With the progressive technological advancement of autonomous vehicles, taxi service
providers are expected to offer driverless taxi systems that alleviate traffic congestion and …

Online ride-hailing platforms have reduced significantly the amounts of the time that taxis are
idle and that passengers spend on waiting. As a key component of these platforms, the fleet …

Proactive taxi dispatching is of great importance to balance taxi demand-supply gaps among
different locations in a city. Recent advances primarily rely on deep reinforcement learning …

In this paper, we define and investigate a novel model-free deep reinforcement learning
framework to solve the taxi dispatch problem. The framework can be used to redistribute …

With the improvement of people's living standards, people's demand of traveling by taxi is
increasing, but the taxi service system is not perfect yet; taxi drivers usually rely on their …

Balancing the supply and demand for ride-sourcing companies is a challenging issue,
especially with real-time requests and stochastic traffic conditions of large-scale congested …

Ride hailing has become prevailing. Central in ride hailing platforms is taxi order
dispatching which involves recommending a suitable driver for each order. Previous works …

Deep Reinforcement Learning (RL) suffer from catastrophic forgetting due to being agnostic
to the timescale of changes in the distribution of experiences. Although, RL algorithms are …

The recent enhancement of taxi dispatch services with information technology has enabled
data-driven pricing and dispatch. However, existing studies failed to address differences in …

The popularity of online ride-hailing platforms has made people travel smarter than ever
before. But people still frequently encounter the dilemma of “taxi drivers hunt passengers …