for Reconstructing a Distribution from a Truncated Sample with an Application to Inferring Car-Sharing Demand","authors":"Evan Fields, C. Osorio, Tianli Zhou","doi":"10.1287/TRSC.2020.1028","DOIUrl":"https://doi.org/10.1287/TRSC.2020.1028","url":null,"abstract":"This paper proposes a method to recover an unknown probability distribution given a censored or truncated sample from that distribution. The proposed method is a novel and conceptually simple detruncation method based on sampling the observed data according to weights learned by solving a simulation-based optimization problem; this method is especially appropriate in cases where little analytic information is available but the truncation process can be simulated. The proposed method is compared with the ubiquitous maximum likelihood estimation (MLE) method in a variety of synthetic validation experiments, where it is found that the proposed method performs slightly worse than perfectly specified MLE and competitively with slightly misspecified MLE. The practical application of this method is then demonstrated via a pair of case studies in which the proposed detruncation method is used alongside a car-sharing service simulator to estimate demand for round-trip car-sharing services in the Boston and New York metropolitan areas.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"34 1","pages":"616-636"},"PeriodicalIF":0.0,"publicationDate":"2021-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87442780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the realm of traffic assignment over a network involving rigid arc capacities, the aim of the present work is to generalize the model of Marcotte, Nguyen, and Schoeb [Marcotte P, Nguyen S, Schoeb A (2004) A strategic flow model of traffic assignment in static capacitated networks. Oper. Res. 52(2):191–212.] by casting it within a stochastic user equilibrium framework. The strength of the proposed model is to incorporate two sources of stochasticity stemming, respectively, from the users’ imperfect knowledge regarding arc costs (represented by a discrete choice model) and the probability of not accessing saturated arcs. Moreover, the arc-based formulation extends the Markovian traffic equilibrium model of Baillon and Cominetti [Baillon JB, Cominetti R ( 2008 ) Markovian traffic equilibrium. Math. Programming 111(1-2):33–56.] through the explicit consideration of capacities. This paper is restricted to the case of acyclic networks, for which we present solution algorithms and numerical experiments.
{"title":"A Strategic Markovian Traffic Equilibrium Model for Capacitated Networks","authors":"Maëlle Zimmermann, Emma Frejinger, P. Marcotte","doi":"10.1287/TRSC.2020.1033","DOIUrl":"https://doi.org/10.1287/TRSC.2020.1033","url":null,"abstract":"In the realm of traffic assignment over a network involving rigid arc capacities, the aim of the present work is to generalize the model of Marcotte, Nguyen, and Schoeb [Marcotte P, Nguyen S, Schoeb A (2004) A strategic flow model of traffic assignment in static capacitated networks. Oper. Res. 52(2):191–212.] by casting it within a stochastic user equilibrium framework. The strength of the proposed model is to incorporate two sources of stochasticity stemming, respectively, from the users’ imperfect knowledge regarding arc costs (represented by a discrete choice model) and the probability of not accessing saturated arcs. Moreover, the arc-based formulation extends the Markovian traffic equilibrium model of Baillon and Cominetti [Baillon JB, Cominetti R ( 2008 ) Markovian traffic equilibrium. Math. Programming 111(1-2):33–56.] through the explicit consideration of capacities. This paper is restricted to the case of acyclic networks, for which we present solution algorithms and numerical experiments.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"117 1","pages":"574-591"},"PeriodicalIF":0.0,"publicationDate":"2021-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75477217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Wentao Zhang, Nelson A. Uhan, M. Dessouky, A. Toriello
Freight consolidation is a logistics practice that improves the cost-effectiveness and efficiency of transportation operations, and also reduces energy consumption and carbon footprint. A “fair” shipping cost-sharing scheme is indispensable to help establish and sustain the cooperation of a group of suppliers in freight consolidation. In this paper, we design a truthful acyclic mechanism to solve the cost-sharing problem in a freight consolidation system with one consolidation center and one common destination. Applying the acyclic mechanism, the consolidation center decides which suppliers’ demands ship via the consolidation center and their corresponding cost shares based on their willingness to pay for the service. The proposed acyclic mechanism is designed based on bin packing solutions that are also strong Nash equilibria for a related noncooperative game. We study the budget-balance of the mechanism both theoretically and numerically. We prove a 2-budget-balance guarantee for the mechanism in general and better budget-balance guarantees under specific problem settings. Empirical tests on budget-balance show that our mechanism performs much better than the guaranteed budget-balance ratio. We also study the economic efficiency of our mechanism numerically to investigate its impact on social welfare under different conditions.
