Proceedings of the 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017)

Two Design of New Toll Station Based on Probability Distributions and Queuing Theory

Authors
Le Wang
Corresponding Author
Le Wang
Available Online May 2017.
DOI
10.2991/msmee-17.2017.132How to use a DOI?
Keywords
Queuing Theory; "shunting" and "series parallel compound" toll station
Abstract

On the basis of Probability Distributions and Queuing Theory[1-4], two kinds of new toll stations are designed, which are the "shunting" toll station and "series parallel compound" toll station. And this article calculates the average queue length, the average waiting time of vehicles, the throughput and costs of the new toll stations and make comparisons with traditional toll stations. The results show that the two new toll stations all make improvements to the shape, size and merge mode and both play a very good role in improving.

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 the 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017)
Series
Advances in Engineering Research
Publication Date
May 2017
ISBN
978-94-6252-346-3
ISSN
2352-5401
DOI
10.2991/msmee-17.2017.132How 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  - Le Wang
PY  - 2017/05
DA  - 2017/05
TI  - Two Design of New Toll Station Based on Probability Distributions and Queuing Theory
BT  - Proceedings of the 2017 2nd International Conference on Materials Science, Machinery and Energy Engineering (MSMEE 2017)
PB  - Atlantis Press
SP  - 685
EP  - 688
SN  - 2352-5401
UR  - https://doi.org/10.2991/msmee-17.2017.132
DO  - 10.2991/msmee-17.2017.132
ID  - Wang2017/05
ER  -