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Road Density Calculations with Unidimensional LiDAR Sensor for Dynamic Intersection Management

Yıl 2023, Cilt: 13 Sayı: 1, 105 - 117, 30.06.2023

Abdülkadir Çıldır Mesud Kahriman Mesut Tigdemir

Öz

The main aim of dynamic intersection management is to make instant detection of vehicles both at the intersection and approaching it. In this sense, vehicle detection sensors have been preferred for dynamic intersection management. In this article, a LiDAR sensor system that can detect the number, velocity, and class of vehicles at intersections with different densities and also the length of vehicle queues that may occur at this intersection has been studied. In this study, 96.36 % success has obtained in the detection of the velocity, and 96.35 % success has also obtained in the queue detection. This study, which includes the data taken from a unidimensional LiDAR sensor and the capabilities of a 3D LiDAR sensor, stands out in terms of price and performance.

Anahtar Kelimeler

Dynamic intersection management Unidimensional LiDAR sensor Queue length detection

Kaynakça

  • Akanbi, L., Olajubu, E.A. 2012. A fuzzy-based intelligent traffic control system for managing VIP-induced chaos at road intersections. African Journal of Computing ICT 5: 109-119.
  • Ban, X. J., Hao, P., Sun, Z. 2011. Real time queue length estimation for signalized intersections using travel times from mobile sensors. Transportation Research Part C: Emerging Technologies 19: 1133-1156.
  • Cai, Y., Zhang, W., Wang H. 2010. Measurement of vehicle queue length based on video processing in intelligent traffic signal control system. IEEE. 2010 International Conference on Measuring Technology and Mechatronics Automation, 615-618, Changsha, China.
  • Chen, J., Xu, H., Wu, J., Yue, R., Yuan, C., Wang, L. 2019. Deer crossing road detection with roadside LiDAR sensor. IEEE Access 7: 65944-65954.
  • Cheung, S. Y., Coleri, S., Dundar, B., Ganesh, S., Tan, C.-W., Varaiya P. 2005. Traffic measurement and vehicle classification with single magnetic sensor. Transportation research record 1917: 173-181.
  • Cheung, S. Y., Varaiya, P. 2006. Traffic surveillance by wireless sensor networks. California Path Program Institute Of Transportation Studies University Of California, Berkeley, Institute Of Transportation Studies University Of California: 13.
  • Chiu, S., Chand, S. 1993. Adaptive traffic signal control using fuzzy logic. IEEE. Fuzzy Systems, 1993., Second IEEE International Conference on, 1371-1376.
  • Choudhary, P. 2018. Analyzing virtual traffic light using state machine in vehicular ad hoc network. Next-generation networks, Springer. Delhi, India, 239-245.
  • Cildir, A., Kahriman, M., Tigdemir, M. 2022. The Intersection Vehicle Delay Optimization For Ideal Traffic Light Cycle Time. International Journal of 3D Printing Technologies and Digital Industry 6: 126-136.
  • Emami, A., Sarvi, M., Bagloee, S. A. 2019. A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles. Journal of Modern Transportation 27: 341-354.
  • Festag, A., Noecker, G., Strassberger, M., Lübke, A., Bochow, B., Torrent-Moreno, M., Schnaufer S., Eigner, R., Catrinescu, C., Kunisch, J. 2008. “NoW - Network on Wheels” : Project Objectives, Technology and Achievements. CiteSeerx. Proceedings of 5rd International Workshop on Intelligent Transportation (WIT), 211-216, Hamburg, Germany.
  • Goodall, N. J. 2017. Fundamental characteristics of Wi‑Fi and wireless local area network re-identification for transportation. IET Intelligent Transport Systems 11: 128-135.
  • Ibisch, A., Stümper, S., Altinger, H., Neuhausen, M., Tschentscher, M., Schlipsing, M., Salinen, J., Knoll, A. 2013. Towards autonomous driving in a parking garage: Vehicle localization and tracking using environment-embedded lidar sensors. IEEE. 2013 IEEE intelligent vehicles symposium (IV), 829-834.
  • Lee, H., Coifman, B. 2015. Using LIDAR to validate the performance of vehicle classification stations. Journal of Intelligent Transportation Systems 19: 355-369.
  • Liu, H. X., Wu, X., Ma, W., Hu, H. 2009. Real-time queue length estimation for congested signalized intersections. Transportation research part C: emerging technologies 17: 412-427.
  • Managuli, M., Deshpande, A., Ayatti, S. H. 2017. Emergent vehicle tracking system using IR sensor. IEEE. 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), 71- 74, Mysuru, India.
  • Margreiter, M. 2016. Fast and Reliable Determination of the Traffic State Using Bluetooth Detection on German Freeways. Transportation Research Procedia. World Conference on Transport Research, Shanghai, China
  • Rani, L. P. J., Kumar, M. K., Naresh, K., Vignesh, S. 2017. Dynamic traffic management system using infrared (IR) and Internet of Things (IoT). IEEE. Third International Conference on Science Technology Engineering & Management (ICONSTEM), 353-357, Chennai, India.
  • Sen, R., Maurya, A., Raman, B., Mehta, R., Kalyanaraman, R., Vankadhara, N., Roy, S., Sharma, P. 2012. Kyun queue: a sensor network system to monitor road traffic queues. ACM. Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, 127-140, India.
  • Skoog, W. (1981). “Principles Industrial and Analysis.” 2. Ed.
  • Sparkfun. (2005). “TF03-180 LiDAR(Long-range distance sensor).” from en.benewake.com.
  • Tiaprasert, K., Zhang, Y., Wang, X. B., Zeng, X. 2015. Queue length estimation using connected vehicle technology for adaptive signal control. IEEE Transactions on Intelligent Transportation Systems 16: 2129-2140.
  • Wu, A., Qi, L., Yang, X. 2013. Mechanism analysis and optimization of signalized intersection coordinated control under oversaturated status. 13th COTA International Conference of Transportation Professionals. Shanghai, China. 96: 1433- 1442.
  • Wu, J., Xu, H., Zhang, Y., Tian, Y., Song, X. 2020. Real-time queue length detection with roadside LiDAR data. Sensors 20: 2342.
  • Xu, B., Chen, M., Xing, C., Zhang, G. 2009. A network traffic identification method based on finite state machine. IEEE. 5th International Conference on Wireless Communications, Networking and Mobile Computing, 1-4, Beijing, China.
  • Yue, X., Wu, B., Seshia, S. A., Keutzer, K., Sangiovanni-Vincentelli, A. L. 2018. A lidar point cloud generator: from a virtual world to autonomous driving. Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, 458-464, Yokohama, Japan.
  • Zhao, J., Xu, H., Liu, H., Wu, J., Zheng, Y., Wu, D. 2019. Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors. Transportation research part C: emerging technologies 100: 68-87.
  • Zhao, Y., Su, Y. 2017. Vehicles detection in complex urban scenes using Gaussian mixture model with FMCW radar. IEEE Sensors Journal 17: 5948-5953.

