Optimizing Ride-Pooling Fares: Balancing Operator Profit and Rider Acceptance with Personalized Pricing

Core Concepts

Personalized fares in ride-pooling, optimized for operator profit by considering rider acceptance probabilities, can improve both financial outcomes and system-wide efficiency compared to flat discounts.

Abstract

• Bibliographic Information: Bujak, M., & Kucharski, R. (2024). Balancing Profit and Traveller Acceptance in Ride-Pooling Personalised Fares. arXiv preprint arXiv:2411.03370v1.
• Research Objective: This paper proposes a novel personalized pricing model for ride-pooling services that aims to maximize operator profit while ensuring rider satisfaction and improving system-wide efficiency.
• Methodology: The authors develop a probabilistic framework that considers the heterogeneous nature of rider preferences, incorporating factors like value of time and willingness to share. They utilize a quantile-based approach to construct a shareability graph, representing feasible ride-pooling combinations. By analyzing rider acceptance probabilities as a function of fare discounts, the model determines optimal personalized discounts for each ride, maximizing the expected profitability for the operator.
• Key Findings: The study reveals that personalized pricing outperforms flat discount strategies in terms of both profitability and system efficiency. The model successfully identifies rides that are beneficial for the system and incentivizes their realization through reduced fares. It also effectively discourages less efficient ride combinations by assigning higher fares, encouraging those riders to opt for private rides.
• Main Conclusions: The research demonstrates that ride-pooling systems employing profit-maximizing personalized fares, considering rider acceptance, are more sustainable and efficient than those using flat discounts. This approach balances operator profitability with rider satisfaction, leading to a more successful and environmentally friendly transportation system.
• Significance: This study provides valuable insights for ride-pooling operators seeking to optimize their pricing strategies. The proposed model offers a practical framework for implementing personalized fares, potentially leading to increased profitability, improved rider experience, and reduced environmental impact.
• Limitations and Future Research: The study assumes a known distribution of rider preferences, which might not always be readily available. Future research could explore methods for dynamically learning and adapting to evolving rider preferences. Additionally, investigating the impact of real-time demand fluctuations on the model's performance would be beneficial.

Stats

A single shared vehicle can potentially replace eleven private cars, significantly reducing traffic congestion and emissions.
A ride-pooling system with a 20% flat discount resulted in 28 out of 150 travelers opting for private rides, while the personalized pricing scheme reduced this number to 14.
The average ride degree (number of passengers per ride) was highest for the personalized pricing strategy (2.23), followed by the 20% discount (2.05) and 15% discount (1.91) strategies.
The personalized pricing strategy achieved an average profitability of 3.29 and a total expected distance of 332.20, outperforming flat discount strategies and a private ride-hailing baseline.

Quotes

"We argue that the best promotional to popularise the ride-pooling service is by proving its commercial potential."
"Our measure (the expected profitability) optimises revenue per mileage. As a result, the vehicle mileage is implicitly reduced, while ramifications for travellers are more complex."
"Clients who are valuable for the service performance are recognised with significantly lower fare and their satisfaction increases while others are discouraged from pooling in favour of the private ride-hailing (higher fare)."

Key Insights Distilled From

Balancing Profit and Traveller Acceptance in Ride-Pooling Personalised Fares

by Michal Bujak... at arxiv.org 11-07-2024

https://arxiv.org/pdf/2411.03370.pdf

Deeper Inquiries

How can ride-pooling platforms effectively communicate the value proposition of personalized pricing to riders, addressing potential concerns about fairness and transparency?

Ride-pooling platforms can employ several strategies to effectively communicate the value proposition of personalized pricing and mitigate concerns about fairness and transparency:
1. Emphasize Individualized Benefits:

Clear Explanation: Platforms should clearly explain how personalized pricing works, emphasizing that discounts are tailored to each rider's specific trip characteristics and level of inconvenience.
Highlight Savings Potential: Focus on how personalized pricing can lead to greater savings compared to flat discounts, especially for riders willing to be flexible with their routes or travel times.
Illustrative Examples: Use real-world examples to demonstrate how different riders benefit from personalized pricing based on factors like detour length, shared ride duration, and time of day.
2. Promote Transparency and Control:

Detailed Fare Breakdown: Provide riders with a transparent breakdown of their fare, clearly showing the base fare, personalized discount, and any other applicable fees.
Trip Comparison Tool: Offer a tool that allows riders to compare the cost and convenience of a private ride versus a shared ride with personalized pricing before booking.
Option to Opt-Out: Allow riders to opt out of personalized pricing and choose a standard fare structure if they prefer, giving them a sense of control.
3. Build Trust and Address Fairness Concerns:

Data Privacy Assurance: Clearly communicate how rider data is used to calculate personalized fares, emphasizing data security and privacy protection measures.
Fairness Algorithm Explanation: Provide a high-level explanation of the algorithm used to determine personalized discounts, assuring riders that it is designed to be fair and unbiased.
Customer Support and Feedback: Offer accessible customer support channels to address rider questions and concerns about personalized pricing, actively incorporating feedback for continuous improvement.
4. Incentivize Rider Education and Engagement:

In-App Tutorials and FAQs: Provide in-app tutorials, FAQs, and blog posts that educate riders about personalized pricing, its benefits, and how it works.
Gamification and Rewards: Consider gamification elements or loyalty programs that reward riders for choosing shared rides and engaging with personalized pricing options.
By implementing these communication and transparency measures, ride-pooling platforms can effectively convey the value proposition of personalized pricing, build rider trust, and encourage wider adoption of this mutually beneficial pricing model.

