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Zhiyuan Liu - One of the best experts on this subject based on the ideXlab platform.
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a cell based dynamic Congestion Pricing scheme considering travel distance and time delay
Transportmetrica B-Transport Dynamics, 2019Co-Authors: Qixiu Cheng, Zhiyuan Liu, W Y SzetoAbstract:This study introduces the dynamic Congestion Pricing (DCP) problem with the consideration of the actual travel distance and time delay (i.e. a joint distance and time-delay toll, JDTDT) in a dynami...
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Congestion Pricing practices and public acceptance a review of evidence
Case studies on transport policy, 2018Co-Authors: Zhiyuan Liu, Qixiu Cheng, Meead SaberiAbstract:Abstract Despite numerous theoretical studies, practical implementation of Congestion Pricing is limited mainly due to the low public acceptance. Existing studies in this respect generally focus on a few selected cases where the results need to be further generalized. With the objective of improving public acceptance of Congestion Pricing, this paper provides a comprehensive overview of the area-based Congestion Pricing practices. An in-depth analysis of public acceptance is presented using a qualitative case study approach. Results show that for the successful implementation of Congestion Pricing, a trial and a referendum are valuable but not necessary, and that an interaction-oriented political process may be more desirable. Four influencing factors, i.e. privacy, equity, complexity and uncertainty, are identified to be critical in establishing strong public support. Taking into account these implementation factors, an extended three-step approach is proposed for further improvement of public acceptance toward Congestion Pricing.
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optimal joint distance and time toll for cordon based Congestion Pricing
Transportation Research Part B-methodological, 2014Co-Authors: Zhiyuan Liu, Shuaian Wang, Qiang MengAbstract:Abstract This paper addresses the optimal toll design problem for the cordon-based Congestion Pricing scheme, where both a time-toll and a nonlinear distance-toll (i.e., joint distance and time toll) are levied for each network user’s trip in a Pricing cordon. The users’ route choice behaviour is assumed to follow the Logit-based stochastic user equilibrium (SUE). We first propose a link-based convex programming model for the Logit-based SUE problem with a joint distance and time toll pattern. A mathematical program with equilibrium constraints (MPEC) is developed to formulate the optimal joint distance and time toll design problem. The developed MPEC model is equivalently transformed into a semi-infinite programming (SIP) model. A global optimization method named Incremental Constraint Method (ICM) is designed for solving the SIP model. Finally, two numerical examples are used to assess the proposed methodology.
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bus based park and ride system a stochastic model on multimodal network with Congestion Pricing schemes
International Journal of Systems Science, 2014Co-Authors: Zhiyuan Liu, Qiang MengAbstract:This paper focuses on modelling the network flow equilibrium problem on a multimodal transport network with bus-based park-and-ride P&R system and Congestion Pricing charges. The multimodal network has three travel modes: auto mode, transit mode and P&R mode. A continuously distributed value-of-time is assumed to convert toll charges and transit fares to time unit, and the users’ route choice behaviour is assumed to follow the probit-based stochastic user equilibrium principle with elastic demand. These two assumptions have caused randomness to the users’ generalised travel times on the multimodal network. A comprehensive network framework is first defined for the flow equilibrium problem with consideration of interactions between auto flows and transit bus flows. Then, a fixed-point model with unique solution is proposed for the equilibrium flows, which can be solved by a convergent cost averaging method. Finally, the proposed methodology is tested by a network example.
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optimal distance tolls under Congestion Pricing and continuously distributed value of time
Transportation Research Part E-logistics and Transportation Review, 2012Co-Authors: Qiang Meng, Zhiyuan Liu, Shuaian WangAbstract:Abstract This paper addresses the optimal distance-based toll design problem for cordon-based Congestion Pricing schemes. The optimal distance tolls are determined by a positive and non-decreasing toll-charge function with respect to the travel distance. Each feasible toll-charge function is evaluated by a probit-based SUE (Stochastic User Equilibrium) problem with elastic demand, asymmetric link travel time functions, and continuously distributed VOT, solved by a convergent Cost Averaging (CA) method. The toll design problem is formulated as a mixed-integer mathematical programming with equilibrium constraints (MPEC) model, which is solved by a Hybrid GA (Genetic Algorithm)–CA method. Finally, the proposed models and algorithms are assessed by two numerical examples.
Qiang Meng - One of the best experts on this subject based on the ideXlab platform.