{"title":"Acyclic Mechanism Design for Freight Consolidation","authors":"Wentao Zhang, Nelson A. Uhan, M. Dessouky, A. Toriello","doi":"10.1287/TRSC.2020.1031","DOIUrl":"https://doi.org/10.1287/TRSC.2020.1031","url":null,"abstract":"Freight consolidation is a logistics practice that improves the cost-effectiveness and efficiency of transportation operations, and also reduces energy consumption and carbon footprint. A “fair” shipping cost-sharing scheme is indispensable to help establish and sustain the cooperation of a group of suppliers in freight consolidation. In this paper, we design a truthful acyclic mechanism to solve the cost-sharing problem in a freight consolidation system with one consolidation center and one common destination. Applying the acyclic mechanism, the consolidation center decides which suppliers’ demands ship via the consolidation center and their corresponding cost shares based on their willingness to pay for the service. The proposed acyclic mechanism is designed based on bin packing solutions that are also strong Nash equilibria for a related noncooperative game. We study the budget-balance of the mechanism both theoretically and numerically. We prove a 2-budget-balance guarantee for the mechanism in general and better budget-balance guarantees under specific problem settings. Empirical tests on budget-balance show that our mechanism performs much better than the guaranteed budget-balance ratio. We also study the economic efficiency of our mechanism numerically to investigate its impact on social welfare under different conditions.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"9 1","pages":"571-584"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81261216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Efficiently handling last-mile deliveries becomes more and more important nowadays. Using drones to support classical vehicles allows improving delivery schedules as long as efficient solution methods to plan last-mile deliveries with drones are available. We study exact solution approaches for some variants of the traveling salesman problem with drone (TSP-D) in which a truck and a drone are teamed up to serve a set of customers. This combination of truck and drone can exploit the benefits of both vehicle types: the truck has a large capacity but usually low travel speed in urban areas; the drone is faster and not restricted to street networks, but its range and carrying capacity are limited. We propose a compact mixed-integer linear program (MILP) for several TSP-D variants that is based on timely synchronizing truck and drone flows; such an MILP is easy to implement but nevertheless leads to competitive results compared with the state-of-the-art MILPs. Furthermore, we introduce dynamic programming recursions to model several TSP-D variants. We show how these dynamic programming recursions can be exploited in an exact branch-and-price approach based on a set partitioning formulation using ng-route relaxation and a three-level hierarchical branching. The proposed branch-and-price can solve instances with up to 39 customers to optimality outperforming the state-of-the-art by more than doubling the manageable instance size. Finally, we analyze different scenarios and show that even a single drone can significantly reduce a route’s completion time when the drone is sufficiently fast.
