Considering the enormous potential application of autonomous mobility-on-demand(AMoD)systems in future ur-ban transportation,the charging behavior of AMoD fleets,as a key link connecting the power system and the trans...Considering the enormous potential application of autonomous mobility-on-demand(AMoD)systems in future ur-ban transportation,the charging behavior of AMoD fleets,as a key link connecting the power system and the transportation system,needs to be guided by a reasonable charging demand management method.This paper uses game theory to investi-gate charging pricing methods for the AMoD fleets.Firstly,an AMoD fleet scheduling model with appropriate scale and mathe-matical complexity is established to describe the spatio-tempo-ral action patterns of the AMoD fleet.Subsequently,using Stackelberg game and Nash bargaining,two game frameworks,i.e.,non-cooperative and cooperative,are designed for the charging station operator(CSO)and the AMoD fleet.Then,the interaction trends between the two entities and the mechanism of charging price formation are discussed,along with an analy-sis of the game implications for breaking the non-cooperative di-lemma and moving towards cooperation.Finally,numerical ex-periments based on real-world city-scale data are provided to validate the designed game frameworks.The results show that the spatio-temporal distribution of charging prices can be cap-tured and utilized by the AMoD fleet.The CSO can then use this action pattern to determine charging prices to optimize the profit.Based on this,negotiated bargaining improves the over-all benefits for stakeholders in urban transportation.展开更多
This paper considers the problem of supply-demand imbalances in Mobility-on-Demand(MoD)services.These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehic...This paper considers the problem of supply-demand imbalances in Mobility-on-Demand(MoD)services.These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehicles to areas where the demand is high.To achieve this,we propose a method that takes into account uncertainties of predicted travel demand while minimizing pick-up time and rebalance mileage for autonomous MoD ride-hailing.More precisely,first travel demand is predicted using Gaussian Process Regression(GPR)which provides uncertainty bounds on the prediction.We then formulate a stochastic model predictive control(MPC)for the autonomous ride-hailing service and integrate the demand predictions with uncertainty bounds.In order to guarantee constraint satisfaction in the optimization under estimated stochastic demand prediction,we employ a probabilistic constraining method with user-defined confidence interval,using Chance Constrained MPC(CCMPC).The benefits of the proposed method are twofold.First,travel demand uncertainty prediction from data can naturally be embedded into the MoD optimization framework,allowing us to keep the imbalance at each station below a certain threshold with a user-defined probability.Second,CCMPC can be relaxed into a Mixed-Integer-Linear-Program(MILP)and the MILP can be solved as a corresponding Linear-Program,which always admits an integral solution.Our transportation simulations show that by tuning the confidence bound on the chance constraint,close to optimal oracle performance can be achieved,with a median customer wait time reduction of 4%compared to using only the mean prediction of the GPR.展开更多
Shared-use autonomous mobility services(SAMSs)have the potential to provide accessible and demand-responsive mobility to passengers,while benefitting from autonomous vehicle(AV)technology and bypassing challenges rela...Shared-use autonomous mobility services(SAMSs)have the potential to provide accessible and demand-responsive mobility to passengers,while benefitting from autonomous vehicle(AV)technology and bypassing challenges related to supply-side incentives or individual driver goals.SAMS operators typically aim to achieve efficiency and improved service quality in their fleet operations,both of which are further enabled by the use of AVs.Specifically,fleet repositioning decisions in anticipation of future demand can improve service quality,but existing approaches in the literature seldom consider the problem of routing repositioning vehicles in a way that further improves SAMS objectives.This paper presents an approach for demand-aware distributed pathfinding for repositioning vehicles,which can supplement existing vehicle repositioning approaches.The problem is formulated with a multi-criteria objective that minimizes the vehicles’total travel time and maximizes their total demand-serving potential,while distributing that potential equitably among the ride-seeking passengers across the transportation network.We evaluate the proposed approach via numerical experiments using an agent-based simulation of SAMS operations in the network of Manhattan in New York City.The proposed approach is compared to a baseline simple shortest path approach for routing the repositioning vehicles.The results demonstrate that mean passenger waiting times for pick-up can be reduced,while also reducing the total vehicle miles and the empty miles travelled due to repositioning.Thus,the proposed approach can help improve the overall system performance in terms of both service quality and efficiency metrics,relative to the baseline approach.展开更多
基金This work was supported by Shanxi Energy Internet Research Institute(No.SXEI2023A 003).