Could surge pricing dynamics in high-demand periods undermine the effectiveness of personalized discounts and rider satisfaction in ride-pooling?

Yes, surge pricing dynamics in high-demand periods have the potential to undermine the effectiveness of personalized discounts and negatively impact rider satisfaction in ride-pooling. Here's why:

Erosion of Perceived Value: Personalized discounts are designed to incentivize riders to choose shared rides by offering lower fares. However, during surge pricing, the base fare itself is inflated, diminishing the perceived value of the personalized discount. Riders might feel that the discount is insignificant compared to the overall surge-inflated price.
Increased Price Sensitivity: Surge pricing often makes riders more price-sensitive. They might be less willing to accept even small detours or longer wait times associated with ride-pooling if the overall price is significantly higher than usual, even with a personalized discount.
Fairness Perception Challenges: If surge pricing is applied inconsistently or opaquely in conjunction with personalized discounts, it can raise fairness concerns among riders. For instance, two riders with similar trip characteristics might perceive unfair price discrepancies due to varying surge multipliers.
Complexity and Confusion: The combination of surge pricing and personalized discounts can create a complex and confusing fare structure for riders. This lack of transparency can lead to frustration and mistrust in the pricing model.
Mitigation Strategies:
To mitigate these potential issues, ride-pooling platforms can consider the following strategies:

Surge-Adjusted Discounts: Dynamically adjust personalized discounts during surge pricing periods to ensure they remain attractive and provide meaningful savings for riders.
Transparent Surge Information: Clearly communicate surge pricing multipliers and their impact on both private and shared ride fares, allowing riders to make informed decisions.
Capping Surge on Shared Rides: Consider implementing policies that limit the maximum surge multiplier applied to shared rides, ensuring that ride-pooling remains a cost-effective option even during peak demand.
Prioritizing Shared Ride Availability: During surge periods, prioritize the allocation of drivers and vehicles to shared rides, ensuring that riders who are willing to share can still access affordable transportation options.
By carefully managing the interplay between surge pricing and personalized discounts, ride-pooling platforms can maintain rider satisfaction, preserve the attractiveness of shared rides, and ensure the long-term sustainability of their services.

How might the increasing prevalence of autonomous vehicles impact the feasibility and adoption of ride-pooling services and their associated pricing models?

The increasing prevalence of autonomous vehicles (AVs) is poised to significantly impact the feasibility, adoption, and pricing models of ride-pooling services:
Increased Feasibility and Adoption:

Lower Operating Costs: AVs eliminate the need for human drivers, drastically reducing operating costs for ride-pooling services. This cost reduction can be passed on to riders through lower fares, making ride-pooling more accessible and appealing.
Improved Efficiency and Reliability: AVs can optimize routes, minimize idling times, and operate continuously, leading to increased efficiency and reliability for ride-pooling services. This enhanced performance can attract more riders and increase the viability of shared rides.
Enhanced Safety and Comfort: AVs have the potential to improve safety by eliminating human error and offer a more comfortable and consistent riding experience, further boosting the appeal of ride-pooling.
Impact on Pricing Models:

Shift from Time-Based to Distance-Based Pricing: With lower operating costs and the ability to precisely calculate distances, ride-pooling services might shift towards predominantly distance-based pricing models, making fares more transparent and predictable.
Dynamic Pricing Based on Occupancy: AVs enable real-time monitoring of vehicle occupancy. Ride-pooling platforms could implement dynamic pricing models where fares fluctuate based on the number of passengers sharing a ride, incentivizing higher occupancy rates.
Subscription-Based Models: The lower operating costs of AVs could facilitate the emergence of subscription-based models for ride-pooling, offering riders unlimited shared rides within a specific area for a fixed monthly fee.
Integration with Mobility-as-a-Service (MaaS): Ride-pooling services using AVs can seamlessly integrate into broader MaaS platforms, offering riders a unified platform to plan and pay for various transportation options, including shared rides, public transit, and micro-mobility.
Challenges and Considerations:

Initial Investment Costs: The upfront investment required to develop and deploy a fleet of AVs remains substantial, potentially hindering the widespread adoption of AV-based ride-pooling in the near term.
Regulatory and Legal Frameworks: Clear regulatory and legal frameworks are crucial for the safe and responsible operation of AVs, and their absence could slow down the integration of AVs into ride-pooling services.
Public Acceptance and Trust: Building public trust in the safety and reliability of AVs is essential for the widespread adoption of autonomous ride-pooling.
Overall, the increasing prevalence of autonomous vehicles presents both opportunities and challenges for ride-pooling services. While AVs have the potential to make ride-pooling more feasible, efficient, and affordable, addressing the associated challenges and adapting pricing models will be crucial for successful implementation and widespread adoption.