{"title":"Exact Methods for the Traveling Salesman Problem with Drone","authors":"R. Roberti, Mario Ruthmair","doi":"10.1287/trsc.2020.1017","DOIUrl":"https://doi.org/10.1287/trsc.2020.1017","url":null,"abstract":"Efficiently handling last-mile deliveries becomes more and more important nowadays. Using drones to support classical vehicles allows improving delivery schedules as long as efficient solution methods to plan last-mile deliveries with drones are available. We study exact solution approaches for some variants of the traveling salesman problem with drone (TSP-D) in which a truck and a drone are teamed up to serve a set of customers. This combination of truck and drone can exploit the benefits of both vehicle types: the truck has a large capacity but usually low travel speed in urban areas; the drone is faster and not restricted to street networks, but its range and carrying capacity are limited. We propose a compact mixed-integer linear program (MILP) for several TSP-D variants that is based on timely synchronizing truck and drone flows; such an MILP is easy to implement but nevertheless leads to competitive results compared with the state-of-the-art MILPs. Furthermore, we introduce dynamic programming recursions to model several TSP-D variants. We show how these dynamic programming recursions can be exploited in an exact branch-and-price approach based on a set partitioning formulation using ng-route relaxation and a three-level hierarchical branching. The proposed branch-and-price can solve instances with up to 39 customers to optimality outperforming the state-of-the-art by more than doubling the manageable instance size. Finally, we analyze different scenarios and show that even a single drone can significantly reduce a route’s completion time when the drone is sufficiently fast.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"1 1","pages":"315-335"},"PeriodicalIF":0.0,"publicationDate":"2021-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75937518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Forecasting short-term ridership of different origin-destination pairs (i.e., OD matrix) is crucial to the real-time operation of a metro system. However, this problem is notoriously difficult due to the large-scale, high-dimensional, noisy, and highly skewed nature of OD matrices. In this paper, we address the short-term OD matrix forecasting problem by estimating a low-rank high-order vector autoregression (VAR) model. We reconstruct this problem as a data-driven reduced-order regression model and estimate it using dynamic mode decomposition (DMD). The VAR coefficients estimated by DMD are the best-fit (in terms of Frobenius norm) linear operator for the rank-reduced full-size data. To address the practical issue that metro OD matrices cannot be observed in real time, we use the boarding demand to replace the unavailable OD matrices. Moreover, we consider the time-evolving feature of metro systems and improve the forecast by exponentially reducing the weights for historical data. A tailored online update algorithm is then developed for the high-order weighted DMD model (HW-DMD) to update the model coefficients at a daily level, without storing historical data or retraining. Experiments on data from two large-scale metro systems show that the proposed HW-DMD is robust to noisy and sparse data, and significantly outperforms baseline models in forecasting both OD matrices and boarding flow. The online update algorithm also shows consistent accuracy over a long time, allowing us to maintain an HW-DMD model at much low costs.
{"title":"Real-Time Forecasting of Metro Origin-Destination Matrices with High-Order Weighted Dynamic Mode Decomposition","authors":"Zhanhong Cheng, M. Trépanier, Lijun Sun","doi":"10.1287/trsc.2022.1128","DOIUrl":"https://doi.org/10.1287/trsc.2022.1128","url":null,"abstract":"Forecasting short-term ridership of different origin-destination pairs (i.e., OD matrix) is crucial to the real-time operation of a metro system. However, this problem is notoriously difficult due to the large-scale, high-dimensional, noisy, and highly skewed nature of OD matrices. In this paper, we address the short-term OD matrix forecasting problem by estimating a low-rank high-order vector autoregression (VAR) model. We reconstruct this problem as a data-driven reduced-order regression model and estimate it using dynamic mode decomposition (DMD). The VAR coefficients estimated by DMD are the best-fit (in terms of Frobenius norm) linear operator for the rank-reduced full-size data. To address the practical issue that metro OD matrices cannot be observed in real time, we use the boarding demand to replace the unavailable OD matrices. Moreover, we consider the time-evolving feature of metro systems and improve the forecast by exponentially reducing the weights for historical data. A tailored online update algorithm is then developed for the high-order weighted DMD model (HW-DMD) to update the model coefficients at a daily level, without storing historical data or retraining. Experiments on data from two large-scale metro systems show that the proposed HW-DMD is robust to noisy and sparse data, and significantly outperforms baseline models in forecasting both OD matrices and boarding flow. The online update algorithm also shows consistent accuracy over a long time, allowing us to maintain an HW-DMD model at much low costs.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"36 1","pages":"904-918"},"PeriodicalIF":0.0,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86621015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Vehicles on roads can be distinguished, each defined by its own set of properties (e.g., fleet length and free-flow speed). The traffic states on roads can be attributed to the longitudinal heterogeneity in vehicles. Vehicles slower than prevailing vehicles are defined as moving bottlenecks. On a multilane road section with multiple vehicle types, slower vehicles create moving bottlenecks and induce overtaking by faster vehicles so as to maintain their higher desired speed. The influence of single-class moving bottlenecks has been studied in the past. However, the impacts of multiple classes of moving bottlenecks have not yet been fully explored. This paper categorizes vehicles into passenger cars, medium trucks, and heavy trucks. By defining medium trucks and heavy trucks as moving bottlenecks, we develop analytical formulas for the fundamental diagram on a multilane road section with heterogeneous moving bottlenecks. The formula confirms that the composition of traffic and the longest truck platoon length influence the fundamental diagram. We then conduct simulations using a first-order kinematic wave model in Lagrangian coordinates to validate the fundamental diagram developed with the analytical formula and obtain promising results. This study provides fundamental knowledge for multiclass traffic modeling and multilane traffic operations.
