Research Article
BibTex RIS Cite

Filo yönetimi için büyük veri temelli yeni sürücü davranış modelleri

Year 2021, Volume: 36 Issue: 1, 543 - 558, 01.12.2020

Ramazan Terzi , Şeref Sağıroğlu , Özge Çöçü Rabia Arkan Merve Tosun Yusuf Tulgar

https://doi.org/10.17341/gazimmfd.598581

Abstract

Bu çalışmada, filo yönetim
sistemlerinin eksikleri hem büyük veri hem de sürücü davranışı bakış açılarıyla
incelenmiş, gerçek veriler üzerinde sürücü/sürüş davranışları analiz edilmiş
bunun sonucunda veri analizlerini gerçekleştirmek için büyük veri tabanlı yeni
modeller önerilmiştir. Büyük veri üzerinde 6 farklı senaryo ile filodaki
sürücülerin davranış farklılıkları, çeşitli lokasyonlardaki davranışları ve
spesifik noktalardaki davranışları büyük veri temelli yaklaşımlar ile tespit
edilmiş yeni modeller geliştirilmiştir. Elde edilen sonuçlardan,  özellikle %50 üzerinde hız limiti aşan
sürücüler arasında belirli sürücülerin diğer sürücülere göre %30 oranında bu
ihlallerde payı olduğu, ortalama hızları aynı olsa bile hız ihlalleri bakımından
sürücüler arasında 6 kat farklılık olabileceği, benzer şekilde hız ihlal
sayıları aynı olsa bile ihlal sürelerinde 2 kata yakın fark olabileceği, mevsimlere
göre hız ihlal sayısının en fazla yaz mevsiminde fakat ihlal süresinin en fazla
sonbaharda olduğu, Ankara ili içinde hız limitlerini aşmaya olanak sağlayan
yolların ilçe bazında %23,6 oranıyla Yenimahalle, mahalle bazında %4,62 oranı
ile Saray, şehirlerarası yol bazında %6,85 oranı ile Eskişehir yolunun, şehir
içi yollarda ise %2,74 oranı ise Anadolu Bulvarı olduğu, son olarak ise radar
noktaların yaklaşık 1 kilometre öncesi ve sonrası hız ihlal sayılarına göre 3
farklı radar noktası incelemesinde %300’e yakın oranlarda farklılıkların
oluştuğu tespit edilmiştir. Sonuç olarak büyük veri analitiği kullanarak
filoların sürücü/sürüş davranış kapsamında daha etkili kullanılabileceği ve
daha kolay yönetilebileceği, gerek maliyet gerekse iş gücü kaybının önlenmesi
için bu modellerden faydalanılabileceği, Ankara ili için yapılan bu analizlerin
diğer iller için de kullanılabileceği, radar noktalarında yapılan analizler
gibi farklı değerler üretilebileceği değerlendirilmektedir.

Keywords

Filo yönetimi sürücü/sürüş davranışı

Supporting Institution

Gazi Üniversitesi BAP

Project Number

06/2015-04

Thanks

Bu makale çalışmasında kullanılan verilerin anonimleştirerek kullanılmasına izin veren 4N Mobile/Netdatasoft şirketine yazarlar teşekkür eder. Ayrıca, yazarlar, 06/2015-04 numaralı BAP projesi kapsamında kurulan ve bu çalışmanın gerçekleşmesi için destek veren Gazi BIDISEC’e ve Gazi Üniversitesi BAP Birimine desteklerinden dolayı teşekkür ederler. Bu makale kapsamında kullanılan veriler ve yapılan çalışmalar Gazi Üniversitesi Etik Kurul Komisyonundan 2017-320 sayılı kararı ile izin alınarak yapılmıştır.

