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Şerit Yoğunluk Tespit ve Bilgilendirme Sistemlerinin Tasarımdan İşletmeye Genel Altyapısının Belirlenmesi: Örnek bir Sistem Tasarımı

Yıl 2023, Cilt: 11 Sayı: 4, 1092 - 1107, 28.12.2023

Metin Mutlu Aydın Başak Bıyık

https://doi.org/10.29109/gujsc.1273234

Öz

Dünya’da ve Türkiye’de trafiğe çıkan araç sayısı gün geçtikçe artmaktadır. Bu durum doğal olarak trafikte oluşan araç yoğunluğunun giderek artmasına ve yolculuklarda önemli gecikmelere sebebiyet verebilmektedir. Birçok insanın günlük hayatını etkileyen bu sorun, özellikle nüfus yoğunluğunun fazla olduğu şehirlerde kendini daha net göstermektedir. Şehiriçi trafikte oluşan yoğunluğun çeşitli sebeplerinin olmasının yanı sıra, trafikte araç sürücülerinin şerit seçim ve kullanım tercihleri de oluşan yoğunluk üzerinde önemli olumsuz bir etkiye sahip olabilmektedir. Sürücülerin yollarda en sol şerit daha hızlı hareket eder düşüncesi ile o şeridi daha çok kullanma arzusu içerisinde olması, tek bir şeritte yığılma oluşmasına neden olabilmektedir. Çalışmada, akıllı ulaşım sistemleri yardımıyla çok şeritli şehiriçi yollarda şerit kullanım yoğunluklarının tespitinin yapılmasını amaçlayan yenilikçi bir akıllı sistem öneri tasarımı sunulmuştur. Önerilen sistem üzerinde etkili olan faktörlerin belirlenmesi amacıyla SWOT analizi yapılmıştır. SWOT analizi ile belirlenen faktörler, beş kişilik bir uzman ekibin değerlendirmeleri ile analitik hiyerarşi prosesi (AHP) yöntemi kullanılarak ağırlıklandırılmıştır. Uygulanan A’WOT analizinden elde edilen sonuçlara göre sistemin en güçlü yönünün önerilen bu sistemden arzulanan doğru şerit kullanımı ile trafikte tıkanıklığın azaltılması olduğu görülmüştür. Önerilen sistemin çıkış noktası olan bu faktörün ön plana çıkması, çalışmanın temel amacını desteklemiş ve bu tür yenilikçi sistemlere ihtiyaç olduğunu net şekilde ortaya koymuştur.

Anahtar Kelimeler

Trafik yoğunluğu akıllı ulaşım akıllı sistemler şerit yoğunluk tespiti şerit kullanımı

Destekleyen Kurum

British Council

Proje Numarası

10

Teşekkür

Bu araştırma, British Council tarafından desteklenen “i-gCar4ITS: Innovative and Green Carrier Development for Intelligent Transportation System Applications” başlıkla proje desteğiyle hazırlanmıştır. Yazarlar destekleri için teşekkür eder.

