Studying Spatial Unevenness of Transport Demand in Cities Using Machine Learning Methods
D Chainikov, D Zakharov, E Kozin, A Pistsov - Applied Sciences, 2024 - mdpi.com
The article discusses the issues of spatial unevenness of transport demand in the city by
various transport modes. It describes the creation of models using an artificial neural …
various transport modes. It describes the creation of models using an artificial neural …
Jointly Optimizing Terahertz based Sensing and Communications in Vehicular Networks: A Dynamic Graph Neural Network Approach
In this paper, the problem of vehicle service mode selection (sensing, communication, or
both) and vehicle connections within terahertz (THz) enabled joint sensing and …
both) and vehicle connections within terahertz (THz) enabled joint sensing and …
Auxiliary Network Enhanced Hierarchical Graph Reinforcement Learning for Vehicle Repositioning
Affected by people's dynamic social activities, the imbalance between vehicle supply and
demand in the Mobility-On-Demand (MOD) system is a common phenomenon. To improve …
demand in the Mobility-On-Demand (MOD) system is a common phenomenon. To improve …
Learning to control autonomous fleets from observation via offline reinforcement learning
Autonomous Mobility-on-Demand (AMoD) systems are an evolving mode of transportation in
which a centrally coordinated fleet of self-driving vehicles dynamically serves travel …
which a centrally coordinated fleet of self-driving vehicles dynamically serves travel …
Anticipatory fleet repositioning for shared-use autonomous mobility services: An optimization and learning-based approach
The development of mobility-on-demand services, rich transportation data sources, and
autonomous vehicles (AVs) creates significant opportunities for shared-use AV mobility …
autonomous vehicles (AVs) creates significant opportunities for shared-use AV mobility …
Approximate Multiagent Reinforcement Learning for On-Demand Urban Mobility Problem on a Large Map
D Garces, S Bhattacharya… - 2024 IEEE International …, 2024 - ieeexplore.ieee.org
In this paper, we focus on the autonomous multiagent taxi routing problem for a large urban
environment where the location and number of future ride requests are unknown a-priori, but …
environment where the location and number of future ride requests are unknown a-priori, but …
Graph reinforcement learning for network control via bi-level optimization
Optimization problems over dynamic networks have been extensively studied and widely
used in the past decades to formulate numerous real-world problems. However,(1) …
used in the past decades to formulate numerous real-world problems. However,(1) …
Surge Routing: Event-informed Multiagent Reinforcement Learning for Autonomous Rideshare
Large events such as conferences, concerts and sports games, often cause surges in
demand for ride services that are not captured in average demand patterns, posing unique …
demand for ride services that are not captured in average demand patterns, posing unique …
Time-to-Green predictions for fully-actuated signal control systems with supervised learning
Recently, efforts have been made to standardize signal phase and timing (SPaT) messages.
These messages contain signal phase timings of all signalized intersection approaches …
These messages contain signal phase timings of all signalized intersection approaches …
Real-time Control of Electric Autonomous Mobility-on-Demand Systems via Graph Reinforcement Learning
A Singhal, D Gammelli, J Luke… - 2024 European …, 2024 - ieeexplore.ieee.org
Operators of Electric Autonomous Mobility-on-Demand (E-AMoD) fleets need to make
several realtime decisions such as matching available cars to ride requests, rebalancing idle …
several realtime decisions such as matching available cars to ride requests, rebalancing idle …