References

  • Xu G., Wang J., Huang G. Q., and Chen C.-H., "Data-Driven Resilient Fleet Management for Cloud Asset-enabled Urban Flood Control," IEEE Trans. Intell. Transp. Syst., vol. 19, no. 6, pp. 1827-1838, June 2018.
  • Gogoulos F. et al., "An Intelligent Trucking Operations Management System," IFAC Proceedings Volumes, vol. 46, no. 25, pp. 49-54, 2013.
  • Chauhan B., Jain A., Chaturvedi T., and Saini S., "User Interactive and Assistive Fleet Management and Eco-Driving System," in 2015 IEEE Region 10 Symposium, Ahmedabad, India, 2015: IEEE, pp. 41-44.
  • Zhang G. et al., "A integrated vehicle health management framework for aircraft—A preliminary report," in 2015 IEEE Conference on Prognostics and Health Management (PHM), Austin, TX, USA, 2015: IEEE, pp. 1-8.
  • Yokoyama D. and Toyoda M., "A large scale examination of vehicle recorder data to understand relationship between drivers' behaviors and their past driving histories," in 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA, 2015: IEEE, pp. 2877-2879.
  • Tilocca P. et al., "Managing data and rethinking applications in an innovative mid-sized bus fleet," Transp. Res. Procedia, vol. 25, pp. 1899-1919, 2017.
  • Stancel I. N. and Surugiu M. C., "Fleet Management System for Truck Platoons - Generating an Optimum Route in Terms of Fuel Consumption," Procedia Eng., vol. 181, pp. 861-867, 2017.
  • Fabbri G., Calenne F., London M., Boccaletti C., Cardoso A. M., and Mascioli F. F., "Development of an on-board unit for the monitoring and management of an electric fleet," in 2012 20th International Conference on Electrical Machines, Marseille, France, 2012: IEEE, pp. 2404-2410.
  • Johanson M., Belenki S., Jalminger J., Fant M., and Gjertz M., "Big automotive data: Leveraging large volumes of data for knowledge-driven product development," in 2014 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 2014: IEEE, pp. 736-741.
  • Gowda V. C. and Gopalakrishna K., "Real time vehicle fleet management and security system," in 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), Trivandrum, India, 2015: IEEE, pp. 417-421.
  • Bélanger V., Kergosien Y., Ruiz A., and Soriano P., "An empirical comparison of relocation strategies in real-time ambulance fleet management," Comput. Ind. Eng., vol. 94, pp. 216-229, 2016.
  • Pérez J., Maldonado S., and López-Ospina H., "A fleet management model for the Santiago Fire Department," Fire Saf. J., vol. 82, pp. 1-11, 2016.
  • Hewicker C., Hogan M., and Mogren A., "Power perspectives 2030:On the road to a decarbonised power sector," European Climate Foundation, 2011. http://www.roadmap2050.eu/attachments/files/ PowerPerspectives2030_FullReport.pdf.
  • Ostermann J. and Koetter F., "Energy-management-as-a-service: Mobility aware energy management for a shared electric vehicle fleet," in 2016 5th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), Rome, Italy, 2016: IEEE, pp. 1-11.
  • Klauenberg J., Rudolph C., and Zajicek J., "Potential Users of Electric Mobility in Commercial Transport – Identification and Recommendations," Transp. Res. Procedia, vol. 16, pp. 202-216, 2016.
  • Awasthi A., Chauhan S. S., Parent M., and Proth J.-M., "Centralized fleet management system for cybernetic transportation," Expert Syst. Appl., vol. 38, no. 4, pp. 3710-3717, 2011.
  • Andre M., Carteret M., Pasquier A., and Liu Y., "Methodology for characterizing vehicle fleet composition and its territorial variability, needed for assessing Low Emission Zones," Transp. Res. Procedia, vol. 25, pp. 3286-3298, 2017.
  • Lee S. S., Park S.-i., and Seo J., "Utilization analysis methodology for fleet telematics of heavy earthwork equipment," Autom. Constr., vol. 92, pp. 59-67, 2018.
  • Entin M. R., Heirichs C. M., Peine A., Warych E. R., Timmerman J., and Cresmore S., "Data Analytics for Snow Plow Trucks Fleet," in 2018 19th IEEE International Conference on Mobile Data Management (MDM), Aalborg, Denmark, 2018: IEEE, pp. 294-295.
  • Driving-tests. Agressive Driving Statistics in The Ultimate List of Driving Statistics for 2019. https://driving-tests.org/driving-statistics/. Erişim tarihi Mart 6, 2019.
  • Wijayasekara D., Manic M., and Gertman D., "Data driven fuel efficient driving behavior feedback for fleet vehicles," in 2015 8th International Conference on Human System Interaction (HSI), Warsaw, Poland, 2015: IEEE, pp. 75-81.
  • Van Nes N., Bärgman J., Christoph M., and Van Schagen I., "The potential of naturalistic driving for in-depth understanding of driver behavior: UDRIVE results and beyond," Saf. Sci., 2019.
  • Díaz-Ramirez J. et al., "Eco-driving key factors that influence fuel consumption in heavy-truck fleets: A Colombian case," Transportation Research Part D: Transport and Environment, vol. 56, pp. 258-270, 2017.
  • Toledo G. and Shiftan Y., "Can feedback from in-vehicle data recorders improve driver behavior and reduce fuel consumption ?," Transportation Research Part A: Policy and Practice, vol. 94, pp. 194-204, 2016.
