A Fuzzy Optimal Route Suggestion Model and Solution Algorithm Considering Inside Bus Occupation Rate

Year 2021, Volume: 25 Issue: 2, 432 - 440, 20.08.2021

Resmiye Nasiboglu

https://doi.org/10.19113/sdufenbed.893994

Abstract

Effective use of public transport is one of the components that will contribute to the smart city concept. In this respect, it is important to develop applications that facilitate the use of public transportation vehicles. In this study, a fuzzy optimal route suggestion model and solution algorithm has been developed that can support the applications in this field. The main difference of this model from other studies is the definition of the fuzzy route preference degree, which also takes into account the occupation density rate of the bus. In today's pandemic process, the importance of the model that can take into account the occupation density in the bus increases even more. In the created model, on the contrary of classical models, each route is suggested with fuzzy preference degree. The solution algorithm is a Dijkstra-like algorithm that uses fuzzy penalty values. Simulative experiments have been conducted using the Izmir public transport network data. It has been observed that the optimal solutions calculated in accordance with the proposed fuzzy model produce solutions with higher preference degrees than the classical approach that does not use the concept of fuzziness.

Keywords

Fuzzy modeling, Optimization, Algorithm, Public transport network, Optimal route

Otobüs İçi Yoğunluk Oranını Dikkate Alan Bulanık Optimal Güzergah Öneri Modeli ve Çözüm Algoritması

Abstract

Toplu taşıma araçlarının etkin şekilde kullanılması, akıllı şehir konseptine katkı sağlayacak bileşenlerden biridir. Bu bakımdan, toplu taşıma araçlarının kullanımını kolaylaştıran uygulamaların geliştirilmesi önem kazanmaktadır. Bu çalışmada, bu alanda yapılacak uygulamalara destek olabilecek bir bulanık optimal güzergah öneri modeli ve çözüm algoritması geliştirilmiştir. Bu modelin diğer çalışmalardan esas farkı otobüs içi yoğunluk oranını da dikkate alan bulanık güzergah tercih derecesinin tanımlanmasıdır. Günümüz pandemi sürecinde otobüs içi yoğunluğu dikkate alabilen modelin önemi daha da artmaktadır. Oluşturulan modelde, klasik modellerin aksine, her güzergah, bulanık tercih derecesi eşliğinde önerilmektedir. Çözüm algoritması, bulanık ceza değerleri kullanan Dijkstra benzeri bir algoritmadır. İzmir toplu taşıma ağı verileri kullanılarak yapılan simülatif deneyler yapılmıştır. Önerilen bulanık modele uygun olarak hesaplanan optimal çözümlerin, bulanıklık konsepti kullanmayan klasik yaklaşımdan tercih dereceleri daha yüksek olan çözümler ürettiği görülmüştür.

Keywords

Bulanık modelleme, Optimizasyon, Algoritma, Toplu taşıma ağı, Optimal güzergah

References

• [1] Wilson, N.H., Zhao, J., Rahbee, A., The potential impact of automated data collection systems on urban public transportation planning. ss 75-99. Wilson, N.H., Nuzzolo. A., ed. 2009. Schedule-based modeling of transportation networks, Springer.
• [2] Pelletier, M.P., Trepanier, M., Morency, C. 2011. Smart card data use in public transit: A literature review. Transportation Research Part C, 19, 557–568.
• [3] Bagchi, M., White, P.R. 2005. The potential of public transport smartcard data. Transport Policy, 12, 464–474.
• [4] Morency, C., Trepanier, M., Agard, B. 2006. Analyzing the variability of transit users’ behaviour with smart card data. Proceedings of the IEEE ITSC, Toronto, Ontario, Canada, 17-20 September, 44-49.
• [5] Ceder, A. 2007. Public transit planning and operation: theory, modelling and practice. Oxford, Butterworth-Heinemann, 626s.
• [6] Nasibov, E.N., Kuvvetli, U., Ozkilcik, M., Eliiyi, U. 2012. Origin-Destination Matrix Generation Using Smart Card Data: Case Study for Izmir. IV International Conference “Problems of Cybernetics and Informatics” (PCI'2012), Sep 12-14, Baku, Azerbaijan, v.1, 188-191.
• [7] Munizaga, M.A., Palma, C. 2012. Estimation of a disaggregate multimodal public transport origin-destination matrix from passive smartcard data from Santiago, Chile. Transportation Research Part C, 24, 9-18.
• [8] Barry, J., Newhouser, R., Rahbee, A., Sayeda, S. 2002. Origin and destination estimation in New York City with automated fare system data. Transportation Research Record, 1817, 183-187.
• [9] Trepanier, M., Tranchant, N., Chapleau, R. 2007. Individual trip destination estimation in a transit smart card automated fare collection system. Journal of Intelligent Transportation Systems, 11(1), 1-14.
• [10] Diker, A. 2015. Usage of fuzzy logic based data mining methods in analysis of public transportation data. Dokuz Eylul University, The Graduate School of Natural and Applied Sciences, Ph.D. Thesis, 29s.
• [11] Bozyigit, A., Nasiboglu, E., Utku, S. 2018. Public Transport Route Recommender Regarding Multiple Factors. 3rd International Conference on Computer Science and Engineering (UBMK-18), Sarajevo, 20-23 September, 12-16.
• [12] Bozyigit, A., Alankus, G., Nasibov, E. 2018. A Public Transport Route Recommender Minimizing the Number of Transfers. Sigma J Eng & Nat Sci, 9(4), 437-446.
• [13] Bozyiğit, A., Alankuş, G., Nasiboğlu, E. 2017. Public transport route planning: Modified dijkstra's algorithm. International Conference on Computer Science and Engineering (UBMK-17), 5-8 Octrober, Antalya, 502-505.
• [14] Wang, H., Hu, M., Xiao, W. 2010. A new public transportation data model and shortest-path algorithms. 2nd International Asia Conference on Informatics in Control Automation and Robotics (CAR 2010), 6-7 March, Wuhan, China, v. 1, 456-459.
• [15] Pun-Cheng, L. S. C., Chan, A. W. F. 2016. Optimal route computation for circular public transport routes with differential fare structure. Travel Behaviour and Society, 3, 71-77.
• [16] Huang, Z., Li, J., Liu, X. 2009. Information Needs of Urban Transit Travelers—The Case of Wuhan China. ICCTP 2009: Critical Issues in Transportation Systems Planning Development and Management, 5-9 August, Harbin, China, 1-7.
• [17] Nasibov, E., Diker, A., Nasibov, E. 2016. A multi criteria route planning model based on fuzzy preference degrees of stops. Applied Soft Computing, 49, 13-26.
• [18] Currie, G., Delbosc, A. 2011. Exploring the trip chaining behaviour of public transport users in Melbourne. Transport Policy, 18(1), 204-210.
• [19] Deng, Y., Chen, Y., Zhang, Y., Mahadevan, S. 2012. Fuzzy Dijkstra algorithm for shortest path problem under uncertain environment. Applied Soft Computing, 12, 1231–1237.
• [20] Kang, J.G. 2013. The Minmax Regret Shortest Path Problem with Interval Arc Lengths. International Journal of Control and Automation, 6(5), 171-180.
• [21] ESHOT resmi web sayfası. 2020. https://www.eshot.gov.tr/ (Erişim tarihi: 16.11.2020).