Dinamik Kavşak Yönetimi İçin Tek-Yönlü LiDAR Sensör ile Yol Yoğunluk Hesabı

Yıl 2023, Cilt: 13 Sayı: 1, 105 - 117, 30.06.2023

Abdülkadir Çıldır Mesud Kahriman Mesut Tigdemir

Öz

Dinamik kavşak yönetimi, kavşaktaki ve kavşağa yaklaşmakta olan araçların anlık tespit edilmesinden geçmektedir. Bu anlamda dinamik kavşak yönetimi için araç tespit sensörleri tercih sebebi olmuştur. Bu makalede farklı yoğunluklara sahip kavşaklardaki araçların sayısını, hızını, sınıfını ve yine bu kavşakta oluşabilecek olan araç kuyruklarının uzunluğunu tespit edebilen lidar sensörlü bir sistem üzerinde çalışılmıştır. Bu çalışmada hız doğruluk tespitinde 96,36 %, kuyruk uzunluğu tespitinde ise 96,35 % başarı elde edilmiştir. Tek boyutlu bir lidar sensörden alınan veriler ile 3D lidar sensörün yapabileceği kabiliyetleri barındıran bu çalışma, fiyat ve performans açısından öne çıkmaktadır.

Anahtar Kelimeler

Dinamik kavşak yönetimi Tek boyutlu LiDAR sensör Kuyruk boyu tespiti

Kaynakça

  • Akanbi, L., Olajubu, E.A. 2012. A fuzzy-based intelligent traffic control system for managing VIP-induced chaos at road intersections. African Journal of Computing ICT 5: 109-119.
  • Ban, X. J., Hao, P., Sun, Z. 2011. Real time queue length estimation for signalized intersections using travel times from mobile sensors. Transportation Research Part C: Emerging Technologies 19: 1133-1156.
  • Cai, Y., Zhang, W., Wang H. 2010. Measurement of vehicle queue length based on video processing in intelligent traffic signal control system. IEEE. 2010 International Conference on Measuring Technology and Mechatronics Automation, 615-618, Changsha, China.
  • Chen, J., Xu, H., Wu, J., Yue, R., Yuan, C., Wang, L. 2019. Deer crossing road detection with roadside LiDAR sensor. IEEE Access 7: 65944-65954.
  • Cheung, S. Y., Coleri, S., Dundar, B., Ganesh, S., Tan, C.-W., Varaiya P. 2005. Traffic measurement and vehicle classification with single magnetic sensor. Transportation research record 1917: 173-181.
  • Cheung, S. Y., Varaiya, P. 2006. Traffic surveillance by wireless sensor networks. California Path Program Institute Of Transportation Studies University Of California, Berkeley, Institute Of Transportation Studies University Of California: 13.
  • Chiu, S., Chand, S. 1993. Adaptive traffic signal control using fuzzy logic. IEEE. Fuzzy Systems, 1993., Second IEEE International Conference on, 1371-1376.
  • Choudhary, P. 2018. Analyzing virtual traffic light using state machine in vehicular ad hoc network. Next-generation networks, Springer. Delhi, India, 239-245.
  • Cildir, A., Kahriman, M., Tigdemir, M. 2022. The Intersection Vehicle Delay Optimization For Ideal Traffic Light Cycle Time. International Journal of 3D Printing Technologies and Digital Industry 6: 126-136.
  • Emami, A., Sarvi, M., Bagloee, S. A. 2019. A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles. Journal of Modern Transportation 27: 341-354.
  • Festag, A., Noecker, G., Strassberger, M., Lübke, A., Bochow, B., Torrent-Moreno, M., Schnaufer S., Eigner, R., Catrinescu, C., Kunisch, J. 2008. “NoW - Network on Wheels” : Project Objectives, Technology and Achievements. CiteSeerx. Proceedings of 5rd International Workshop on Intelligent Transportation (WIT), 211-216, Hamburg, Germany.
  • Goodall, N. J. 2017. Fundamental characteristics of Wi‑Fi and wireless local area network re-identification for transportation. IET Intelligent Transport Systems 11: 128-135.