  • Terzi R., Sagiroglu S., and Demirezen M. U., "Big Data Perspective for Driver/Driving Behavior," IEEE Intell. Transp. Syst. Mag., pp. 1-1, 10.1109/MITS.2018.2879220, 2018.
  • Bassoo V., Hurbungs V., Ramnarain-Seetohul V., Fowdur T. P., and Beeharry Y., "A framework for safer driving in Mauritius," Future Comput. Inf. J., vol. 2, no. 2, pp. 125-132, 2017.
  • Rutty M., Matthews L., Andrey J., and Matto T. D., "Eco-driver training within the City of Calgary’s municipal fleet: Monitoring the impact," Transportation Research Part D: Transport and Environment, vol. 24, pp. 44-51, 2013.
  • Yokoyama D. and Toyoda M., "Do Drivers' Behaviors Reflect Their Past Driving Histories? - Large Scale Examination of Vehicle Recorder Data," in 2016 IEEE International Congress on Big Data (BigData Congress), San Francisco, CA, USA, 2016: IEEE, pp. 361-368.
  • Sagiroglu S. and Sinanc D., "Big data: A review," in 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA, 2013: IEEE, pp. 42-47.
  • Orlovska J., Wickman C., and Söderberg R., "Big Data Usage Can Be a Solution for User Behavior Evaluation: An Automotive Industry Example," in Procedia CIRP 2018 51st CIRP Conference on Manufacturing Systems, 2018, vol. 72, pp. 117-122.
  • Luo X. et al., "Analysis on spatial-temporal features of taxis' emissions from big data informed travel patterns: a case of Shanghai, China," J. Cleaner Prod., vol. 142, pp. 926-935, 2017.
  • Chen J., Wu Y., Huang H., Wu B., and Hou G., "Driving-Data-Driven Platform of Driving Behavior Spectrum for Vehicle Networks," presented at the 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, United Kingdom, United Kingdom, 2018.
  • Paffumi E., De Gennaro M., and Martini G., "European-wide study on big data for supporting road transport policy," Case Studies on Transport Policy, vol. 6, no. 4, pp. 785-802, 2018.
  • Zhang M., Wo T., Xie T., Lin X., and Liu Y., "CarStream: an industrial system of big data processing for internet-of-vehicles," Proceedings of the VLDB Endowment, vol. 10, no. 12, pp. 1766-1777, 2017.
  • Yin J.-L. and Chen B.-H., "An Advanced Driver Risk Measurement System for Usage-Based Insurance on Big Driving Data," IEEE Trans. Intell. Veh., vol. 3, no. 4, pp. 585-594, 2018.
  • Birek L., Grzywaczewski A., Iqbal R., Doctor F., and Chang V., "A novel Big Data analytics and intelligent technique to predict driver's intent," Comput. Ind., vol. 99, pp. 226-240, 2018.
  • Verizon Connect. Fleet management solutions. https://www.verizonconnect.com/solutions/. Erişim tarihi Ocak 28, 2019.
  • 4N Araç Takip Sistemi. Araç Takip Sistemi. http://4n.com.tr/. Erişim tarihi Ocak 20, 2019.
  • Asis Elektronik. Filo Yönetimi. https://www.asiselektronik.com.tr/anasayfa. Erişim tarihi Ocak 29, 2019.
  • Scania Türkiye. Ürünler ve Hizmetler. http://scaniasuruculigi.com/StaticPage/sdsFilo. Erişim tarihi Ocak 29, 2019.
  • InfoMobil. Kullanımı En Kolay Mobil Araç Takip Uygulaması. http://www.infomobil.com.tr/#services. Erişim tarihi Ocak 29, 2019.
  • Petrol Ofisi. AutoMatic Plus ile Araç Takibi. http://www.automaticplus.com.tr/automatic-plus-nedir.html. Erişim tarihi Ocak 29, 2019.
  • Lenoi. Araç Takip Sistemi. http://lenoi.com.tr/arac-takip-sistemi/. Erişim tarihi Ocak 29, 2019.
  • Shell. Shell Filoplatform. https://www.shell.com.tr/kurumsal-musteriler/shell-fuel-card/shell-film-platform.html?cid=ppc:google:CommercialFleetSearchTurkey:april2017. Erişim tarihi Ocak 29, 2019.
  • Vodafone. Araç Takip Sistemi Nedir? https://www.vodafone.com.tr/VodafoneBusiness/Arac-Takip-Kampanyasi.php. Erişim tarihi Ocak 29, 2019.
  • Arvento. Arvento Araç Takip ve Filo Yönetim Sistemi. http://a19.arvento.com/arvento_help/index.html. Erişim tarihi Ocak 29, 2019.
  • Turkcell. Akıllı Araç (Araç Takip). https://www.turkcell.com.tr/kurumsal/kurumsal-cozumler/akilli-arac. Erişim tarihi Ocak 28, 2019.
  • Opet. Otokonum. https://www.opet.com.tr/Otokonum. Erişim tarihi Ocak 28, 2019.
  • Geotab. myGeotab. https://www.geotab.com/fleet-management-software/. Erişim tarihi Ocak 28, 2019.
  • Ctrack. Ctrack Vehicle Tracking Systems. https://www.ctrack.com/. Erişim tarihi Ocak 28, 2019.
  • Chevin Fleet Solutions. Fleet Management Software. https://www.chevinfleet.com/gb/asset-fleet-management-software-system/. Erişim tarihi Ocak 27, 2019.
  • Gps Trackit. Fleet Management. https://gpstrackit.com/solutions/fleet-management/. Erişim tarihi Ocak 26, 2019.
  • Samsara. Samsara for Fleets. https://www.samsara.com/fleet. Erişim tarihi Ocak 28, 2019.
  • Silent Passenger. Manage a More Efficient, Productive, and Profitable Fleet. https://www.silentpassenger.com/. Erişim tarihi Ocak 27, 2019.
  • Fleetio. Fleetio Manage. https://www.fleetio.com/manage. Erişim tarihi Ocak 29, 2019.
  • Gazi Bidisec. http://bigdatacenter.gazi.edu.tr/. Erişim tarihi Ocak 20, 2019.

