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Adaptive Traffic Light Control with Wireless Sensor Networks
Abstract
In this paper, we propose a novel decentralized traffic light control using wireless sensor network. The system architecture is classified into three layers; the wireless sensor network, the localized traffic flow model policy, and the higher level coordination of the traffic lights agents. The wireless sensors are deployed on the lanes going in and out the intersection. These sensors detect vehicles' number, speed, etc. and send their data to the nearest Intersection Control Agent (ICA) which, determines the flow model of the inter- section depending on sensors' data (e.g., number of vehicles approaching a specific intersection). Coping with dynamic changes in the traffic volume is one of the biggest challenges in intelligent transportation system (ITS). Our main contribution is the real-time adaptive control of the traffic lights. Our aim is to maximize the flow of vehicles and reduce the waiting time while maintaining fairness among the other traffic lights. Each traffic light controlled intersection has an intersection control agent that collects information from the sensor nodes. An intersection control agent manages its intersection by controlling its traffic lights. Multiple intersection agents can exchange information among themselves to control a wider area.
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“The real-time adaptive control of the traffic signals is the key contribution. This will also enhance the movement of cars.”
From Smart Transportation: An Overview of Technologies and Applications (2023) · cites this paper as [114]
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No references could be resolved yet.
Research works printed but not linked: 8
- 10.1109/IVS.2004.1336426 · DOI
- dresner, 2005, Multiagent traffic management: an improved intersection control mechanism, 4rd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005) July 25–29 2005, 471
- bret, 2006, A distributed mobile sensor computing system, 4th ACM SenSys
- 10.1109/IMSCCS.2006.286 · DOI
- 10.1109/ICPPW.2005.16 · DOI
- de oliveira, 2005, Using cooperative mediation to coordinate traffic lights: a case study, 4rd International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2005) July 25–29 2005, 463
- 10.1007/s10458-004-6975-9 · DOI
- cheung, 2006, Traffic measurement and vehicle classification with a single magnetic sensor, Journal of the Transportation Research Board
Cited by
Sorted by citations
Smart Transportation: An Overview of Technologies and Applications
Oladimeji, D., Gupta, K., Kose, N. A., Gundogan, K., Ge, L., & Liang, F. (2023) · Sensors · doi:10.3390/s23083880 · Cited by 386
Metadata
- Authors
- Tubaishat, M., Shang, Y., & Shi, H.
- Affiliations
- University of Missouri (US)
- ORCID
- Shang, Yi
- Venue
- 2007 4th IEEE Consumer Communications and Networking Conference
- Publisher
- IEEE
- Published
- 2007-01
- Pages
- 187-191
- ISSN
- 2331-9852 (ISSN-L)
- OpenAlex
- W2159016605
- Cited by
- 139
- References
- 0
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2023: 8 · 2022: 7 · 2021: 6 · 2020: 14 · 2019: 14 · 2018: 5 · 2017: 7 · 2016: 9 · 2015: 7 · 2014: 9 · 2013: 10 · 2012: 20
- FWCI
- 5.15
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Consumer Communications and Networking Conference (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Traffic control and management
- Social Sciences › Transportation › Traffic Prediction and Management Techniques
- Physical Sciences › Engineering › Civil and Structural Engineering › Road Transport and Highway Systems
- Keywords
- adaptive traffic signal control, wireless sensor networks, decentralized control, traffic flow modeling, intelligent transportation systems, vehicle detection, waiting time reduction, multi-agent coordination
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