Traffic signal control on similarity logic reasoning

Niittymäki, Jarkko; Turunen, Esko

doi:10.1016/s0165-0114(02)00128-8

Cited by 42 publications

(14 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A large number of traffic light control approaches have been proposed in the past decades, most of them [10], [11], [12], [13], [14], [15] do not deal with the traffic lights sequence adjustment when scheduling traffic lights, which is also a challenging issue in adaptive traffic light control.…”

Section: Traffic Control Is An Important Component Of Intelligent Tramentioning

confidence: 99%

An Adaptive Traffic Light Control Scheme and its Implementationin WSN-Based ITS

Zhou

Cao

2013

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

Subsequently, the optimal traffic light length can be determined according to the local traffic and predicted traffic data. Simulation results demonstrate that the proposed scheme can achieve much higher performance, in terms of throughput and average waiting time. We also implement proposed scheme into our WSN-based ITS project, iSensNet, and the result shows that our scheme is effective and practical.

show abstract

Section: Traffic Control Is An Important Component Of Intelligent Tramentioning

confidence: 99%

An Adaptive Traffic Light Control Scheme and its Implementationin WSN-Based ITS

Zhou

Cao

2013

International Journal on Smart Sensing and Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…There are some applications of fuzzy logic in traffic control in the cities (Anderson et al, 1998;Niittymäki and Turunen, 2003;Kosonen, 2003). Anderson et al (1998) investigated the implementation of a traffic signal controller based on a fuzzy inference system that uses travel times and emissions as inputs.…”

Section: Fuzzy Knowledge Base and Membership Functionsmentioning

confidence: 99%

Benefits and challenges of controlling a LED AFS (adaptive front-lighting system) using fuzzy logic

Lukacs

Dassanayake

Magalhaes

et al. 2011

Int.J Automot. Technol.

View full text Add to dashboard Cite

The vehicular illumination system has undergone considerable technological advances in recent decades such as the use of a Light Emitting Diode (LED) Adaptive Front-lighting System (AFS), which represents an industry breakthrough in lighting technology and is rapidly becoming one of the most important innovative technologies around the world in the lighting community. This paper presents AFS control alternatives using fuzzy logic (types 1 and 2) to determine its operating parameters taking into consideration the road conditions in the state of São Paulo (Brazil). Fuzzy logic is a well-known extension of the conventional (Boolean) logic that enables the treatment of uncertainty present in the information through the definition of intermediary membership values between the "completely true" and the "completely false". This technique or modeling strategy is particularly important when a multi-parameter decision must be taken or the decisions are based on the human knowledge. The results show the potential of the methodology proposed and its suitability for light control providing safer nighttime driving.

show abstract

“…The main intention at [16] was to tie fuzzy reasoning to a logical framework of many values, similarity logic many-valued Lukasiewicz based fuzzy control algorithm was introduced and tested in three realistic traffic lights systems control.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Urban Traffic Flow Control: A Case Study on Fuzzy Logic Application

Lamas¹

2017

IJTET

View full text Add to dashboard Cite

This work uses concepts, tools, and methodologies associated to Operational Research and Artificial Intelligence to turn more efficient a system that controls traffic lights at a road crossing. Using Operational Research, through Linear Programming method, the intelligent traffic light operation is described as a mathematical formulation and constraints. That information is used for MacVicar-Whelam table elaboration that relates system pertinence rules through fuzzy logic techniques. It is pled, with the described development, to obtain of solid results for validation of the developed methodology, as well as its efficient control of intelligent traffic lights.

show abstract

Traffic signal control on similarity logic reasoning

Cited by 42 publications

References 16 publications

An Adaptive Traffic Light Control Scheme and its Implementationin WSN-Based ITS

An Adaptive Traffic Light Control Scheme and its Implementationin WSN-Based ITS

Benefits and challenges of controlling a LED AFS (adaptive front-lighting system) using fuzzy logic

Intelligent Urban Traffic Flow Control: A Case Study on Fuzzy Logic Application

Contact Info

Product

Resources

About