Year 2021, Volume: 36 Issue: 1, 543 - 558, 01.12.2020

Ramazan Terzi , Şeref Sağıroğlu , Özge Çöçü Rabia Arkan Merve Tosun Yusuf Tulgar

https://doi.org/10.17341/gazimmfd.598581

Abstract

Project Number

06/2015-04

References

  • Xu G., Wang J., Huang G. Q., and Chen C.-H., "Data-Driven Resilient Fleet Management for Cloud Asset-enabled Urban Flood Control," IEEE Trans. Intell. Transp. Syst., vol. 19, no. 6, pp. 1827-1838, June 2018.
  • Gogoulos F. et al., "An Intelligent Trucking Operations Management System," IFAC Proceedings Volumes, vol. 46, no. 25, pp. 49-54, 2013.
  • Chauhan B., Jain A., Chaturvedi T., and Saini S., "User Interactive and Assistive Fleet Management and Eco-Driving System," in 2015 IEEE Region 10 Symposium, Ahmedabad, India, 2015: IEEE, pp. 41-44.
  • Zhang G. et al., "A integrated vehicle health management framework for aircraft—A preliminary report," in 2015 IEEE Conference on Prognostics and Health Management (PHM), Austin, TX, USA, 2015: IEEE, pp. 1-8.
  • Yokoyama D. and Toyoda M., "A large scale examination of vehicle recorder data to understand relationship between drivers' behaviors and their past driving histories," in 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, USA, 2015: IEEE, pp. 2877-2879.
  • Tilocca P. et al., "Managing data and rethinking applications in an innovative mid-sized bus fleet," Transp. Res. Procedia, vol. 25, pp. 1899-1919, 2017.
  • Stancel I. N. and Surugiu M. C., "Fleet Management System for Truck Platoons - Generating an Optimum Route in Terms of Fuel Consumption," Procedia Eng., vol. 181, pp. 861-867, 2017.
  • Fabbri G., Calenne F., London M., Boccaletti C., Cardoso A. M., and Mascioli F. F., "Development of an on-board unit for the monitoring and management of an electric fleet," in 2012 20th International Conference on Electrical Machines, Marseille, France, 2012: IEEE, pp. 2404-2410.
  • Johanson M., Belenki S., Jalminger J., Fant M., and Gjertz M., "Big automotive data: Leveraging large volumes of data for knowledge-driven product development," in 2014 IEEE International Conference on Big Data (Big Data), Washington, DC, USA, 2014: IEEE, pp. 736-741.
  • Gowda V. C. and Gopalakrishna K., "Real time vehicle fleet management and security system," in 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS), Trivandrum, India, 2015: IEEE, pp. 417-421.
  • Bélanger V., Kergosien Y., Ruiz A., and Soriano P., "An empirical comparison of relocation strategies in real-time ambulance fleet management," Comput. Ind. Eng., vol. 94, pp. 216-229, 2016.
  • Pérez J., Maldonado S., and López-Ospina H., "A fleet management model for the Santiago Fire Department," Fire Saf. J., vol. 82, pp. 1-11, 2016.
  • Hewicker C., Hogan M., and Mogren A., "Power perspectives 2030:On the road to a decarbonised power sector," European Climate Foundation, 2011. http://www.roadmap2050.eu/attachments/files/ PowerPerspectives2030_FullReport.pdf.
  • Ostermann J. and Koetter F., "Energy-management-as-a-service: Mobility aware energy management for a shared electric vehicle fleet," in 2016 5th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), Rome, Italy, 2016: IEEE, pp. 1-11.
  • Klauenberg J., Rudolph C., and Zajicek J., "Potential Users of Electric Mobility in Commercial Transport – Identification and Recommendations," Transp. Res. Procedia, vol. 16, pp. 202-216, 2016.