Kaynakça

  • [1] Ilıcalı, M., & Saraç, S. (2019). Trafik sıkışıklığının azaltılmasında ulaşım çözümlerinin etkisi. Trafik ve Ulaşım Araştırmaları Dergisi, 2(2), 93-107.
  • [2] Vera-Gómez, J. A., Quesada-Arencibia, A., García, C. R., Suárez Moreno, R., & Guerra Zeroual, A., Harrou, F., & Sun, Y. (2019). Road traffic density estimation and congestion detection with a hybrid observer-based strategy. Sustainable Cities and Society, 46, 101411.
  • [3] TÜİK. (2023). Türkiye İstatistik Kurumu, Motorlu Kara Taşıtları İstatistikleri, https://data.tuik.gov.tr/Bulten/Index?p=Motorlu-Kara-Tasitlari-Ocak-2023-49433
  • [4] Delice, M. (2015). Trafik kazalarına etki eden sürücüyle ilgili faktörlerin çoklu regresyon analiziyle incelenmesi. Uhbab Journal, 4(11), 198-210.
  • [5] Ozan, C., Başkan, Ö., Haldenbilen, S., & Derici, E. (2010). Trafik kazalarının tehlike indeksi metodu ile analizi: Denizli örneği. Pamukkale University Journal of Engineering Sciences, 16(3), 325-333.
  • [6] Civelekoğlu, G., & Bıyık, Y. (2018). Ulaşım sektöründen kaynaklı karbon ayak izi değişiminin incelenmesi. Bilge International Journal of Science and Technology Research, 2(2), 157-166.
  • [7] Cao, Z., Jiang, S., Zhang, J., & Guo, H. (2016). A unified framework for vehicle rerouting and traffic light control to reduce traffic congestion. IEEE Transactions on Intelligent Transportation Systems, 18(7), 1958-1973.
  • [8] Ikiriwatte, A. K., Perera, D. D. R., Samarakoon, S. M. M. C., Dissanayake, D. M. W. C. B., & Rupasignhe, P. L. (2019). Traffic density estimation and traffic control using convolutional neural network. In 2019 IEEE International Conference on Advancements in Computing (ICAC), pp. 323-328.
  • [9] Basavaraju, A., Doddigarla, S., Naidu, N., & Malgatti, S. (2014). Vehicle density sensor system to manage traffic. IJRET: International Journal of Research in Engineering and Technology, 2319-1163.
  • [10] Jagadeesh, Y. M., Suba, G. M., Karthik, S., & Yokesh, K. (2015). Smart autonomous traffic light switching by traffic density measurement through sensors. In 2015 IEEE International Conference on Computers, Communications, and Systems (ICCCS), pp. 123-126.
  • [11] Zhang, Y., & Ioannou, P. A. (2016). Combined variable speed limit and lane change control for highway traffic. IEEE Transactions on Intelligent Transportation Systems, 18(7), 1812-1823.
  • [12] Li, Y., & Liu, Q. (2020). Intersection management for autonomous vehicles with vehicle-to-infrastructure communication. PLoS one, 15(7), e0235644.
  • [13] Arifin, A. S., & Zulkifli, F. Y. (2021). Recent development of smart traffic lights. IAES International Journal of Artificial Intelligence, 10(1), 224.
  • [14] Madrigal Arteaga, V. M., Pérez Cruz, J. R., Hurtado-Beltrán, A., & Trumpold, J. (2022). Efficient Intersection Management Based on an Adaptive Fuzzy-Logic Traffic Signal. Applied Sciences, 12(12), 6024.
  • [15] Knorr, F., Baselt, D., Schreckenberg, M., & Mauve, M. (2012). Reducing traffic jams via VANETs. IEEE Transactions on Vehicular Technology, 61(8), 3490-3498.
  • [16] Kanungo, A., Sharma, A., & Singla, C. (2014). Smart traffic lights switching and traffic density calculation using video processing. In 2014 IEEE Recent Advances in Engineering and Computational Sciences (RAECS), pp. 1-6.
  • [17] Kavya, G., & Saranya, B. (2015). Density based intelligent traffic signal system using PIC microcontroller. International Journal of Research in Applied Science & Engineering Technology (IJRASET), 3(1), 205-209.
  • [18] Ghazal, B., ElKhatib, K., Chahine, K., & Kherfan, M. (2016). Smart traffic light control system. In 2016 IEEE Third International Conference On Electrical, Electronics, Computer Engineering and Their Applications (EECEA), pp. 140-145.
  • [19] Bauza, R., & Gozálvez, J. (2013). Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications. Journal of Network and Computer Applications, 36(5), 1295-1307.
  • [20] Soomro, S., Miraz, M. H., Prasanth, A., & Abdullah, M. (2018). Artificial intelligence enabled IoT: traffic congestion reduction in smart cities. In IET Smart Cities Symposium 2018 (SCS '18), 22-23 April 2018, pp. 81-86.
  • [21] Hu, H., Gao, Z., Sheng, Y., Zhang, C., & Zheng, R. (2019). Traffic density recognition based on image global texture feature. International Journal of Intelligent Transportation Systems Research, 17, 171-180.
  • [22] Frank, A., Al Aamri, Y. S. K., & Zayegh, A. (2019). IoT based smart traffic density control using image processing. In 2019 IEEE 4th MEC International Conference on Big Data and Smart City (ICBDSC), pp. 1-4.
  • [23] Biswas, D., Su, H., Wang, C., Stevanovic, A., & Wang, W. (2019). An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD. Physics and Chemistry of the Earth, Parts A/B/C, 110, 176-184.
  • [24] Altay, A. B. & Demirhan, A. (2023). Boş park yerlerinin tespiti ve kullanıcıya mobil uygulama ile yol tarifi verilmesi. Gazi University Journal of Science Part C: Design and Technology, 11(1), 68-80.
  • [25] Alisoltani, N., Leclercq, L., & Zargayouna, M. (2021). Can dynamic ride-sharing reduce traffic congestion? Transportation Research Part B: Methodological, 145, 212-246.
