[go: up one dir, main page]

Cheng et al., 2015 - Google Patents

Automatic incident detection for urban expressways based on segment traffic flow density

Cheng et al., 2015

Document ID
5003152700162882399
Author
Cheng Y
Zhang M
Yang D
Publication year
Publication venue
Journal of Intelligent Transportation Systems

External Links

Snippet

Urban expressways play an important role in urban road networks. Although automatic incident detection (AID) methods have been studied for a long time, most of the existing AID algorithms are designed for freeways and do not explicitly consider the detection of incidents …
Continue reading at www.tandfonline.com (other versions)

Classifications

    • 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
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • 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
    • G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/08—Controlling traffic signals according to detected number or speed of vehicles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30861—Retrieval from the Internet, e.g. browsers
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • 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
    • G06Q30/00—Commerce, e.g. shopping or e-commerce
    • G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • 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
    • G—PHYSICS
    • G08—SIGNALLING
    • G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
    • G08B13/00—Burglar, theft or intruder alarms

Similar Documents

Publication Publication Date Title
Kuang et al. Predicting duration of traffic accidents based on cost-sensitive Bayesian network and weighted K-nearest neighbor
Cheng et al. Automatic incident detection for urban expressways based on segment traffic flow density
Martin et al. Incident detection algorithm evaluation
He et al. Incident duration prediction with hybrid tree-based quantile regression
US20200278894A1 (en) Notifying entities of relevant events
Haghighat et al. A computer vision‐based deep learning model to detect wrong‐way driving using pan–tilt–zoom traffic cameras
US20130253808A1 (en) Estimating Incident Duration
Dubey et al. Internet of Things based adaptive traffic management system as a part of Intelligent Transportation System (ITS)
Pirdavani et al. Application of a rule-based approach in real-time crash risk prediction model development using loop detector data
Zhu et al. Spatio-temporal point processes with attention for traffic congestion event modeling
Weng et al. Cluster-based lognormal distribution model for accident duration
Abdel-Aty et al. Real-time big data analytics and proactive traffic safety management visualization system
Evans et al. Evolution and future of urban road incident detection algorithms
US9622048B2 (en) SNS based incident management
Jose et al. Identifying and classifying highway bottlenecks based on spatial and temporal variation of speed
US20200050611A1 (en) Utilizing satisified rules as input signals
Cheng et al. A systematic approach for evaluating spatiotemporal characteristics of traffic violations and crashes at road intersections: an empirical study
Moriano et al. Spatiotemporal features of traffic help reduce automatic accident detection time
Kayes et al. Comparison of two intelligent transportation systems wrong-way driving countermeasures currently deployed on Florida toll roads
Qi et al. Recurrent and non-recurrent bottleneck analysis based on traffic state rank distribution
Evans et al. A random forest incident detection algorithm that incorporates contexts
Li et al. Estimation and prediction of freeway traffic congestion propagation using tagged vehicle positioning data
Markou et al. Use of taxi-trip data in analysis of demand patterns for detection and explanation of anomalies
Abdi et al. A Two‐Stage Sequential Framework for Traffic Accident Post‐Impact Prediction Utilizing Real‐Time Traffic, Weather, and Accident Data
Salanova Grau et al. Multisource data framework for road traffic state estimation