KaydedilenlerDerginizi ekleyin

Dergiler / Journal of Transportation and Logistics / 2026 / Volume: 8 Issue: 1

Traffic Analysis Model with Bayesian Network and Social Media Data: D100 Highway Travel İnformation System

Cihan Çiftçi0000-0002-3649-8010
Halim Kazan(ISTANBUL UNIVERSITY, FACULTY OF BUSINESS ADMINISTRATION)0000-0002-9234-6214
Sayfa
—
Yayın Tarihi
2023-08-10
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

With these models, the infrastructure of the early warning system has been created for region-specific traffic density situations and possible traffic events.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:The traffic problem in Intelligent Transportation Systems has recently become a very important issue.
📊
Temel Bulgular & Veri:In the analysis of traffic incidents, 36627 traffic incidents were digitized.
💡
Sonuç & Katkı:With these models, the infrastructure of the early warning system has been created for region-specific traffic density situations and possible traffic events.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2016 Verileri
⚙️Değişkenler:Traffic management · traffic analysis · machine learning

Abstract

The traffic problem in Intelligent Transportation Systems has recently become a very important issue. Thanks to Intelligent Transportation Systems, the formation of large amounts of traffic data has led to the formation of data-oriented models. There is a growing interest in predicting traffic measures by modeling complex scenarios based on big data with data mining and machine learning methods. In this study, traffic events from Twitter traffic notifications and vehicle density from sensor data were obtained. Traffic density analysis and traffic incident analysis were performed with the machine learning method. In the analysis of traffic incidents, 36627 traffic incidents were digitized. This data was separated into categories including type of accident; day; month; year; season; left, right or middle lane; and vehicle failure, maintenance-repair work and accident notification. Between 2016 and 2020, 1400 daily vehicle data logs were obtained from the sensor data located at 59 points of the D100 highway. Traffic density and parameters affecting traffic incidents on the Anatolian and European sides of the D100 highway in Istanbul were determined. Traffic density and accident event models were designed with the Bayesian network approach. In the sensitivity analysis of the model, it was concluded that the parameter that has the strongest effect on traffic events and density formation on the D100 highway line is the strips. With these models, the infrastructure of the early warning system has been created for region-specific traffic density situations and possible traffic events.

🔖

Bu Makaleye Atıf Yap

Çiftçi, C., Kazan, H. (2023). Traffic Analysis Model with Bayesian Network and Social Media Data: D100 Highway Travel İnformation System. Journal of Transportation and Logistics, Volume: 8 Issue: 1. https://doi.org/10.26650/JTL.2023.1179093

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (53)