  • [26] Budiarto, J., Sulistyo, S., Mustika, I. W., & Infantono, A. (2014). Road density prediction: Updated methods of turning probabilities and highway capacities manual for achieving the best route. In 2014 IEEE International Conference on Electrical Engineering and Computer Science (ICEECS), pp. 168-173.
  • [27] De Souza, A. M., Yokoyama, R. S., Botega, L. C., Meneguette, R. I., & Villas, L. A. (2015). Scorpion: A solution using cooperative rerouting to prevent congestion and improve traffic condition. In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 497-503.
  • [28] Barrachina, J., Garrido, P., Fogue, M., Martinez, F. J., Cano, J. C., Calafate, C. T., & Manzoni, P. (2015). A V2I-based real-time traffic density estimation system in urban scenarios. Wireless Personal Communications, 83, 259-280.
  • [29] Khan, U., Basaras, P., Schmidt-Thieme, L., Nanopoulos, A., & Katsaros, D. (2014). Analyzing cooperative lane change models for connected vehicles. In 2014 IEEE International Conference on Connected Vehicles and Expo (ICCVE), pp. 565-570.
  • [30] Roncoli, C., Bekiaris-Liberis, N., & Papageorgiou, M. (2017). Lane-changing feedback control for efficient lane assignment at motorway bottlenecks. Transportation Research Record, 2625(1), 20-31.
  • [31] Ramezani, M., & Ye, E. (2019). Lane density optimisation of automated vehicles for highway congestion control. Transportmetrica B: Transport Dynamics, 7(1), 1096-1116.
  • [32] Toledo, T., & Zohar, D. (2007). Modeling duration of lane changes. Transportation Research Record, 1999(1), 71-78.
  • [33] Meneguette, R. I., Filho, G. P., Guidoni, D. L., Pessin, G., Villas, L. A., & Ueyama, J. (2016). Increasing intelligence in inter-vehicle communications to reduce traffic congestions: Experiments in urban and highway environments. PLoS one, 11(8), e0159110.
  • [34] Kurttila, M., Pesonen, M., Kangas, J., & Kajanus, M. (2000). Utilizing the analytic hierarchy process (AHP) in SWOT analysis—a hybrid method and its application to a forest-certification case. Forest policy and economics, 1(1), 41-52.
  • [35] Bourhim, E. M., & Labti, O. (2022, December). Application of Combined SWOT and AHP Analysis to Assess the Virtual Reality and Select the Priority Factors for Education. In International Conference on Intelligent Systems Design and Applications (pp. 512-521). Cham: Springer Nature Switzerland.
  • [36] Zhang, H., Mehrotra, D. V., & Shen, J. (2023). AWOT and CWOT for genotype and genotype-by-treatment interaction joint analysis in pharmacogenetics GWAS. Bioinformatics, 39(1), btac834.
  • [37] Bottero, M., D’Alpaos, C., & Marello, A. (2020). An application of the a’WOT analysis for the management of cultural heritage assets: the case of the historical farmhouses in the aglie castle (Turin). Sustainability, 12(3), 1071.
  • [38] Lee, S., Kim, D., Park, S., & Lee, W. (2021). A study on the strategic decision making used in the revitalization of fishing village tourism: using A’WOT analysis. Sustainability, 13(13), 7472.
  • [39] Bourhim, E. M., & Cherkaoui, A. (2020). Exploring the potential of virtual reality in fire training research using A’WOT hybrid method. In Intelligent Systems, Technologies and Applications: Proceedings of Fifth ISTA 2019, India (pp. 157-167). Springer Singapore.
  • [40] Jozi, S. A., Dehghani, M., & Zarei, M. (2013). Rural waste management strategic plan by A'WOT Method (Case study: Minab). Journal of Environmental Studies, 38(4), 93-108.
  • [41] Bottero, M., D’Alpaos, C., & Marello, A. (2020). An application of the a’WOT analysis for the management of cultural heritage assets: the case of the historical farmhouses in the aglie castle (Turin). Sustainability, 12(3), 1071.
  • [42] Öztaş Karlı, R. G., & Karlı, H. (2022, October). Assessment of the Role of Micromobility in ITS by A’WOT Analysis. In The Proceedings of the International Conference on Smart City Applications (pp. 226-236). Cham: Springer International Publishing.
  • [43] Aydın, M. M. (2021). Method for modeling lane-based driving discipline of drivers on divided multilane urban roads. Journal of Transportation Engineering, Part A: Systems, 147(4), 04021011.
  • [44] Ulaştırma ve Altyapı Bakanlığı (UAB) (2023). Ağırlık ve boyut kontrolü, https://www.uab.gov.tr/uploads/pages/kutuphane/a3f43dbbb7bb488.pdf
  • [45] Lee, S., Kim, D., Park, S., & Lee, W. (2021). A study on the strategic decision making used in the revitalization of fishing village tourism: using A’WOT analysis. Sustainability, 13(13), 7472.
  • [46] Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal Of Mathematical Psychology, 15(3), 234-281.
  • [47] Aydın, M. M., Yıldırım, M. S., Karpuz, O., & Ghasemlou, K. (2014). Modeling of driver lane choice behavior with artificial neural networks (ANN) and linear regression (LR) analysis on deformed roads. Computer Science, 4(1), 47-57.
  • [48] Aydın, M. M., & Topal, A. (2018). Effects of pavement surface deformations on lane-changing behaviours. Proceedings of the Institution of Civil Engineers–Transport, 171(3), 136-145.

