2010
DOI: 10.26552/com.c.2010.3.80-84
|View full text |Cite
|
Sign up to set email alerts
|

Abstract: Short-term traffic flow forecast plays an important role in transit scheduling. A high-order generalized neural network model is constructed to actualize dynamic forecast on-line and a hybrid genetic algorithm and identical dimension recurrence idea are performed to optimize the structure and shape of neural network dynamically so as to enhance its forecast accuracy. With data collected from Dazhi Str., Harbin as the system input, the experimental result indicates that the average relative error of forecast is… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2013
2013
2014
2014

Publication Types

Select...
1
1

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(2 citation statements)
references
References 7 publications
0
2
0
Order By: Relevance
“…For a typical urban freeway section including one on-ramp and one off-ramp, the traffic flow passing the section during time period (t Ϫ 1, t) can be estimated as: (5) where α is a smoothing parameter that is set to 0.5. q u (t) and q d (t) are traffic flows of the upstream and downstream boundaries within (t Ϫ 1, t). q on (t) and q off (t) are total on-ramp and off-ramp traffic flows within (t Ϫ 1, t).…”
Section: B Traffic Flow Theory Based Methodsmentioning
confidence: 99%
See 1 more Smart Citation
“…For a typical urban freeway section including one on-ramp and one off-ramp, the traffic flow passing the section during time period (t Ϫ 1, t) can be estimated as: (5) where α is a smoothing parameter that is set to 0.5. q u (t) and q d (t) are traffic flows of the upstream and downstream boundaries within (t Ϫ 1, t). q on (t) and q off (t) are total on-ramp and off-ramp traffic flows within (t Ϫ 1, t).…”
Section: B Traffic Flow Theory Based Methodsmentioning
confidence: 99%
“…The neural network optimized by generic algorithm of models for the real time forecast of traffic flow is presented in [5]. Traffic flow is a better predicable parameter than travel time is.…”
Section: Literature Reviewmentioning
confidence: 99%