Proceedings of 3rd International Symposium on Social Science (ISSS 2017)

The Optimal Equipping Based on M/G/K Algorithm

Authors
Xuan Wang
Corresponding Author
Xuan Wang
Available Online May 2017.
DOI
10.2991/isss-17.2017.28How to use a DOI?
Keywords
Lanes Distribution, Traffic Capacity, M/G/K Algorithm.
Abstract

I develop a model to determine an optimal model to improve the traffic capacity of the toll plaza. My model contains four parts: In the first part, I consider the principles of designing the toll plaza. Then I set out to reach to an optimal model on the basis of the principle above. In the meantime, I take the accident prevention, heavy traffic and the throughput of the toll plaza into consideration. After that, I present the emergency area and portable toll collector in order to deal with emergency situations. Moreover, I analyze the traffic flow in various periods every day, after which I we get the scheme of mixed lanes. Then I get the distribution of toll plaza. Then, I change the numerical value of traffic flow when it at peak to examine the sensitivity of my model. What's more, my model is broad enough to accommodate any optimization problem. The result shows that my model is robust.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of 3rd International Symposium on Social Science (ISSS 2017)
Series
Advances in Social Science, Education and Humanities Research
Publication Date
May 2017
ISBN
978-94-6252-341-8
ISSN
2352-5398
DOI
10.2991/isss-17.2017.28How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xuan Wang
PY  - 2017/05
DA  - 2017/05
TI  - The Optimal Equipping Based on M/G/K Algorithm
BT  - Proceedings of 3rd International Symposium on Social Science (ISSS 2017)
PB  - Atlantis Press
SP  - 128
EP  - 131
SN  - 2352-5398
UR  - https://doi.org/10.2991/isss-17.2017.28
DO  - 10.2991/isss-17.2017.28
ID  - Wang2017/05
ER  -