A Novel Emergent Intelligence Technique for Public Transport Vehicle Allocation Problem in a Dynamic Transportation System

Chavhan, Suresh; Gupta, Deepak; Chandana, B N; Chidambaram, Ramesh Kumar; Khanna, Ashish; Rodrigues, Joel J. P. C.

doi:10.1109/tits.2020.3011198

Cited by 16 publications

(9 citation statements)

References 36 publications

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“…Emission rates of vehicles depend mainly on the vehicles traic characteristics [1][2][3][28][29][30][31][32], including vehicles acceleration and deceleration, traic speed, vehicle traic situation, idling-stop and go-free low, vehicles queuing on road, and time headway. Hence, in order to provide sustainable transportation and controlling greenhouse gas emission from vehicle traic is determined by the capability of managing and controlling vehicles traic characteristics to reduce emissions.…”

Section: Origin Of Problem Statementmentioning

confidence: 99%

“…Therefore, the ITS contribute in reducing the GHG emission in the environment, vehicles mileage improvement, lower energy consumption, etc. The research work in this paper is inspired by the concept of connected vehicles and vehicle routings technique which help to reduce the efect of traic congestion by integrating a traic prediction mechanism for traic congestion assessment and mitigation [21,24,28,31,34,36]. The origin of the proposal is from the following scenario.…”

Section: Origin Of Problem Statementmentioning

confidence: 99%

See 1 more Smart Citation

Edge Computing AI-IoT Integrated Energy-efficient Intelligent Transportation System for Smart Cities

Chavhan

Gupta

Gochhayat

et al. 2022

ACM Trans. Internet Technol.

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Advancement of information and communication technologies (ICTs) there is high-scale utilization of IoT and adoption of AI in the transportation system to improve the utilization of energy, reduce greenhouse gas (GHG) emissions, increase quality of services and many extensive benefits to the commuters’ and transportation authorities. In this paper, we propose a novel edge-based AI-IoT integrated energy efficient intelligent transport system for smart cities by using distributed multi-agent system. An urban area is divided into multiple number of regions and each region is sub-divided into finite number of zones. At each zone optimal number of RSUs are installed along with the edge computing devices. The MAS deployed at each RSU collects the huge volume of data from the various sensors, devices and infrastructures. The edge computing device uses the collected raw data from the MAS to process, analyze and predict. The predicted information will be shared with the neighborhood RSUs, vehicles, and cloud by using MAS with the help of IoT. The predicted information can be used by freight vehicles to maintain the smooth and steady movement which result into reduction in the GHG emissions and energy consumptions, and finally improves the freight vehicles mileage by reducing the traffic congestion in the urban areas. We have exhaustively carried out the simulation results and demonstrated the effectiveness of the proposed system.

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Section: Origin Of Problem Statementmentioning

confidence: 99%

Section: Origin Of Problem Statementmentioning

confidence: 99%

Edge Computing AI-IoT Integrated Energy-efficient Intelligent Transportation System for Smart Cities

Chavhan

Gupta

Gochhayat

et al. 2022

ACM Trans. Internet Technol.

Self Cite

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“…Also, the EI method's ability for tackling public vehicle framework issues is illustrated. EI method keeps up recorded data, commuters' appearance rates, availability of resources, and deficit in resources; an EI strategy is used to gather, investigate, share, and ideally assign transport resources adequately [ 20 ].…”

Section: Related Workmentioning

confidence: 99%

Analysis on the Bus Arrival Time Prediction Model for Human-Centric Services Using Data Mining Techniques

Shanthi¹,

Sathishkumar

Babu

et al. 2022

Computational Intelligence and Neuroscience

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The human-computer interaction has become inevitable in digital world. HCI helps humans to incorporate technology to resolve even their day-to-day problems. The main objective of the paper is to utilize HCI in Intelligent Transportation Systems. In India, the most common and convenient mode of transportation is the buses. Every state government provides the bus transportation facility to all routes at an affordable cost. The main difficulty faced by the passengers (humans) is lack of information about bus numbers available for the particular route and Estimated Time of Arrival (ETA) of the buses. There may be different reasons for the bus delay. These include heavy traffic, breakdowns, and bad weather conditions. The passengers waiting in the bus stops are neither aware of the delay nor the bus arrival time. These issues can be resolved by providing an HCI-based web/mobile application for the passengers to track their bus locations in real time. They can also check the Estimated Time of Arrival (ETA) of a particular bus, calculated using machine learning techniques by considering the impacts of environmental dynamics, and other factors like traffic density and weather conditions and track their bus locations in real time. This can be achieved by developing a real-time bus management system for the benefit of passengers, bus drivers, and bus managers. This system can effectively address the problems related to bus timing transparency and arrival time forecasting. The buses are equipped with real-time vehicle tracking module containing Raspberry Pi, GPS, and GSM. The traffic density in the current location of the bus and weather data are some of the factors used for the ETA prediction using the Support Vector Regression algorithm. The model showed RMSE of 27 seconds when tested. The model is performing well when compared with other models.

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“…The existing system should be modified and upgraded to implement IoE operations [51][52][53][54][55]. In addition to it, the security techniques against cyberattacks should also be integrated within the network [56][57][58][59].…”

Section: Upgradation Of the Energy Production Systemmentioning

confidence: 99%

A comprehensive review on IoT‐based infrastructure for smart grid applications

Pal

Chavhan

Gupta

et al. 2021

IET Renewable Power Gen

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Currently, there is a huge production in the automotive industries and energy consumption resulted into spike in the cost and emissions of greenhouse gases. Hence, an efficient energy utilisation mechanism has become a prime most important factor. Energy and its conservation problem also have raised and become one of the prominent critical factors needed to address. Therefore, the development of smart grid infrastructure is one of the solutions to address the above issue.This article discusses different methods and mechanisms require to manage energy efficiently within the smart grid network using communication technologies and protocols and proposed an integration method of electric vehicles and smart grid using communication networks for charging or discharging electricity and exchanging relevant information. In addition, this paper discusses different integration strategies and multi-agent system implementation in the smart grid network. The proposed multi-agent system shows the efficient method for monitoring power flow and maintaining the stability of the grid. The paper also presents the optimal scheduling of charging of electric vehicles in the smart grid network. Finally, the authors investigated and presented many standard communication protocols and their comparisons with respect to different scenarios. This paper discuss different methods and concepts which would require to manage energy efficiently by using recent communication technologies, communication protocols, and sensor technologies. INTRODUCTIONDue to the growth of industries and population density, energy demand has been increasing exponentially. Hence, the energy consumption is under focus in order to reduce the energy crisis in the future. In this 21st century, conventional electrical energy and network will not meet up with the industries due to the lack of reliability, efficiency, security, seamless connectivity, etc. Therefore, a lot of new technologies (communication and sensor) have evolved to provide above features. The evolved communication and sensor technologies applied to the power grid to make smarter, that is, Smart Grid (SG) [1,2]. The SG infrastructure is the backbone of the future smart cities and the connected electric mobility. The connected infrastructureThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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A Novel Emergent Intelligence Technique for Public Transport Vehicle Allocation Problem in a Dynamic Transportation System

Cited by 16 publications

References 36 publications

Edge Computing AI-IoT Integrated Energy-efficient Intelligent Transportation System for Smart Cities

Edge Computing AI-IoT Integrated Energy-efficient Intelligent Transportation System for Smart Cities

Analysis on the Bus Arrival Time Prediction Model for Human-Centric Services Using Data Mining Techniques

A comprehensive review on IoT‐based infrastructure for smart grid applications

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