[go: up one dir, main page]

Bai et al., 2025 - Google Patents

A Dynamic Unmanned Aerial Vehicle Routing Framework for Urban Traffic Monitoring

Bai et al., 2025

View PDF
Document ID
11235741340573009673
Author
Bai Y
Feng Y
Publication year
Publication venue
arXiv preprint arXiv:2501.09249

External Links

Snippet

Unmanned Aerial Vehicles (UAVs) have great potential in urban traffic monitoring due to their rapid speed, cost-effectiveness, and extensive field-of-view, while being unconstrained by traffic congestion. However, their limited flight duration presents critical challenges in …
Continue reading at arxiv.org (PDF) (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063—Operations research or analysis
    • G06Q10/0639—Performance analysis
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in preceding groups
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled

Similar Documents

Publication Publication Date Title
Katare et al. A survey on approximate edge AI for energy efficient autonomous driving services
US11567495B2 (en) Methods and systems for selecting machine learning models to predict distributed computing resources
Li et al. A traffic prediction enabled double rewarded value iteration network for route planning
Merchant et al. Optimality conditions for a dynamic traffic assignment model
Chandra Shit Crowd intelligence for sustainable futuristic intelligent transportation system: a review
Annaswamy et al. Transactive control in smart cities
Bai et al. A Dynamic Unmanned Aerial Vehicle Routing Framework for Urban Traffic Monitoring
Ben-Akiva et al. DTA2012 Symposium: combining disaggregate route choice estimation with aggregate calibration of a dynamic traffic assignment model
Liu et al. A refinement and abstraction method of the SPZN formal model for intelligent networked vehicles systems.
Tresca et al. Robo-taxi fleet coordination at scale via reinforcement learning
Ullah et al. Intelligent transportation channels for smart cities
Kazemdehbashi et al. An exact coverage path planning algorithm for UAV-based search and rescue operations
Park et al. Anticipatory dynamic traffic sensor location problems with connected vehicle technologies
Gupta et al. A Vehicle Congestion Prediction Approach for Smart City Traffic Management
ZRIGUI et al. ADAPTIVE TRAFFIC SIGNAL CONTROL USING AI AND DISTRIBUTED SYSTEMS FOR SMART URBAN MOBILITY
Subrahmanyam Future Trends and Research Directions
Ahmed et al. Optimal path recommendation in dynamic traffic networks using the hybrid Tabu-A* algorithm
Khan et al. Intelligent transportation system for smart-cities using fuzzy logic
Boubaker et al. Electric vehicles charging station allocation based on load profile forecasting and Dijkstra’s algorithm for optimal path planning
Khanal et al. Learning-Augmented Model-Based Multi-Robot Planning for Time-Critical Search and Inspection Under Uncertainty
Solmaz et al. Digital twins in the future networking: A use case in IOWN
Vihurskyi Optimizing urban traffic management with machine learning techniques: A systematic review
Chu et al. Adaptive data sharing and computation offloading in cloud-edge computing with resource constraints
US11132100B1 (en) 4D interactive mission analytics for visualization of unmanned vehicle performance indicators
Adeyinka et al. Modeling Traffic Congestion With Graphs