Analysis of Sidewalk Traffic Lights Setting Modes Optimization
- 1 Guangzhou Huamei International School
* Author to whom correspondence should be addressed.
Abstract
There are often some problems with the traditional setting mode of traffic lights, especially on some non-main roads. The unreasonable setting of pedestrian traffic lights can easily lead to empty spaces for people and vehicles, waste resources, and increase the risks of traffic congestion, Although the push-button traffic lights improves traffic efficiency, there is still a waste of waiting time for pedestrians and vehicles due to the fixed setting of the traffic time. This article mainly studies the use of machine learning to solve the recognition, motion direction, and time models of people and vehicles, and optimize pedestrian traffic light signals. By identifying pedestrians and vehicles and allocating release signal time reasonably, the waiting time of pedestrians and vehicles crossing the road can be reduced, and traffic accidents can be minimized. The optimized traffic system is applied to sidewalks on non-main roads, it can replace the push-button traffic lights, reduce manual intervention, save construction costs, and improve traffic efficiency.
Keywords
intelligent traffic lights, automatic execution, artificial intelligence, push-button traffic lights replacement, traffic efficiency improvement
[1]. Wei Min Talks About Things. (2020) Why is the push-button traffic light not promoted in China, this is the original reason. September 14. https://baijiahao.baidu.com/s?id=1677774612046534092&wfr=spider&for=pc,.
[2]. Dongming Xu. (2020) A face age recognition method based on cost sensitive convolutional neural networks [J]. Computer application research, (11):3516-3520.
[3]. Haiyang Chen. (2014) Autonomous intelligent decision-making of traffic lights based on dynamic Bayesian networks [J]. Journal of Xi'an Engineering University, (4):474-479.
[4]. Song Jiuqing, Huang Yalou, Kang Yewei, et al. (2006) Data Processing and Analysis in Intelligent Transportation Systems [J]. Computer Engineering and Applications, 42(8):5. DOI:10.3321/j.issn:1002-8331.2006.08.064.
[5]. Jinpeng Li. (2019) Intelligent Traffic Light Scheduling Method Based on GA and LSTM [J]. Internet of Things Technology, (12):50-54.
[6]. Zhihua Zhou. (2016) Machine Learning, Tsinghua University Press: Chapter 6 Support Vector Machines, Integrated Learning, pp.137-180, Chapter 5: Neural Networks. pp.114.
[7]. Redmon, J., Divvala, S., Girshick, R. and Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 779-788.
[8]. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R. and Fei-Fei, L. (2014). Large-scale video classification with convolutional neural networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, pp. 1725-1732.
[9]. Tran, D., Bourdev, L., Fergus, R., Torresani, L. and Paluri, M. (2015). Learning spatiotemporal features with 3d convolutional networks. In Proceedings of the IEEE international conference on computer vision, pp. 4489-4497.
[10]. Donahue, J., Anne Hendricks, L., Guadarrama, S., Rohrbach, M., Venugopalan, S., Saenko, K. and Darrell, T., (2015). Long-term recurrent convolutional networks for visual recognition and description. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2625-2634.
[11]. Xipeng Qiu. (2020) Neural Networks and Deep Learning, Chapter 6: Recurrent Neural Networks,Chapter 14, Deep Reinforcement Learning. Github Inc., June 14.
[12]. Fern, X. (2008) Reinforcement Learning. In CS 434: Machine Learning and Data Mining. School of Electrical Engineering and Computer Science, Oregon State University.
[13]. Panin, A. and Shvechikov, P. (2017) Practical Reinforcement Learning. Coursera and National Research University Higher School of Economics.
Cite this article
Pan,Z. (2024).Analysis of Sidewalk Traffic Lights Setting Modes Optimization.Communications in Humanities Research,45,32-39.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content
About volume
Volume title: Proceedings of the 3rd International Conference on Art, Design and Social Sciences
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).