Predictive Vehicle Route Optimization in Intelligent Transportation Systems

Mohamad Abdul-Hak; Nizar Al-Holou; Youssef Bazzi; Malok Alamir Tamer

doi:doi:10.11648/j.ijdst.20190501.13

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Predictive Vehicle Route Optimization in Intelligent Transportation Systems

Mohamad Abdul-Hak, Nizar Al-Holou, Youssef Bazzi, Malok Alamir Tamer

Published in International Journal on Data Science and Technology (Volume 5, Issue 1)

Received: 7 March 2019 Accepted: 26 April 2019 Published: 20 May 2019

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Abstract

Through the adoption of dedicated short-range communication (DSRC) wireless communication technology, intelligent transportation systems (ITS) will spur a new revolution in the U.S. transportation system. This paper is structured around providing drivers with the least-congested transportation route choices enabled by the ITS-envisioned vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communication platforms. Recent research in vehicle navigation systems has proposed energy consumption and emission optimized routing methodologies using historical traffic data modeling. More than 50% of congestion in U.S. cities is nonrecurring congestion. Nonrecurring congestion reduces the availability of the traffic network, thus rendering historical traffic data-based systems insufficient in more than 50% of the cases. Real-time traffic data modeling provides an enhanced performance in traffic congestion assessment; however, greater performance is expected with a predictive traffic congestion model with increased certainty. This paper compares the conventional shortest path and fastest path vehicle routing methodologies and establish the improvement for environmentally friendly routing in a dynamic and predictive cost dependent traffic network based on Petri Net Modeling. The proposed routing algorithm is validated using a computer-based tool of choice.

Published in	International Journal on Data Science and Technology (Volume 5, Issue 1)
DOI	10.11648/j.ijdst.20190501.13
Page(s)	14-28
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Intelligent Transportation Systems (ITS), Predictive Traffic Information, Environmentally Friendly Navigation, Emission, Dedicated Short-Range Communication (DSRC)

References

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APA Style

Mohamad Abdul-Hak, Nizar Al-Holou, Youssef Bazzi, Malok Alamir Tamer. (2019). Predictive Vehicle Route Optimization in Intelligent Transportation Systems. International Journal on Data Science and Technology, 5(1), 14-28. https://doi.org/10.11648/j.ijdst.20190501.13

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ACS Style

Mohamad Abdul-Hak; Nizar Al-Holou; Youssef Bazzi; Malok Alamir Tamer. Predictive Vehicle Route Optimization in Intelligent Transportation Systems. Int. J. Data Sci. Technol. 2019, 5(1), 14-28. doi: 10.11648/j.ijdst.20190501.13

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AMA Style

Mohamad Abdul-Hak, Nizar Al-Holou, Youssef Bazzi, Malok Alamir Tamer. Predictive Vehicle Route Optimization in Intelligent Transportation Systems. Int J Data Sci Technol. 2019;5(1):14-28. doi: 10.11648/j.ijdst.20190501.13

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@article{10.11648/j.ijdst.20190501.13,
  author = {Mohamad Abdul-Hak and Nizar Al-Holou and Youssef Bazzi and Malok Alamir Tamer},
  title = {Predictive Vehicle Route Optimization in Intelligent Transportation Systems},
  journal = {International Journal on Data Science and Technology},
  volume = {5},
  number = {1},
  pages = {14-28},
  doi = {10.11648/j.ijdst.20190501.13},
  url = {https://doi.org/10.11648/j.ijdst.20190501.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20190501.13},
  abstract = {Through the adoption of dedicated short-range communication (DSRC) wireless communication technology, intelligent transportation systems (ITS) will spur a new revolution in the U.S. transportation system. This paper is structured around providing drivers with the least-congested transportation route choices enabled by the ITS-envisioned vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communication platforms. Recent research in vehicle navigation systems has proposed energy consumption and emission optimized routing methodologies using historical traffic data modeling. More than 50% of congestion in U.S. cities is nonrecurring congestion. Nonrecurring congestion reduces the availability of the traffic network, thus rendering historical traffic data-based systems insufficient in more than 50% of the cases. Real-time traffic data modeling provides an enhanced performance in traffic congestion assessment; however, greater performance is expected with a predictive traffic congestion model with increased certainty. This paper compares the conventional shortest path and fastest path vehicle routing methodologies and establish the improvement for environmentally friendly routing in a dynamic and predictive cost dependent traffic network based on Petri Net Modeling. The proposed routing algorithm is validated using a computer-based tool of choice.},
 year = {2019}
}

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TY  - JOUR
T1  - Predictive Vehicle Route Optimization in Intelligent Transportation Systems
AU  - Mohamad Abdul-Hak
AU  - Nizar Al-Holou
AU  - Youssef Bazzi
AU  - Malok Alamir Tamer
Y1  - 2019/05/20
PY  - 2019
N1  - https://doi.org/10.11648/j.ijdst.20190501.13
DO  - 10.11648/j.ijdst.20190501.13
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 14
EP  - 28
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20190501.13
AB  - Through the adoption of dedicated short-range communication (DSRC) wireless communication technology, intelligent transportation systems (ITS) will spur a new revolution in the U.S. transportation system. This paper is structured around providing drivers with the least-congested transportation route choices enabled by the ITS-envisioned vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communication platforms. Recent research in vehicle navigation systems has proposed energy consumption and emission optimized routing methodologies using historical traffic data modeling. More than 50% of congestion in U.S. cities is nonrecurring congestion. Nonrecurring congestion reduces the availability of the traffic network, thus rendering historical traffic data-based systems insufficient in more than 50% of the cases. Real-time traffic data modeling provides an enhanced performance in traffic congestion assessment; however, greater performance is expected with a predictive traffic congestion model with increased certainty. This paper compares the conventional shortest path and fastest path vehicle routing methodologies and establish the improvement for environmentally friendly routing in a dynamic and predictive cost dependent traffic network based on Petri Net Modeling. The proposed routing algorithm is validated using a computer-based tool of choice.
VL  - 5
IS  - 1
ER  -

