* Corresponding Author
Hide
Show all
Traffic Density Classification Using Twitter Data and GPS Based On Android Application
https://doi.org/10.22146/ijccs.55761 Mohammad Afrizal(1*), Idham Ananta Timur(2)
(1) Master Program of Computer Science; FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author
Abstract
Increasing the number of vehicles in Special Region of Yogyakarta caused by congestion occurred at various traffic points in Special Region of Yogyakarta. The solution to reducing congestion is by increasing the use of public transportation within the city, but it still not in demand by the public. Optimizing daily activities, community always tries to avoid the traffic density on the road to be bypassed.
Some research on social media has been used to detect traffic density anomalies. However, the system still cannot provide traffic density information on roads that will be passed by the user because it is just a mapping. Based on this problem, this study aims to classify the traffic density on the road that will be passed by users in the Special Region of Yogyakarta into the category of high traffic and low traffic by utilizing Twitter and GPS data.
The results show that Android Applications are able to classify traffic density on the road to be traversed using Geonames.org API. Using the naïve bayes classification algorithm, the system can classify traffic density on 14 streets with an average accuracy of 77.5%, 90% precision, 79.1% recall, and 82.8% f-score.
Keywords
Full Text:
PDFReferences
D. A. Kurniawan, S. Wibirama, and N. A. Setiawan, “Real-time traffic classification with Twitter data mining,” 2016 8th Int. Conf. Inf. Technol. Electr. Eng., pp. 1–5, 2016.
[2] K. R. Pandhare and M. A. Shah, “Real time road traffic event detection using Twitter and spark,” Proc. 2017 Int. Conf. Inven. Commun. Comput. Technol., no. Icicct, pp. 445–449, 2017.
[3] B. Yang, W. Guo, B. Chen, G. Yang, and J. Zhang, “Estimating Mobile Traffic Demand Using Twitter,” IEEE Wirel. Commun. Lett., vol. 5, no. 4, pp. 380–383, 2016.
[4] R. Y. K. Lau, “Toward a social sensor based framework for intelligent transportation,” 2017 IEEE 18th Int. Symp. A World Wireless, Mob. Multimed. Networks, pp. 1–6, 2017.
[5] Y. Chen, Y. Lv, X. Wang, and F.-Y. Wang, “A convolutional neural network for traffic information sensing from social media text,” 2017 IEEE 20th Int. Conf. Intell. Transp. Syst., pp. 1–6, 2017.
[6] I. Salas, A., Georgakis, P., Nwagboso, C., Ammari, A. and Petalas, “Traffic Event Detection Framework Using Social Media,” IEEE Int. Conf. Smart Grid Smart Cities, no. July, p. 5, 2017.
[7] I. P. Windasari, F. N. Uzzi, and K. I. Satoto, “Sentiment Analysis on Twitter Posts : An analysis of Positive or Negative Opinion on GoJek,” pp. 266–269, 2017.
[8] N. R. Fatahillah, P. Suryati, and C. Haryawan, “Implementation of Naive Bayes classifier algorithm on social media (Twitter) to the teaching of Indonesian hate speech,” 2017 Int. Conf. Sustain. Inf. Eng. Technol., pp. 128–131, 2017.
[9] B. Akilesh, N. Kumar, B. Reddy, and M. Singh, “TRAFAN : Road Traffic Analysis Using Social Media Web Pages,” pp. 655–659.
[10] I. Hanifah and B. N. Prastowo, “Uji GPS Tracking Dalam Skala Transportasi Antar Kota,” vol. 6, no. 2, pp. 175–186, 2016.
[11] S. Rodiyansyah and E. Winarko, “Klasifikasi Posting Twitter Kemacetan Lalu Lintas Kota Bandung Menggunakan Naive Bayesian Classification,” Indones. J. Comput. Cybern. Syst., vol. 6, no. 1, pp. 91–100, 2012.
DOI: https://doi.org/10.22146/ijccs.55761
Article Metrics
Abstract views : 2234
| 
views : 1870
Refbacks
- There are currently no refbacks.
Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
View My Stats1