Smart Density Based Traffic Light System

Firdous, Anam; Indu,; Niranjan, Vandana

doi:10.1109/icrito48877.2020.9197940

Cited by 16 publications

(10 citation statements)

References 8 publications

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“…With proper integration of STMLS, most congestion problems can be solved. Indeed, multiple solutions have been proposed in the STMLS specialized literature, either for a single intersection [1,2,8,10,12,[16][17][18] or for a set of intersections [11-13, 3, 5, 19] and that according to different approaches [1][2][3][4][5][6][7][8][9][10][11].…”

Section: Context Of the Workmentioning

confidence: 99%

“…Firdous, et al [18] proposed a prototype of a functional model for an intelligent trafic signal that adjusts its synchrony automatically according to the trafic flow by exploiting solar energy from a solar panel. The system manages a four-way intersection using four infrared digital captors, one for each way, and eight LEDs to serve as signal lights for each way.…”

Section: Sensor-based Approachesmentioning

confidence: 99%

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Smart Cities Trafic Light Management Systems Review: Models and Approaches

ZERROUG,

ALIOUAT,

ALIOUAT

et al. 2023

Preprint

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In large cities, the number of vehicles in daily circulation is increasing significantly. In parallel with the evolution of urban structures, the road infrastructure is struggling to keep up with this flow of vehicles. Such a situation could become more and more cumbersome until it leads to unmanageable conditions that could reach complete congestion of the crossroads. Thus, due to its negative impact on the daily lives of vehicle users, many studies have addressed this problem, but the issue is still relevant today and is attracting more attention from researchers, especially with the emerging paradigm of smart cities. To this end, different approaches have been developed to overcome the waiting time hindrance at road junctions and avoid the costly and stressful situation of trafic congestion. In this context, the significant studies conducted so far are analyzed in this paper in order to synthesize the different approaches used. The objective is to highlight the important elements of a successful solution for trafic signal planning at intersections. Such a solution must be consistent with the requirements and environment of the smart city concept. Thus, different solutions to the problem posed, in the form of Smart Trafic Light Management Systems (STLMS), have been based on models applied to one or more intersections and using mathematical optimization techniques, wireless sensor networks, or both. Nevertheless, none of the proposed methods has addressed the problem as a whole, i.e., the coordination and cooperation of STLMS at all intersections of a smart city and the integration of the overall system into the IoT environment.

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Section: Context Of the Workmentioning

confidence: 99%

Section: Sensor-based Approachesmentioning

confidence: 99%

Smart Cities Trafic Light Management Systems Review: Models and Approaches

ZERROUG,

ALIOUAT,

ALIOUAT

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Optimal Traffic Signal Control (TSC) using dynamic cycle of traffic lights instead of a fixed cycle, in which red and green lights are adjusted according to the density of the traffic has been suggested (Firdous and Niranjan, 2020). Different algorithms for smart transportation and smart junctions using traffic congestion estimation and monitoring have been developed with the help of sensors and cameras in the framework of the Internet of Things (IoT), alongside with the implementation of various technologies such as Artificial Intelligence (AI) and image processing.…”

Section: Introductionmentioning

confidence: 99%

“…Different algorithms for smart transportation and smart junctions using traffic congestion estimation and monitoring have been developed with the help of sensors and cameras in the framework of the Internet of Things (IoT), alongside with the implementation of various technologies such as Artificial Intelligence (AI) and image processing. All these enable the traffic light controller to make time setting decisions based on real-time data (Ata et al, 2019;Chong and Ng, 2016;Lana, et al, 2018;Firdous and Niranjan, 2020;Patil et al, 2020;Hartanti et al, 2019). Finding the best approach for optimizing traffic lights is still an open question for researchers.…”

Section: Introductionmentioning

confidence: 99%

Using Machine Learning Techniques to Incorporate Social Priorities in Traffic Monitoring in a Junction with a Fast Lane

Barzilai

Rika

Voloch

et al. 2023

Transport and Telecommunication Journal

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Traffic lights monitoring that considers only traffic volumes is not necessarily the optimal way to time the green/red allocation in a junction. A “smart” allocation should also consider the necessities of the vehicle’s passengers and the needs of the people those passengers ought to serve. This paper deals with a “smart” junction, where several cars approach the intersection from different directions and a traffic light is set to comply to a sequence of time intervals of red and green lights in each direction. The novel approach presented here is based not only on traffic congestion parameters, but also on the social and economic characteristics of the passengers (e.g. a handicapped person, a medical doctor, an employee who is extremely required in a certain organization due to an emergency situation). This paper proposes to enhance the smart junction with a fast lane, which has a flexible entry permit based on social and economic criteria. Machine learning (specifically, Reinforcement Learning (RL)) is added to the junction’s algorithm with the aim of optimizing the social utility of the junction. For the purposes of this study, the utility of the junction is defined by the total social and economic potential benefits given a certain red/green time allocation is set. This is defined as the measure of the reward function which contains positive factors for vehicles which crossed the junction or advanced their position and a negative factor for vehicles which remains in their positions. In addition, a weight value for the vehicles with high priority is also part of the equation. A simplified version of the smart junction has been used, serving as a model for incorporating RL into the “smart’ junction with Fast Lane (FL). Specifically, the Q-Learning algorithm is used to maximize the reward function. Simulation results show that prioritizing high priority vehicles via FL is influenced by the weights and factors given to the reward components. Farther research should enhance the “Smart” junction with FL to a more complex and realistic one using a varying amount of vehicles crossing the junction.

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“…In [2] a development towards wireless connectivity for traffic light control is presented. However, the development of intelligent systems for traffic light control presents a wide field of development, for example, based on the number of vehicles on the road [3], the action that may require image processing algorithms for this identification [4], [5], among other similar [6]- [9]. One of them is a study [10], which presents an intelligent traffic light system for emergency vehicles, using strategies for route identification and traffic light status change, mainly using the message queuing telemetry transport (MQTT) protocol.…”

Section: Introductionmentioning

confidence: 99%

Ambulance detection for smart traffic light applications with fuzzy controller

Moreno

Baquero²,

Umaña³

2022

IJECE

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<p>In the development of intelligent cities, the automation of vehicular mobility is one of the strong points of research, where intelligent traffic lights stand out. It is essential in this field to prioritize emergency vehicles that can help save lives, where every second counts in favor of the transfer of a patient or injured person. This paper presents an artificial intelligence algorithm based on two stages, one is the recognition of emergency vehicles through a ResNet-50 and the other is a fuzzy inference system for timing control of a traffic light, both lead to an intelligent traffic light. An application of traffic light vehicular flow control for automatic preemption when detecting emergency vehicles, specifically ambulances, is oriented. The training parameters of the network, which achieves 100% accuracy with confidence levels between 65% with vehicle occlusion and 99% in direct view, are presented. The traffic light cycles are able to extend the green time of the traffic light with almost 50% in favor of the road that must yield the priority, in relation to not using the fuzzy inference system.</p>

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Smart Density Based Traffic Light System

Cited by 16 publications

References 8 publications

Smart Cities Trafic Light Management Systems Review: Models and Approaches

Smart Cities Trafic Light Management Systems Review: Models and Approaches

Using Machine Learning Techniques to Incorporate Social Priorities in Traffic Monitoring in a Junction with a Fast Lane

Ambulance detection for smart traffic light applications with fuzzy controller

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