{"title":"Multiclass Traffic Flow Dynamics: An Endogenous Model","authors":"K. Yuan, H. Lo","doi":"10.1287/trsc.2020.1015","DOIUrl":"https://doi.org/10.1287/trsc.2020.1015","url":null,"abstract":"Vehicles on roads can be distinguished, each defined by its own set of properties (e.g., fleet length and free-flow speed). The traffic states on roads can be attributed to the longitudinal heterogeneity in vehicles. Vehicles slower than prevailing vehicles are defined as moving bottlenecks. On a multilane road section with multiple vehicle types, slower vehicles create moving bottlenecks and induce overtaking by faster vehicles so as to maintain their higher desired speed. The influence of single-class moving bottlenecks has been studied in the past. However, the impacts of multiple classes of moving bottlenecks have not yet been fully explored. This paper categorizes vehicles into passenger cars, medium trucks, and heavy trucks. By defining medium trucks and heavy trucks as moving bottlenecks, we develop analytical formulas for the fundamental diagram on a multilane road section with heterogeneous moving bottlenecks. The formula confirms that the composition of traffic and the longest truck platoon length influence the fundamental diagram. We then conduct simulations using a first-order kinematic wave model in Lagrangian coordinates to validate the fundamental diagram developed with the analytical formula and obtain promising results. This study provides fundamental knowledge for multiclass traffic modeling and multilane traffic operations.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"95 1","pages":"456-474"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89153211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bolor Jargalsaikhan, Ward Romeijnders, K. J. Roodbergen
We consider the capacitated single vehicle one-to-one pickup and delivery problem with divisible pickups and deliveries (PDPDPD). In this problem, we do not make the standard assumption of one-to-one pickup and delivery problems (PDPs) that each location has only one transportation request. Instead we assume there are multiple requests per location that may be performed individually. This may result in multiple visits to a location. We provide a new compact arc-based integer linear programming (ILP) formulation for the PDPDPD by deriving time-consistency constraints that identify the order in which selected outgoing arcs from a node are actually traversed. The formulation can also easily be applied to the one-to-one PDP by restricting the number of times that a node can be visited. Numerical results on standard one-to-one PDP test instances from the literature show that our compact formulation is almost competitive with tailor-made solution methods for the one-to-one PDP. Moreover, we observe that significant cost savings of up to 15% on average may be obtained by allowing divisible pickups and deliveries in one-to-one PDPs. It turns out that divisible pickups and deliveries are not only beneficial when the vehicle capacity is small, but also when this capacity is unrestrictive.
