A Self-Adaptive Collaborative Multi-Agent based Traffic Signal Timing System

被引：0

作者：

Torabi, Behnam ^{[1
]}

Wenkstern, Rym Z. ^{[1
]}

Saylor, Robert ^{[2
]}

机构：

[1] Univ Texas Dallas, Dept Comp Sci, Richardson, TX 75080 USA

[2] City Richardsons Div Traff & Transportat, Richardson, TX 75080 USA

来源：

2018 IEEE INTERNATIONAL SMART CITIES CONFERENCE (ISC2) | 2018年

关键词：

NETWORKS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, we present DALI, a self-adaptive, collaborative multi-agent Traffic Signal Timing system (TST). Intersection controller agents collaborate with one another and adapt their timing plans based on the traffic conditions. Reinforcement learning is used to optimize values for the various thresholds necessary to dynamically determine the scope of collaboration between the agents. DALI was implement in MATISSE 3.0, a large-scale agent-based micro-simulator. Experimental results show an improvement over traditional and reinforcement learning TSTs.

引用

页数：8

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