Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2905055.2905121acmotherconferencesArticle/Chapter ViewAbstractPublication PagesictcsConference Proceedingsconference-collections
research-article

Centralized Approach towards Intelligent Traffic Signal Control

Published: 04 March 2016 Publication History

Abstract

For better transportation system it is needed to make our traffic signal well instructed with the help of Vehicle-to-Infrastructure (V2I) communication for transferring the information about vehicles to the central system. This paper presents the centralized approach in three phases. First phase gives the complete information of Central System (CS), second phase shows the working of Area Controller Systems (ACS) and Area Border Controller Systems (ABCS) and phase three shows the Onsite Unit (OU).

References

[1]
Nan Ding and Qing He. Modeling Traffic Control Agency Decision Behavior for Multimodal Manual Signal Control Under Event Occurrences. In IEEE Transactions on Intelligent Transportation Systems, 2015.
[2]
Bhushan S. Atote, Dr. Mangesh Bedekar and Suja S. Panicker. Traffic Signal Control For Urban Area: A Survey. In International Journal of Engineering Sciences & Research Technology, ISSN: 2277-9655, Vol. 4 Issue 11- Nov, 2015.
[3]
Nan Xiao and Emilio Frazzoli. Throughput Optimality of Extended Back-pressure Traffic Signal Control Algorithm. In 23rd Mediterranean Conference on Control and Automation (MED), June 16-19, 2015.
[4]
Guilherme B. Castro and José Sidnei C. Martini. Biologically-Inspired Neural Network for Traffic Signal Control. In 17th International Conference on Intelligent Transportation Systems (ITSC), October 8-11, 2014.
[5]
Yu Wang, Danwei Wang and Nan Xiao. Iterative Tuning Strategy for Setting Phase Splits in Traffic Signal Control. In 17th International Conference on Intelligent Transportation Systems (ITSC), October 8-11, 2014.
[6]
Juan Chen. A Robust Multi- objective Compatible Optimization Control Algorithm for Traffic Signal Control. In 17th International Conference on Intelligent Transportation Systems (ITSC), October 8-11, 2014.
[7]
Prabuchandran K.J. and Hemanth Kumar A.N. Multi-agent Reinforcement Learning for Traffic Signal Control. In 17th International Conference on Intelligent Transportation Systems (ITSC), October 8-11, 2014.
[8]
Stelios Timotheou and Christos G. Panayiotou. Online Distributed Network Traffic Signal Control using the Cell Transmission Model. In 17th International Conference on Intelligent Transportation Systems (ITSC), October 8-11, 2014.
[9]
Erfan Shaghaghi and Ali Jalooli,. Intelligent Traffic Signal Control for Urban Central Using Vehicular Ad-hoc Network. In APWiMob 2014, August 2014.
[10]
Y.-F. Zhao and H. Gao, Y.-S. Lv. Latent Factor Model for Traffic Signal Control. 2014
[11]
Jiaxing Xu and Weihua Sunt. GreenSwirl: Combining Traffic Signal Control and Route Guidance for Reducing Traffic Congestion. In IEEE Vehicular Networking Conference (VNC), October 8-11, 2014.
[12]
Kai Zeng and Yue-Jiao Gong. Real-time Traffic Signal Control with Dynamic Evolutionary Computation. In 3rd International Conference on Advanced Applied Informatics, 2014.
[13]
http://mpcb.gov.in/envtdata/envtair.php
[14]
Daniel Krajzewicz, Jakob Erdmann, Michael Behrisch, and Laura Bieker. Recent Development and Applications of SUMO - Simulation of Urban Mobility. In International Journal On Advances in Systems and Measurements, 5 (3&4):128--138, December 2012.
[15]
http://veins.car2x.org/
[16]
http://vision-traffic.ptvgroup.com/en-us/products/ptv-vissim/
[17]
http://www.trafficware.com/synchro-studio.html

Cited By

View all
  • (2021)Intelligent Traffic Signal Control Based on Reinforcement Learning with State Reduction for Smart CitiesACM Transactions on Internet Technology10.1145/341868221:4(1-24)Online publication date: 22-Jul-2021

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICTCS '16: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies
March 2016
843 pages
ISBN:9781450339629
DOI:10.1145/2905055
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 March 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Centralized Approach
  2. Intelligent Traffic Signal Control
  3. Traffic profiling
  4. Vehicle
  5. Vehicle-to-Infrastructure communication

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICTCS '16

Acceptance Rates

Overall Acceptance Rate 97 of 270 submissions, 36%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)0
  • Downloads (Last 6 weeks)0
Reflects downloads up to 02 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2021)Intelligent Traffic Signal Control Based on Reinforcement Learning with State Reduction for Smart CitiesACM Transactions on Internet Technology10.1145/341868221:4(1-24)Online publication date: 22-Jul-2021

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media