[HTML][HTML] How machine learning informs ride-hailing services: A survey
In recent years, online ride-hailing services have emerged as an important component of
urban transportation system, which not only provide significant ease for residents' travel …
urban transportation system, which not only provide significant ease for residents' travel …
Reinforcement learning for ridesharing: An extended survey
In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement
learning approaches to decision optimization problems in a typical ridesharing system …
learning approaches to decision optimization problems in a typical ridesharing system …
Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform
The vehicle dispatching system is one of the most critical problems in online ride-hailing
platforms, which requires adapting the operation and management strategy to the dynamics …
platforms, which requires adapting the operation and management strategy to the dynamics …
Neural approximate dynamic programming for on-demand ride-pooling
On-demand ride-pooling (eg, UberPool, LyftLine, GrabShare) has recently become popular
because of its ability to lower costs for passengers while simultaneously increasing revenue …
because of its ability to lower costs for passengers while simultaneously increasing revenue …
Optimization of ride-sharing with passenger transfer via deep reinforcement learning
D Wang, Q Wang, Y Yin, TCE Cheng - Transportation Research Part E …, 2023 - Elsevier
With the emergence of the sharing economy and the rapid growth of mobile communications
technologies, many novel sharing service models have been developed stemming from ride …
technologies, many novel sharing service models have been developed stemming from ride …
Economies and diseconomies of scale in on-demand ridepooling systems
We analyse the sources of economies and diseconomies of scale in On-Demand
Ridepooling (ODRP), disentangling three effects: when demand grows, average costs are …
Ridepooling (ODRP), disentangling three effects: when demand grows, average costs are …
Courier routing and assignment for food delivery service using reinforcement learning
We consider a Markov decision process model mimicking a real-world food delivery service
where the objective is to maximize the revenue derived from served requests given a limited …
where the objective is to maximize the revenue derived from served requests given a limited …
Supplier menus for dynamic matching in peer-to-peer transportation platforms
Peer-to-peer transportation platforms dynamically match requests (eg, a ride, a delivery) to
independent suppliers who are not employed nor controlled by the platform. Thus, the …
independent suppliers who are not employed nor controlled by the platform. Thus, the …
Reinforcement learning for ridesharing: A survey
In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement
learning approaches to ridesharing problems. Papers on the topics of rideshare matching …
learning approaches to ridesharing problems. Papers on the topics of rideshare matching …
Real-time dispatching of large-scale ride-sharing systems: Integrating optimization, machine learning, and model predictive control
This paper considers the dispatching of large-scale real-time ride-sharing systems to
address congestion issues faced by many cities. The goal is to serve all customers (service …
address congestion issues faced by many cities. The goal is to serve all customers (service …