文摘Considering the enormous potential application of autonomous mobility-on-demand(AMoD)systems in future ur-ban transportation,the charging behavior of AMoD fleets,as a key link connecting the power system and the transportation system,needs to be guided by a reasonable charging demand management method.This paper uses game theory to investi-gate charging pricing methods for the AMoD fleets.Firstly,an AMoD fleet scheduling model with appropriate scale and mathe-matical complexity is established to describe the spatio-tempo-ral action patterns of the AMoD fleet.Subsequently,using Stackelberg game and Nash bargaining,two game frameworks,i.e.,non-cooperative and cooperative,are designed for the charging station operator(CSO)and the AMoD fleet.Then,the interaction trends between the two entities and the mechanism of charging price formation are discussed,along with an analy-sis of the game implications for breaking the non-cooperative di-lemma and moving towards cooperation.Finally,numerical ex-periments based on real-world city-scale data are provided to validate the designed game frameworks.The results show that the spatio-temporal distribution of charging prices can be cap-tured and utilized by the AMoD fleet.The CSO can then use this action pattern to determine charging prices to optimize the profit.Based on this,negotiated bargaining improves the over-all benefits for stakeholders in urban transportation.
基金co-funded by Vinnova,Sweden through the project:Simulation,analysis and modeling of future efficient traffic systems.
文摘This paper considers the problem of supply-demand imbalances in Mobility-on-Demand(MoD)services.These imbalances occur due to uneven stochastic travel demand and can be mitigated by proactively rebalancing empty vehicles to areas where the demand is high.To achieve this,we propose a method that takes into account uncertainties of predicted travel demand while minimizing pick-up time and rebalance mileage for autonomous MoD ride-hailing.More precisely,first travel demand is predicted using Gaussian Process Regression(GPR)which provides uncertainty bounds on the prediction.We then formulate a stochastic model predictive control(MPC)for the autonomous ride-hailing service and integrate the demand predictions with uncertainty bounds.In order to guarantee constraint satisfaction in the optimization under estimated stochastic demand prediction,we employ a probabilistic constraining method with user-defined confidence interval,using Chance Constrained MPC(CCMPC).The benefits of the proposed method are twofold.First,travel demand uncertainty prediction from data can naturally be embedded into the MoD optimization framework,allowing us to keep the imbalance at each station below a certain threshold with a user-defined probability.Second,CCMPC can be relaxed into a Mixed-Integer-Linear-Program(MILP)and the MILP can be solved as a corresponding Linear-Program,which always admits an integral solution.Our transportation simulations show that by tuning the confidence bound on the chance constraint,close to optimal oracle performance can be achieved,with a median customer wait time reduction of 4%compared to using only the mean prediction of the GPR.
基金supported by the University of Connecticut Office of the Vice President for Research(OVPR)via the Research Excellence Program(REP)grant.
文摘Shared-use autonomous mobility services(SAMSs)have the potential to provide accessible and demand-responsive mobility to passengers,while benefitting from autonomous vehicle(AV)technology and bypassing challenges related to supply-side incentives or individual driver goals.SAMS operators typically aim to achieve efficiency and improved service quality in their fleet operations,both of which are further enabled by the use of AVs.Specifically,fleet repositioning decisions in anticipation of future demand can improve service quality,but existing approaches in the literature seldom consider the problem of routing repositioning vehicles in a way that further improves SAMS objectives.This paper presents an approach for demand-aware distributed pathfinding for repositioning vehicles,which can supplement existing vehicle repositioning approaches.The problem is formulated with a multi-criteria objective that minimizes the vehicles’total travel time and maximizes their total demand-serving potential,while distributing that potential equitably among the ride-seeking passengers across the transportation network.We evaluate the proposed approach via numerical experiments using an agent-based simulation of SAMS operations in the network of Manhattan in New York City.The proposed approach is compared to a baseline simple shortest path approach for routing the repositioning vehicles.The results demonstrate that mean passenger waiting times for pick-up can be reduced,while also reducing the total vehicle miles and the empty miles travelled due to repositioning.Thus,the proposed approach can help improve the overall system performance in terms of both service quality and efficiency metrics,relative to the baseline approach.
