The DOI matches its Crossref record.
Adaptive Traffic Light Control in Wireless Sensor Network-Based Intelligent Transportation System
Abstract
We investigate the problem of adaptive traffic light control using real-time traffic information collected by a wireless sensor network (WSN). Existing studies mainly focused on determining the green light length in a fixed sequence of traffic lights. In this paper, we propose an adaptive traffic light control algorithm that adjusts both the sequence and length of traffic lights in accordance with the real time traffic detected. Our algorithm considers a number of traffic factors such as traffic volume, waiting time, vehicle density, etc., to determine green light sequence and the optimal green light length. Simulation results demonstrate that our algorithm produces much higher throughput and lower vehicle's average waiting time, compared with a fixed-time control algorithm and an actuated control algorithm. We also implement proposed algorithm on our transportation testbed, iSensNet, and the result shows that our algorithm is effective and practical.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
The Fog Computing Paradigm: Scenarios and Security Issues
Stojmenovic, I., & Wen, S. (2014) · Annals of Computer Science and Information Systems · doi:10.15439/2014f503 · Cited by 1,037
An overview of Fog computing and its security issues
Stojmenovic, I., Wen, S., Huang, X., & Luan, H. (2016) · Concurrency and Computation: Practice and Experience · doi:10.1002/cpe.3485 · Cited by 408
A Survey on Urban Traffic Management System Using Wireless Sensor Networks
Nellore, K., & Hancke, G. (2016) · Sensors · doi:10.3390/s16020157 · Cited by 384
Qian, B., Su, J., Wen, Z., Jha, D. N., Li, Y., Guan, Y., Puthal, D., James, P., Yang, R., Zomaya, A. Y., et al. (2021) · ACM Computing Surveys · doi:10.1145/3398020 · Cited by 132
Metadata
- Authors
- Zhou, B., Cao, J., Zeng, X., & Wu, H.
- Venue
- 2010 IEEE 72nd Vehicular Technology Conference - Fall
- Publisher
- IEEE
- Published
- 2010-09
- Pages
- 1-5
- OpenAlex
- W2092909612
- Cited by
- 174
- References
- 0
- FWCI
- 16.51
- Type
- conference paper
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Adaptive Traffic Light Control in Wireless Sensor Network-Based Intelligent Transportation System. https://app.cochat.ai/papers/10.1109/vetecf.2010.5594435
- The paper
- Zhou, B., Cao, J., Zeng, X., & Wu, H. (2010). Adaptive Traffic Light Control in Wireless Sensor Network-Based Intelligent Transportation System. 2010 IEEE 72nd Vehicular Technology Conference - Fall. https://doi.org/10.1109/vetecf.2010.5594435
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