{"title":"A Compact Arc-Based ILP Formulation for the Pickup and Delivery Problem with Divisible Pickups and Deliveries","authors":"Bolor Jargalsaikhan, Ward Romeijnders, K. J. Roodbergen","doi":"10.1287/trsc.2020.1016","DOIUrl":"https://doi.org/10.1287/trsc.2020.1016","url":null,"abstract":"We consider the capacitated single vehicle one-to-one pickup and delivery problem with divisible pickups and deliveries (PDPDPD). In this problem, we do not make the standard assumption of one-to-one pickup and delivery problems (PDPs) that each location has only one transportation request. Instead we assume there are multiple requests per location that may be performed individually. This may result in multiple visits to a location. We provide a new compact arc-based integer linear programming (ILP) formulation for the PDPDPD by deriving time-consistency constraints that identify the order in which selected outgoing arcs from a node are actually traversed. The formulation can also easily be applied to the one-to-one PDP by restricting the number of times that a node can be visited. Numerical results on standard one-to-one PDP test instances from the literature show that our compact formulation is almost competitive with tailor-made solution methods for the one-to-one PDP. Moreover, we observe that significant cost savings of up to 15% on average may be obtained by allowing divisible pickups and deliveries in one-to-one PDPs. It turns out that divisible pickups and deliveries are not only beneficial when the vehicle capacity is small, but also when this capacity is unrestrictive.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"1 1","pages":"336-352"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76513788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
M. Hoogeboom, Y. Adulyasak, W. Dullaert, Patrick Jaillet
In practice, there are several applications in which logistics service providers determine the service time windows at the customers, for example, in parcel delivery, retail, and repair services. These companies face uncertain travel times and service times that have to be taken into account when determining the time windows and routes prior to departure. The objective of the proposed robust vehicle routing problem with time window assignments (RVRP-TWA) is to simultaneously determine routes and time window assignments such that the expected travel time and the risk of violating the time windows are minimized. We assume that the travel time probability distributions are not completely known but that some statistics, such as the mean, minimum, and maximum, can be estimated. We extend the robust framework based on the requirements’ violation index, which was originally developed for the case where the specific requirements (time windows) are given as inputs, to the case where they are also part of the decisions. The subproblem of finding the optimal time window assignment for the customers in a given route is shown to be convex, and the subgradients can be derived. The RVRP-TWA is solved by iteratively generating subgradient cuts from the subproblem that are added in a branch-and-cut fashion. Experiments address the performance of the proposed solution approach and examine the trade-off between expected travel time and risk of violating the time windows.
{"title":"The Robust Vehicle Routing Problem with Time Window Assignments","authors":"M. Hoogeboom, Y. Adulyasak, W. Dullaert, Patrick Jaillet","doi":"10.1287/trsc.2020.1013","DOIUrl":"https://doi.org/10.1287/trsc.2020.1013","url":null,"abstract":"In practice, there are several applications in which logistics service providers determine the service time windows at the customers, for example, in parcel delivery, retail, and repair services. These companies face uncertain travel times and service times that have to be taken into account when determining the time windows and routes prior to departure. The objective of the proposed robust vehicle routing problem with time window assignments (RVRP-TWA) is to simultaneously determine routes and time window assignments such that the expected travel time and the risk of violating the time windows are minimized. We assume that the travel time probability distributions are not completely known but that some statistics, such as the mean, minimum, and maximum, can be estimated. We extend the robust framework based on the requirements’ violation index, which was originally developed for the case where the specific requirements (time windows) are given as inputs, to the case where they are also part of the decisions. The subproblem of finding the optimal time window assignment for the customers in a given route is shown to be convex, and the subgradients can be derived. The RVRP-TWA is solved by iteratively generating subgradient cuts from the subproblem that are added in a branch-and-cut fashion. Experiments address the performance of the proposed solution approach and examine the trade-off between expected travel time and risk of violating the time windows.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"64 1","pages":"395-413"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84833841","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study analyzes the performance of a credit-based mobility management scheme considering travelers’ budgeting behaviors for credit consumption under uncertainty. In the scheme, government agencies periodically distribute a certain number of credits to travelers; travelers must pay a credit charge for driving to complete their trips. Otherwise, they can take public transit free of credit charge. Consequently, within a credit-releasing cycle, travelers must budget their credit consumption to fulfill their mobility needs. Such budgeting behaviors can be viewed as a multistage decision-making process under uncertainty. Considering a transportation system with a credit scheme, we propose parsimonious models to investigate how the uncertainty associated with individual mobility needs and the subsequent travelers’ credit-budgeting behavior influence the multistage equilibrium of the transportation system, as well as the performance of the credit scheme on managing the transportation system. Both analytical and numerical results suggest that travelers tend to restrict their credit consumption in the early stage of a credit-releasing cycle to hedge against the risks associated with using up all credits, which compromises the performances of credit-based schemes. Moreover, a negative attitude toward risk aggravates the discrepancy between the credit consumption of the early and late stages. Last, we propose a contingency credit scheme to mitigate the negative impact incurred by travelers’ budgeting behaviors.