  • Ibisch, A., Stümper, S., Altinger, H., Neuhausen, M., Tschentscher, M., Schlipsing, M., Salinen, J., Knoll, A. 2013. Towards autonomous driving in a parking garage: Vehicle localization and tracking using environment-embedded lidar sensors. IEEE. 2013 IEEE intelligent vehicles symposium (IV), 829-834.
  • Lee, H., Coifman, B. 2015. Using LIDAR to validate the performance of vehicle classification stations. Journal of Intelligent Transportation Systems 19: 355-369.
  • Liu, H. X., Wu, X., Ma, W., Hu, H. 2009. Real-time queue length estimation for congested signalized intersections. Transportation research part C: emerging technologies 17: 412-427.
  • Managuli, M., Deshpande, A., Ayatti, S. H. 2017. Emergent vehicle tracking system using IR sensor. IEEE. 2017 International Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), 71- 74, Mysuru, India.
  • Margreiter, M. 2016. Fast and Reliable Determination of the Traffic State Using Bluetooth Detection on German Freeways. Transportation Research Procedia. World Conference on Transport Research, Shanghai, China
  • Rani, L. P. J., Kumar, M. K., Naresh, K., Vignesh, S. 2017. Dynamic traffic management system using infrared (IR) and Internet of Things (IoT). IEEE. Third International Conference on Science Technology Engineering & Management (ICONSTEM), 353-357, Chennai, India.
  • Sen, R., Maurya, A., Raman, B., Mehta, R., Kalyanaraman, R., Vankadhara, N., Roy, S., Sharma, P. 2012. Kyun queue: a sensor network system to monitor road traffic queues. ACM. Proceedings of the 10th ACM Conference on Embedded Network Sensor Systems, 127-140, India.
  • Skoog, W. (1981). “Principles Industrial and Analysis.” 2. Ed.
  • Sparkfun. (2005). “TF03-180 LiDAR(Long-range distance sensor).” from en.benewake.com.
  • Tiaprasert, K., Zhang, Y., Wang, X. B., Zeng, X. 2015. Queue length estimation using connected vehicle technology for adaptive signal control. IEEE Transactions on Intelligent Transportation Systems 16: 2129-2140.
  • Wu, A., Qi, L., Yang, X. 2013. Mechanism analysis and optimization of signalized intersection coordinated control under oversaturated status. 13th COTA International Conference of Transportation Professionals. Shanghai, China. 96: 1433- 1442.
  • Wu, J., Xu, H., Zhang, Y., Tian, Y., Song, X. 2020. Real-time queue length detection with roadside LiDAR data. Sensors 20: 2342.
  • Xu, B., Chen, M., Xing, C., Zhang, G. 2009. A network traffic identification method based on finite state machine. IEEE. 5th International Conference on Wireless Communications, Networking and Mobile Computing, 1-4, Beijing, China.
  • Yue, X., Wu, B., Seshia, S. A., Keutzer, K., Sangiovanni-Vincentelli, A. L. 2018. A lidar point cloud generator: from a virtual world to autonomous driving. Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval, 458-464, Yokohama, Japan.
  • Zhao, J., Xu, H., Liu, H., Wu, J., Zheng, Y., Wu, D. 2019. Detection and tracking of pedestrians and vehicles using roadside LiDAR sensors. Transportation research part C: emerging technologies 100: 68-87.
  • Zhao, Y., Su, Y. 2017. Vehicles detection in complex urban scenes using Gaussian mixture model with FMCW radar. IEEE Sensors Journal 17: 5948-5953.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Araştırma Makaleleri
Yazarlar

