Congestion Pricing for Efficiency and Equity: Theory and Applications to the San Francisco Bay Area

• URL: http://arxiv.org/abs/2401.16844v1
• Date: Tue, 30 Jan 2024 09:35:02 GMT
• Title: Congestion Pricing for Efficiency and Equity: Theory and Applications to the San Francisco Bay Area
• Authors: Chinmay Maheshwari and Kshitij Kulkarni and Druv Pai and Jiarui Yang and Manxi Wu and Shankar Sastry
• Abstract summary: We propose a new class of congestion pricing schemes that minimize congestion levels and incorporate an equity objective. We evaluate our pricing schemes in the calibrated freeway network of the San Francisco Bay Area. Our results show that pricing schemes charging differentiated prices to traveler populations with varying willingness-to-pay lead to a more equitable distribution of travel costs.
• Score: 5.056456697289351
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Congestion pricing, while adopted by many cities to alleviate traffic congestion, raises concerns about widening socioeconomic disparities due to its disproportionate impact on low-income travelers. In this study, we address this concern by proposing a new class of congestion pricing schemes that not only minimize congestion levels but also incorporate an equity objective to reduce cost disparities among travelers with different willingness-to-pay. Our analysis builds on a congestion game model with heterogeneous traveler populations. We present four pricing schemes that account for practical considerations, such as the ability to charge differentiated tolls to various traveler populations and the option to toll all or only a subset of edges in the network. We evaluate our pricing schemes in the calibrated freeway network of the San Francisco Bay Area. We demonstrate that the proposed congestion pricing schemes improve both efficiency (in terms of reduced average travel time) and equity (the disparities of travel costs experienced by different populations) compared to the current pricing scheme. Moreover, our pricing schemes also generate a total revenue comparable to the current pricing scheme. Our results further show that pricing schemes charging differentiated prices to traveler populations with varying willingness-to-pay lead to a more equitable distribution of travel costs compared to those that charge a homogeneous price to all.

Related papers

• A Primal-Dual Online Learning Approach for Dynamic Pricing of Sequentially Displayed Complementary Items under Sale Constraints [54.46126953873298]
  We address the problem of dynamically pricing complementary items that are sequentially displayed to customers. Coherent pricing policies for complementary items are essential because optimizing the pricing of each item individually is ineffective. We empirically evaluate our approach using synthetic settings randomly generated from real-world data, and compare its performance in terms of constraints violation and regret.
  arXiv  Detail & Related papers  (2024-07-08T09:55:31Z)
• Fair collaborative vehicle routing: A deep multi-agent reinforcement learning approach [49.00137468773683]
  Collaborative vehicle routing occurs when carriers collaborate through sharing their transportation requests and performing transportation requests on behalf of each other. Traditional game theoretic solution concepts are expensive to calculate as the characteristic function scales exponentially with the number of agents. We propose to model this problem as a coalitional bargaining game solved using deep multi-agent reinforcement learning.
  arXiv  Detail & Related papers  (2023-10-26T15:42:29Z)
• Designing Optimal Personalized Incentive for Traffic Routing using BIG Hype algorithm [3.7597202216941783]
  We study the problem of optimally routing plug-in electric and conventional fuel vehicles on a city level. In our model, commuters selfishly aim to minimize a local cost that combines travel time, and the monetary cost of using city facilities, parking or service stations. We formalize the problem of designing these monetary incentives optimally as a large-scale bilevel game.
  arXiv  Detail & Related papers  (2023-04-24T11:13:10Z)
• Studying the Impact of Semi-Cooperative Drivers on Overall Highway Flow [76.38515853201116]
  Semi-cooperative behaviors are intrinsic properties of human drivers and should be considered for autonomous driving. New autonomous planners can consider the social value orientation (SVO) of human drivers to generate socially-compliant trajectories. We present study of implicit semi-cooperative driving where agents deploy a game-theoretic version of iterative best response.
  arXiv  Detail & Related papers  (2023-04-23T16:01:36Z)
• A Data-driven Pricing Scheme for Optimal Routing through Artificial Currencies [1.3419982985275638]
  Mobility systems often suffer from a high price of anarchy due to the uncontrolled behavior of selfish users. This paper presents a data-driven approach to automatically adapt artificial-currency tolls within repetitive-game settings.
  arXiv  Detail & Related papers  (2022-11-27T11:23:29Z)
• Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy [17.513581783749707]
  Vehicle platooning can potentially reduce traffic congestion by increasing road capacity via vehicle platooning. We consider a network of parallel roads with two modes of transportation: (i) human drivers, who will choose the quickest route available to them, and (ii) a ride hailing service, which provides an array of autonomous vehicle route options, each with different prices, to users. We formalize a model of vehicle flow in mixed autonomy and a model of how autonomous service users make choices between routes with different prices and latencies.
  arXiv  Detail & Related papers  (2021-05-06T03:01:46Z)
• Balancing Fairness and Efficiency in Traffic Routing via Interpolated Traffic Assignment [29.556405472628402]
  Interpolated Traffic Assignment Problem (I-TAP) is a convex program that interpolates between a fairness-promoting and an efficiency-promoting traffic-assignment objective. We present a numerical comparison between I-TAP and a state-of-the-art algorithm on a range of transportation networks.
  arXiv  Detail & Related papers  (2021-03-31T20:32:52Z)
• Fairness, Welfare, and Equity in Personalized Pricing [88.9134799076718]
  We study the interplay of fairness, welfare, and equity considerations in personalized pricing based on customer features. We show the potential benefits of personalized pricing in two settings: pricing subsidies for an elective vaccine, and the effects of personalized interest rates on downstream outcomes in microcredit.
  arXiv  Detail & Related papers  (2020-12-21T01:01:56Z)
• Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations [81.05412704590707]
  We propose a novel end-to-end learnable network that performs joint perception, prediction and motion planning for self-driving vehicles. Our network is learned end-to-end from human demonstrations.
  arXiv  Detail & Related papers  (2020-08-13T14:40:46Z)
• A validated multi-agent simulation test bed to evaluate congestion pricing policies on population segments by time-of-day in New York City [7.5800592082103035]
  We validate the first open-source multi-agent simulation model for New York City, called MATSim-NYC. The model is used to evaluate a congestion pricing plan proposed by the Regional Plan Association.
  arXiv  Detail & Related papers  (2020-07-31T16:07:34Z)
• Congestion-aware Evacuation Routing using Augmented Reality Devices [96.68280427555808]
  We present a congestion-aware routing solution for indoor evacuation, which produces real-time individual-customized evacuation routes among multiple destinations. A population density map, obtained on-the-fly by aggregating locations of evacuees from user-end Augmented Reality (AR) devices, is used to model the congestion distribution inside a building.
  arXiv  Detail & Related papers  (2020-04-25T22:54:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.