When Efficiency meets Equity in Congestion Pricing and Revenue Refunding Schemes

Jalota, Devansh; Solovey, Kiril; Gopalakrishnan, Karthik; Zoepf, Stephen; Balakrishnan, Hamsa; Pavone, Marco

doi:10.1145/3465416.3483296

Cited by 6 publications

(5 citation statements)

References 62 publications

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“…They also developed an absolute correlation term as fairness regularizers to mitigate the mode-choice prediction disparities among different population groups. Fairness concerns were also addressed in traffic assignment problems [55], [56], where the objective is usually to obtain optimal travel flows that minimize the discrepancy of user travel time sharing the same link. In addition to four-step model, researchers also developed unfairness correction methods for other tasks, such as traffic safety [11] and infrastructure planning [57].…”

Section: B Addressing Ai Fairness Issues In Travel Demand Predictionmentioning

confidence: 99%

Travel Demand Forecasting: A Fair AI Approach

Zhang,

Ke,

Zhao

2024

IEEE Trans. Intell. Transport. Syst.

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Artificial Intelligence (AI) and machine learning have been increasingly adopted for travel demand forecasting. The AI-based travel demand forecasting models, though generate accurate predictions, may produce prediction biases and raise fairness issues. Using such biased models for decision-making may lead to transportation policies that exacerbate social inequalities. However, limited studies have been focused on addressing the fairness issues of these models. Therefore, in this study, we propose a novel methodology to develop fairness-aware, highly-accurate travel demand forecasting models. Particularly, the proposed methodology can enhance the fairness of AI models for multiple protected attributes (such as race and income) simultaneously. Specifically, we introduce a new fairness regularization term, which is explicitly designed to measure the correlation between prediction accuracy and multiple protected attributes, into the loss function of the travel demand forecasting model. We conduct two case studies to evaluate the performance of the proposed methodology using real-world ridesourcing-trip data in Chicago, IL and Austin, TX, respectively. Results highlight that our proposed methodology can effectively enhance fairness for multiple protected attributes while preserving prediction accuracy. Additionally, we have compared our methodology with three state-of-the-art methods that adopt the regularization term approach, and the results demonstrate that our approach significantly outperforms them in both preserving prediction accuracy and enhancing fairness. This study can provide transportation professionals with a new tool to achieve fair and accurate travel demand forecasting.

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Section: B Addressing Ai Fairness Issues In Travel Demand Predictionmentioning

confidence: 99%

Travel Demand Forecasting: A Fair AI Approach

Zhang,

Ke,

Zhao

2024

IEEE Trans. Intell. Transport. Syst.

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“…The paper (Guo and Yang, 2010) studied the problem of designing Pareto-improving pricing schemes combined with revenue refund. (Jalota et al, 2021) extended this line of research by developing optimal revenue refunding schemes to minimize the congestion and inequity together. In both (Guo and Yang, 2010) and (Jalota et al, 2021), the tolls minimize the weighted sum of total travel time with weights being each population's value of time.…”

Section: Related Workmentioning

confidence: 99%

“…(Jalota et al, 2021) extended this line of research by developing optimal revenue refunding schemes to minimize the congestion and inequity together. In both (Guo and Yang, 2010) and (Jalota et al, 2021), the tolls minimize the weighted sum of total travel time with weights being each population's value of time. This objective is different from our goal of minimizing the actual congestion level (unweighted total travel time), which is a more suitable metric to assess the environmental impact of congestion.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Tolling for Inducing Socially Optimal Traffic Loads

Maheshwari

Kulkarni

et al. 2022

2022 American Control Conference (ACC)

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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.

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“…In the context of traffic routing, road tolls are a commonly used mechanism to cope with the inefficiency loss due to the selfishness of users and enforce the system-optimum solution as a user equilibrium [11]. However, the computation of these tolls typically relies on solving a centralized optimization problem, which assumes complete information on users' trip attributes [12,13,14], such as their origin-destination (O-D) travel information and their values of time [15]. In practice, this information is typically not available since this would violate user privacy and can thus confound the successful deployment of a tolling scheme to regulate road traffic.…”

Section: Introductionmentioning

confidence: 99%

Online Learning for Traffic Routing under Unknown Preferences

Jalota¹,

Gopalakrishnan²,

Azizan³

et al. 2022

Preprint

Self Cite

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In transportation networks, users typically choose routes in a decentralized and self-interested manner to minimize their individual travel costs, which, in practice, often results in inefficient overall outcomes for society. As a result, there has been a growing interest in designing road tolling schemes to cope with these efficiency losses and steer users toward a system-efficient traffic pattern. However, the efficacy of road tolling schemes often relies on having access to complete information on users' trip attributes, such as their origin-destination (O-D) travel information and their values of time, which may not be available in practice.Motivated by this practical consideration, we propose an online learning approach to set tolls in a traffic network to drive heterogeneous users with different values of time toward a system-efficient traffic pattern. In particular, we develop a simple yet effective algorithm that adjusts tolls at each time period solely based on the observed aggregate flows on the roads of the network without relying on any additional trip attributes of users, thereby preserving user privacy. In the setting where the O-D pairs and values of time of users are drawn i.i.d. at each period, we show that our approach obtains an expected regret and road capacity violation of O( √ T ), where T is the number of periods over which tolls are updated. Our regret guarantee is relative to an offline oracle that has complete information on users' trip attributes. We further establish a Ω( √ T ) lower bound on the regret of any algorithm, which establishes that our algorithm is optimal up to constants. Finally, we demonstrate the superior performance of our approach relative to several benchmarks on a real-world transportation network, thereby highlighting its practical applicability.

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When Efficiency meets Equity in Congestion Pricing and Revenue Refunding Schemes

Cited by 6 publications

References 62 publications

Travel Demand Forecasting: A Fair AI Approach

Travel Demand Forecasting: A Fair AI Approach

Dynamic Tolling for Inducing Socially Optimal Traffic Loads

Online Learning for Traffic Routing under Unknown Preferences

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