Spatial-Aware Deep Reinforcement Learning for the Traveling Officer Problem

• URL: http://arxiv.org/abs/2401.05969v1
• Date: Thu, 11 Jan 2024 15:16:20 GMT
• Title: Spatial-Aware Deep Reinforcement Learning for the Traveling Officer Problem
• Authors: Niklas Strau{\ss}, Matthias Schubert
• Abstract summary: The traveling officer problem (TOP) is a challenging optimization task. A major challenge in TOP is the dynamic nature of parking offenses, which randomly appear and disappear after some time. This paper proposes SATOP, a novel spatial-aware deep reinforcement learning approach for TOP.
• Score: 2.8781483086625537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The traveling officer problem (TOP) is a challenging stochastic optimization task. In this problem, a parking officer is guided through a city equipped with parking sensors to fine as many parking offenders as possible. A major challenge in TOP is the dynamic nature of parking offenses, which randomly appear and disappear after some time, regardless of whether they have been fined. Thus, solutions need to dynamically adjust to currently fineable parking offenses while also planning ahead to increase the likelihood that the officer arrives during the offense taking place. Though various solutions exist, these methods often struggle to take the implications of actions on the ability to fine future parking violations into account. This paper proposes SATOP, a novel spatial-aware deep reinforcement learning approach for TOP. Our novel state encoder creates a representation of each action, leveraging the spatial relationships between parking spots, the agent, and the action. Furthermore, we propose a novel message-passing module for learning future inter-action correlations in the given environment. Thus, the agent can estimate the potential to fine further parking violations after executing an action. We evaluate our method using an environment based on real-world data from Melbourne. Our results show that SATOP consistently outperforms state-of-the-art TOP agents and is able to fine up to 22% more parking offenses.

