Personalized Vehicle Repositioning to Balance Supply and Demand in Ride-Hailing Services

Core Concepts

i-Rebalance, a personalized vehicle repositioning technique, aims to balance supply and demand while maximizing driver acceptance of repositioning recommendations.

Abstract

i-Rebalance is a personalized vehicle repositioning technique that addresses the challenge of balancing supply and demand in ride-hailing services. It consists of two key components:

Driver Behavior Modeling:

Predicts each driver's cruising preferences using a lightweight LSTM network based on their historical trajectories.
Estimates the probability of a driver accepting a repositioning recommendation through an on-field user study involving 99 real drivers.
Integrates the driver preference prediction and acceptance probability models into the simulation environment.

Sequential Vehicle Repositioning with Dual DRL Agents:

Employs a Grid Agent to determine the optimal order for recommending idle vehicles within a grid, considering the nearby supply-demand gap and driver preferences.
Uses a Vehicle Agent to provide personalized repositioning recommendations for each vehicle in the pre-defined order, aiming to optimize both supply-demand balance and driver preference satisfaction.
The sequential learning strategy with the two agents facilitates more effective policy training within a smaller action space compared to traditional joint-action methods.

Evaluation on real-world taxi trajectory data shows that i-Rebalance significantly improves driver acceptance rate by 38.07% and total driver income by 9.97% compared to baseline techniques.

Stats

The supply-demand gap δg
t of grid g at time t is the difference between the number of idle vehicles and the number of ride requests within grid g.
The preference satisfaction reward Ri
P(Si
V, ai
V) measures how much a reposition recommendation satisfies driver i's preference, calculated as the min-max normalized rankings of driver i's preferences on the 9 neighboring grids.

Quotes

"Imposing repositioning mandates on drivers would lead to unpleasant working experiences and cause driver loss on ride-hailing platforms."
"Earlier driver decisions affect subsequent ones and thereby the resulting supply-demand balance."

Key Insights Distilled From

i-Rebalance

by Haoyang Chen... at arxiv.org 04-03-2024

https://arxiv.org/pdf/2401.04429.pdf

Deeper Inquiries

How can the sequential repositioning framework be generalized to coordinate multi-agents for personalized decision-making in other domains

The sequential repositioning framework used in i-Rebalance can be generalized to coordinate multi-agents for personalized decision-making in various domains by adapting the principles and methodologies to suit the specific requirements of each domain. One way to achieve this is by incorporating domain-specific features and constraints into the framework. For instance, in a manufacturing setting, the framework could be modified to optimize production line reconfiguration by considering the unique preferences and constraints of each machine or workstation. By defining appropriate states, actions, and rewards tailored to the manufacturing environment, the framework can effectively coordinate multiple agents to make personalized decisions that enhance overall efficiency and productivity.
Another approach to generalizing the sequential repositioning framework is to apply it to dynamic resource allocation problems in fields such as healthcare or logistics. By modeling the states, actions, and rewards based on the specific needs and objectives of these domains, the framework can facilitate personalized decision-making among multiple agents to optimize resource utilization, minimize costs, and improve service quality. For example, in healthcare, the framework could be used to allocate medical staff or equipment based on real-time demand and individual preferences, ensuring efficient and effective resource management.
Overall, by customizing the sequential repositioning framework to accommodate the unique characteristics of different domains and leveraging its ability to coordinate multi-agent interactions, it can be effectively applied to a wide range of personalized decision-making scenarios beyond ride-hailing supply-demand balancing.

What are the potential drawbacks or limitations of the driver preference and acceptance models used in i-Rebalance, and how could they be further improved

While the driver preference and acceptance models used in i-Rebalance are effective in improving driver satisfaction and overall performance, there are potential drawbacks and limitations that could be addressed for further enhancement:

Limited Generalization: The models may have limited generalization capabilities beyond the specific dataset or context they were trained on. To improve this, the models could be fine-tuned on diverse datasets from various locations to capture a broader range of driver preferences and behaviors.

Assumption of Rationality: The models assume that drivers make decisions based on rational preferences and expected income. However, in real-world scenarios, drivers' decisions may be influenced by other factors such as emotions, fatigue, or external conditions. Incorporating additional features or sentiment analysis could help capture these nuances.

Dynamic Preferences: Driver preferences and acceptance rates may change over time due to various factors like traffic conditions, weather, or events. Implementing a mechanism to adaptively update the models based on real-time feedback could improve their accuracy and relevance.

Bias and Fairness: The models should be evaluated for bias and fairness to ensure that they do not inadvertently discriminate against certain drivers based on demographic or other factors. Techniques like fairness-aware learning and bias mitigation strategies can be employed to address these concerns.

By addressing these limitations and continuously refining the models based on feedback and new data, the driver preference and acceptance models in i-Rebalance can be further improved to enhance their effectiveness and applicability in diverse scenarios.

What other types of incentives or mechanisms, beyond personalized repositioning recommendations, could be explored to encourage driver participation and collaboration in ride-hailing supply-demand balancing

In addition to personalized repositioning recommendations, several other types of incentives or mechanisms could be explored to encourage driver participation and collaboration in ride-hailing supply-demand balancing:

Gamification: Introducing gamification elements such as rewards, badges, or leaderboards based on drivers' performance in accepting reposition recommendations and balancing supply-demand could incentivize active participation and engagement.

Dynamic Pricing: Implementing dynamic pricing strategies that offer financial incentives for drivers who accept reposition recommendations during peak demand periods or in areas with high ride requests can motivate drivers to proactively reposition themselves.

Driver Feedback and Recognition: Establishing a feedback system where drivers can provide input on the effectiveness of reposition recommendations and recognizing top-performing drivers through rewards or acknowledgments can foster a sense of ownership and pride in contributing to supply-demand balance.

Training and Education: Providing training sessions or resources to educate drivers on the benefits of supply-demand balancing, the impact on their earnings, and best practices for accepting reposition recommendations can increase awareness and encourage active participation.

Collaborative Decision-Making: Involving drivers in the decision-making process by seeking their input on reposition strategies, preferences, and challenges can empower them to take ownership of the balancing process and enhance their commitment to collaborative efforts.

By combining personalized reposition recommendations with these additional incentives and mechanisms, ride-hailing platforms can create a more engaging and rewarding experience for drivers while optimizing supply-demand balance and improving overall operational efficiency.