Decentralized Optimization of Vehicle Route Planning -- A Cross-City Comparative Study

• URL: http://arxiv.org/abs/2001.03384v1
• Date: Fri, 10 Jan 2020 11:02:51 GMT
• Title: Decentralized Optimization of Vehicle Route Planning -- A Cross-City Comparative Study
• Authors: Brionna Davis, Grace Jennings, Taylor Pothast, Ilias Gerostathopoulos, Evangelos Pournaras, Raphael E. Stern
• Abstract summary: We conduct a study to compare different levels of agent altruism and the resulting effect on the network-level traffic performance. The main finding is that, with increased vehicle altruism, it is possible to balance traffic flow among the links of the network.
• Score: 7.74034002629298
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: New mobility concepts are at the forefront of research and innovation in smart cities. The introduction of connected and autonomous vehicles enables new possibilities in vehicle routing. Specifically, knowing the origin and destination of each agent in the network can allow for real-time routing of the vehicles to optimize network performance. However, this relies on individual vehicles being "altruistic" i.e., being willing to accept an alternative non-preferred route in order to achieve a network-level performance goal. In this work, we conduct a study to compare different levels of agent altruism and the resulting effect on the network-level traffic performance. Specifically, this study compares the effects of different underlying urban structures on the overall network performance, and investigates which characteristics of the network make it possible to realize routing improvements using a decentralized optimization router. The main finding is that, with increased vehicle altruism, it is possible to balance traffic flow among the links of the network. We show evidence that the decentralized optimization router is more effective with networks of high load while we study the influence of cities characteristics, in particular: networks with a higher number of nodes (intersections) or edges (roads) per unit area allow for more possible alternate routes, and thus higher potential to improve network performance.

Related papers

• Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks [1.9413548770753521]
  This paper proposes a reinforcement learning framework for coordinating mixed traffic across interconnected intersections. Our key contribution is a neighbor-aware reward mechanism that enables RVs to maintain balanced distribution across the network. Results show that our method reduces average waiting times by 39.2% compared to the state-of-the-art single-intersection control policy.
  arXiv  Detail & Related papers  (2024-12-17T07:35:56Z)
• Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions using Reinforcement Learning [2.362412515574206]
  This study explores Reinforcement Learning (RL) methods for evolving ROW compositions. We implement a centralised paradigm and a distributive learning paradigm to separately perform the dynamic control on several road network configurations. Experimental results indicate that the algorithms have the potential to improve traffic flow efficiency and allocate more space for pedestrians.
  arXiv  Detail & Related papers  (2023-03-22T03:42:39Z)
• Multi-agent Reinforcement Learning with Graph Q-Networks for Antenna Tuning [60.94661435297309]
  The scale of mobile networks makes it challenging to optimize antenna parameters using manual intervention or hand-engineered strategies. We propose a new multi-agent reinforcement learning algorithm to optimize mobile network configurations globally. We empirically demonstrate the performance of the algorithm on an antenna tilt tuning problem and a joint tilt and power control problem in a simulated environment.
  arXiv  Detail & Related papers  (2023-01-20T17:06:34Z)
• Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network [47.29123145759976]
  We propose a learning-based framework for handover parameter configuration. First, we introduce a novel approach to imitate how the network responds to different network states and parameter values. During the parameter configuration stage, instead of solving the global optimization problem, we design a local multi-objective optimization strategy.
  arXiv  Detail & Related papers  (2022-12-29T18:51:36Z)
• An ASP Framework for Efficient Urban Traffic Optimization [0.0]
  This paper presents a framework which allows to efficiently simulate and optimize traffic flow in a large roads' network with hundreds of vehicles. The framework leverages on an Answer Set Programming (ASP) encoding to formally describe the movements of vehicles inside a network. It is then possible to optimize the routes of vehicles inside the network to reduce a range of relevant metrics.
  arXiv  Detail & Related papers  (2022-08-05T10:50:38Z)
• Deep Reinforcement Learning Aided Packet-Routing For Aeronautical Ad-Hoc Networks Formed by Passenger Planes [99.54065757867554]
  We invoke deep reinforcement learning for routing in AANETs aiming at minimizing the end-to-end (E2E) delay. A deep Q-network (DQN) is conceived for capturing the relationship between the optimal routing decision and the local geographic information observed by the forwarding node. We further exploit the knowledge concerning the system's dynamics by using a deep value network (DVN) conceived with a feedback mechanism.
  arXiv  Detail & Related papers  (2021-10-28T14:18:56Z)
• Urban traffic dynamic rerouting framework: A DRL-based model with fog-cloud architecture [0.7046417074932257]
  This study proposes a two-stage model that combines GAQ (Graph Attention Network - Deep Q Learning) and EBkSP (Entropy Based k Shortest Path) First, GAQ analyzes the traffic conditions on each road and for each fog area, and then assigns a road index based on the information attention from both local and neighboring areas. Second, EBkSP assigns the route for each vehicle based on the vehicle priority and route popularity.
  arXiv  Detail & Related papers  (2021-10-11T18:15:44Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• Optimal transport in multilayer networks [68.8204255655161]
  We propose a model where optimal flows on different layers contribute differently to the total cost to be minimized. As an application, we consider transportation networks, where each layer is associated to a different transportation system. We show an example of this result on the real 2-layer network of the city of Bordeaux with bus and tram, where in certain regimes the presence of the tram network significantly unburdens the traffic on the road network.
  arXiv  Detail & Related papers  (2021-06-14T07:33:09Z)
• Incentivizing Efficient Equilibria in Traffic Networks with Mixed Autonomy [17.513581783749707]
  Vehicle platooning can potentially reduce traffic congestion by increasing road capacity via vehicle platooning. We consider a network of parallel roads with two modes of transportation: (i) human drivers, who will choose the quickest route available to them, and (ii) a ride hailing service, which provides an array of autonomous vehicle route options, each with different prices, to users. We formalize a model of vehicle flow in mixed autonomy and a model of how autonomous service users make choices between routes with different prices and latencies.
  arXiv  Detail & Related papers  (2021-05-06T03:01:46Z)
• On Topology Optimization and Routing in Integrated Access and Backhaul Networks: A Genetic Algorithm-based Approach [70.85399600288737]
  We study the problem of topology optimization and routing in IAB networks. We develop efficient genetic algorithm-based schemes for both IAB node placement and non-IAB backhaul link distribution. We discuss the main challenges for enabling mesh-based IAB networks.
  arXiv  Detail & Related papers  (2021-02-14T21:52:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.