Predictive Vehicle Route Optimization in Intelligent Transportation Systems
International Journal on Data Science and Technology
Volume 5, Issue 1, March 2019, Pages: 14-28
Received: Mar. 7, 2019; Accepted: Apr. 26, 2019; Published: May 20, 2019
Views 550      Downloads 86
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
Article Tools
Follow on us
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.
Intelligent Transportation Systems (ITS), Predictive Traffic Information, Environmentally Friendly Navigation, Emission, Dedicated Short-Range Communication (DSRC)
To cite this article
Mohamad Abdul-Hak, Nizar Al-Holou, Youssef Bazzi, Malok Alamir Tamer, Predictive Vehicle Route Optimization in Intelligent Transportation Systems, International Journal on Data Science and Technology. Vol. 5, No. 1, 2019, pp. 14-28. doi: 10.11648/j.ijdst.20190501.13
Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
National oceanic and atmospheric administration (NOAA), [Online], “” , April 12, 2019, [April 15, 2019]
Arrhenius, Svante, 1896. On the Influence of Carbonic Acid in the Air upon the Temperature of the Ground. Philosophical Magazine ser. 5, vol. 41, 237–276.
U. S. Environmental Protection Agency, “Inventory of U. S. Greenhouse Gas Emissions and sinks: 1990-2017”, April 11 2019, EPA 430-R-19-001.
U. S. Department of Transportation, [Online], ““, March 18, 2019, [April16, 2019]
Fallouh. S; Abdul-Hak. M; Al-Holou. N, “Performance Evaluation of Data Communication Protocols in Intelligent Transportation”, 2018 International Conference on Computer and Applications (ICCA), August 2018, DOI: 10.1109/COMAPP.2018.8460226
U. S. Energy Information Administration, “Inventory of U. S Greenhouse Gas Emissions and Sinks: 1990-2016”, April 2018.
M. A. S. Kamal, Hayakawa, T., & Imura, J.-i. (2018, Apr). Road-speed profile for enhanced perception of traffic conditions in a partially connected vehicle environment. IEEE Transactions on Vehicular Technology, vol. 67, no. 8, pp. 6824-6837, Aug. 2018.
United States Department of Transportation - Federal Highway Administration, [Online], “”, Feb 1, 2017, [April 17, 2019].
P. Hao, G. Wu, K. Boriboonsomsin and M. J. Barth, "Eco-Approach and Departure (EAD) Application for Actuated Signals in Real-World Traffic," in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 1, pp. 30-40, Jan. 2019. doi: 10.1109/TITS.2018.2794509
S. Kidane Zegeye, B. De Schutter, H. Hellendoorn, E. Breunesse, “Reduction of Travel Times and Traffic Emissions Using Model Predictive Control” 2009 American Control Conference Hyatt Regency Riverfront, St. Louis, MO, USA June 10-12, 2009.
Shahzada, “Dynamic vehicle navigation: An A* algorithm based approach using traffic and road information”, Computer Applications and Industrial Electronics (ICCAIE), 2011 IEEE International Conference on Computer Applications and Industrial Electronics, Dec, 7, 2011, Page: 514-518
Cascetta, E., A. Nuzzolo, F. Russo, and A. Vitetta (1996). “A Modified Logit Route Choice Model Overcoming Path Overlapping Problems: Specification and Some Calibration Results for Interurban Networks.” In J. B. Lesort (ed.), Transportation and Traffic Theory. Proceedings from the Thirteenth International Symposium on Transportation and Traffic Theory, Lyon, France, Pergamon pp. 697–711.
C. Gawron. “An iterative algorithm to determine the dynamic user equilibrium in a traffic simulation model”, International Journal of Modern Physics C, 9(3): 393–407, 1998
Palubinskas, Gintautas and Kurz, Franz and Reinartz, Peter (2008) Detection of traffic congestion in optical remote sensing imagery. In: International Geoscience and Remote Sensing Symposium. IEEE. IGARSS08 , 2008-07-06 - 2008-07-11 , Boston, USA.
Cherrett, T., Waterson, B. and McDonald, M. (2005) Remote automatic incident detection using inductive loops. Proceedings of the Institution of Civil Engineers: Transport, 158, (3), PP. 149-155.
J. H. Banks, “Introduction to transportation engineering. McGraw-Hill”, 2002.
Bellman, Richard, “Adaptive Control Processes: A Guided Tour”, Princeton University Press, 1961
S. Hausberger, M. Rexeis, M. Zallinger, R. Luz, “Emission Factors from the Model PHEM for the HBEFA Version 3”, Report Nr. I-20/2009 Haus-Em 33/08/679 from 07.12.2009
DLR,”An Integrated Wireless and Traffic Platform for Real-Time Road Traffic Management Solutions D3.1 – Traffic Modelling: Environmental Factors”, February, 13, 2009
C. G. Cassandras and S. Lafortune, “Introduction to Discrete Event Systems” - Second Edition, Springer, 2008. ISBN 978-0-387-33332-8
Ng, K. M.; Reaz, M. B. I.; Ali, M. A. M., "A Review on the Applications of Petri Nets in Modeling, Analysis, and Control of Urban Traffic," Intelligent Transportation Systems, IEEE Transactions on , vol. 14, no. 2, pp. 858, 870, June 2013
McMillan, K. L.: Using unfoldings to avoid the state explosion problem in the verification of asynchronous circuits. In: Computer Aided Verification, 4th Inter- national Workshop (CAV’92). Volume 663 of Lecture Notes in Computer Science., Springer (1992) 164–177.
Dijkstra, E. W. “A note on two problems in connection with graphs. Numerische Mathematik”, 1959.
D. B. Rehunathan, B. C. Seet, T. T. Luong, Federating of MITSIMLab and ns-2 for realistic vehicular network simulation, Proc. Mobility Conference 2007 - the 4th Int. Conf. Mobile Technology, Applications and Systems, Mobility 2007, Incorporating the 1st Int. Symp. Computer Human Interaction in Mobile Technology, IS-CHI 2007, p 62-67, 2007.
H. Park, A. Miloslavov, J. Lee, M. Veeraraghavan, B. Park, B. L. Smith, Integrated Traffic/Communications Simulation Evaluation Environment for IntelliDriveSM Applications Using SAE J2735 Dedicated Short Range Communications Message Sets to be presented at the 2011 Annual Meeting of the Transportation, University of Virginia, United States, 2010.
B. Liu, B. Khorashadi, H. Du , D. Ghosal,C. N. Chuah, M. Zhang, \VGSim: An integrated networking and microscopic vehicular mobility simulation platform" IEEE Communications Magazine, v 47, n 5, p 134-141, 2009.
C. Sommer, Z. Yao, R. German, F. Dressler, On the need for bidirectional coupling of road traffic microsimulation and network simulation, Proc. 9th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), p 41-48, 2008.
Introduction to iTETRIS, [Online],””, [April 21, 2019]
Daniel Krajzewicz, Jakob Erdmann, Michael Behrisch, and Laura Bieker. Recent Development and Applications of SUMO - Simulation of Urban Mobility. International Journal On Advances in Systems and Measurements, 5 (3&4): 128-138, December 2012.
Network Simulator, ns-3, [Online], ””, [April 21, 2019].
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186