Deployment of Leader-Follower Automated Vehicle Systems for Smart Work Zone Applications with a Queuing-based Traffic Assignment Approach

• URL: http://arxiv.org/abs/2308.03764v1
• Date: Sun, 23 Jul 2023 16:35:05 GMT
• Title: Deployment of Leader-Follower Automated Vehicle Systems for Smart Work Zone Applications with a Queuing-based Traffic Assignment Approach
• Authors: Qing Tang, Xianbiao Hu
• Abstract summary: This manuscript focuses on optimizing the routing for ATMA vehicles in a network to minimize the system cost associated with the slow-moving operation. A queuing-based traffic assignment approach is proposed to identify the system cost caused by the ATMA system. The methodology is validated using a small-size and a large-size network and compared with two benchmark models to analyze the benefit of capacity drop modeling and QBTD travel time function.
• Score: 1.0355894890759376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The emerging technology of the Autonomous Truck Mounted Attenuator (ATMA), a leader-follower style vehicle system, utilizes connected and automated vehicle capabilities to enhance safety during transportation infrastructure maintenance in work zones. However, the speed difference between ATMA vehicles and general vehicles creates a moving bottleneck that reduces capacity and increases queue length, resulting in additional delays. The different routes taken by ATMA cause diverse patterns of time-varying capacity drops, which may affect the user equilibrium traffic assignment and lead to different system costs. This manuscript focuses on optimizing the routing for ATMA vehicles in a network to minimize the system cost associated with the slow-moving operation. To achieve this, a queuing-based traffic assignment approach is proposed to identify the system cost caused by the ATMA system. A queuing-based time-dependent (QBTD) travel time function, considering capacity drop, is introduced and applied in the static user equilibrium traffic assignment problem, with a result of adding dynamic characteristics. Subsequently, we formulate the queuing-based traffic assignment problem and solve it using a modified path-based algorithm. The methodology is validated using a small-size and a large-size network and compared with two benchmark models to analyze the benefit of capacity drop modeling and QBTD travel time function. Furthermore, the approach is applied to quantify the impact of different routes on the traffic system and identify an optimal route for ATMA vehicles performing maintenance work. Finally, sensitivity analysis is conducted to explore how the impact changes with variations in traffic demand and capacity reduction.

Related papers

• Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks [9.485363025495225]
  We present a novel semantic traffic control system that entrusts semantic encoding responsibilities to the vehicles themselves. This system processes driving decisions obtained from a Reinforcement Learning (RL) agent, streamlining the decision-making process.
  arXiv  Detail & Related papers  (2024-06-26T20:12:48Z)
• DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback [109.84667902348498]
  Traffic Signal Control (TSC) aims to reduce the average travel time of vehicles in a road network. Most prior TSC methods leverage deep reinforcement learning to search for a control policy. We propose DenseLight, a novel RL-based TSC method that employs an unbiased reward function to provide dense feedback on policy effectiveness.
  arXiv  Detail & Related papers  (2023-06-13T05:58:57Z)
• PDFormer: Propagation Delay-Aware Dynamic Long-Range Transformer for Traffic Flow Prediction [78.05103666987655]
  spatial-temporal Graph Neural Network (GNN) models have emerged as one of the most promising methods to solve this problem. We propose a novel propagation delay-aware dynamic long-range transFormer, namely PDFormer, for accurate traffic flow prediction. Our method can not only achieve state-of-the-art performance but also exhibit competitive computational efficiency.
  arXiv  Detail & Related papers  (2023-01-19T08:42:40Z)
• Reinforcement Learning based Cyberattack Model for Adaptive Traffic Signal Controller in Connected Transportation Systems [61.39400591328625]
  In a connected transportation system, adaptive traffic signal controllers (ATSC) utilize real-time vehicle trajectory data received from vehicles to regulate green time. This wirelessly connected ATSC increases cyber-attack surfaces and increases their vulnerability to various cyber-attack modes. One such mode is a'sybil' attack in which an attacker creates fake vehicles in the network. An RL agent is trained to learn an optimal rate of sybil vehicle injection to create congestion for an approach(s)
  arXiv  Detail & Related papers  (2022-10-31T20:12:17Z)
• Learning energy-efficient driving behaviors by imitating experts [75.12960180185105]
  This paper examines the role of imitation learning in bridging the gap between control strategies and realistic limitations in communication and sensing. We show that imitation learning can succeed in deriving policies that, if adopted by 5% of vehicles, may boost the energy-efficiency of networks with varying traffic conditions by 15% using only local observations.
  arXiv  Detail & Related papers  (2022-06-28T17:08:31Z)
• Traffic Management of Autonomous Vehicles using Policy Based Deep Reinforcement Learning and Intelligent Routing [0.26249027950824505]
  We propose a DRL-based signal control system that adjusts traffic signals according to the current congestion situation on intersections. To deal with the congestion on roads behind the intersection, we used re-routing technique to load balance the vehicles on road networks.
  arXiv  Detail & Related papers  (2022-06-28T02:46:20Z)
• AI-aided Traffic Control Scheme for M2M Communications in the Internet of Vehicles [61.21359293642559]
  The dynamics of traffic and the heterogeneous requirements of different IoV applications are not considered in most existing studies. We consider a hybrid traffic control scheme and use proximal policy optimization (PPO) method to tackle it.
  arXiv  Detail & Related papers  (2022-03-05T10:54:05Z)
• 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)
• Data-Driven Intersection Management Solutions for Mixed Traffic of Human-Driven and Connected and Automated Vehicles [0.0]
  This dissertation proposes two solutions for urban traffic control in the presence of connected and automated vehicles. First, a centralized platoon-based controller is proposed for the cooperative intersection management problem. Second, a data-driven approach is proposed for adaptive signal control in the presence of connected vehicles.
  arXiv  Detail & Related papers  (2020-12-10T01:44:45Z)
• Network Impacts of Automated Mobility-on-Demand: A Macroscopic Fundamental Diagram Perspective [0.0]
  Automated Mobility on Demand (AMOD) is a promising solution that may improve future urban mobility. This paper investigates the network impacts of AMOD through high-fidelity activity- and agent-based traffic simulation.
  arXiv  Detail & Related papers  (2020-11-10T13:39:35Z)
• Integrated Traffic Simulation-Prediction System using Neural Networks with Application to the Los Angeles International Airport Road Network [39.975268616636]
  The proposed system includes an optimization-based OD matrix generation method, a Neural Network (NN) model trained to predict OD matrices via the pattern of traffic flow and a microscopic traffic simulator. We test the proposed system on the road network of the central terminal area (CTA) of the Los Angeles International Airport (LAX)
  arXiv  Detail & Related papers  (2020-08-05T01:41:10Z)

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.