Simulation-based pricing optimization for improving network-wide travel time reliability

Chen, Xiqun; Zhang, Lei; He, Xiang; Xiong, Chenfeng; Zhu, Zheng

doi:10.1080/23249935.2017.1379038

Cited by 17 publications

(12 citation statements)

References 45 publications

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“…A general-purpose Kriging metamodel is used to address a five-dimensional problem for a large-scale network with nonlinear network topology. They also apply similar simulation-based optimization techniques for improving the travel time reliability of the network (Chen et al 2018). The advantage of using a generalpurpose metamodel is that the approach can be directly applied to a variety of problem formulations.…”

Section: Introductionmentioning

confidence: 99%

Efficient Simulation-Based Toll Optimization for Large-Scale Networks

Osorio

Atasoy

2021

Transportation Science

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This paper proposes a simulation-based optimization technique for high-dimensional toll optimization problems of large-scale road networks. We formulate a novel analytical network model. The latter is embedded within a metamodel simulation-based optimization (SO) algorithm. It provides analytical and differentiable structural information of the underlying problem to the SO algorithm. Hence, the algorithm no longer treats the simulator as a black box. The analytical model is formulated as a system of nonlinear equations that can be efficiently evaluated with standard solvers. The dimension of the system of equations scales linearly with network size. It scales independently of the dimension of the route choice set and of link attributes such as link length. Hence, it is a scalable formulation suitable for the optimization of large-scale networks. For instance, the model is used in the case study of the paper for toll optimization of a Singapore network with more than 4,050 OD (origin-destination) pairs and 18,200 feasible routes. The corresponding analytical model is implemented as a system of 860 nonlinear equations. The analytical network model is validated based on one-dimensional toy network problems. It captures the main trends of the simulation-based objective function and, more importantly, accurately locates the global optimum for all experiments. The proposed SO approach is then used to optimize a set of 16 tolls for the network of expressways and major arterials of Singapore. The proposed method is compared with a general-purpose algorithm. The proposed method identifies good quality solutions at the very first iteration. The benchmark method identifies solutions with similar performance after 2 days of computation or similarly after more than 30 points have been simulated. The case study indicates that the analytical structural information provided to the algorithm by the analytical network model enables it to (i) identify good quality solutions fast and (ii) become robust to both the quality of the initial points and to the stochasticity of the simulator. The final solutions identified by the proposed algorithm outperform those of the benchmark method by an average of 18%.

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Section: Introductionmentioning

confidence: 99%

Efficient Simulation-Based Toll Optimization for Large-Scale Networks

Osorio

Atasoy

2021

Transportation Science

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“…Several studies have used dynamic traffic models within simulation-based optimization frameworks to increase the degree of accuracy of traffic models so as to better inform decision-makers. In particular, network pricing problems have been frequently solved using simulation-based optimization methods [40][41][42][43][44].…”

Section: Introductionmentioning

confidence: 99%

Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion

Song

Monteil

Ygnace

et al. 2021

Journal of Advanced Transportation

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Traffic congestion is largely due to the high proportion of solo drivers during peak hours. Ridesharing, in the sense of carpooling, has emerged as a travel mode with the potential to reduce congestion by increasing the average vehicle occupancy rates and reduce the number of vehicles during commuting periods. In this study, we propose a simulation-based optimization framework to explore the potential of subsidizing ridesharing users, drivers, and riders, so as to improve social welfare and reduce congestion. We focus our attention on a realistic case study representative of the morning commute on Sydney’s M4 Motorway in Australia. We synthesize a network model and travel demand data from open data sources and use a multinomial logistic model to capture users’ preferences across different travel roles, including solo drivers, ridesharing drivers, ridesharing passengers, and a reserve option that does not contribute to congestion on the freeway network. We use a link transmission model to simulate traffic congestion on the freeway network and embed a fixed-point algorithm to equilibrate users’ mode choice in the long run within the proposed simulation-based optimization framework. Our numerical results reveal that ridesharing incentives have the potential to improve social welfare and reduce congestion. However, we find that providing too many subsidies to ridesharing users may increase congestion levels and thus be counterproductive from a system performance standpoint. We also investigate the impact of transaction fees to a third-party ridesharing platform on social welfare and traffic congestion. We observe that increasing the transaction fee for ridesharing passengers may help in mitigating congestion effects while improving social welfare in the system.

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“…The concept of smart pricing has been under study as a new way to perform traffic assignment, being the implementation of road pricing usually considered an effective measure to reduce environmental externalities (Vonk Noordegraaf et al, 2014, Cavallaro et al, 2018, Wen and Eglese, 2016. Dynamic pricing involves a tradeoff between multiple objectives including efficiency, safety, pollution, reliability, economy, supply and demand (He et al, 2016, Chen et al, 2018. When assessing a multi-objective routing problem with environmental goals, the variables under study are not static, for example, emissions may vary by a factor of 1,4 while the potential number of exposed individuals can vary by a factor of 10 (Bandeira et al, 2018).…”

Section: Introduction and Objectivesmentioning

confidence: 99%

Integrating environmental impacts in an intercity corridor level pricing scheme

Coelho¹,

Sampaio²,

Macedo³

et al. 2020

Preprint

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A significant part of the transport sector externalities occurs in intercity corridors, which account for 65% of the total of the kilometers travelled in for example, Portugal (for 2017). A thorough analysis of intercity corridors characteristics has been receiving less attention compared to urban roads. The objective of this work is to propose a methodology to tackle intercity corridors issues with respect to environmental impacts. It will focus on suggesting smart and dynamic toll systems, integration of impacts in pricing schemes, and optimization of public transport fares, coupled with a scheme based on the "polluter pays" principle. This vision paper presents the main objectives and methodology of an ongoing research in which the final objective is to lead to a more efficient usage of the infrastructures. The optimization is mainly focused on an environmental perspective, which can be important for decision-makers to improve specific intercity corridor measures/policies.

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Simulation-based pricing optimization for improving network-wide travel time reliability

Cited by 17 publications

References 45 publications

Efficient Simulation-Based Toll Optimization for Large-Scale Networks

Efficient Simulation-Based Toll Optimization for Large-Scale Networks

Incentives for Ridesharing: A Case Study of Welfare and Traffic Congestion

Integrating environmental impacts in an intercity corridor level pricing scheme

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