STRATEGIC ANALYSIS OF INTELLIGENT TRANSPORTATION SYSTEMS

Year 2019, BEYKOZ AKADEMİ 2019 ÖZEL SAYI, 148 - 158, 01.10.2019

Esin Mukul Gülçin Büyüközkan Merve Güler

https://doi.org/10.14514/byk.m.26515393.2019.sp/148-158

Cited By: 3

Abstract

Transportation is one of the most critical factors affecting the economic development and
welfare of a country. Effective transport systems create socio-economic opportunities and
benefits by facilitating access to markets, jobs, and investments. Moreover, transportation shows
a rapid change in today's world of globalization and economic growth. With the rapid
development of information and technology, the demand for higher, faster, safer, and more
comfortable transportation is emphasized. On the other hand, with the development of the
automotive industry, increased vehicle traffic volumes cause congestion, delays, travel time,
resource consumption, environmental problems, and accidents. Systems need to be designed to
be more efficient, effective, safe, and economical to reduce these adverse outcomes of
transportation systems and meet user demands. For this reason, the concept of "Intelligent
Transportation Systems (ITS)" has emerged. ITS provide economic, environmental, and socially
sustainable solutions, in particular by ensuring that information is accessed quickly and
efficiently. The analysis of ITS are very complicated since it has many conflicting objectives
and many different criteria. Multi-criteria decision-making (MCDM) is a powerful tool widely
used for solving this type of problems. Therefore, in this study, we aim to propose a strategic
analysis of ITS by using MCDM methods. In the proposed methodology, ITS criteria are
weighted with fuzzy Analytic Hierarchy Process (AHP) and fuzzy Evaluation Based on
Distance from Average Solution (EDAS) is used to select the most appropriate ITS strategy.
Finally, an application is provided to demonstrate the potential use of the proposed
methodology

Keywords

Fuzzy logic, intelligent transportation systems, MCDM methods, strategic analysis

References

• Ayağ, Z. (2005). A Fuzzy AHP-Based Simulation Approach To Concept Evaluation In A NPD Environment. IIE transactions, 37(9), 827-842.
• Bacciu, D., Carta, A., Gnesi, S., &Semini, L. (2017). An Experience In Using Machine Learning For Short-Term Predictions In Smart Transportation Systems. Journal of Logical and Algebraic Methods in Programming, 87, 52-66.
• Bask, A., Spens, K., Stefansson, G., &Lumsden, K. (2009). Performance Issues Of Smart Transportation Management Systems. International Journal of Productivity and Performance Management.
• Büyüközkan, G., and Çifçi, G. (2012). A Combined Fuzzy AHP And Fuzzy TOPSIS Based Strategic Analysis Of Electronic Service Quality In Healthcare Industry. Expert systems with applications, 39(3), 2341-2354.
• Catapult. (2014). Exploring Intelligent Mobility.
• Civitas. (2015). Intelligent Transport Systems And Traffic Management In Urban Areas, Policy Note.
• Dia, H., Panwai, S. (2014, December). Intelligent Mobility for Smart Cities: Driver Behaviour Models for Assessment of Sustainable Transport. In 2014 IEEE Fourth International Conference on Big Data and Cloud Computing (pp. 625-632). IEEE.
• Ghorabaee, M. K., Zavadskas, E. K., Amiri, M., &Turskis, Z. (2016). Extended EDAS Method For Fuzzy Multi-Criteria Decision-Making: An Application To Supplier Selection. International Journal of Computers Communications & Control, 11(3), 358-371.
• Ilıcalı, M., Toprak, T., Özen, H., Tapkın, S., Öngel, A., Camkesen, N, and Kantarcı, M. (2016). Akıcı- Güvenli Trafik için Akıllı Ulaşım Sistemleri.
• Keshavarz Ghorabaee, M., Zavadskas, E. K., Olfat, L., &Turskis, Z. (2015). Multi-Criteria Inventory Classification Using A New Method Of Evaluation Based On Distance From Average Solution (EDAS). Informatica, 26(3), 435-451.
• Kim, H. J., Lee, J., Park, G. L., Kang, M. J., & Kang, M. (2010, September). An Efficient Scheduling Scheme On Charging Stations For Smart Transportation. In International Conference on Security-Enriched Urban Computing and Smart Grid (pp. 274-278).Springer, Berlin, Heidelberg.
• Kirch, M., Poenicke, O., and Richter, K. (2017). RFID in Logistics and Production– Applications, Research and Visions for Smart Logistics Zones. Procedia Engineering, 178, 526-533.
• Kolosz, B., Grant-Muller, S., and Djemame, K. (2013). Modelling Uncertainty In The Sustainability Of Intelligent Transport Systems For Highways Using Probabilistic Data Fusion. Environmental Modelling & Software, 49, 78-97.
• Kumbhar, M. A. (2012). Wireless Sensor Networks: A Solution For Smart Transportation. Journal of Emerging Trends in Computing and Information Sciences, 3(4).
• Kutlu Gündoğdu, F., Kahraman, C., &Civan, H. N. (2018). A Novel Hesitant Fuzzy EDAS Method And Its Application To Hospital Selection. Journal of Intelligent & Fuzzy Systems, (Preprint), 1-13.
• Pomorel, J. C., & Romero, S. B. (2000). Multicriterion Decision in Management. Principle and Practice.
• Saaty, T. L. (1980). The analytic hierarchy process McGraw-Hill. New York, 324.
• Schewel, L., and Kammen, D. M. (2010). Smart Transportation: Synergizing Electrified Vehicles And Mobile Information Systems. Environment, 52(5), 24-35.
• T. C. Ministry of Transport, Maritime Affairs and Communications (UHDB). (2014). National Intelligent Transportation Systems Strategy Document and Action Plan: 2014-2023.
• UNECE, (2012). Intelligent Transport Systems (ITS) For Sustainable Mobility.
• Wang, F. Y. (2010). Parallel Control And Management For Intelligent Transportation Systems: Concepts, Architectures, And Applications. IEEE Transactions on Intelligent Transportation Systems, 11(3), 630-638.
• Wang, M., and Kexin, L. (2013). Transportation model application for the planning of low carbon city–take Xining city in China as example. Procedia Computer Science, 19, 835-840.
• Yardım, M. S. and Akyıldız, G. (2005). Intelligent Transportation Systems and Applications in Turkey. 6th Transportation Congress Proceeding, Istanbul: TMMOB Civil Engineers.
• Zanelli, P. (2016). Intelligent Mobility. CATAPULT Transport Systems Report.
• Zhang, J., Wang, F. Y., Wang, K., Lin, W. H., Xu, X., & Chen, C. (2011). Data-driven intelligent transportation systems: A survey. IEEE Transactions on Intelligent Transportation Systems, 12(4), 1624-1639.