Comparison Study of Credit vs. Discount-Based Congestion Pricing

Concepts de base

Credit-based and discount-based congestion pricing policies have distinct impacts on traffic flow patterns, influenced by factors such as toll levels and user values of time.

Résumé

The content compares credit-based and discount-based congestion pricing policies in a single-edge traffic network with one eligible and one ineligible user group. Theoretical analyses are provided along with sensitivity experiments.

Introduction

Congestion pricing as a traffic management policy.
Criticism of existing schemes for inequities.

Credit vs. Discount-Based Policies

Credit-based policies offer fixed budgets to low-income users.
Discount-based policies provide toll discounts.

Equilibrium Analysis

Existence of equilibria for both types of policies.
Convex programs to compute equilibria efficiently.

Comparative Analysis

Single eligible group scenario: DBCP outperforms CBCP at low discount levels.
Single eligible and single ineligible group scenario: Impact of VoT differences on policy effectiveness.

Experimental Results

Sensitivity analysis on theoretical characterizations.

Further Research Directions

Real-world case study implications.

Stats

Eligible users' VoT vE = 1.0, ineligible users' VoT vI > 1.0 assumed in theoretical analysis.

Citations

"Under Assumptions 1 and 2, there exists a unique α1 ∈(0, 1/2) such that vEℓ(α1) + τ = vEℓ(1 −α1)."
"If τ < 2vEℓ′(1), then yD(α) < yC(α) ∀α ∈ (0, 1 −vE/vI), and yD(α) > yC(α) ∀α ∈ (1 −vE/vI, 1)."

Idées clés tirées de

Credit vs. Discount-Based Congestion Pricing

by Chih-Yuan Ch... à arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.13923.pdf

Questions plus approfondies

How do variations in toll levels impact the equilibrium flow patterns under different congestion pricing policies

Variations in toll levels have a significant impact on equilibrium flow patterns under different congestion pricing policies. In the context of credit-based congestion pricing (CBCP), where eligible users receive travel credits, an increase in toll levels can lead to a decrease in the number of eligible users utilizing the express lane. This is because higher tolls reduce the value of the travel credits provided, making it less attractive for eligible users to use them. As a result, there may be a shift towards more ineligible users using the express lane.
On the other hand, in discount-based congestion pricing (DBCP), where eligible users receive toll discounts, variations in toll levels can influence how these discounts affect user behavior. Higher tolls combined with lower discounts may still incentivize eligible users to utilize the express lane but at reduced rates compared to when discounts are more substantial. The equilibrium flow patterns will reflect this balance between toll levels and discount effectiveness in encouraging eligible user participation.
Overall, changes in toll levels can alter the distribution of traffic between eligible and ineligible users on tolled roads under both CBCP and DBCP policies.

What real-world factors could influence the effectiveness of credit-based versus discount-based congestion pricing

Several real-world factors could influence the effectiveness of credit-based versus discount-based congestion pricing strategies. One key factor is income disparity among road users. Credit-based policies might be more effective if there is a significant proportion of low-income individuals who would benefit from receiving travel credits to offset high transportation costs. On the other hand, discount-based policies could be more impactful if there are clear distinctions between income groups and providing direct financial relief through discounted tolls would better address equity concerns.
Additionally, infrastructure considerations play a role in policy effectiveness. The availability and design of alternative transportation options such as public transit or carpooling services could impact how well credit or discount incentives encourage mode shifts away from single-occupancy vehicles during peak hours.
Moreover, public perception and acceptance of each policy type can significantly influence their success. If one approach is perceived as fairer or easier to understand by stakeholders such as commuters, policymakers, and advocacy groups, it may garner greater support and compliance leading to better outcomes.

How might changes in user demand over time affect the outcomes predicted by the theoretical models

Changes in user demand over time can have varying effects on predicted outcomes by theoretical models for congestion pricing policies like CBCP and DBCP.
For instance:

Shifts in peak hours: Fluctuations in peak traffic times could impact when certain user groups are most affected by congestion charges or discounts.

Seasonal variations: Changes due to seasonal factors like holidays or weather conditions might alter overall demand for transportation services.

Economic fluctuations: During economic downturns or upswings, user behaviors related to commuting choices may change based on job opportunities or financial constraints.
These dynamic changes highlight that theoretical models need flexibility built-in to account for evolving scenarios affecting traffic patterns over time accurately.