  1. Afrin, T., & Yodo, N. (2021). A probabilistic estimation of traffic congestion using Bayesian network. Measurement, 174, 109051. google scholar
  2. Agarwal, S., Kachroo, P., & Regentova, E. (2016). A hybrid model using logistic regression and wavelet transformation to detect traffic incidents. IATSS Research, 40(1), 56-63. https://doi.Org/10.1016/j.iatssr.2016.06.001. google scholar
  3. Ali, F., Ali, A., Imran, M., Naqvi, R. A., Siddiqi, M. H., & Kwak, K. S. (2021). Traffic accident detection and condition analysis based on social networking data. Accident Analysis & Prevention, 151, 105973. google scholar
  4. Alkouz, B., & Al Aghbari, Z. (2020). SNSJam: Road traffic analysis and prediction by fusing data from multiple social networks. Information Processing & Management, 57(1), 102139. google scholar
  5. Alkouz, B., & Al Aghbari, Z. (2022, March). Fusion of Multiple Arabic Social Media Streams for Traffic Events Detection. In 2022 7th International Conference on Big Data Analytics (ICBDA) (pp. 231-235). IEEE. google scholar
  6. Bao, J., Liu, P., Yu, H., & Xu, C. (2017). Incorporating twitter-based human activity information in spatial analysis of crashes in urban areas. Accident analysis & prevention, 106, 358-369. google scholar
  7. Blackwell, D., & Ajoodha, R. (2022). A Bayesian Approach to Understanding the Influence of Traffic Congestion Given the Road Structure. In Proceedings of Sixth International Congress on Information and Communication Technology (pp. 271-279). Springer, Singapore. google scholar
  8. Chen, H., Zhao, Y., Ma, X. (2020). Critical factors analysis of severe traffic accidents based on Bayesian network in China. Journal of advanced transportation, 2020. google scholar
  9. Contreras, E., Torres-Trevino, L., & Torres, F. (2018). Prediction of car accidents using amaximum sensitivity neural network. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 213, 86-95. https://doi.org/10.1007/978-3-319-73323-4_9. google scholar
  10. Dabiri, S., & Heaslip, K. (2019). Developing a Twitter-based traffic event detection model using deep learning architectures. Expert systems with applications, 118, 425-439. google scholar
  11. Daly, E. M., Lecue, F., & Bicer, V. (2013, March). Westland row why so slow? Fusing social media and linked data sources for understanding real-time traffic conditions. In Proceedings of the 2013 international conference on Intelligent user interfaces (pp. 203-212). google scholar
  12. De Ona, J., Lopez, G., Mujalli, R., & Calvo, F. J. (2013). Analysis of traffic accidents on rural highways using Latent Class Clustering and Bayesian Networks. Accident Analysis & Prevention, 51, 1-10. google scholar
  13. De Ona, J., Mujalli, R. O., & Calvo, F. J. (2011). Analysis of traffic accident injury severity on Spanish rural highways using Bayesian networks. Accident Analysis & Prevention, 43(1), 402-411. google scholar
  14. Diao, Z., Wang, X., Zhang, D., Liu, Y., Xie, K., & He, S. (2019). Dynamic Spatial-Temporal Graph Convolutional Neural Networks for Traffic Forecasting. https://doi.org/10.1609/aaai.v33i01.330189. google scholar
  15. Dogru, N., & Subasi, A. (2018). Traffic accident detection using random forest classifier. 2018 15th Learning and Technology Conference, L and T 2018, 40-45. https://doi.org/10.1109/LT.2018.8368509. google scholar
  16. Febres, J. D., Mohamadi, F., Mariscal, M., Herrera, S., & Garaa-Herrero, S. (2019). The role of journey purpose in road traffic injuries: a Bayesian network approach. Journal of Advanced Transportation, 2019. google scholar
  17. Fu, H., & Zhou, Y. (2011). The traffic accident prediction based on neural network. Proceedings of the 2011 2nd International Conference on Digital Manufacturing and Automation, ICDMA 2011, 1349-1350. https://doi.org/10.1109/ICDMA.2011.331. google scholar
  18. Geetha Ramani, R., & Shanthi, S. (2012). Classifier prediction evaluation in modeling road traffic accident data. 2012 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2012. google scholar
