An experience in using machine learning for short-term predictions in smart transportation systems

Bacciu, Davide; Carta, Antonio; Gnesi, Stefania; Semini, Laura

doi:10.1016/j.jlamp.2016.11.002

Cited by 29 publications

(15 citation statements)

References 19 publications

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“…The data was collected and analyzed by python3.5 and sklearn [ 38 ] to achieve machine learning. Orange3.11 [ 39 ] was used for cluster analysis and removing unqualified pulse wave.…”

Section: Methodsmentioning

confidence: 99%

A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave

Luo

Cui

et al. 2018

BioMed Research International

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Objective In this study, machine learning was utilized to classify and predict pulse wave of hypertensive group and healthy group and assess the risk of hypertension by observing the dynamic change of the pulse wave and provide an objective reference for clinical application of pulse diagnosis in traditional Chinese medicine (TCM). Method The basic information from 450 hypertensive cases and 479 healthy cases was collected by self-developed H20 questionnaires and pulse wave information was acquired by self-developed pulse diagnostic instrument (PDA-1). H20 questionnaires and pulse wave information were used as input variables to obtain different machine learning classification models of hypertension. This method was aimed at analyzing the influence of pulse wave on the accuracy and stability of machine learning model, as well as the feature contribution of hypertension model after removing noise by K-means. Result Compared with the classification results before removing noise, the accuracy and the area under the curve (AUC) had been improved. The accuracy rates of AdaBoost, Gradient Boosting, and Random Forest (RF) were 86.41%, 86.41%, and 85.33%, respectively. AUC were 0.86, 0.86, and 0.85, respectively. The maximum accuracy of SVM increased from 79.57% to 83.15%, and the AUC stability increased from 0.79 to 0.83. In addition, the features of importance on traditional statistics and machine learning were consistent. After removing noise, the features with large changes were h1/t1, w1/t, t, w2, h2, t1, and t5 in AdaBoost and Gradient Boosting (top10). The common variables for machine learning and traditional statistics were h1/t1, h5, t, Ad, BMI, and t2. Conclusion Pulse wave-based diagnostic method of hypertension has significant value in reference. In view of the feasibility of digital-pulse-wave diagnosis and dynamically evaluating hypertension, it provides the research direction and foundation for Chinese medicine in the dynamic evaluation of modern disease diagnosis and curative effect.

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“…The data was collected and analyzed by python3.5 and sklearn [ 38 ] to achieve machine learning. Orange3.11 [ 39 ] was used for cluster analysis and removing unqualified pulse wave.…”

Section: Methodsmentioning

confidence: 99%

A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave

Luo

Cui

et al. 2018

BioMed Research International

View full text Add to dashboard Cite

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“…In [ 23 ], the authors discussed how a machine learning approach could be used to implement and assess predictive services for the users of a bike-sharing system. The models used in this study were trained on real-world historical usage data comprising of more than 280 000 entries covering all hires in Pisa for two years.…”

Section: Related Workmentioning

confidence: 99%

An Intelligent Machine Learning-Based Real-Time Public Transport System

Skhosana

Ezugwu

Rana

et al. 2020

Computational Science and Its Applications – ICCSA 2020

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More often than not, commuters are left stranded at pick-up spots – clueless about the availability and proximity of public transport vehicles hence the stigma of public transport being unreliable, especially in developing countries. This is a result of poorly managed fleets, caused by varying demands and rigid schedules. In this paper, we present an intelligent real-time transport information system to keep commuters informed about the status of buses currently in transit, and also provide an insight to bus managers based on ridership data and commuter behavior. The system is composed of three subsystems designed to cater for commuters, bus-drivers and bus managers respectively. This system is developed on the Backend-as-a-Service (BaaS) platform Firebase. Furthermore, a neural network is trained to provide predictions to bus managers on the expected ridership numbers per route. The trained model is integrated with a web application for bus managers. An Android application used by bus drivers collects the ridership data being fed to the network. The proposed system was evaluated with a real-world data set that contains the daily ridership on a per-route basis dating back to 2001. Evaluation results confirm the effectiveness of the new system in reducing the total mileage used to deliver commuters, reducing fuel costs, increasing the profit of bus operators, and increasing the percentage of satisfied ridership requests.

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“…Applications of these learning models are manifold in the transportation realm, including self‐driving (e.g. in real‐time perception and prediction of traffic scenes [82]); ride‐sharing platforms such as Uber (e.g. demand forecasting [83]); or by crowd‐sourced video scene analysis companies such as Nexar (e.g.…”

Section: Literature Surveymentioning

confidence: 99%

Big Data for transportation and mobility: recent advances, trends and challenges

Torre-Bastida

Ser

Laña

et al. 2018

IET Intelligent Transport Systems

116

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An experience in using machine learning for short-term predictions in smart transportation systems

Cited by 29 publications

References 19 publications

A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave

A Study of Machine-Learning Classifiers for Hypertension Based on Radial Pulse Wave

An Intelligent Machine Learning-Based Real-Time Public Transport System

Big Data for transportation and mobility: recent advances, trends and challenges

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