Impact of Artificial Intelligence in the Transportation Sector
Keywords:
Transportation, Artificial Intelligence, Technology, Machine LearningAbstract
Purpose: The purpose of the present article is to highlight the role of Artificial Intelligence (AI)
in the transportation industry and the various technologies being integrated to improve the
service and practical applications. The expected changes and challenges in transportation in the
future are focused in this paper.
Design/methodology/approach: A systematic study on the emerging technologies of AI and
applied in the transportation sector is presented in the form of a viewpoint.
Findings: AI certainly enhances the transportation services, however it cannot surpass the
human touch which is an essential determinant of experiential transportation services. AI acts as
an effective complementary dimension to the future of transportation.
Practical implications: The AI applications in transport are being constantly updated in various
frameworks. The three fundamental cases are the following:
(i)
The utilization of AI in corporate choice-making, arranging, and overseeing
transportation frameworks. This can be imperative to overcome the issue of a ceaselessly
rising request with restricted street supply. This incorporates a better utilization of
precise forecasting and location models pointing toward growing figure activity
volumes, activity conditions, and occurrences.
(ii)
Applications of AI aiming to improve public transport is also discussed. It is due to the
understanding that open transportation is respected as an economical mode of versatility.
Originality/value : The present viewpoint discusses the application and role of AI in the
transportation industry with the help of relevant examples and theory. The present paper
highlights the different technologies and mathematical models being used and will be used in the
future.
Warning: file_put_contents(): Only -1 of 23 bytes written, possibly out of free disk space in /home2/openjtg/public_html/lib/pkp/classes/cache/FileCache.inc.php on line 90
References
SADEK, A. Artificial Intelligence in Transportation. Transp. Res. Circ. 2007. E-C113 , 72–79.
YEGNANARAYANA, B. Artificial Neural Networks ; PHI Learning Pvt. Ltd.: New Delhi,
India, 1999; p. 476.
ABRAHAM, A. 2005. Artificial Neural Networks. Handbook of Measuring System Design ;
Sydenham, P.H., Thorn, R., Eds.; John Wiley & Sons, Ltd.: Hoboken, NJ, USA.
WOLPERT, D.H. Bayesian Backpropagation Over I-O Functions Rather Than Weights.
Dia, H. 2001. An object-oriented neural network approach to short-term traffic forecasting. Eur.
J. Oper. Res. 131 , 253–261.
HUANG, W.; SONG, G.; HONG, H.; XIE, K. 2014. Deep architecture for traffic flow
prediction: Deep belief networks with multitask learning. IEEE Trans. Intell. Transp. Syst. , 15 ,
–2201.
LEDOUX, C. An urban traffic flow model integrating neural networks. 1997. Transp. Res. Part
C Emerg. Technol. 5 , 287–300.
LV, Y.; DUAN, Y.; KANG, W.; LI, Z.; WANG, F.Y. 2014. Traffic Flow Prediction with Big
Data: A Deep Learning Approach. IEEE Trans. Intell. Transp. Syst. 16 , 865–873.
BELL, J.E.; McMullen, P.R. 2004. Ant colony optimization techniques for the vehicle routing
problem. Adv. Eng. Inform. 18 , 41–48.
FRIES, R.; CHOWDHURY, M.; BRUMMOND, J. 2008. Transportation Infrastructure Security
Utilizing Intelligent Transportation Systems ; John Wiley & Sons: Hoboken, NJ, USA,
NUZZOLO, A.; COMI, A. 2016. Advanced public transport and intelligent transport systems:
New modelling challenges. Transp. A Transp. Sci. 12 , 674–699.
HU, J.; YANG, Z.; JIAN, F. 2001. Study on the optimization methods of transit network based
on Ant Algorithm. In Proceedings of the IEEE International Vehicle Electronics Conference
, Tottori, Japan, 25–28; pp. 215–219.
JEONG, R.; RILETT, R. 2004. Bus arrival time prediction using artificial neural network model.
In Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems,
Washington, DC, USA, 3–6; pp. 988–993.
Published
How to Cite
Issue
Section
Copyright (c) 2020 Raul Rodriguez
This work is licensed under a Creative Commons Attribution 4.0 International License.
Published in Journal of Technology & Governance. Published by Creactos Projects (OPC) Private Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode
