The transition to autonomous cars, the redesign of cities and the future of urban sustainability
Autonomous cars controlled by an artificial intelligence are increasingly being integrated in
the transport portfolio of cities, with strong repercussions for the design and sustainability of …
the transport portfolio of cities, with strong repercussions for the design and sustainability of …
Deep reinforcement learning-based charging pricing for autonomous mobility-on-demand system
The autonomous mobility-on-demand (AMoD) system plays an important role in the urban
transportation system. The charging behavior of AMoD fleet becomes a critical link between …
transportation system. The charging behavior of AMoD fleet becomes a critical link between …
Robust matching-integrated vehicle rebalancing in ride-hailing system with uncertain demand
With the rapid growth of the mobility-on-demand (MoD) market in recent years, ride-hailing
companies have become an important element of the urban mobility system. There are two …
companies have become an important element of the urban mobility system. There are two …
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 …
A real-time dispatching strategy for shared automated electric vehicles with performance guarantees
Car-sharing has emerged as a competitive technology for urban mobility. Combined with the
upward trend in vehicle electrification and the promise of automation, it is expected that …
upward trend in vehicle electrification and the promise of automation, it is expected that …
Deep neural networks for choice analysis: A statistical learning theory perspective
Although researchers increasingly use deep neural networks (DNN) to analyze individual
choices, overfitting and interpretability issues remain obstacles in theory and practice. This …
choices, overfitting and interpretability issues remain obstacles in theory and practice. This …
A modular and transferable reinforcement learning framework for the fleet rebalancing problem
Mobility on demand (MoD) systems show great promise in realizing flexible and efficient
urban transportation. However, significant technical challenges arise from operational …
urban transportation. However, significant technical challenges arise from operational …
Multi-agent transfer learning in reinforcement learning-based ride-sharing systems
A Castagna, I Dusparic - arXiv preprint arXiv:2112.00424, 2021 - arxiv.org
Reinforcement learning (RL) has been used in a range of simulated real-world tasks, eg,
sensor coordination, traffic light control, and on-demand mobility services. However, real …
sensor coordination, traffic light control, and on-demand mobility services. However, real …
Urban computing: The technological framework for smart cities
M Bouroche, I Dusparic - Handbook of smart cities, 2021 - Springer
Increased urbanization is putting a strain on the limited shared urban resources, for
example, road space, energy, and clean air and water. Smart cities leverage technology to …
example, road space, energy, and clean air and water. Smart cities leverage technology to …
Learning model predictive controllers for real-time ride-hailing vehicle relocation and pricing decisions
E Yuan, P Van Hentenryck - arXiv preprint arXiv:2111.03204, 2021 - arxiv.org
Large-scale ride-hailing systems often combine real-time routing at the individual request
level with a macroscopic Model Predictive Control (MPC) optimization for dynamic pricing …
level with a macroscopic Model Predictive Control (MPC) optimization for dynamic pricing …