Determining the General Infrastructure of Lane Density Detection and Information Systems from Design to Operation: An Example System Design

Yıl 2023, Cilt: 11 Sayı: 4, 1092 - 1107, 28.12.2023

Metin Mutlu Aydın Başak Bıyık

https://doi.org/10.29109/gujsc.1273234

Öz

The number of vehicles on the road in the world and in Turkey is increasing day by day. This situation naturally may cause an increase on the density of vehicles in traffic and may cause significant delays in journeys. This problem, which affects the daily life of many people, shows itself more clearly, in cities with a high population density. In addition to the various reasons for the density in urban traffic, the lane choice and utilization preferences of vehicle drivers in traffic can also have a significant negative effect on the density. The fact that the drivers want to use that lane more with the thought that the leftmost lane moves faster on the roads can cause congestion in a single lane. In the study, an innovative intelligent system proposal design which aims to determine the lane usage densities on multi-lane urban roads with the help of intelligent transportation systems is presented. A SWOT analysis was conducted to determine the factors which affecting the proposed system. The determined factors via the SWOT analysis were weighted using the Analytical Hierarchy Process (AHP) method with the evaluations of a team of five experts. According to the obtained results from the applied A'WOT analysis, it has been seen that the strongest aspect of the system is the reduction of traffic congestion by using the desired correct lane. The prominence of this factor, which is the starting point of the proposed system, supported the main purpose of the study and clearly demonstrated the need for such innovative systems in urban roads.