  19. Gu, X., Li, T., Wang, Y., Zhang, L., Wang, Y., & Yao, J. (2018). Traffic fatalities prediction using support vector machine with hybrid particle swarm optimization. Journal of Algorithms and Computational Technology, 12(1), 20-29. https://doi.org/10.1177/1748301817729953. google scholar
  20. Gu, Y., Qian, Z. S., & Chen, F. (2016). From Twitter to detector: Real-time traffic incident detection using social media data. Transportation research part C: emerging technologies, 67, 321-342. google scholar
  21. Guo, S., Lin, Y., Feng, N., Song, C., & Wan, H.(2019).Attention Based Spatial-Temporal Graph Convolutional Networks for Traf-fic Flow Forecasting. AAAI Conference on Artificial Intelligence Twenty-Eighth AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v33i01.3301922 google scholar
  22. He, Z. (2019). STCNN: A Spatio-Temporal Convolutional Neural Network for Long-Term Traffic Prediction. https://doi.org/10.1109/mdm.2019.00-53. google scholar
  23. Kim, J., & Wang, G. (2016). Diagnosis and prediction of traffic congestion on urban road networks using Bayesian networks. Transportation Research Record, 2595(1), 108-118. google scholar
  24. Lalika, L., Kitali, A. E., Haule, H. J., Kidando, E., Sando, T., & Alluri, P. (2022). What are the leading causes of fatal and severe injury crashes involving older pedestrian? Evidence from Bayesian network model. Journal of safety research, 80, 281-292. google scholar
  25. Li, C., Wu, X., Zhang, Z., Ma, Z., Zhu, Y., & Chen, Y. (2022, January). Freeway traffic accident severity prediction based on multi-dimensional and multi-layer Bayesian network. In 2022 IEEE 2nd International Conference on Power, Electronics and Computer Applications (ICPECA) (pp. 1032-1035). IEEE. google scholar
  26. Liu, L., Ye, X., Wang, T., Yan, X., Chen, J., & Ran, B. (2022). Key factors analysis of severity of automobile to two-wheeler traffic accidents based on Bayesian network. International journal of environmental research and public health, 19(10), 6013. google scholar
  27. Mai, E., & Hranac, R. (2013). Twitter interactions as a data source for transportation incidents (No. 13-1636). google scholar
  28. Mbakwe, A. C., Saka, A. A., Choi, K., & Lee, Y. J. (2016). Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network. Accident Analysis & Prevention, 93, 135-146. google scholar
  29. Mohamed, E. A. (2014). Predicting Causes of Traffic Road Accidents Using Multi-class Support Vector Machines. Journal of Communication and Computer, 11, 403-411. https://doi.org/10.17265/1548-7709/2014.05. google scholar
  30. Mujalli, R. O., & De Ona, J. (2011). A method for simplifying the analysis of traffic accidents injury severity on two-lane highways using Bayesian networks. Journal of safety research, 42(5), 317-326. google scholar
  31. Nguyen, H., Liu, W., Rivera, P., & Chen, F. (2016, April). Trafficwatch: Real-time traffic incident detection and monitoring using social media. In Pacific-asia conference on knowledge discovery and data mining (pp. 540-551). Springer, Cham. google scholar
  32. Ozbayoglu, M., Kucukayan, G., & Dogdu, E. (2016). A real-time autonomous highway accident detection model based on big data pro-cessing and computational intelligence. Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016, 1807-1813. https://doi.org/10.1109/BigData.2016.7840798. google scholar
  33. Pascale, A., & Nicoli, M. (2011, June). Adaptive Bayesian network for traffic flow prediction. In 2011 IEEE Statistical Signal Processing Workshop (SSP) (pp. 177-180). IEEE. google scholar
  34. Paule, J. D. G., Sun, Y., & Moshfeghi, Y. (2019). On fine-grained geolocalisation of tweets and real-time traffic incident detection. Information Processing & Management, 56(3), 1119-1132. google scholar
  35. Razzaq, S., Riaz, F., Mehmood, T., & Ratyal, N. I. (2016). Multi-Factors Based Road Accident Prevention System. 2016 International Conference on Computing, Electronic and Electrical Engineering, ICE Cube 2016 - Proceedings, 190-195. https://doi.org/10.1109/ICECUBE.2016.7495221. google scholar