Related papers

• Deep Learning for On-Street Parking Violation Prediction [0.0]
  Illegal parking along with the lack of available parking spaces are among the biggest issues faced in many large cities.<n>In this paper, we employ a Deep Learning (DL)-based model to predict fine-grained parking violation rates for on-street parking systems.<n>We demonstrate, using experiments on real data collected in Thessaloniki, Greece, that the developed system can indeed provide accurate parking violation predictions.
  arXiv  Detail & Related papers  (2025-05-11T03:02:26Z)
• GPT-Augmented Reinforcement Learning with Intelligent Control for Vehicle Dispatching [82.19172267487998]
  GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching. This paper introduces GARLIC: a framework of GPT-Augmented Reinforcement Learning with Intelligent Control for vehicle dispatching.
  arXiv  Detail & Related papers  (2024-08-19T08:23:38Z)
• Smart Camera Parking System With Auto Parking Spot Detection [1.0512475026060208]
  We provide a novel approach called PakSta for identifying the state of parking spots automatically. Our method utilizes object detector from PakLoc to simultaneously determine the occupancy status of all parking lots within a video frame. The efficacy of our proposed methodology on the PKLot dataset results in a significant reduction in human labor of 94.25%.
  arXiv  Detail & Related papers  (2024-07-07T19:00:11Z)
• Crowdsourced reviews reveal substantial disparities in public perceptions of parking [2.3034861262968453]
  This study introduces a cost-effective and widely accessible data source, crowdsourced online reviews, to investigate public perceptions of parking across the U.S. We examine 4,987,483 parking-related reviews for 1,129,460 points of interest (POIs) across 911 core-based statistical areas (CBSAs) sourced from Google Maps. Findings reveal significant variations in parking sentiment across POI types and CBSAs, with Restaurant POIs showing the most negative.
  arXiv  Detail & Related papers  (2024-07-06T15:17:17Z)
• Truck Parking Usage Prediction with Decomposed Graph Neural Networks [15.291200515217513]
  Truck parking on freight corridors faces the major challenge of insufficient parking spaces. It has been shown that providing accurate parking usage prediction can be a cost-effective solution to reduce unsafe parking practices. This paper presents the Regional Temporal Conal Network (Reg-TCN) to predict parking usage across the entire state.
  arXiv  Detail & Related papers  (2024-01-23T17:14:01Z)
• Multi-Agent Deep Reinforcement Learning for Dynamic Avatar Migration in AIoT-enabled Vehicular Metaverses with Trajectory Prediction [70.9337170201739]
  We propose a model to predict the future trajectories of intelligent vehicles based on their historical data. We show that our proposed algorithm can effectively reduce the latency of executing avatar tasks by around 25% without prediction.
  arXiv  Detail & Related papers  (2023-06-26T13:27:11Z)
• SHINE: Deep Learning-Based Accessible Parking Management System [1.7109513360384465]
  An increase in the number of privately owned vehicles has led to the abuse of disabled parking spaces. Traditional license plate recognition (LPR) systems have proven inefficient in addressing such a problem in real-time. We have proposed a novel system called, Shine, which uses the deep learning-based object detection algorithm for detecting the vehicle.
  arXiv  Detail & Related papers  (2023-02-02T02:46:52Z)
• Crowd-sensing Enhanced Parking Patrol using Sharing Bikes' Trajectories [20.705097835958245]
  Illegal vehicle parking is a common urban problem faced by major cities in the world, as it incurs traffic jams, which lead to air pollution and traffic accidents. The massive and high-quality sharing bike trajectories from Mobike offer us a unique opportunity to design a ubiquitous illegal parking detection approach. The detection result can guide the patrol schedule, i.e. send the patrol policemen to the region with higher illegal parking risks, and further improve the patrol efficiency.
  arXiv  Detail & Related papers  (2021-10-29T05:48:51Z)
• Robust Reinforcement Learning Under Minimax Regret for Green Security [50.03819244940543]
  Green security domains feature defenders who plan patrols in the face of uncertainty about the adversarial behavior of poachers, illegal loggers, and illegal fishers. We focus on robust sequential patrol planning for green security following the minimax regret criterion, which has not been considered in the literature. We formulate the problem as a game between the defender and nature who controls the parameter values of the adversarial behavior and design an algorithm MIRROR to find a robust policy.
  arXiv  Detail & Related papers  (2021-06-15T20:11:12Z)
• MoParkeR : Multi-objective Parking Recommendation [5.970994932728028]
  Existing parking recommendation solutions mainly focus on finding and suggesting parking spaces based on the unoccupied options only. More importantly, these factors may change over time and conflict with each other which makes the recommendations produced by current parking recommender systems ineffective. We present a solution by designing a multi-objective parking recommendation engine called MoParkeR that considers various conflicting factors together.
  arXiv  Detail & Related papers  (2021-06-10T10:57:09Z)
• Contingencies from Observations: Tractable Contingency Planning with Learned Behavior Models [82.34305824719101]
  Humans have a remarkable ability to make decisions by accurately reasoning about future events. We develop a general-purpose contingency planner that is learned end-to-end using high-dimensional scene observations. We show how this model can tractably learn contingencies from behavioral observations.
  arXiv  Detail & Related papers  (2021-04-21T14:30:20Z)
• ParkPredict: Motion and Intent Prediction of Vehicles in Parking Lots [65.33650222396078]
  We develop a parking lot environment and collect a dataset of human parking maneuvers. We compare a multi-modal Long Short-Term Memory (LSTM) prediction model and a Convolution Neural Network LSTM (CNN-LSTM) to a physics-based Extended Kalman Filter (EKF) baseline. Our results show that 1) intent can be estimated well (roughly 85% top-1 accuracy and nearly 100% top-3 accuracy with the LSTM and CNN-LSTM model); 2) knowledge of the human driver's intended parking spot has a major impact on predicting parking trajectory; and 3) the semantic representation of the environment
  arXiv  Detail & Related papers  (2020-04-21T20:46:32Z)
• Intelligent Roundabout Insertion using Deep Reinforcement Learning [68.8204255655161]
  We present a maneuver planning module able to negotiate the entering in busy roundabouts. The proposed module is based on a neural network trained to predict when and how entering the roundabout throughout the whole duration of the maneuver.
  arXiv  Detail & Related papers  (2020-01-03T11:16:41Z)

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.