Anahtar Kelimeler

Traffic density intelligent transportation intelligent systems lane density detection lane utilization

Proje Numarası

10

Kaynakça

  • [1] Ilıcalı, M., & Saraç, S. (2019). Trafik sıkışıklığının azaltılmasında ulaşım çözümlerinin etkisi. Trafik ve Ulaşım Araştırmaları Dergisi, 2(2), 93-107.
  • [2] Vera-Gómez, J. A., Quesada-Arencibia, A., García, C. R., Suárez Moreno, R., & Guerra Zeroual, A., Harrou, F., & Sun, Y. (2019). Road traffic density estimation and congestion detection with a hybrid observer-based strategy. Sustainable Cities and Society, 46, 101411.
  • [3] TÜİK. (2023). Türkiye İstatistik Kurumu, Motorlu Kara Taşıtları İstatistikleri, https://data.tuik.gov.tr/Bulten/Index?p=Motorlu-Kara-Tasitlari-Ocak-2023-49433
  • [4] Delice, M. (2015). Trafik kazalarına etki eden sürücüyle ilgili faktörlerin çoklu regresyon analiziyle incelenmesi. Uhbab Journal, 4(11), 198-210.
  • [5] Ozan, C., Başkan, Ö., Haldenbilen, S., & Derici, E. (2010). Trafik kazalarının tehlike indeksi metodu ile analizi: Denizli örneği. Pamukkale University Journal of Engineering Sciences, 16(3), 325-333.
  • [6] Civelekoğlu, G., & Bıyık, Y. (2018). Ulaşım sektöründen kaynaklı karbon ayak izi değişiminin incelenmesi. Bilge International Journal of Science and Technology Research, 2(2), 157-166.
  • [7] Cao, Z., Jiang, S., Zhang, J., & Guo, H. (2016). A unified framework for vehicle rerouting and traffic light control to reduce traffic congestion. IEEE Transactions on Intelligent Transportation Systems, 18(7), 1958-1973.
  • [8] Ikiriwatte, A. K., Perera, D. D. R., Samarakoon, S. M. M. C., Dissanayake, D. M. W. C. B., & Rupasignhe, P. L. (2019). Traffic density estimation and traffic control using convolutional neural network. In 2019 IEEE International Conference on Advancements in Computing (ICAC), pp. 323-328.
  • [9] Basavaraju, A., Doddigarla, S., Naidu, N., & Malgatti, S. (2014). Vehicle density sensor system to manage traffic. IJRET: International Journal of Research in Engineering and Technology, 2319-1163.
  • [10] Jagadeesh, Y. M., Suba, G. M., Karthik, S., & Yokesh, K. (2015). Smart autonomous traffic light switching by traffic density measurement through sensors. In 2015 IEEE International Conference on Computers, Communications, and Systems (ICCCS), pp. 123-126.
  • [11] Zhang, Y., & Ioannou, P. A. (2016). Combined variable speed limit and lane change control for highway traffic. IEEE Transactions on Intelligent Transportation Systems, 18(7), 1812-1823.
  • [12] Li, Y., & Liu, Q. (2020). Intersection management for autonomous vehicles with vehicle-to-infrastructure communication. PLoS one, 15(7), e0235644.
  • [13] Arifin, A. S., & Zulkifli, F. Y. (2021). Recent development of smart traffic lights. IAES International Journal of Artificial Intelligence, 10(1), 224.
  • [14] Madrigal Arteaga, V. M., Pérez Cruz, J. R., Hurtado-Beltrán, A., & Trumpold, J. (2022). Efficient Intersection Management Based on an Adaptive Fuzzy-Logic Traffic Signal. Applied Sciences, 12(12), 6024.
  • [15] Knorr, F., Baselt, D., Schreckenberg, M., & Mauve, M. (2012). Reducing traffic jams via VANETs. IEEE Transactions on Vehicular Technology, 61(8), 3490-3498.
  • [16] Kanungo, A., Sharma, A., & Singla, C. (2014). Smart traffic lights switching and traffic density calculation using video processing. In 2014 IEEE Recent Advances in Engineering and Computational Sciences (RAECS), pp. 1-6.
  • [17] Kavya, G., & Saranya, B. (2015). Density based intelligent traffic signal system using PIC microcontroller. International Journal of Research in Applied Science & Engineering Technology (IJRASET), 3(1), 205-209.
  • [18] Ghazal, B., ElKhatib, K., Chahine, K., & Kherfan, M. (2016). Smart traffic light control system. In 2016 IEEE Third International Conference On Electrical, Electronics, Computer Engineering and Their Applications (EECEA), pp. 140-145.
  • [19] Bauza, R., & Gozálvez, J. (2013). Traffic congestion detection in large-scale scenarios using vehicle-to-vehicle communications. Journal of Network and Computer Applications, 36(5), 1295-1307.
  • [20] Soomro, S., Miraz, M. H., Prasanth, A., & Abdullah, M. (2018). Artificial intelligence enabled IoT: traffic congestion reduction in smart cities. In IET Smart Cities Symposium 2018 (SCS '18), 22-23 April 2018, pp. 81-86.
  • [21] Hu, H., Gao, Z., Sheng, Y., Zhang, C., & Zheng, R. (2019). Traffic density recognition based on image global texture feature. International Journal of Intelligent Transportation Systems Research, 17, 171-180.
  • [22] Frank, A., Al Aamri, Y. S. K., & Zayegh, A. (2019). IoT based smart traffic density control using image processing. In 2019 IEEE 4th MEC International Conference on Big Data and Smart City (ICBDSC), pp. 1-4.
  • [23] Biswas, D., Su, H., Wang, C., Stevanovic, A., & Wang, W. (2019). An automatic traffic density estimation using Single Shot Detection (SSD) and MobileNet-SSD. Physics and Chemistry of the Earth, Parts A/B/C, 110, 176-184.
  • [24] Altay, A. B. & Demirhan, A. (2023). Boş park yerlerinin tespiti ve kullanıcıya mobil uygulama ile yol tarifi verilmesi. Gazi University Journal of Science Part C: Design and Technology, 11(1), 68-80.
  • [25] Alisoltani, N., Leclercq, L., & Zargayouna, M. (2021). Can dynamic ride-sharing reduce traffic congestion? Transportation Research Part B: Methodological, 145, 212-246.
  • [26] Budiarto, J., Sulistyo, S., Mustika, I. W., & Infantono, A. (2014). Road density prediction: Updated methods of turning probabilities and highway capacities manual for achieving the best route. In 2014 IEEE International Conference on Electrical Engineering and Computer Science (ICEECS), pp. 168-173.
  • [27] De Souza, A. M., Yokoyama, R. S., Botega, L. C., Meneguette, R. I., & Villas, L. A. (2015). Scorpion: A solution using cooperative rerouting to prevent congestion and improve traffic condition. In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 497-503.
  • [28] Barrachina, J., Garrido, P., Fogue, M., Martinez, F. J., Cano, J. C., Calafate, C. T., & Manzoni, P. (2015). A V2I-based real-time traffic density estimation system in urban scenarios. Wireless Personal Communications, 83, 259-280.
  • [29] Khan, U., Basaras, P., Schmidt-Thieme, L., Nanopoulos, A., & Katsaros, D. (2014). Analyzing cooperative lane change models for connected vehicles. In 2014 IEEE International Conference on Connected Vehicles and Expo (ICCVE), pp. 565-570.
  • [30] Roncoli, C., Bekiaris-Liberis, N., & Papageorgiou, M. (2017). Lane-changing feedback control for efficient lane assignment at motorway bottlenecks. Transportation Research Record, 2625(1), 20-31.
  • [31] Ramezani, M., & Ye, E. (2019). Lane density optimisation of automated vehicles for highway congestion control. Transportmetrica B: Transport Dynamics, 7(1), 1096-1116.
  • [32] Toledo, T., & Zohar, D. (2007). Modeling duration of lane changes. Transportation Research Record, 1999(1), 71-78.
  • [33] Meneguette, R. I., Filho, G. P., Guidoni, D. L., Pessin, G., Villas, L. A., & Ueyama, J. (2016). Increasing intelligence in inter-vehicle communications to reduce traffic congestions: Experiments in urban and highway environments. PLoS one, 11(8), e0159110.
  • [34] Kurttila, M., Pesonen, M., Kangas, J., & Kajanus, M. (2000). Utilizing the analytic hierarchy process (AHP) in SWOT analysis—a hybrid method and its application to a forest-certification case. Forest policy and economics, 1(1), 41-52.