  • Awasthi A., Chauhan S. S., Parent M., and Proth J.-M., "Centralized fleet management system for cybernetic transportation," Expert Syst. Appl., vol. 38, no. 4, pp. 3710-3717, 2011.
  • Andre M., Carteret M., Pasquier A., and Liu Y., "Methodology for characterizing vehicle fleet composition and its territorial variability, needed for assessing Low Emission Zones," Transp. Res. Procedia, vol. 25, pp. 3286-3298, 2017.
  • Lee S. S., Park S.-i., and Seo J., "Utilization analysis methodology for fleet telematics of heavy earthwork equipment," Autom. Constr., vol. 92, pp. 59-67, 2018.
  • Entin M. R., Heirichs C. M., Peine A., Warych E. R., Timmerman J., and Cresmore S., "Data Analytics for Snow Plow Trucks Fleet," in 2018 19th IEEE International Conference on Mobile Data Management (MDM), Aalborg, Denmark, 2018: IEEE, pp. 294-295.
  • Driving-tests. Agressive Driving Statistics in The Ultimate List of Driving Statistics for 2019. https://driving-tests.org/driving-statistics/. Erişim tarihi Mart 6, 2019.
  • Wijayasekara D., Manic M., and Gertman D., "Data driven fuel efficient driving behavior feedback for fleet vehicles," in 2015 8th International Conference on Human System Interaction (HSI), Warsaw, Poland, 2015: IEEE, pp. 75-81.
  • Van Nes N., Bärgman J., Christoph M., and Van Schagen I., "The potential of naturalistic driving for in-depth understanding of driver behavior: UDRIVE results and beyond," Saf. Sci., 2019.
  • Díaz-Ramirez J. et al., "Eco-driving key factors that influence fuel consumption in heavy-truck fleets: A Colombian case," Transportation Research Part D: Transport and Environment, vol. 56, pp. 258-270, 2017.
  • Toledo G. and Shiftan Y., "Can feedback from in-vehicle data recorders improve driver behavior and reduce fuel consumption ?," Transportation Research Part A: Policy and Practice, vol. 94, pp. 194-204, 2016.
  • Terzi R., Sagiroglu S., and Demirezen M. U., "Big Data Perspective for Driver/Driving Behavior," IEEE Intell. Transp. Syst. Mag., pp. 1-1, 10.1109/MITS.2018.2879220, 2018.
  • Bassoo V., Hurbungs V., Ramnarain-Seetohul V., Fowdur T. P., and Beeharry Y., "A framework for safer driving in Mauritius," Future Comput. Inf. J., vol. 2, no. 2, pp. 125-132, 2017.
  • Rutty M., Matthews L., Andrey J., and Matto T. D., "Eco-driver training within the City of Calgary’s municipal fleet: Monitoring the impact," Transportation Research Part D: Transport and Environment, vol. 24, pp. 44-51, 2013.
  • Yokoyama D. and Toyoda M., "Do Drivers' Behaviors Reflect Their Past Driving Histories? - Large Scale Examination of Vehicle Recorder Data," in 2016 IEEE International Congress on Big Data (BigData Congress), San Francisco, CA, USA, 2016: IEEE, pp. 361-368.
  • Sagiroglu S. and Sinanc D., "Big data: A review," in 2013 International Conference on Collaboration Technologies and Systems (CTS), San Diego, CA, USA, 2013: IEEE, pp. 42-47.
  • Orlovska J., Wickman C., and Söderberg R., "Big Data Usage Can Be a Solution for User Behavior Evaluation: An Automotive Industry Example," in Procedia CIRP 2018 51st CIRP Conference on Manufacturing Systems, 2018, vol. 72, pp. 117-122.
  • Luo X. et al., "Analysis on spatial-temporal features of taxis' emissions from big data informed travel patterns: a case of Shanghai, China," J. Cleaner Prod., vol. 142, pp. 926-935, 2017.
