An Atypical Induction Control with User-Defined Rules
- DOI
- 10.2991/978-94-6463-010-7_50How to use a DOI?
- Keywords
- traffic engineering; actuated control; user rules; demand response
- Abstract
Under traditional induction control methods, phase sequence combination can’t be adjusted dynamically by traffic flow characteristics. To solve this problem, this paper put forward an atypical induction control with user-defined rules, which is supported by real-time traffic data such as vehicle presence, queue length, and the number of vehicles in the interval. Under this control, users can define the identification and triggering rules of the traffic state and design the combination rules of the conventional and extended stage chain according to the characteristics of the actual scene. Based on the analysis of the local high-resolution log data of the signal controller at the intersection of Jiaotong Avenue and Tianxian Road in Xiaogan City, it is concluded that the maximum queuing length of the average intersection cycle decreases from 160 m to 95 m after the implementation of the scheme, a decrease up to 40.6%. The results show that the atypical induction control with user-defined rules has good flexibility, controllability, and expansibility and is extremely suitable for traffic scenes with dynamic traffic characteristics.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Tao-tao Zhang AU - Hai-long Ding AU - Ju-yuan Wu PY - 2022 DA - 2022/12/02 TI - An Atypical Induction Control with User-Defined Rules BT - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022) PB - Atlantis Press SP - 471 EP - 480 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-010-7_50 DO - 10.2991/978-94-6463-010-7_50 ID - Zhang2022 ER -