A Testbed for Intelligent Control of Traffic Lights at Pedestrian Crossings on a Road

Tian, Yanshan; Ma, Xu; Luo, Chu; Zhang, Yuehui

doi:10.1109/icicip.2018.8606678

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…Information is transmitted via low power wide area network to a dedicated control center, for further analyzes, where a server controls the traffic lights scheduling process at the road junctions. A testbed for testers to validate their intelligent traffic light control programs at pedestrian crossings on a road is also proposed (Tian et al , 2018). Given the traffic light controls the testbed conducts the simulation and provides visualized performance measures including the waiting time of vehicles and pedestrians.…”

Section: Related Workmentioning

confidence: 99%

A multi-agent system for distributed smartphone sensing cycling in smart cities

Anagnostopoulos

Luo

Ramson

et al. 2020

JSIT

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Purpose The purpose of this paper is to propose a distributed smartphone sensing-enabled system, which assumes an intelligent transport signaling (ITS) infrastructure that operates traffic lights in a smart city (SC). The system is able to handle priorities between groups of cyclists (crowd-cycling) and traffic when approaching traffic lights at road junctions. Design/methodology/approach The system takes into consideration normal probability density function (PDF) and analytics computed for a certain group of cyclists (i.e. crowd-cycling). An inference model is built based on real-time spatiotemporal data of the cyclists. As the system is highly distributed – both physically (i.e. location of the cyclists) and logically (i.e. different threads), the problem is treated under the umbrella of multi-agent systems (MAS) modeling. The proposed model is experimentally evaluated by incorporating a real GPS trace data set from the SC of Melbourne, Australia. The MAS model is applied to the data set according to the quantitative and qualitative criteria adopted. Cyclists’ satisfaction (CS) is defined as a function, which measures the satisfaction of the cyclists. This is the case where the cyclists wait the least amount of time at traffic lights and move as fast as they can toward their destination. ITS system satisfaction (SS) is defined as a function that measures the satisfaction of the ITS system. This is the case where the system serves the maximum number of cyclists with the fewest transitions between the lights. Smart city satisfaction (SCS) is defined as a function that measures the overall satisfaction of the cyclists and the ITS system in the SC based on CS and SS. SCS defines three SC policies (SCP), namely, CS is maximum and SS is minimum then the SC is cyclist-friendly (SCP1), CS is average and SS is average then the SC is equally cyclist and ITS system friendly (SCP2) and CS is minimum and SS is maximum then the SC is ITS system friendly (SCP3). Findings Results are promising toward the integration of the proposed system with contemporary SCs, as the stakeholders are able to choose between the proposed SCPs according to the SC infrastructure. More specifically, cyclist-friendly SCs can adopt SCP1, SCs that treat cyclists and ITS equally can adopt SCP2 and ITS friendly SCs can adopt SCP3. Originality/value The proposed approach uses internet connectivity available in modern smartphones, which provide users control over the data they provide to us, to obviate the installation of additional sensing infrastructure. It extends related study by assuming an ITS system, which turns traffic lights green by considering the normal PDF and the analytics computed for a certain group of cyclists. The inference model is built based on the real-time spatiotemporal data of the cyclists. As the system is highly distributed – both physically (i.e. location of the cyclists) and logically (i.e. different threads), the system is treated under the umbrella of MAS. MAS has been used in the literature to model complex systems by incorporating intelligent agents. In this study, the authors treat agents as proxy threads running in the cloud, as they require high computation power not available to smartphones.

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Section: Related Workmentioning

confidence: 99%

A multi-agent system for distributed smartphone sensing cycling in smart cities

Anagnostopoulos

Luo

Ramson

et al. 2020

JSIT

Self Cite

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“…The system consisted of a light traffic control and a context-aware and intelligent system. The system was tested in real-like conditions on the street [11]. The Method of Pedestrian Detection Based on the YOLOv3 Model and Image Enhancement by Retinex was examined by Qu.…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Crossing Safety System at Traffic Lights based on Decision Tree Algorithm

Hardiyanto¹,

Iswanto²,

Sartika³

et al. 2019

IJACSA

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Pedestrians are one of the street users who have the right to get priority on security. Highway users such as vehicle drivers sometimes violate the traffic lights that is endanger pedestrians and make pedestrians feel insecure when crossing the street. Based on this problem, a tool is designed to provide a warning for the drivers or riders violating the traffic lights and prevent traffic accident by spraying water. The system is able to detect traffic violation based on changes in the value of the vehicle position on the stop line obtained from the Ultrasonic HC-SR04 sensor. When a violation is detected, a decision tree algorithm turns on the pump to spray water to the traffic violators as a deterrent effect. The results show that the vehicle located closest to the sensor has 94% precision, 88% recall and 85% accuracy, the vehicle located in the middle has 73% precision, 100% recall, and 75% accuracy, and the vehicle located furthest to the sensor has 75% precision, 100% recall and 80% accuracy.

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A Testbed for Intelligent Control of Traffic Lights at Pedestrian Crossings on a Road

Cited by 2 publications

References 22 publications

A multi-agent system for distributed smartphone sensing cycling in smart cities

A multi-agent system for distributed smartphone sensing cycling in smart cities

Pedestrian Crossing Safety System at Traffic Lights based on Decision Tree Algorithm

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