  • Chen J., Wu Y., Huang H., Wu B., and Hou G., "Driving-Data-Driven Platform of Driving Behavior Spectrum for Vehicle Networks," presented at the 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Exeter, United Kingdom, United Kingdom, 2018.
  • Paffumi E., De Gennaro M., and Martini G., "European-wide study on big data for supporting road transport policy," Case Studies on Transport Policy, vol. 6, no. 4, pp. 785-802, 2018.
  • Zhang M., Wo T., Xie T., Lin X., and Liu Y., "CarStream: an industrial system of big data processing for internet-of-vehicles," Proceedings of the VLDB Endowment, vol. 10, no. 12, pp. 1766-1777, 2017.
  • Yin J.-L. and Chen B.-H., "An Advanced Driver Risk Measurement System for Usage-Based Insurance on Big Driving Data," IEEE Trans. Intell. Veh., vol. 3, no. 4, pp. 585-594, 2018.
  • Birek L., Grzywaczewski A., Iqbal R., Doctor F., and Chang V., "A novel Big Data analytics and intelligent technique to predict driver's intent," Comput. Ind., vol. 99, pp. 226-240, 2018.
  • Verizon Connect. Fleet management solutions. https://www.verizonconnect.com/solutions/. Erişim tarihi Ocak 28, 2019.
  • 4N Araç Takip Sistemi. Araç Takip Sistemi. http://4n.com.tr/. Erişim tarihi Ocak 20, 2019.
  • Asis Elektronik. Filo Yönetimi. https://www.asiselektronik.com.tr/anasayfa. Erişim tarihi Ocak 29, 2019.
  • Scania Türkiye. Ürünler ve Hizmetler. http://scaniasuruculigi.com/StaticPage/sdsFilo. Erişim tarihi Ocak 29, 2019.
  • InfoMobil. Kullanımı En Kolay Mobil Araç Takip Uygulaması. http://www.infomobil.com.tr/#services. Erişim tarihi Ocak 29, 2019.
  • Petrol Ofisi. AutoMatic Plus ile Araç Takibi. http://www.automaticplus.com.tr/automatic-plus-nedir.html. Erişim tarihi Ocak 29, 2019.
  • Lenoi. Araç Takip Sistemi. http://lenoi.com.tr/arac-takip-sistemi/. Erişim tarihi Ocak 29, 2019.
  • Shell. Shell Filoplatform. https://www.shell.com.tr/kurumsal-musteriler/shell-fuel-card/shell-film-platform.html?cid=ppc:google:CommercialFleetSearchTurkey:april2017. Erişim tarihi Ocak 29, 2019.
  • Vodafone. Araç Takip Sistemi Nedir? https://www.vodafone.com.tr/VodafoneBusiness/Arac-Takip-Kampanyasi.php. Erişim tarihi Ocak 29, 2019.
  • Arvento. Arvento Araç Takip ve Filo Yönetim Sistemi. http://a19.arvento.com/arvento_help/index.html. Erişim tarihi Ocak 29, 2019.
  • Turkcell. Akıllı Araç (Araç Takip). https://www.turkcell.com.tr/kurumsal/kurumsal-cozumler/akilli-arac. Erişim tarihi Ocak 28, 2019.
  • Opet. Otokonum. https://www.opet.com.tr/Otokonum. Erişim tarihi Ocak 28, 2019.
  • Geotab. myGeotab. https://www.geotab.com/fleet-management-software/. Erişim tarihi Ocak 28, 2019.
  • Ctrack. Ctrack Vehicle Tracking Systems. https://www.ctrack.com/. Erişim tarihi Ocak 28, 2019.
  • Chevin Fleet Solutions. Fleet Management Software. https://www.chevinfleet.com/gb/asset-fleet-management-software-system/. Erişim tarihi Ocak 27, 2019.
  • Gps Trackit. Fleet Management. https://gpstrackit.com/solutions/fleet-management/. Erişim tarihi Ocak 26, 2019.
  • Samsara. Samsara for Fleets. https://www.samsara.com/fleet. Erişim tarihi Ocak 28, 2019.
  • Silent Passenger. Manage a More Efficient, Productive, and Profitable Fleet. https://www.silentpassenger.com/. Erişim tarihi Ocak 27, 2019.
  • Fleetio. Fleetio Manage. https://www.fleetio.com/manage. Erişim tarihi Ocak 29, 2019.
  • Gazi Bidisec. http://bigdatacenter.gazi.edu.tr/. Erişim tarihi Ocak 20, 2019.
There are 56 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Makaleler
Authors