  36. Ren, H., Song, Y., Wang, J., Hu, Y., & Lei, J. (2018). A Deep Learning Approach to the Citywide Traffic Accident Risk Prediction. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2018-November, 3346-3351. https://doi.org/10.1109/ITSC.2018.8569437. google scholar
  37. S. S. Ribeiro Jr., C. A. Davis Jr., D. R. R. Oliveira, W. Meira Jr., T. S. Gonçalves, and G. L. Pappa, “Traffic Observatory: A System to Detect and Locate Traffic Events and Conditions Using Twitter,” in Proceedings of the 5th ACM SIGSPATIAL International Workshop on Location-Based Social Networks, New York, NY, USA, 2012, pp. 5-11. google scholar
  38. Salas, A., Georgakis, P., & Petalas, Y. (2017, October). Incident detection using data from social media. In 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC) (pp. 751-755). IEEE. google scholar
  39. Salazar, J.C.; Torres-Ruiz, M.; Davis, C.A., Jr.; Moreno-Ibarra, M. Geocoding of traffic-related events from Twitter. In Proceedings of the XVI Brazilian Symposium of Geoinformatics GEOINFO, Campos do Jordao, SP, Brazil, 29 November-2 December 2015; GEOINFO Series; pp. 14-25. google scholar
  40. Shi, Q., & Abdel-Aty, M. (2015 ).Big Data applications in real-time traffic operation and safety monitoring and improvement on urban expressways. Transportation Research Part C: Emerging Technologies, 58, 380-394. https://doi.org/10.1016/j.trc.2015.02.022 google scholar
  41. Suat-Rojas, N., Gutierrez-Osorio, C., & Pedraza, C. (2022). Extraction and Analysis of Social Networks Data to Detect Traffic Accidents. Information, 13(1), 26. google scholar
  42. Sumalee, Agachai, and Hung Wai Ho. 2018. “Smarter and more connected: Future intelligent transportation system.” IATSS Research. https://doi.org/10.1016/j.iatssr.2018.05.005. google scholar
  43. Sun, M., Zhou, R., Jiao, C., & Sun, X. (2022). Severity analysis of hazardous material road transportation crashes with a Bayesian network using Highway Safety Information System data. International journal of environmental research and public health, 19(7), 4002. google scholar
  44. Sun, S., Zhang, C., & Yu, G. (2006). A Bayesian network approach to traffic flow forecasting. IEEE Transactions on intelligent transportation systems, 7(1), 124-132. google scholar
  45. Taamneh, M., Alkheder, S., & Taamneh, S. (2017). Data-mining techniques for traffic accident modeling and prediction in the United Arab Emirates. Journal of Transportation Safety and Security, 9(2), 146-166. https://doi.org/10.1080/19439962.2016.1152338. google scholar
  46. Vasavi, S. (2016). A Survey on Extracting Hidden Patterns within Road Accident Data using Machine Learning Techniques. Communications on Applied Electronics, 6(4), 1-6. https://doi.org/10.5120/cae2016652455. google scholar
  47. Wu S, Hofman JM, Mason WA, Watts DJ (2011) Who says what to whom on Twitter. In: Proceedings of the 20th international world wide web conference, pp 705-714. google scholar
  48. Yang, Y., Wang, K., Yuan, Z., & Liu, D. (2022). Predicting freeway traffic crash severity using XGBoost-Bayesian network model with consideration of features interaction. Journal of advanced transportation, 2022. google scholar
  49. Yao, W., & Qian, S. (2021). From Twitter to traffic predictor: Next-day morning traffic prediction using social media data. Transportation Research Part C: Emerging Technologies, 124, 102938. https://doi.org/10.1016/j.trc.2020.102938. google scholar
  50. Zhang, Z., He, Q., Gao, J., & Ni, M. (2018). A deep learning approach for detecting traffic accidents from social media data. Transportation research part C: emerging technologies, 86, 580-596. google scholar
  51. Zhu, L., Guo, F., Krishnan, R., & Polak, J. W. (2018). A Deep Learning Approach for Traffic Incident Detection in Urban Networks. https://doi.org/10.1109/itsc.2018.8569402. google scholar
  52. Zong, F., Chen, X., Tang, J., Yu, P., & Wu, T. (2019). Analyzing traffic crash severity with combination of information entropy and Bayesian network. IEEE Access, 7, 63288-63302. google scholar
  53. Zong, F., Xu, H., & Zhang, H. (2013). Prediction for traffic accident severity: comparing the Bayesian network and regression models. MathematiA2:A48cal Problems in Engineering, 2013. google scholar