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global convergence of the trial and error method for the traffic restraint Congestion Pricing scheme with day to day flow dynamics
Transportation Research Part C-emerging Technologies, 2016Co-Authors: Qiang Meng, Zhongxiang HuangAbstract:Abstract The traffic-restraint Congestion-Pricing scheme (TRCPS) aims to maintain traffic flow within a desirable threshold for some target links by levying the appropriate link tolls. In this study, we propose a trial-and-error method using observed link flows to implement the TRCPS with the day-to-day flow dynamics. Without resorting to the origin–destination (O–D) demand functions, link travel time functions and value of time (VOT), the proposed trial-and-error method works as follows: tolls for the traffic-restraint links are first implemented each time (trial) and they are subsequently updated using observed link flows in a disequilibrium state at any arbitrary time interval. The trial-and-error method has the practical significance because it is necessary only to observe traffic flows on those tolled links and it does not require to wait for the network flow pattern achieving the user equilibrium (UE) state. The global convergence of the trial-and-error method is rigorously demonstrated under mild conditions. We theoretically show the viability of the proposed trial-and-error method, and numerical experiments are conducted to evaluate its performance. The result of this study, without doubt, enhances the confidence of practitioners to adopt this method.
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optimal joint distance and time toll for cordon based Congestion Pricing
Transportation Research Part B-methodological, 2014Co-Authors: Zhiyuan Liu, Shuaian Wang, Qiang MengAbstract:Abstract This paper addresses the optimal toll design problem for the cordon-based Congestion Pricing scheme, where both a time-toll and a nonlinear distance-toll (i.e., joint distance and time toll) are levied for each network user’s trip in a Pricing cordon. The users’ route choice behaviour is assumed to follow the Logit-based stochastic user equilibrium (SUE). We first propose a link-based convex programming model for the Logit-based SUE problem with a joint distance and time toll pattern. A mathematical program with equilibrium constraints (MPEC) is developed to formulate the optimal joint distance and time toll design problem. The developed MPEC model is equivalently transformed into a semi-infinite programming (SIP) model. A global optimization method named Incremental Constraint Method (ICM) is designed for solving the SIP model. Finally, two numerical examples are used to assess the proposed methodology.
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bus based park and ride system a stochastic model on multimodal network with Congestion Pricing schemes
International Journal of Systems Science, 2014Co-Authors: Zhiyuan Liu, Qiang MengAbstract:This paper focuses on modelling the network flow equilibrium problem on a multimodal transport network with bus-based park-and-ride P&R system and Congestion Pricing charges. The multimodal network has three travel modes: auto mode, transit mode and P&R mode. A continuously distributed value-of-time is assumed to convert toll charges and transit fares to time unit, and the users’ route choice behaviour is assumed to follow the probit-based stochastic user equilibrium principle with elastic demand. These two assumptions have caused randomness to the users’ generalised travel times on the multimodal network. A comprehensive network framework is first defined for the flow equilibrium problem with consideration of interactions between auto flows and transit bus flows. Then, a fixed-point model with unique solution is proposed for the equilibrium flows, which can be solved by a convergent cost averaging method. Finally, the proposed methodology is tested by a network example.
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optimal distance tolls under Congestion Pricing and continuously distributed value of time
Transportation Research Part E-logistics and Transportation Review, 2012Co-Authors: Qiang Meng, Zhiyuan Liu, Shuaian WangAbstract:Abstract This paper addresses the optimal distance-based toll design problem for cordon-based Congestion Pricing schemes. The optimal distance tolls are determined by a positive and non-decreasing toll-charge function with respect to the travel distance. Each feasible toll-charge function is evaluated by a probit-based SUE (Stochastic User Equilibrium) problem with elastic demand, asymmetric link travel time functions, and continuously distributed VOT, solved by a convergent Cost Averaging (CA) method. The toll design problem is formulated as a mixed-integer mathematical programming with equilibrium constraints (MPEC) model, which is solved by a Hybrid GA (Genetic Algorithm)–CA method. Finally, the proposed models and algorithms are assessed by two numerical examples.
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impact analysis of cordon based Congestion Pricing on mode split for a bimodal transportation network
Transportation Research Part C-emerging Technologies, 2012Co-Authors: Qiang Meng, Zhiyuan LiuAbstract:This paper investigates the impact of cordon-based Congestion Pricing scheme on the mode-split of a bimodal transportation network with auto and rail travel modes. For any given toll-charge pattern, its impact on the mode-split can be estimated by solving a combined mode-split and traffic-assignment problem. Using a binary logit model for the mode-split, the combined problem is converted into a traffic-assignment problem with elastic demand. Probit-based stochastic user equilibrium (SUE) principle is adopted for this traffic-assignment problem, and a continuously distributed value of time (VOT) is assumed to convert the toll charges and transit fares into time-units. This combined mode-split and traffic-assignment problem is then formulated as a fixed-point model, which can be solved by a convergent Cost Averaging method. The combined mode-split and traffic-assignment problem is then used to analyze a multimodal toll design problem for cordon-based Congestion Pricing scheme, with the aim of increasing the mode-share of public transport system to a targeted level. Taking the fixed-point model as a constraint, the multimodal toll design problem is thus formulated as a mathematical programming with equilibrium constraints (MPEC) model. A genetic algorithm (GA) is employed to solve this MPEC model, which is then numerical validated by a network example.
