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Research on Traffic Signal Control Algorithm Based on Deep Reinforcement Learning Cover

Research on Traffic Signal Control Algorithm Based on Deep Reinforcement Learning

By: Hanfeng Xue,  Pingping Liu and  Zhen Mu  
Open Access
|Sep 2025

Figures & Tables

Figure 1

Single Fork Simulation Road
Single Fork Simulation Road

Figure 2

Average waiting time for high load vehicles
Average waiting time for high load vehicles

Figure 3

Average waiting time for medium-load vehicles
Average waiting time for medium-load vehicles

Figure 4

Average waiting time for low load vehicles
Average waiting time for low load vehicles

Figure 5

Average passage time for high-load vehicles
Average passage time for high-load vehicles

Figure 6

Average travel time for medium-load vehicles
Average travel time for medium-load vehicles

Figure 7

Average low load vehicle travel time
Average low load vehicle travel time

Figure 8

High load average cumulative rewards
High load average cumulative rewards

Figure 9

Average cumulative reward for medium Load
Average cumulative reward for medium Load

Figure 10

Low load average cumulative reward
Low load average cumulative reward

INDICATOR MEANING AND ASSESSMENT DIMENSIONS

Indicator nameExplanation of meaningAssessment dimensions
Average vehicle waiting timeAverage length of time a bicycle stays at a red light, in secondsAccessibility
Average vehicle travel timeTotal time required from entry to exit, in secondsSystem-level scheduling efficiency
Signal switching frequencyNumber of signal changes per unit of time, unit: times/minuteDecision stability and control smoothing

ABLATION EXPERIMENT FORM

Program namefixed phaseperceptual abilitylearning to predict ability
FIXED××
ADAPTIVE×
DB-DRL
Language: English
Page range: 72 - 80
Published on: Sep 30, 2025
Published by: Xi’an Technological University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Hanfeng Xue, Pingping Liu, Zhen Mu, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.