Ramazan Terzi 0000-0003-2345-8666

Şeref Sağıroğlu 0000-0003-0805-5818

Özge Çöçü This is me 0000-0003-2763-708X

Rabia Arkan This is me 0000-0003-3837-8052

Merve Tosun This is me 0000-0003-4229-509X

Yusuf Tulgar This is me 0000-0003-3345-7149

Project Number 06/2015-04
Publication Date December 1, 2020
Submission Date July 30, 2019
Acceptance Date September 27, 2020
Published in Issue Year 2021 Volume: 36 Issue: 1

Cite

APA Terzi, R., Sağıroğlu, Ş., Çöçü, Ö., Arkan, R., et al. (2020). Filo yönetimi için büyük veri temelli yeni sürücü davranış modelleri. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 36(1), 543-558. https://doi.org/10.17341/gazimmfd.598581
AMA Terzi R, Sağıroğlu Ş, Çöçü Ö, Arkan R, Tosun M, Tulgar Y. Filo yönetimi için büyük veri temelli yeni sürücü davranış modelleri. GUMMFD. December 2020;36(1):543-558. doi:10.17341/gazimmfd.598581
Chicago Terzi, Ramazan, Şeref Sağıroğlu, Özge Çöçü, Rabia Arkan, Merve Tosun, and Yusuf Tulgar. “Filo yönetimi için büyük Veri Temelli Yeni sürücü davranış Modelleri”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36, no. 1 (December 2020): 543-58. https://doi.org/10.17341/gazimmfd.598581.
EndNote Terzi R, Sağıroğlu Ş, Çöçü Ö, Arkan R, Tosun M, Tulgar Y (December 1, 2020) Filo yönetimi için büyük veri temelli yeni sürücü davranış modelleri. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36 1 543–558.
IEEE R. Terzi, Ş. Sağıroğlu, Ö. Çöçü, R. Arkan, M. Tosun, and Y. Tulgar, “Filo yönetimi için büyük veri temelli yeni sürücü davranış modelleri”, GUMMFD, vol. 36, no. 1, pp. 543–558, 2020, doi: 10.17341/gazimmfd.598581.
ISNAD Terzi, Ramazan et al. “Filo yönetimi için büyük Veri Temelli Yeni sürücü davranış Modelleri”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 36/1 (December 2020), 543-558. https://doi.org/10.17341/gazimmfd.598581.
JAMA Terzi R, Sağıroğlu Ş, Çöçü Ö, Arkan R, Tosun M, Tulgar Y. Filo yönetimi için büyük veri temelli yeni sürücü davranış modelleri. GUMMFD. 2020;36:543–558.
MLA Terzi, Ramazan et al. “Filo yönetimi için büyük Veri Temelli Yeni sürücü davranış Modelleri”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 36, no. 1, 2020, pp. 543-58, doi:10.17341/gazimmfd.598581.
Vancouver Terzi R, Sağıroğlu Ş, Çöçü Ö, Arkan R, Tosun M, Tulgar Y. Filo yönetimi için büyük veri temelli yeni sürücü davranış modelleri. GUMMFD. 2020;36(1):543-58.