Aya Aboudina - One of the best experts on this subject based on the ideXlab platform.
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mode shift impacts of optimal time dependent Congestion Pricing in large networks a simulation based case study in the greater toronto area
Case studies on transport policy, 2020Co-Authors: Sami Hasnine, Aya Aboudina, Baher Abdulhai, Khandker Nurul HabibAbstract:Abstract This paper presents a case study on commuter’s departure time, route and mode choice responses to optimized tolling in the Toronto, Ontario, Canada. The study integrates a toll optimization component into an integrated framework of econometric departure time choice, dynamic traffic assignment model, and random utility maximization (RUM) based mode choice model. An iterative optimization algorithm is used to generate optimal tolling structure which explicitly considers heterogeneous user preferences through econometric departure time choice modelling component. The mode choice component is exogenously retrofitted in an integrated econometric departure time choice modelling component and dynamic operational traffic assignment model to capture individual mode choice behaviour in response to variable Congestion Pricing. The integrated model results show that individuals are more likely to switch driving routes and departure times than commuting modes when making tradeoffs between schedule delay cost, travel time cost, and toll cost. It is found that the optimal toll scenario improves the network-wide travel time. The modelling framework presented in this study can test a wide range of policy scenarios including the time-variable tolling of congested highways, and high occupancy toll lanes. While the integrated framework presented here is tested at a large scale on the Greater Toronto Area (GTA), we believe the approach can be applied to large-scale networks globally where the objective is seeking the best network-wide spatial and temporal traffic distribution.
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a bi level distributed approach for optimizing time dependent Congestion Pricing in large networks a simulation based case study in the greater toronto area
Transportation Research Part C-emerging Technologies, 2017Co-Authors: Aya Aboudina, Baher AbdulhaiAbstract:Abstract Congestion Pricing is one of the most widely contemplated methods to manage traffic Congestion by charging fees for the use of roads, more where and when it is congested, and less where and when it is not. This study presents a bi-level distributed approach for optimal time-dependent Congestion Pricing in large networks. The bi-level procedure involves a theoretical model of dynamic Congestion Pricing and a distributed optimization algorithm. The bi-level toll optimization module is integrated into a testbed of hybrid departure time choice and dynamic traffic assignment simulation models for the Greater Toronto Area (GTA). The integrated system provides a unified (location- and time-specific) Congestion Pricing system that determines optimal tolling and evaluates its impact on road traffic Congestion and travellers’ behavioural choices, including departure time and route choices. For the system’s large-scale nature and the consequent computational challenges, the optimization algorithm is executed concurrently on a parallel computing cluster. The system is applied to a simulation-based case study of tolling major highways in the GTA while capturing the network-wide regional effects of tolling. The travel demand and drivers’ attributes are extracted from regional household travel survey data that reflect travellers’ heterogeneity. The main results indicate that: (1) optimal variable Pricing reflects Congestion patterns and induces departure time re-scheduling and rerouting patterns, resulting in improved average travel times and schedule delays, (2) optimal tolls intended to manage traffic demand are significantly lower than those intended to maximize toll revenues, (3) tolled routes have different sensitivities to identical toll changes, (4) the start times of longer trips are more sensitive (elastic) to variable distance-based tolling policies compared to shorter trips, (5) toll payers benefit from tolling even before toll revenues are spent, and (6) the optimal tolling policies determined offer a win–win solution in which travel times are improved while also raising funds to invest in sustainable transportation infrastructure.
Hai Yang - One of the best experts on this subject based on the ideXlab platform.
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a trial and error Congestion Pricing scheme for networks with elastic demand and link capacity constraints
Transportation Research Part B-methodological, 2015Co-Authors: Michiel C J Bliemer, Bojian Zhou, Hai YangAbstract:Abstract This paper proposes a combination of trial-and-error Congestion Pricing schemes that have been studied in the literature. It not only considers the minimization of the total system cost but also addresses the capacity constraints. A two-level iteration method is proposed for solving the hybrid problem, in which the approximate subgradient projection method is used for the outer level iteration phase, and the partial linearization method is used for the inner level iteration phase. We prove the convergence of the two-level iteration method, under the condition that the subproblem for the inner level iteration is only solved approximately, which makes the method efficient and practical. A numerical example is presented to illustrate the application of the two-level iteration method to the trial-and-error Congestion Pricing scheme.
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a pareto improving hybrid policy for transportation networks
Journal of Advanced Transportation, 2014Co-Authors: Ziqi Song, Siriphong Lawphongpanich, Yafeng Yin, Hai YangAbstract:SUMMARY This paper investigates an innovative Pareto-improving hybrid policy that combines two policy instruments, that is, Congestion Pricing and road space rationing, and takes advantage of the synergistic effects between these instruments. Mathematical formulations for developing Pareto-improving pure road space rationing schemes and hybrid policies are presented. Numerical examples demonstrate that the proposed hybrid policy offers greater flexibility and is more prominent in leading to Pareto improvement than both pure Congestion Pricing and road space rationing schemes. Copyright © 2013 John Wiley & Sons, Ltd.