Abdülkadir Çıldır 0000-0003-1789-6088

Mesud Kahriman 0000-0003-0731-0936

Mesut Tigdemir 0000-0002-5303-2722

Yayımlanma Tarihi 30 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 13 Sayı: 1

Kaynak Göster

APA Çıldır, A., Kahriman, M., & Tigdemir, M. (2023). Road Density Calculations with Unidimensional LiDAR Sensor for Dynamic Intersection Management. Karaelmas Fen Ve Mühendislik Dergisi, 13(1), 105-117. https://doi.org/10.7212/karaelmasfen.1193517
AMA Çıldır A, Kahriman M, Tigdemir M. Road Density Calculations with Unidimensional LiDAR Sensor for Dynamic Intersection Management. Karaelmas Fen ve Mühendislik Dergisi. Haziran 2023;13(1):105-117. doi:10.7212/karaelmasfen.1193517
Chicago Çıldır, Abdülkadir, Mesud Kahriman, ve Mesut Tigdemir. “Road Density Calculations With Unidimensional LiDAR Sensor for Dynamic Intersection Management”. Karaelmas Fen Ve Mühendislik Dergisi 13, sy. 1 (Haziran 2023): 105-17. https://doi.org/10.7212/karaelmasfen.1193517.
EndNote Çıldır A, Kahriman M, Tigdemir M (01 Haziran 2023) Road Density Calculations with Unidimensional LiDAR Sensor for Dynamic Intersection Management. Karaelmas Fen ve Mühendislik Dergisi 13 1 105–117.
IEEE A. Çıldır, M. Kahriman, ve M. Tigdemir, “Road Density Calculations with Unidimensional LiDAR Sensor for Dynamic Intersection Management”, Karaelmas Fen ve Mühendislik Dergisi, c. 13, sy. 1, ss. 105–117, 2023, doi: 10.7212/karaelmasfen.1193517.
ISNAD Çıldır, Abdülkadir vd. “Road Density Calculations With Unidimensional LiDAR Sensor for Dynamic Intersection Management”. Karaelmas Fen ve Mühendislik Dergisi 13/1 (Haziran 2023), 105-117. https://doi.org/10.7212/karaelmasfen.1193517.
JAMA Çıldır A, Kahriman M, Tigdemir M. Road Density Calculations with Unidimensional LiDAR Sensor for Dynamic Intersection Management. Karaelmas Fen ve Mühendislik Dergisi. 2023;13:105–117.
MLA Çıldır, Abdülkadir vd. “Road Density Calculations With Unidimensional LiDAR Sensor for Dynamic Intersection Management”. Karaelmas Fen Ve Mühendislik Dergisi, c. 13, sy. 1, 2023, ss. 105-17, doi:10.7212/karaelmasfen.1193517.
Vancouver Çıldır A, Kahriman M, Tigdemir M. Road Density Calculations with Unidimensional LiDAR Sensor for Dynamic Intersection Management. Karaelmas Fen ve Mühendislik Dergisi. 2023;13(1):105-17.
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