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Author Information

Mohamad Abdul-Hak

Mercedes Benz Research and Development North America Inc., Redford, Michigan, USA
Nizar Al-Holou

Department of Electrical & Computer Engineering, University of Detroit Mercy Detroit, Michigan, USA
Youssef Bazzi

Faculty of Engineering, Lebanese University, Beirut, Lebanon
Malok Alamir Tamer

Department of Electrical & Computer Engineering, University of Detroit Mercy Detroit, Michigan, USA

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Plain Text BibTeX RIS

APA Style

Mohamad Abdul-Hak, Nizar Al-Holou, Youssef Bazzi, Malok Alamir Tamer. (2019). Predictive Vehicle Route Optimization in Intelligent Transportation Systems. International Journal on Data Science and Technology, 5(1), 14-28. https://doi.org/10.11648/j.ijdst.20190501.13

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ACS Style

Mohamad Abdul-Hak; Nizar Al-Holou; Youssef Bazzi; Malok Alamir Tamer. Predictive Vehicle Route Optimization in Intelligent Transportation Systems. Int. J. Data Sci. Technol. 2019, 5(1), 14-28. doi: 10.11648/j.ijdst.20190501.13

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AMA Style

Mohamad Abdul-Hak, Nizar Al-Holou, Youssef Bazzi, Malok Alamir Tamer. Predictive Vehicle Route Optimization in Intelligent Transportation Systems. Int J Data Sci Technol. 2019;5(1):14-28. doi: 10.11648/j.ijdst.20190501.13

Copy | Download

@article{10.11648/j.ijdst.20190501.13,
  author = {Mohamad Abdul-Hak and Nizar Al-Holou and Youssef Bazzi and Malok Alamir Tamer},
  title = {Predictive Vehicle Route Optimization in Intelligent Transportation Systems},
  journal = {International Journal on Data Science and Technology},
  volume = {5},
  number = {1},
  pages = {14-28},
  doi = {10.11648/j.ijdst.20190501.13},
  url = {https://doi.org/10.11648/j.ijdst.20190501.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20190501.13},
  abstract = {Through the adoption of dedicated short-range communication (DSRC) wireless communication technology, intelligent transportation systems (ITS) will spur a new revolution in the U.S. transportation system. This paper is structured around providing drivers with the least-congested transportation route choices enabled by the ITS-envisioned vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communication platforms. Recent research in vehicle navigation systems has proposed energy consumption and emission optimized routing methodologies using historical traffic data modeling. More than 50% of congestion in U.S. cities is nonrecurring congestion. Nonrecurring congestion reduces the availability of the traffic network, thus rendering historical traffic data-based systems insufficient in more than 50% of the cases. Real-time traffic data modeling provides an enhanced performance in traffic congestion assessment; however, greater performance is expected with a predictive traffic congestion model with increased certainty. This paper compares the conventional shortest path and fastest path vehicle routing methodologies and establish the improvement for environmentally friendly routing in a dynamic and predictive cost dependent traffic network based on Petri Net Modeling. The proposed routing algorithm is validated using a computer-based tool of choice.},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - Predictive Vehicle Route Optimization in Intelligent Transportation Systems
AU  - Mohamad Abdul-Hak
AU  - Nizar Al-Holou
AU  - Youssef Bazzi
AU  - Malok Alamir Tamer
Y1  - 2019/05/20
PY  - 2019
N1  - https://doi.org/10.11648/j.ijdst.20190501.13
DO  - 10.11648/j.ijdst.20190501.13
T2  - International Journal on Data Science and Technology
JF  - International Journal on Data Science and Technology
JO  - International Journal on Data Science and Technology
SP  - 14
EP  - 28
PB  - Science Publishing Group
SN  - 2472-2235
UR  - https://doi.org/10.11648/j.ijdst.20190501.13
AB  - Through the adoption of dedicated short-range communication (DSRC) wireless communication technology, intelligent transportation systems (ITS) will spur a new revolution in the U.S. transportation system. This paper is structured around providing drivers with the least-congested transportation route choices enabled by the ITS-envisioned vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I), and infrastructure-to-vehicle (I2V) communication platforms. Recent research in vehicle navigation systems has proposed energy consumption and emission optimized routing methodologies using historical traffic data modeling. More than 50% of congestion in U.S. cities is nonrecurring congestion. Nonrecurring congestion reduces the availability of the traffic network, thus rendering historical traffic data-based systems insufficient in more than 50% of the cases. Real-time traffic data modeling provides an enhanced performance in traffic congestion assessment; however, greater performance is expected with a predictive traffic congestion model with increased certainty. This paper compares the conventional shortest path and fastest path vehicle routing methodologies and establish the improvement for environmentally friendly routing in a dynamic and predictive cost dependent traffic network based on Petri Net Modeling. The proposed routing algorithm is validated using a computer-based tool of choice.
VL  - 5
IS  - 1
ER  -

Copy | Download