Pelican Crossing System for Control a Green Man Light with Predicted Age
Keywords:
AgeNet Method, Ages-Predicted, Artificial Intelligent, FaceNet Method, Pelican Crossing System, Traffic light green man, Zebra Cross
Abstract
Traffic lights are generally used to regulate the control flow of traffic at an intersection from all directions, including a pelican crossing system with traffic signals for pedestrians. There are two facilities for walker crossing, namely using a pedestrian bridge and a zebra cross. In general, the traffic signals of the pelican crossing system are a fixed time, whereas other pedestrians need "green man" traffic lights with duration time arrangement. Our research proposes a prototype intelligent pelican crossing system for somebody who crosses the road at zebra crossings, but the risk of falling while crossing is not expected, especially in the elderly age group or pedestrians who are pregnant or carrying children. On the other hand, the problem is that the average step length or stride length (distance in centimeter), cadence or walking rate (in steps per minute), and the possibility of accidents are very high for pedestrians to make sure do crossing during the lights “green man”. The new idea of our research aims to set the adaptive time arrangement on the pelican crossing intelligent system of the traffic lights “green man” based on the age of the pedestrians with artificial intelligence using two combined methods of the FaceNet and AgeNet. The resulting measure can predict the age of pedestrians of the training dataset of 66.67% and testing prototype in real-time with participants on the pelican crossing system of 73% (single face) and 76% (multi faces).
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References
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[31] L. Zahara, P. Musa, E. Prasetyo Wibowo, I. Karim, and S. Bahri Musa, “The Facial Emotion Recognition (FER-2013) Dataset for Prediction System of Micro-Expressions Face Using the Convolutional Neural Network (CNN) Algorithm based Raspberry Pi,” 2020 5th Int. Conf. Informatics Comput. ICIC 2020, vol. 7, 2020, doi: 10.1109/ICIC50835.2020.9288560.
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[2] H. Halim and R. Abdullah, “Equivalent noise level response to number of vehicles: A comparison between a high traffic flow and low traffic flow highway in Klang Valley, Malaysia,” Front. Environ. Sci., 2014, doi: 10.3389/fenvs.2014.00013.
[3] M. Pi, H. Yeon, H. Son, and Y. Jang, “Visual Cause Analytics for Traffic Congestion,” IEEE Trans. Vis. Comput. Graph., 2021, doi: 10.1109/TVCG.2019.2940580.
[4] D. R. Aleko and S. Djahel, “An efficient adaptive traffic light control system for urban road traffic congestion reduction in smart cities,” Inf., 2020, doi: 10.3390/info11020119.
[5] M. Eom and B. I. Kim, “The traffic signal control problem for intersections: a review,” European Transport Research Review. 2020, doi: 10.1186/s12544-020-00440-8.
[6] P. Musa, N. Yuliani, and M. Lamsani, “Rancang Bangun Pengendali Pintu Automatis Berdasarkan Ciri Wajah Menggunakan Metode Euclidean Distance Dan Fuzzy C-mean,” J. Ilm. Inform. Komput. Univ. Gunadarma, vol. 13, no. 1, p. 35, 2009.
[7] M. B. Subaweh and E. P. Wibowo, “Implementation of Pixel Based Adaptive Segmenter method for tracking and counting vehicles in visual surveillance,” 2017, doi: 10.1109/IAC.2016.7905679.
[8] A. L. Ramadhani, P. Musa, and E. P. Wibowo, “Human face recognition application using PCA and eigenface approach,” in Proceedings of the 2nd International Conference on Informatics and Computing, 2018, vol. ICIC 2017, pp. 1–5, doi: 10.1109/IAC.2017.8280652.
[9] Direktur Jenderal Bina Marga, Keputusan Direktur Jenderal Bina Marga Nomor: 76/KPTS/Db/1999 tentang Pedoman Perencanaan Jalur Pejalan Kaki Pada Jalan Umum, no. 032. 1999.
[10] Kementerian Pekerjaan Umum dan Perumahan Rakyat, “Pedoman Bahan Konstruksi Bangunan dan Rekayasa Sipil: Perencanaan Teknis Fasilitas Pejalan Kaki,” SE Menteri PUPR, pp. 1–43, 2017.
[11] P. Hastuti, L. E. Nugroho, and I. W. Mustika, “Application for Detection of Pedestrian Position on Zebra Cross,” 2020, doi: 10.1109/ISRITI51436.2020.9315342.
[12] A. Muhammad Mulyadi, “Pedestrian Perception about Facility of Pedestrian Crossings,” 2018, doi: 10.1051/matecconf/201814702009.
[13] D. HALDEN, “Civilising pedestrian road crossing facilities,” Proc. ETC 2005, STRASBOURG, Fr. 18-20 Sept. 2005 - Transp. POLICY Oper. - TRAFFIC Eng. Str. Manag. - STREETS 21ST CEBTURY I, 2005.
[14] K. Alnaqbi, “Investigation of pedestrian accidents: Analysis at signalised pedestrian crossings in Edinburgh,” 2012, doi: 10.47556/b.outlook2012.10.26.
[15] M. Kieu, A. D. Bagdanov, M. Bertini, and A. del Bimbo, “Task-Conditioned Domain Adaptation for Pedestrian Detection in Thermal Imagery,” 2020, doi: 10.1007/978-3-030-58542-6_33.
[16] G. L. Djavendra, S. Aisyah, and E. R. Jamzuri, “Desain Sistem Pengatur Lampu Lalu Lintas dengan Identifikasi Kepadatan Kendaraan Menggunakan Metode Subtraction,” J. Nas. Tek. ELEKTRO, 2018, doi: 10.25077/jnte.v7n2.541.2018.
