The Analysis of Highway Bottleneck during Long Weekend: A Case Study of Mittraphap Road, Nakhon Ratchasima–Khon Kaen Section
Main Article Content
Abstract
During long weekends, there are a high number of vehicles in a highway especially private cars resulting in traffic congestion. It is observed that the traffic flow on these highways is under stop-and-go driving condition due to the bottlenecks. This study aimed to explore factors related to the causes of the bottlenecks on highways. Data were collected by driving along Mittraphap Road from Nakhon Ratchasima to Khon Kaen during the beginning of New Year weekends (December 27–29, 2013) totally 16 trips with 3 hour interval. A designated survey form was used to record the locations of the bottlenecks as well as factors contributing to their occurrences. The data were processed and analyzed to screen the results. The findings revealed 97 locations of the bottlenecks and 12 factors. Binary logistic regression model was utilized to analyze and develop forecasting models, which differed primarily in such factors as types of gas stations and overpasses . It was found that the most efficient model was the model using PTT gas stations and unclassified overpasses.
Article Details
The articles published are the opinion of the author only. The author is responsible for any legal consequences. That may arise from that article.
References
[2] M. J. Cassidy and R. L.Bertini, “Some traffic features at freeway bottlenecks,” Transportation Research Part B: Methodological, vol. 33, no. 1, pp. 25–42, 1999.
[3] R. L. Bertini and A. M. Myton, “Use of performance measurement system data to diagnose freeway bottleneck location empirically in Orange country, Calofornia,” Journal of the Transportation Research Board, vol. 1925, 2005.
[4] Z. Horowitz and R. L. Bertini, “Using PORTAL data to empirically diagnose freeway bottlenecks located on Oregon Highway 217,” presented at Institute of Transportation Engineering District 6 Annual Meeting, July 15–18, 2007.
[5] J. Wieczorek, H. Li, R. J. Fernandez-Moctezuma, and R. L. Bertitni, “Integration an automated bottleneck detection tool into an online freeway data archive,” presented at 88th Annual Meeting of the Transportation Research Board, January 11–15, 2009.
[6] J. Wieczorek, H. Li, R. J. Fernandez-Moctezuma, and R. L. Bertitni, “Techniques for validating an automatic bottleneck detection tool using archived freeway sensor data,” presented at 89th Annual Meeting of the Transportation Research Board, January 10–14, 2010.
[7] C. Chen, A. Skabardonis, and P. Varaiya, “Systematic identification of freeway bottlenecks,” Transportation Research Record: Journal of the Transportation Research Board, vol. 1867, pp. 46–52, 2004.
[8] R. L. Bertitni , R. J. Fernandez-Moctezuma, J. Wieczorek, and H. Li, “Using archived its data to automatically identify freeway bottleneck in Portland, Oregon,” presented at the 15th World Congress on Intelligent Transport Systems and ITS America's 2008 Annual Meeting, Washington, DC, 2008.
[9] P. Jin, S. Parker, J. Fang, B. Ran, and C. M. Walton, “Freeway recurrent bottleneck identification algorithms considering detector data quality issues,” Journal of Transportation Engineering, vol. 138, no. 10, pp. 1205–1214, 2012.
[10] J. M. Hibe, Logistic regression models. Chapman & Hall/CRC. Boca Raton: Taylor & Francis Group, 2009.
[11] D. G. Kleinbaum and M. Klein, Logistic regression A Self-learning text, 3rd Edition. New York:Springer, 2010.
