Nothing Special   »   [go: up one dir, main page]

Thomas et al., 2009 - Google Patents

Predictions of urban volumes in single time series

Thomas et al., 2009

View PDF
Document ID
9854255110267527609
Author
Thomas T
Weijermars W
Van Berkum E
Publication year
Publication venue
IEEE Transactions on Intelligent Transportation Systems

External Links

Snippet

Congestion is increasing in many urban areas. This has led to a growing awareness of the importance of accurate traffic-flow predictions. In this paper, we introduce a prediction scheme that is based on an extensive study of volume patterns that were collected at about …
Continue reading at www.utwente.nl (PDF) (other versions)

Classifications

    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/01Detecting movement of traffic to be counted or controlled
    • G08G1/0104Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125Traffic data processing
    • G08G1/0129Traffic data processing for creating historical data or processing based on historical data
    • GPHYSICS
    • G08SIGNALLING
    • G08GTRAFFIC CONTROL SYSTEMS
    • G08G1/00Traffic control systems for road vehicles
    • G08G1/07Controlling traffic signals
    • G08G1/08Controlling traffic signals according to detected number or speed of vehicles
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis

Similar Documents

Publication Publication Date Title
Thomas et al. Predictions of urban volumes in single time series
CN109035761B (en) Travel time estimation method based on auxiliary supervised learning
Hou et al. Repeatability and similarity of freeway traffic flow and long-term prediction under big data
Chien et al. Dynamic travel time prediction with real-time and historic data
Van Arem et al. Recent advances and applications in the field of short-term traffic forecasting
D’Angelo et al. Travel-time prediction for freeway corridors
Li et al. Identifying important variables for predicting travel time of freeway with non-recurrent congestion with neural networks
CN109658695A (en) A kind of multifactor Short-time Traffic Flow Forecasting Methods
Lam et al. Short-term hourly traffic forecasts using Hong Kong annual traffic census
CN108648445B (en) Dynamic traffic situation prediction method based on traffic big data
CN110634292A (en) Travel time reliability estimation method based on road resistance performance function
Huisken et al. A comparative analysis of short-range travel time prediction methods
Rupnik et al. Travel time prediction on highways
CN108281033A (en) A kind of parking guidance system and method
Wu et al. Multiple-clustering ARMAX-based predictor and its application to freeway traffic flow prediction
Van Lint et al. Travel time reliability on freeways
Doğan Short-term traffic flow prediction using artificial intelligence with periodic clustering and elected set
Qiao et al. Short-term traffic flow forecast based on parallel long short-term memory neural network
Pamuła Road traffic parameters prediction in urban traffic management systems using neural networks
Zhang et al. Stochastic volatility modeling approach that accounts for uncertainties in travel time reliability forecasting
Mane et al. Link-level travel time prediction using artificial neural network models
Turki et al. A Markova-Chain Approach to Model Vehicles Traffic Behavior
Lee et al. A new travel time prediction method for intelligent transportation systems
Li et al. Combined neural network approach for short-term urban freeway traffic flow prediction
Rasaizadi et al. The ensemble learning process for short-term prediction of traffic state on rural roads