{"title":"Credit-Based Mobility Management Considering Travelers' Budgeting Behaviors Under Uncertainty","authors":"Xi Lin, Yafeng Yin, Fang He","doi":"10.1287/trsc.2020.1014","DOIUrl":"https://doi.org/10.1287/trsc.2020.1014","url":null,"abstract":"This study analyzes the performance of a credit-based mobility management scheme considering travelers’ budgeting behaviors for credit consumption under uncertainty. In the scheme, government agencies periodically distribute a certain number of credits to travelers; travelers must pay a credit charge for driving to complete their trips. Otherwise, they can take public transit free of credit charge. Consequently, within a credit-releasing cycle, travelers must budget their credit consumption to fulfill their mobility needs. Such budgeting behaviors can be viewed as a multistage decision-making process under uncertainty. Considering a transportation system with a credit scheme, we propose parsimonious models to investigate how the uncertainty associated with individual mobility needs and the subsequent travelers’ credit-budgeting behavior influence the multistage equilibrium of the transportation system, as well as the performance of the credit scheme on managing the transportation system. Both analytical and numerical results suggest that travelers tend to restrict their credit consumption in the early stage of a credit-releasing cycle to hedge against the risks associated with using up all credits, which compromises the performances of credit-based schemes. Moreover, a negative attitude toward risk aggravates the discrepancy between the credit consumption of the early and late stages. Last, we propose a contingency credit scheme to mitigate the negative impact incurred by travelers’ budgeting behaviors.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"27 1","pages":"297-314"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85926939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Previous studies have shown traffic oscillations can be induced by special network topology. In the simplest case, a network of two intersections connected by two parallel roads would produce oscillatory traffic, when the split of drivers between the two roads falls into certain range. To understand how traffic information may affect such oscillations, a subset of drivers is allowed to be “reactive” in this study; that is, their route choice varies according to information about prevailing traffic conditions on the roads. We show that, depending on the ratio of reactive drivers, the system displays six new decaying, periodic oscillatory, or stable patterns. All solutions are obtained analytically in closed form and validated by macroscopic traffic simulation. Of all the solutions discovered, only one both is stable and fully utilizes the road space between the two intersections, and hence it is more desirable than the other solutions. The findings reveal the link between information provision and topology-induced oscillations, which may help practitioners design strategies that contribute to mitigating the adverse impact of such oscillations.
{"title":"Impact of Information on Topology-Induced Traffic Oscillations","authors":"Yanhong Wang, R. Jiang, Y. Nie, Ziyou Gao","doi":"10.1287/trsc.2020.1032","DOIUrl":"https://doi.org/10.1287/trsc.2020.1032","url":null,"abstract":"Previous studies have shown traffic oscillations can be induced by special network topology. In the simplest case, a network of two intersections connected by two parallel roads would produce oscillatory traffic, when the split of drivers between the two roads falls into certain range. To understand how traffic information may affect such oscillations, a subset of drivers is allowed to be “reactive” in this study; that is, their route choice varies according to information about prevailing traffic conditions on the roads. We show that, depending on the ratio of reactive drivers, the system displays six new decaying, periodic oscillatory, or stable patterns. All solutions are obtained analytically in closed form and validated by macroscopic traffic simulation. Of all the solutions discovered, only one both is stable and fully utilizes the road space between the two intersections, and hence it is more desirable than the other solutions. The findings reveal the link between information provision and topology-induced oscillations, which may help practitioners design strategies that contribute to mitigating the adverse impact of such oscillations.","PeriodicalId":23247,"journal":{"name":"Transp. Sci.","volume":"10 1","pages":"475-490"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89786026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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