Quantifying Autonomy Levels of Traffic Signal Control Within Autonomous Traffic Systems Based on AHP–TOPSIS
Year: 2025
Type: article
Abstract: With the increasing complexity of transportation systems, traditional qualitative descriptions fail to objectively reflect the level of autonomy in traffic signal control systems—especially the lack of a systematic evaluation framework that links technology synergy, task autonomy, and system-level autonomy. To address this critical systematic gap, this study integrates the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to develop a systematic quantitative classification model for assessing system autonomy. The model constructs a three-level indicator framework—“technology–task–system”—based on the systematic closed-loop architecture of traffic signal control systems (upper interaction layer + lower technology chain layer), thereby enabling a holistic and quantitative evaluation of traffic signal control system autonomy. Results indicate that human involvement in the system decreases from 86% at the non-autonomous L0 level to 13% at the fully autonomous L3 level. This systematic quantitative method first reveals the inherent evolution logic of system autonomy (technology → task → system). Additionally, it provides a theoretical foundation for two key applications: the performance comparison across different traffic signal control systems and the planning of their intelligent development pathways—filling the gap of scattered, non-systematic evaluations in existing research. It also serves as a practical tool for these applications.
Source: Systems
Authors: Mingli Shi, Hong Zhu, Kai Li, Yanyue Liu, Keshuang Tang
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G1008200509, G1358888780, G6378218809
OpenAlex ID: W4416506048