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design of more equitable Congestion Pricing and tradable credit schemes for multimodal transportation networks
Transportation Research Part B-methodological, 2012Co-Authors: Di Wu, Siriphong Lawphongpanich, Hai YangAbstract:This paper develops a modeling framework that considers the effect of income on travelers’ choices of trip generation, mode and route on multimodal transportation networks and explicitly captures the distributional impacts of Congestion–mitigation policies on different income and geographic groups. The modeling framework is applied to design more equitable yet efficient Congestion Pricing and tradable credit schemes. The design models are formulated as mathematical programs with equilibrium constraints, and solved by derivative-free solution algorithms. Numerical examples are presented to demonstrate the models and offer insight on the mechanisms of achieving better equity under Congestion Pricing or tradable credit schemes.
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pareto improving Congestion Pricing and revenue refunding with multiple user classes
Transportation Research Part B-methodological, 2010Co-Authors: Xiaolei Guo, Hai YangAbstract:Abstract This study investigates Pareto-improving Congestion Pricing and revenue refunding schemes in general transportation networks, which make every road user better off as compared with the situation without Congestion Pricing. We consider user heterogeneity in value of time (VOT) by adopting a multiclass user model with fixed origin–destination (OD) demands. We first prove that an OD and class-based Pareto-improving refunding scheme exists if and only if the total system monetary travel disutility is reduced. In view of the practical difficulty in identifying individual user’s VOT, we further investigate class-anonymous refunding schemes that give the same amount of refund to all user classes traveling between the same OD pair regardless of their VOTs. We establish a sufficient condition for the existence of such OD-specific but class-anonymous Pareto-improving refunding schemes, which needs information only on the average toll paid and average travel time for trips between each OD pair.
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pareto improving and revenue neutral Congestion Pricing schemes in two mode traffic networks
Netnomics, 2009Co-Authors: Yang Liu, Xiaolei Guo, Hai YangAbstract:This paper studies a Pareto-improving and revenue-neutral Congestion Pricing scheme on a simple two-mode (highway and transit) network: this scheme aims at simultaneously improving system performance, making every individual user better off, and having zero total revenue. Different Pareto-improving situations are explored when a two-mode transportation system serves for travel groups with different value-of-time (VOT) distributions. Since the Congestion Pricing scheme suggested here charges transit users negative tolls and automobile users positive tolls, it can be considered as a proper way to implement Congestion Pricing and transit subsidy in one step, while offsetting the inequity for the poor. For a general VOT distribution of commuters, the condition of Pareto-improving is established, and the impact of the VOT distribution on solving the inequity issue is explored. For a uniform VOT distribution, we show that a Pareto-improving and revenue-neutral Pricing scheme always exists for any target modal split pattern that reduces the total system travel time.
Qi Luo - One of the best experts on this subject based on the ideXlab platform.
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dynamic Congestion Pricing for ridesourcing traffic a simulation optimization approach
Winter Simulation Conference, 2019Co-Authors: Qi LuoAbstract:Despite the documented benefits of ridesourcing services, recent studies show that they can slow down traffic in the densest cities significantly. To implement Congestion Pricing policies upon those vehicles, regulators need to estimate the degree of Congestion effect. This paper studies simulation-based approaches to address the two technical challenges arising from the representation of system dynamics and the optimization for Congestion price mechanisms. To estimate the traffic state, we use a metamodel representation for traffic flow and a numerical method for data interpolation. To reduce the burden of replicating evaluation in stochastic optimization, we use a simulation optimization approach to compute the optimal Congestion price. This data-driven approach can potentially be extended to solve large-scale Congestion Pricing problems with unobservable states.
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dynamic Congestion Pricing for ridesourcing traffic a simulation optimization approach
Social Science Research Network, 2019Co-Authors: Qi Luo, Zhiyuan Huang, Henry LamAbstract:Despite the documented benefits of ride-sourcing services, recent studies show that they can slow down traffic in the densest cities significantly. To implement Congestion Pricing policies upon those vehicles, regulators need to estimate how much their Congestion effects are. This paper studies simulation-based approaches to address the two technical challenges arising from the representation of system dynamics and the optimization for Congestion price mechanisms. To estimate the traffic conditions, we use a meta-model representation for traffic flow and a numerical method for data interpolation. To reduce the burden of replicating evaluation in stochastic optimization, we use a simulation optimization approach to compute the optimal Congestion price. This data-driven approach can potentially be extended to solve large-scale Congestion Pricing problems with unobservable states.