modelling signal controlled traffic based on driving behaviors
Clicks: 102
ID: 216006
2015
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Steady Performance
30.0
/100
102 views
7 readers
AI Quality Assessment
Not analyzed
Readership in this journal
SteadyRanked #161 of 266 articles by views in Journal of the American Heart Association
Most read
Least read
Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 266 in total.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
In urban traffic, of particular interest the traffic breakdown which is primarily resulted from the driving behaviors is emerged to respond to the traffic signal. To investigate the influences of driving behaviors on the traffic breakdown, a cellular automaton model has been developed by incorporating a number of driving behaviors typically manifesting during the different stages when the vehicle approaching a traffic light. Numerical simulations have been performed based on a road segment consisting of three sections and each section is associated with a set of rules. The numerical simulations have demonstrated that the proposed model is capable of producing the time-delayed traffic breakdown and the dissolution of the oversaturated traffic. Furthermore, it has been evidenced that the probability of the traffic breakdown can be increased by involving the slow-to-start behavior. However, the activation of the anticipatory behavior can effectively impede the transition from undersaturated to oversaturated traffic. Finally, the contributions of the driving behaviors on the traffic breakdown have been quantitatively examined.
| Reference Key |
wang2015discretemodelling
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Yang Wang;Yanyan Chen;Ning Chen |
| Journal | Journal of the American Heart Association |
| Year | 2015 |
| DOI |
10.1155/2015/219574
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.