Please use this identifier to cite or link to this item:
http://hdl.handle.net/11375/14477
Title: | A model for investigating traffic assignment and control in a freeway corridor |
Authors: | Easa, Said M |
Keywords: | Engineering;Engineering |
Publication Date: | Mar-1977 |
Abstract: | <p>A procedure for predicting traffic behaviour in a freeway corridor is developed. Traffic demand can vary over time and is assigned to the freeway and surrounding network subject to the hypothesis that individual travellers will minimize their travel time. The impact of queueing time on minimum paths is included by utilizing a traffic diversion model. The model is capable of diverting all, some, or none of the traffic from a particulat queueing path, and can therefore be used to investigate the effects of freeway entrance ramp control upon the adjacent road system.</p> <p>A computer program was developed and incorporates several other new features for network traffic flow assignment. These include turning volume calculations without the requirement of separate turning links, the ability to impose turning movement prohibitions at critical locations, and a procedure for identifying illogical paths.</p> <p>A new minimum path algorithm was developed to ensure that illogical paths were not used, and prelimiary tests indicate that it is more computationally efficient than previous versions. Due to such improvements in the assignment procedure, link-node representation was simplified and use of the model requires considerably less coding effort as well.</p> <p>The new model was fully tested using a relatively large freeway corridor network, and results show that it can be useful for evaluating ramp control strategies and predicting the resulting flows and queues corresponding to any desired case of diversion.</p> |
URI: | http://hdl.handle.net/11375/14477 |
Identifier: | ee4bi6/72 1071 1883787 |
Appears in Collections: | EE 4BI6 Electrical Engineering Biomedical Capstones |
Files in This Item:
File | Size | Format | |
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fulltext.pdf | 4.42 MB | Adobe PDF | View/Open |
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