The Collection of Road Traffic Incidents in Bangkok from Twitter Data based on Deep Learning Algorithm

Main Article Content

Korn Puangnak
Natworapol Rachsiriwatcharabul


Text processing technology from Twitter to report notification formats that are known in many countries with verification on different languages. This research presents the development of a neural network memory learning model. To solve the problem of classifying incidence patterns and identifying severity of incidents from Thai social media messages. For gathering incident data and reporting incidents externally from a single reporting platform by using deep learning models like MLP, CNN and LSTM which is designed by dividing the study into 3 types, including examination traffic incidence identification pattern that can identify the report as general news or traffic reporting Incident Identification Patterns. These include traffic conditions, accidents, disasters, damaged roads, or other than the aforementioned patterns, and the pattern indicating the severity of the incidence consists of normal level, medium level and lane blocking or stationary levels. The results demonstrated the ability of LSTM learning with the best results in incidence detection and incidence pattern identification at 93.44% and 87.40%, respectively, and the CNN method was able to State the severity of the incidence at best, reaching 91.42%.

Article Details

How to Cite
K. Puangnak and N. Rachsiriwatcharabul, “The Collection of Road Traffic Incidents in Bangkok from Twitter Data based on Deep Learning Algorithm”, ECTI-CIT Transactions, vol. 16, no. 3, pp. 267–276, Jun. 2022.
Research Article


B. -H. Lin and S. -F. Tseng, “A predictive analysis of citizen hotlines 1999 and traffic accidents: A case study of Taoyuan city,” 2017 IEEE International Conference on Big Data and Smart Computing (BigComp), pp.374-376, 2017.

L. Cao, F. Zhu, X. Dong, Z. Shen, J. Yu, B. Hu and G. Xiong, “Big data platform & typical APP services for urban public transportation,” 2017 Chinese Automation Congress (CAC), pp. 7565-7570, 2017.

J. Xie and J. Luo, “Construction for the city taxi trajectory data analysis system by Hadoop platform,” 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), pp. 527-531, 2017.

M. Zhang, C. Chen, T. Wo, T. Xie, M. Z. A. Bhuiyan and X. Lin, “SafeDrive, Online Driv- ing Anomaly Detection from Large-Scale Vehi- cle Data,” in IEEE Transactions on Industrial Informatics, vol. 13, no. 4, pp. 2087-2096, 2017.

A. Ciociola, M. Cocca, D. Giordano, M. Mel- lia, A. Morichetta, A. Putina and F. Salutari, “UMAP, Urban mobility analysis platform to harvest car sharing data,” 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Com- puting & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, pp. 1-8, 2017.

G. Buroni, Y. Le Borgne, G. Bontempi and K. Determe, “Cluster Analysis of On-Board-Unit Truck Big Data from the Brussels Capital Region,” 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2074-2079, 2018.

A. Kaplunovich and Y. Yesha, “Consolidating billions of Taxi rides with AWS EMR and Spark in the Cloud Tuning, Analytics and Best Practices,” 2018 IEEE International Conference on Big Data (Big Data), pp. 4501-4507, 2018.

L. Lin, J. Li, F. Chen, J. Ye and J. Huai, “Road Traffic Speed Prediction: A Probabilistic Model Fusing Multi-Source Data,” in IEEE Transactions on Knowledge and Data Engineering, vol. 30, no. 7, pp. 1310-1323, 2018.

S. A. A. Balamurugan, J. F. Lilian and S. Sasikala, “The Future of India Creeping up in Building a Smart City Intelligent Traffic Analysis Platform,” 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), pp. 518-523, 2018.

S. Nguyen, Z. Salcic and X. Zhang, “Big Data Processing in Fog - Smart Parking Case Study,” 2018 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Ubiquitous Computing & Communications, Big Data & Cloud Computing, Social Computing & Networking, Sustainable Computing & Communications (ISPA/IUCC/BDCloud/SocialCom/SustainCom), pp. 127-134, 2018.

Q. Ren, K. L. Man, M. Li and B. Gao, “Using Blockchain to Enhance and Optimize IoT-based Intelligent Traffic System,” 2019 International Conference on Platform Technology and Service (PlatCon), pp. 1-4, 2019.

Z. -X. Yang and M. -H. Zhu, “A Dynamic Prediction Model of Real-Time Link Travel Time Based on Traffic Big Data,” 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS), pp. 330-333, 2019.

L. Zhu, F. R. Yu, Y. Wang, B. Ning and T. Tang, “Big Data Analytics in Intelligent Transportation Systems A Survey,” in IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 1, pp. 383-398, 2019.

P. Vateekul and T. Koomsubha, “A study of sentiment analysis using deep learning techniques on Thai Twitter data,” 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 1-6, 2016.

S. Klaithin and C. Haruechaiyasak “Traffic information extraction and classification from Thai Twitter,” 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE), pp. 1-6, 2016.

R. Bishnoi, M. Ebrahimi, F. Oboril and M. B. Tahoori, “Improving Write Performance for STT-MRAM,” in IEEE Transactions on Magnetics, vol. 52, no. 8, pp. 1-11, Aug. 2016.

Z. Zhang, Q. He, J. Gao and M. Ni, “A deep learning approach for detecting traffic accidents from social media data,” Transportation Research Part C: Emerging Technologies, vol. 86, pp. 580-596, 2018.

R. Neuhold, H. Gursch, R. Kern and M. Cik, “Driver's dashboard - using social media data as additional information for motorway operators,” IET Intelligent Transport Systems, vol. 12, no. 9, pp. 1116-1122, 2018.

F. Jin and H. Liu, “Detect Hidden Road Hazards combining Multiple Social Media Data,” 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA, 2018.

H. El Alaoui El Abdallaoui, A. El Fazziki, F. Z. Ennaji and M. Sadgal, “Decision Support System for the Analysis of Traffic Accident Big Data,” 2018 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 514-521, 2018.

T. Yamazaki, “Analysis of Traffic Accident Occurrence in Niigata Prefecture of Japan using Open Data,” 2019 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1-4, 2019.

Y. Chen, Y. Lv, X. Wang, L. Li and F.-Y. Wang, “Detecting Traffic Information from Social Media Texts With Deep Learning Approaches,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 8, pp. 3049-3058, 2019.