An optimal control strategy for emergency vehicle priority system in smart cities using edge computing and IOT sensors

Rosayyan, Prakash; Paul, Jasmine; Subramaniam, Sreeramanan; Ilango, G. Saravana

doi:10.1016/j.measen.2023.100697

Cited by 11 publications

(7 citation statements)

References 13 publications

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“…Thirdly, due to long transmission distances, the communication process between EMVs and TSCs or other SVs might be hindered by communication delays in the V2X environment [107]. However, limited studies have considered this factor [87,107,108].…”

Section: Discussionmentioning

confidence: 99%

“…The proposed method was field-tested at an intersection in India and evaluated by an external agency. To enhance the performance of EMV-TPC strategies, researchers are exploring more sophisticated techniques aimed at obtaining precise spatial-temporal information on EMVs such as RFID [85] and high-precision GPS [87]. However, given that EMV rescue routes frequently traverse a number of signalized intersections, node-based optimization methods would disrupt regular traffic flow patterns for each intersection sequentially, which inevitably entails significant adverse effects on SVs.…”

Section: Stage 3: Priority Controlmentioning

confidence: 99%

“…Rather than exclusively optimizing EMVs, future studies should concentrate more on the negative impacts on SVs and select suitable indicators to quantify the negative impact, including SV waiting times, queue lengths, average number of stops, and passing capacity [71,72,86,87,90]. Moreover, future research can incorporate appropriate transition algorithms [93] and smaller rescue vehicles [106,112] in their models, which can help reduce the negative impacts and transition times [79,92].…”

Section: Concentrating More On the Negative Impacts On Svsmentioning

confidence: 99%

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Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

Hao,

Wang,

Yang

2024

Sustainability

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Emergency vehicles (EMVs) play an important role in saving human lives and mitigating property losses in urban traffic systems. Due to traffic congestion and improper priority control strategies along the rescue route, EMVs may not be able to arrive at rescue spots on time, which also increases traffic risk and has a negative impact on social vehicles (SVs). The greater the negative impact on SVs, such as increased delay times and queue length, the more profound the negative impacts on urban environmental sustainability. Proper rescue route selection and priority control strategies are essential for addressing this problem. Consequently, this paper systematically reviews the studies on EMV routing and priority control. First, a general bibliometric analysis is conducted using VOSviewer. This study also classifies the existing studies into three parts: EMV travel time prediction (EMV-TTP), EMV routing optimization (EMV-RO), and EMV traffic priority control (EMV-TPC). Finally, this study provides future research suggestions on five aspects: 1. uncovering authentic demand characteristics through EMV data mining, 2. incorporating the distinct characteristics of EMV in EMV-RO models, 3. implementing active EMV-TPC strategies, 4. concentrating more on the negative impacts on SVs, and 5. embracing the emerging technologies in the future urban traffic environment.

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Section: Discussionmentioning

confidence: 99%

Section: Stage 3: Priority Controlmentioning

confidence: 99%

Section: Concentrating More On the Negative Impacts On Svsmentioning

confidence: 99%

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Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

Hao,

Wang,

Yang

2024

Sustainability

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“…The topic of this study [24] is the application of edge computing and Internet of Things sensors to an optimal control method for emergency vehicle priority. Throughout this construction, emergency vehicles will always take precedence, causing no disruption to normal traffic.…”

Section: Related Workmentioning

confidence: 99%

A Vehicle Density Estimation Traffic Light Control System Using a Two-Dimensional Convolution Neural Network

Mathiane,

Tu,

Adewale

et al. 2023

Vehicles

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One of the world’s challenges is the amount of traffic on the roads. Waiting for the green light is a major cause of traffic congestion. Low throughput rates and eventual congestion come from many traffic signals that are hard coded, irrespective of the volume of the amount of traffic. Instead of depending on predefined time intervals, it is essential to build a traffic signal control system that can react to changing vehicle densities. Emergency vehicles, like ambulances, must be given priority at the intersection so as not to spend more time at the traffic light. Computer vision techniques can be used to improve road traffic signal control and reduce real-time traffic delays at intersections without the requirement for substantial infrastructure analysis. Long wait times and significant energy consumption are just two of the problems of the current traffic signal control system. To optimal efficiency, the traffic signal’s duration must be dynamically changed to account for current traffic volume. To lessen congestion, the approach taken in this research focuses on modifying traffic signal time determined by the density of vehicles at the crossroads. The main purpose of this article is to demonstrate heavy traffic and emergency vehicle prioritization from all directions at the traffic intersection for a speedy passage. Using the Pygame tool, the proposed method in this study, which includes a mechanism for estimating traffic density and prioritization by counting vehicles at a traffic junction, is demonstrated. The vehicle throughput for the adaptive traffic light built using Pygame is compared with the vehicle pass rate for the adaptive traffic light built using Simulation of Urban Mobility (SUMO). The simulation results show that the adaptive traffic light built using Pygame achieves 90% throughput compared to the adaptive traffic light built using SUMO. A Two-Dimensional Convolutional Neural Network (2D-CNN) is implemented using Tensorflow for vehicle classification. The 2D-CNN model demonstrated 96% accuracy in classifying vehicles using the test dataset. Additionally, emergency vehicles, such as ambulances, are given priority for quick passing.

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“…Recently, the internet of things (IoT) has gained worldwide traction, culminating in the development of smart cities worldwide. [1][2][3] Smart cities utilize technological means to create and promote solutions to problems caused by urbanization. These cities aim to enhance efficiency, promote sustainability, elevate the quality of life, and minimize energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Multi-phase time-zooming high-precision ultrasonic water flow meter

Oda,

Kaga,

Johguchi

2024

Jpn. J. Appl. Phys.

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In this study, we have developed a high-precision ultrasonic water flow meter with a 0.18 µm standard CMOS technology. Three methods are proposed to improve the performance: implementation of an active bandpass filter, threshold voltage optimization, and integration of a multi-phase time-zooming technique. The active bandpass filter effectively reduces noise in the received signal, thereby contributing to the overall accuracy of propagation measurements. Based on a threshold voltage generator, our proposed system identifies the optimum threshold voltage, ensuring robust noise durability. Furthermore, we have extended the conventional time-zooming technique by incorporating multiple phases of internal clocks. This innovative multiphase time-zooming technique offers a theoretical improvement in time resolution. The results demonstrate a remarkable suppression of flow rate measurement variations, achieving an impressive reduction to 1/10th of the original values. This affirms the effectiveness of the developed ultrasonic water flow meter in ensuring precise and reliable flow rate measurements.

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An optimal control strategy for emergency vehicle priority system in smart cities using edge computing and IOT sensors

Cited by 11 publications

References 13 publications

Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

Every Second Counts: A Comprehensive Review of Route Optimization and Priority Control for Urban Emergency Vehicles

A Vehicle Density Estimation Traffic Light Control System Using a Two-Dimensional Convolution Neural Network

Multi-phase time-zooming high-precision ultrasonic water flow meter

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