6,228 research outputs found
A State-of-the-art Integrated Transportation Simulation Platform
Nowadays, universities and companies have a huge need for simulation and
modelling methodologies. In the particular case of traffic and transportation,
making physical modifications to the real traffic networks could be highly
expensive, dependent on political decisions and could be highly disruptive to
the environment. However, while studying a specific domain or problem,
analysing a problem through simulation may not be trivial and may need several
simulation tools, hence raising interoperability issues. To overcome these
problems, we propose an agent-directed transportation simulation platform,
through the cloud, by means of services. We intend to use the IEEE standard HLA
(High Level Architecture) for simulators interoperability and agents for
controlling and coordination. Our motivations are to allow multiresolution
analysis of complex domains, to allow experts to collaborate on the analysis of
a common problem and to allow co-simulation and synergy of different
application domains. This paper will start by presenting some preliminary
background concepts to help better understand the scope of this work. After
that, the results of a literature review is shown. Finally, the general
architecture of a transportation simulation platform is proposed
Smart balancing of E-scooter sharing systems via deep reinforcement learning: a preliminary study
Nowadays, micro-mobility sharing systems have become extremely popular. Such systems consist in fleets of dockless electric vehicles which are deployed in cities, and used by citizens to move in a more ecological and flexible way. Unfortunately, one of the issues related to such technologies is its intrinsic load imbalance, since users can pick up and drop off the electric vehicles where they prefer. In this paper we present ESB-DQN, a multi-agent system for E-Scooter Balancing (ESB) based on Deep Reinforcement Learning where agents are implemented as Deep Q-Networks (DQN). ESB-DQN offers suggestions to pick or return e-scooters in order to make the fleet usage and sharing as balanced as possible, still ensuring that the original plans of the user undergo only minor changes. The main contributions of this paper include a careful analysis of the state of the art, an innovative customer-oriented rebalancing strategy, the integration of state-of-the-art libraries for deep Reinforcement Learning into the existing ODySSEUS simulator of mobility sharing systems, and preliminary but promising experiments that suggest that our approach is worth further exploration
Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications
We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches
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