A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning (2010.01755v2)

Abstract: Significant development of ride-sharing services presents a plethora of opportunities to transform urban mobility by providing personalized and convenient transportation while ensuring efficiency of large-scale ride pooling. However, a core problem for such services is route planning for each driver to fulfill the dynamically arriving requests while satisfying given constraints. Current models are mostly limited to static routes with only two rides per vehicle (optimally) or three (with heuristics). In this paper, we present a dynamic, demand aware, and pricing-based vehicle-passenger matching and route planning framework that (1) dynamically generates optimal routes for each vehicle based on online demand, pricing associated with each ride, vehicle capacities and locations. This matching algorithm starts greedily and optimizes over time using an insertion operation, (2) involves drivers in the decision-making process by allowing them to propose a different price based on the expected reward for a particular ride as well as the destination locations for future rides, which is influenced by supply-and demand computed by the Deep Q-network, (3) allows customers to accept or reject rides based on their set of preferences with respect to pricing and delay windows, vehicle type and carpooling preferences, and (4) based on demand prediction, our approach re-balances idle vehicles by dispatching them to the areas of anticipated high demand using deep Reinforcement Learning (RL). Our framework is validated using the New York City Taxi public dataset; however, we consider different vehicle types and designed customer utility functions to validate the setup and study different settings. Experimental results show the effectiveness of our approach in real-time and large scale settings.

• The paper introduces a dynamic reinforcement learning framework that jointly optimizes ride matching, pricing, and dispatching.
• It leverages a Deep Q-network for real-time route optimization and driver pricing participation, enhancing operational efficiency.
• The system incorporates customer preferences and idle vehicle rebalancing to improve user satisfaction and reduce wait times.

The paper "A Distributed Model-Free Ride-Sharing Approach for Joint Matching, Pricing, and Dispatching using Deep Reinforcement Learning" tackles the complex problem of route planning for ride-sharing services. The core innovation of the paper is a dynamic and integrated framework that addresses the challenges of passenger-vehicle matching, pricing, and dispatching in real-time using a deep reinforcement learning approach.

Key Contributions:

1. Dynamic Route Generation: The proposed framework dynamically generates optimal routes for each vehicle based on real-time demand, the pricing of each ride, vehicle capacities, and their current locations. This dynamic matching algorithm begins with a greedy method and subsequently optimizes the route through insertion operations.
2. Driver Participation in Pricing: Drivers are given an active role in the decision-making process by allowing them to propose different prices for rides. This pricing is influenced by the expected reward of a prospective ride and future ride opportunities, calculated using a Deep Q-network. This enables drivers to adjust prices based on supply and demand forecasts.
3. Customer Preferences: Customers have the flexibility to accept or reject rides based on a set of preferences. These preferences include pricing considerations, acceptable delay windows, vehicle type, and carpooling options. This customization ensures a better user experience and aligns ride availability with customer expectations.
4. Idle Vehicle Rebalancing: The system also includes a mechanism for rebalancing idle vehicles based on demand predictions. This is accomplished through dispatching idle vehicles to areas where high demand is anticipated, guided by deep reinforcement learning. This proactive rebalancing helps in reducing wait times and improving efficiency.

Methodology:

The framework employs a deep reinforcement learning approach:

• Deep Q-network: Used to compute expected rewards and enable dynamic pricing adjustments.
• Reinforcement Learning: Guides the strategic rebalancing of idle vehicles to areas with forecasted high demand.

Experimental Validation:

The approach is validated using the New York City Taxi public dataset. The experiment included testing different vehicle types and designed customer utility functions to observe various settings.

Results:

Experimental results reveal that the proposed framework is effective in real-time and large-scale scenarios. Key findings demonstrate that dynamic and demand-aware route planning significantly improves the operational efficiency and user satisfaction in ride-sharing services.

This framework presents a substantial advancement in the field by integrating dynamic routing, pricing, and strategic dispatching into a cohesive system enhanced by deep reinforcement learning techniques. The involvement of both drivers and customers in the decision-making process and the proactive rebalancing of idle vehicles are particularly noteworthy innovations contributing to the ride-sharing ecosystem.