Research Article


DOI :10.26650/JTL.2023.1179093   IUP :10.26650/JTL.2023.1179093    Full Text (PDF)

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

Cihan ÇiftçiHalim Kazan

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.


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APA

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, 8(1), 48-61. https://doi.org/10.26650/JTL.2023.1179093


AMA

Çiftçi C, Kazan H. Traffic Analysis Model with Bayesian Network and Social Media Data: D100 Highway Travel İnformation System. Journal of Transportation and Logistics. 2023;8(1):48-61. https://doi.org/10.26650/JTL.2023.1179093


ABNT

Çiftçi, C.; Kazan, H. Traffic Analysis Model with Bayesian Network and Social Media Data: D100 Highway Travel İnformation System. Journal of Transportation and Logistics, [Publisher Location], v. 8, n. 1, p. 48-61, 2023.


Chicago: Author-Date Style

Çiftçi, Cihan, and Halim Kazan. 2023. “Traffic Analysis Model with Bayesian Network and Social Media Data: D100 Highway Travel İnformation System.” Journal of Transportation and Logistics 8, no. 1: 48-61. https://doi.org/10.26650/JTL.2023.1179093


Chicago: Humanities Style

Çiftçi, Cihan, and Halim Kazan. Traffic Analysis Model with Bayesian Network and Social Media Data: D100 Highway Travel İnformation System.” Journal of Transportation and Logistics 8, no. 1 (Jul. 2024): 48-61. https://doi.org/10.26650/JTL.2023.1179093


Harvard: Australian Style

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, vol. 8, no. 1, pp. 48-61, viewed 13 Jul. 2024, https://doi.org/10.26650/JTL.2023.1179093


Harvard: Author-Date Style

Çiftçi, C. and Kazan, H. (2023) ‘Traffic Analysis Model with Bayesian Network and Social Media Data: D100 Highway Travel İnformation System’, Journal of Transportation and Logistics, 8(1), pp. 48-61. https://doi.org/10.26650/JTL.2023.1179093 (13 Jul. 2024).


MLA

Çiftçi, Cihan, and Halim Kazan. Traffic Analysis Model with Bayesian Network and Social Media Data: D100 Highway Travel İnformation System.” Journal of Transportation and Logistics, vol. 8, no. 1, 2023, pp. 48-61. [Database Container], https://doi.org/10.26650/JTL.2023.1179093


Vancouver

Çiftçi C, Kazan H. Traffic Analysis Model with Bayesian Network and Social Media Data: D100 Highway Travel İnformation System. Journal of Transportation and Logistics [Internet]. 13 Jul. 2024 [cited 13 Jul. 2024];8(1):48-61. Available from: https://doi.org/10.26650/JTL.2023.1179093 doi: 10.26650/JTL.2023.1179093


ISNAD

Çiftçi, Cihan - Kazan, Halim. Traffic Analysis Model with Bayesian Network and Social Media Data: D100 Highway Travel İnformation System”. Journal of Transportation and Logistics 8/1 (Jul. 2024): 48-61. https://doi.org/10.26650/JTL.2023.1179093



TIMELINE


Submitted10.10.2022
Accepted14.12.2022
Published Online10.08.2023

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