  • [35] Bourhim, E. M., & Labti, O. (2022, December). Application of Combined SWOT and AHP Analysis to Assess the Virtual Reality and Select the Priority Factors for Education. In International Conference on Intelligent Systems Design and Applications (pp. 512-521). Cham: Springer Nature Switzerland.
  • [36] Zhang, H., Mehrotra, D. V., & Shen, J. (2023). AWOT and CWOT for genotype and genotype-by-treatment interaction joint analysis in pharmacogenetics GWAS. Bioinformatics, 39(1), btac834.
  • [37] Bottero, M., D’Alpaos, C., & Marello, A. (2020). An application of the a’WOT analysis for the management of cultural heritage assets: the case of the historical farmhouses in the aglie castle (Turin). Sustainability, 12(3), 1071.
  • [38] Lee, S., Kim, D., Park, S., & Lee, W. (2021). A study on the strategic decision making used in the revitalization of fishing village tourism: using A’WOT analysis. Sustainability, 13(13), 7472.
  • [39] Bourhim, E. M., & Cherkaoui, A. (2020). Exploring the potential of virtual reality in fire training research using A’WOT hybrid method. In Intelligent Systems, Technologies and Applications: Proceedings of Fifth ISTA 2019, India (pp. 157-167). Springer Singapore.
  • [40] Jozi, S. A., Dehghani, M., & Zarei, M. (2013). Rural waste management strategic plan by A'WOT Method (Case study: Minab). Journal of Environmental Studies, 38(4), 93-108.
  • [41] Bottero, M., D’Alpaos, C., & Marello, A. (2020). An application of the a’WOT analysis for the management of cultural heritage assets: the case of the historical farmhouses in the aglie castle (Turin). Sustainability, 12(3), 1071.
  • [42] Öztaş Karlı, R. G., & Karlı, H. (2022, October). Assessment of the Role of Micromobility in ITS by A’WOT Analysis. In The Proceedings of the International Conference on Smart City Applications (pp. 226-236). Cham: Springer International Publishing.
  • [43] Aydın, M. M. (2021). Method for modeling lane-based driving discipline of drivers on divided multilane urban roads. Journal of Transportation Engineering, Part A: Systems, 147(4), 04021011.
  • [44] Ulaştırma ve Altyapı Bakanlığı (UAB) (2023). Ağırlık ve boyut kontrolü, https://www.uab.gov.tr/uploads/pages/kutuphane/a3f43dbbb7bb488.pdf
  • [45] Lee, S., Kim, D., Park, S., & Lee, W. (2021). A study on the strategic decision making used in the revitalization of fishing village tourism: using A’WOT analysis. Sustainability, 13(13), 7472.
  • [46] Saaty, T. L. (1977). A scaling method for priorities in hierarchical structures. Journal Of Mathematical Psychology, 15(3), 234-281.
  • [47] Aydın, M. M., Yıldırım, M. S., Karpuz, O., & Ghasemlou, K. (2014). Modeling of driver lane choice behavior with artificial neural networks (ANN) and linear regression (LR) analysis on deformed roads. Computer Science, 4(1), 47-57.
  • [48] Aydın, M. M., & Topal, A. (2018). Effects of pavement surface deformations on lane-changing behaviours. Proceedings of the Institution of Civil Engineers–Transport, 171(3), 136-145.
Toplam 48 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Tasarım ve Teknoloji
Yazarlar

Metin Mutlu Aydın 0000-0001-9470-716X

Başak Bıyık 0000-0002-0648-7409

Proje Numarası 10
Erken Görünüm Tarihi 30 Kasım 2023
Yayımlanma Tarihi 28 Aralık 2023
Gönderilme Tarihi 29 Mart 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 11 Sayı: 4

Kaynak Göster

APA Aydın, M. M., & Bıyık, B. (2023). Şerit Yoğunluk Tespit ve Bilgilendirme Sistemlerinin Tasarımdan İşletmeye Genel Altyapısının Belirlenmesi: Örnek bir Sistem Tasarımı. Gazi University Journal of Science Part C: Design and Technology, 11(4), 1092-1107. https://doi.org/10.29109/gujsc.1273234
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