Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Short-term traffic flow forecasting method based on hidden Markov model

A technology of short-term traffic flow and prediction method, which is applied in the field of short-term traffic flow state prediction based on hidden Markov model, and achieves the effect of reasonable design

Active Publication Date: 2013-11-27
TAIYUAN UNIV OF TECH
View PDF7 Cites 46 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0014] The purpose of the present invention is to provide a short-term method for predicting the traffic flow state of a road section, solve the problem of predicting the short-term traffic flow state, and provide a short-term traffic flow state prediction method based on hidden Markov model

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Short-term traffic flow forecasting method based on hidden Markov model
  • Short-term traffic flow forecasting method based on hidden Markov model
  • Short-term traffic flow forecasting method based on hidden Markov model

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0039] Specific embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0040] In this embodiment, the hidden Markov model determined based on the traffic flow state is called "traffic hidden Markov model", abbreviated as "THMM". Set the parameter acquisition period δ to 30s, then there are 2880 sets of data in 24 hours a day, and a total of 1001 sets of morning peak data sequences from 500 to 1500 per day are used as the data of this embodiment. In order to facilitate the construction of THMM, three time windows are defined: prediction window Φ, transfer window Δ, acquisition window (period) δ.

[0041] Definition 1: Prediction window Φ, the purpose is to predict the short-term traffic status in the future, and set the time length of 5 minutes as the prediction window, that is, the predicted future "short-term" is 5 minutes.

[0042] Definition 2: Transition window Δ, which refers to the time window from time t ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention relates to the field of the intelligent transportation system, and especially relates to short-term traffic flow forecasting with use of a parameter value sequence of a road segment. A short-term traffic flow forecasting method based on a hidden Markov model comprises the following steps: collected data are processed and counted; a prediction window is set; a starting time measured value of the prediction window, and an average parameter value and a sequence contrast ratio in the prediction window are discretized, so as to form a hidden state and an observation state set of the hidden Markov model; and a Baum Welch algorithm combined with training data are used for learning the model parameter. Finally, for a certain prediction window, a Viterbi algorithm is used to obtain an optimal hidden state sequence based on a known observation state sequence, and a last state of the optimal hidden state sequence is a prediction state. The short-term traffic flow forecasting method based on the hidden Markov model can be used to predict the short-term traffic state in the future, and is an effective method of prediction of the short-term traffic state.

Description

technical field [0001] The invention relates to the field of intelligent traffic systems, in particular to predicting short-term traffic flow states by using traffic flow parameter value sequences of road sections, and in particular to a short-term traffic flow state prediction method based on a hidden Markov model. Background technique [0002] With the continuous development and deepening of the country's urbanization level and the continuous improvement of people's living standards, cars have entered everyone's life and have a profound impact on everyone's work, life and study. Accompanied by the lagging of transportation infrastructure and the inefficiency of transportation service level, what is more serious is the resulting traffic congestion, environmental pollution and energy waste, which have caused great economic losses. Therefore, the Intelligent Transportation System (Intelligent Transportation System, ITS) came into being. Based on the existing transportation in...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G08G1/065
Inventor 谢刚阎高伟续欣莹陈泽华窦寿军杨江波
Owner TAIYUAN UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com