A review of bus arrival time prediction using artificial intelligence

Singh, Nisha; Kumar, Kranti

doi:10.1002/widm.1457

Cited by 7 publications

(2 citation statements)

References 79 publications

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“…The authors in [22] reviewed different AI-based methods and algorithms to predict bus arrival times. It explores a range of branches in AI methodologies and identifies some effective methods such as support vector machines.…”

Section: Related Workmentioning

confidence: 99%

LocateMyBus: IoT-Driven Smart Bus Transit

Desingu¹,

Isaac²,

P.³

et al. 2023

JCOMSS

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Uncertainty of traffic in cities makes it difficult for metropolitan buses to adhere to predetermined schedules, making it strenuous for commuters to plan travel reliably. The proposed LocateMyBus system leverages Internet of Things(IoT) set-ups at bus stops and buses, and Machine Learning(ML) to assuage this uncertainty by allowing commuters to track live-runningstatus of buses, disseminate tentative and live-status to commuters through Public Announcement(PA) systems at bus-stops and a web-application interface. The schedule prediction module provides a tentative schedule of buses with stop-wise arrival times estimated using ML based on historic and real-time route data. Arrival times of two bus-routes in the Massachusetts Bay Area were collected for a period of four months by periodically querying its real-time General Transit Feed Systems(GTFS). This dataset was used to train and validate the proposed ML methods. The IoT system was modeled on Proteus, and validated with a miniature prototype. LocateMyBus is proposed as a step forward toward minimal intervention algorithmic set-ups to ease the uncertainty associated with bus commute in cities. It enables commuters to track live running status and avail ML-predicted tentative schedules. Furthermore, it eradicates the computation requirements of GPS-based systems, whilst ensuring stop-level tracking granularity. LocateMyBus's ability to log bus arrival times at each stop paves the way to building real-time GTFSs.

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Section: Related Workmentioning

confidence: 99%

LocateMyBus: IoT-Driven Smart Bus Transit

Desingu¹,

Isaac²,

P.³

et al. 2023

JCOMSS

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show abstract

“…e key to judging the choice of innovation strategy of lm and television works is to judge the matching and the type of innovation strategy as stated by Sun et al [3]. Film and television production theory can not only judge the nonlinear relationship between feature solution and innovation strategy selection as described by Sorkhabi et al [4] but also solve the nonlinear and high-latitude innovation strategy selection problem, which is the main method to judge the innovation strategy selection of lm and television works at present as described by Singh and Kumar [5]. Literature research shows that the theory of lm and television production can classify the decomposed harmonic signals, shorten the diagnosis time, and improve the judgment e ect of harmonic signals as stated by Sikka et al [6].…”

Section: Introductionmentioning

confidence: 99%

Study Film and Television Postproduction and Innovation Strategy Based on an Artificial Intelligence Algorithm

Han

Shen

2022

Mobile Information Systems

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In the process of choosing the best scheme in the artificial intelligence algorithm, it is impossible to accurately judge the nonlinear relationship between the innovation strategy and the film and television postproduction scheme. An improved artificial intelligence algorithm based on the integration of dynamic factors and the artificial intelligence algorithm is proposed to reduce the disturbance ability of the artificial intelligence algorithm and improve the analysis level of film and television postproduction and innovation strategy. Firstly, the initial innovation strategy set of the production set is established by using dynamic factors, which makes it discrete and reduces the influence of the scheme selection error on the results. Then, the production set is divided into dynamic subproduction sets by using the film and television production theory, and each subproduction set seeks its own parallel innovation strategy. Finally, under the guidance of film and television production theory, each subproduction set shares the matching of optimal solutions. Through MATLAB simulation analysis and verification, the improved dynamic artificial intelligence algorithm can improve the accuracy of judging the innovation strategy of film and television works in an uncertain environment and shorten the convergence time of global feature solution and is superior to the original selection method of film and television production strategy. In addition, under that condition the initial weight scheme and the threshold scheme are set. The artificial intelligence algorithm is used to analyze the innovation strategy selection of youth idol works. The results show that under different film and television production requirements, the innovation strategy selection judgment of the artificial intelligence algorithm is accurate and superior to the original film and television production strategy selection method, which further verifies the effectiveness of the artificial intelligence algorithm proposed in this paper.

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Bus Journey Time Prediction: A Comparison of Whole Route and Segment Journey Time Predictions Using Machine Learning

Dunne

McArdle

2023

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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A review of bus arrival time prediction using artificial intelligence

Cited by 7 publications

References 79 publications

LocateMyBus: IoT-Driven Smart Bus Transit

LocateMyBus: IoT-Driven Smart Bus Transit

Study Film and Television Postproduction and Innovation Strategy Based on an Artificial Intelligence Algorithm

Bus Journey Time Prediction: A Comparison of Whole Route and Segment Journey Time Predictions Using Machine Learning

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