[17] S. Suhanda, “Rancangan Simulasi Antrian Kendaraan pada Persimpangan Jalan Berbasis Multi Agent menggunakan Logika Fuzzy,” ENSAINS J., 2018, doi: 10.31848/ensains.v1i1.52.
[18] N. Widyaningsih and O. Daniel, “Analisis Karakteristik Dan Perilaku Penyeberangan Orang pada Fasilitas Penyeberangan Zebra Cross dan Pelican Cross (Studi Kasus Ruas Jalan M. H. Thamrin),” J. Pengemb. Rekayasa dan Teknol., 2019, doi: 10.26623/jprt.v15i1.1486.
[19] B. Iswanto and B. Setioko, “Faktor yang Mempengaruhi Pejalan Kaki dalam Memilih Lokasi Penyeberangan Jalan di Kawasan Malioboro Yogyakarta,” J. Pembang. Wil. \& KOTA, 2020, doi: 10.14710/pwk.v16i2.26143.
[20] X. Hao et al., “Pedestrian Crossing Behaviour At Signalised Crossings,” Assoc. Eur. Transp. Contrib. 2008, 2008.
[21] B. JAPS, “MINIMISING Pedestrian Delays at Signal Controlled Crossings,” Traffic Eng. Control, 2000.
[22] A. Angelova, A. Krizhevsky, V. Vanhoucke, A. Ogale, and D. Ferguson, “Real-Time Pedestrian Detection with Deep Network Cascades,” 2015, doi: 10.5244/c.29.32.
[23] P. Dollár, C. Wojek, B. Schiele, and P. Perona, “Pedestrian detection: An evaluation of the state of the art,” IEEE Trans. Pattern Anal. Mach. Intell., 2012, doi: 10.1109/TPAMI.2011.155.
[24] P. Kuang, T. Ma, F. Li, and Z. Chen, “Real-Time Pedestrian Detection Using Convolutional Neural Networks,” Int. J. Pattern Recognit. Artif. Intell., 2018, doi: 10.1142/S0218001418560141.
[25] R. P. Resha, R. F. Rachmadi, S. M. S. Nugroho, and I. Ketut Eddy Purnama, “Pelican Crossing Adaptive Time Arrangement using Convolutional Neural Network,” 2019, doi: 10.1109/CENIM48368.2019.8973343.
[26] N. Ould Taleb, A. Chergui, M. L. Ben Maati, and M. F. Nanne, “Overview on automatic detection of human body,” 2017, doi: 10.1109/ICMCS.2016.7905638.
[27] E. Cho, S. Lee, F. Woyano, and S. Park, “Study on requirements and architecture for enhancing pedestrian mobility,” 2017, doi: 10.1109/ICTC.2017.8190879.
[28] N. A. Termida, M. M. Rohani, B. D. Daniel, N. Omar, and D. B. E. Dharmowijoyo, “Behavioural observations of adult-child pairs at a Pelican crossing: A case study in Kuala Lumpur,” Int. J. Integr. Eng., 2019, doi: 10.30880/ijie.2019.11.02.027.
[29] R. Romero-Ortuno, L. Cogan, C. U. Cunningham, and R. A. Kenny, “Do older pedestrians have enough time to cross roads in Dublin? A critique of the Traffic Management Guidelines based on clinical research findings,” Age Ageing, 2009, doi: 10.1093/ageing/afp206.
[30] Z. Zhang, Y. Song, and H. Qi, “Age progression/regression by conditional adversarial autoencoder,” 2017, doi: 10.1109/CVPR.2017.463.
[31] L. Zahara, P. Musa, E. Prasetyo Wibowo, I. Karim, and S. Bahri Musa, “The Facial Emotion Recognition (FER-2013) Dataset for Prediction System of Micro-Expressions Face Using the Convolutional Neural Network (CNN) Algorithm based Raspberry Pi,” 2020 5th Int. Conf. Informatics Comput. ICIC 2020, vol. 7, 2020, doi: 10.1109/ICIC50835.2020.9288560.
[32] F. Schroff, D. Kalenichenko, and J. Philbin, “FaceNet: A unified embedding for face recognition and clustering,” 2015, doi: 10.1109/CVPR.2015.7298682.
[33] P. F. T. Madio, “A FaceNet-Style Approach to Facial Recognition on the Google Coral Development board,” Towards Data Science, 2019. https://towardsdatascience.com/a-facenet-style-approach-to-facial-recognition-dc0944efe8d1.
[34] S. Melangi, “Klasifikasi Usia Berdasarkan Citra Wajah Menggunakan Algoritma Artificial Neural Network dan Gabor Filter,” Jambura J. Electr. Electron. Eng., 2020, doi: 10.37905/jjeee.v2i2.6956.
[35] O. Agbo-Ajala and S. Viriri, “Deep learning approach for facial age classification: a survey of the state-of-the-art,” Artif. Intell. Rev., 2021, doi: 10.1007/s10462-020-09855-0.
Published
2022-03-31
How to Cite
Musa, P., Wibowo, E. P., Musa, S. B., & Baihaqi, I. (2022). Pelican Crossing System for Control a Green Man Light with Predicted Age. MATRIK : Jurnal Manajemen, Teknik Informatika Dan Rekayasa Komputer, 21(2), 293-306. https://doi.org/https://doi.org/10.30812/matrik.v21i2.1508
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