Big data and machine learning approach for an efficient intelligent logistics transportation

Z. MOUAMMINE; H. KHOULIMI; O. EL IMRANI; M. CHRAYAH; A. AMMOUMOU; B. NSIRI

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Big data and machine learning approach for an efficient intelligent logistics transportation

Author(s)

Z. MOUAMMINE Laboratory Industrial Engineering, Information Processing and Logistics (GITIL) Faculty of Science Ain Chock. University Hassan II, Casablanca, Morocco

H. KHOULIMI Laboratory Industrial Engineering, Information Processing and Logistics (GITIL) Faculty of Science Ain Chock. University Hassan II, Casablanca, Morocco

O. EL IMRANI FSJESTE, Abdelmalek Essaadi University, Tetouan, Morocco

M. CHRAYAH Ensate, Abdelmalek Essaadi University, Tetouan, 93000, Morocco

A. AMMOUMOU Applied Mathematics and Computing Laboratory, High School of Technology University Hassan II, Casablanca, Morocco.

B. NSIRI Research Center STIS, M2CS, National School of Arts and Crafts of Rabat (ENSAM), Mohammed V University in Rabat, Morocco

Abstract

Logistics is the pillar of any industrial activity, transportation and itineraries management are the essential functions of logistics, within the appearance of smart city infrastructure, intelligent transportation appeared as a cutting-edge technological newcomer, though, all researches curried out about intelligent transport/logistics require basically the existence of intelligent ground such smart city infrastructure, sensors or “IoT” so as to work, however, this is still challenging for developing countries. Thus, there is a need for an alternative framework allowing “ITS” to work independently to the infrastructure. To fulfill that demand, authors suggest in this paper an innovative model to enable smarter transportation no matter there is an intelligent ground or not. It enables automatic monitoring of road traffic state and transportation conditions via near-real time detection of road events. Facebook and twitter are used as sources of social big data that are needed for our framework. “PNL” is applied to process Moroccan Dialect texts. The obtained results prove the effectiveness of our approach, to the best of our knowledge; this is the first work treating traffic event detection from tweets written in Moroccan dialect language using machine learning and Apache Spark big data platform.

Publication Details

Page(s) 3739-3749

DOI DOI not available

Published Journal: Journal of Theoretical and Applied Information Technology, Volume: 100, Issue: 11, Year: 2022

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Keywords

Logistic regression, machine learning, support vector machine (SVM), Apache Spark, Big Data, Naïve Bayes (NB), Social big data, Logistic transportation, Smart transportation

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