Article
DEVELOPMENT OF CONGESTION CAUSAL PIE CHARTS FOR ARTERIAL ROADWAYS
DOI: 10.7708/ijtte.2017.7(1).09
7 / 1 / 117-133 Pages
Author(s)
Ali Soltani-Sobh - Department of Transportation City of Miami Beach, FL, USA -
Abstract
Urban congestion is being increased by a rapid growth in travel demand and a limited ability to expand physical infrastructure. If transportation agencies could accurately quantify the impacts of various congestion causes, they would be able to prioritize their strategies more efficiently. The Federal Highway Administration developed a well-known congestion causal pie chart in 2004, but this development process did not have extensive access to field data. Recent advancements in both traffic measurement technologies and data-driven analysis are making it possible to quantify congestion impacts more accurately. However, an assessment of congestion causes on signalized arterials presents many challenges, due to complexity of the required data and the interaction of traffic demand and control. The objective of this study is to create congestion pie charts which demonstrate the proportion of average experienced delay components on arterial corridors. A multivariate linear regression model of observed delay is used to demonstrate contributing factors to arterial street congestion. The methodology is explained using a section of Broward Boulevard in Fort Lauderdale, FL. The findings from the model demonstrate that a considerable part of arterial congestion can be attributed to travel demands and intersection signals.
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References:
Agarwal, M.; Maze, T.H.; Souleyrette, S. 2005. Impacts of weather on urban freeway traffic flow characteristics and facility capacity. In Proceedings of the 2005 mid-continent transportation research symposium, 18-19.
Ahmadi, L.; Merkley, G.P. 2009. Planning and management modeling for treated wastewater usage, Irrigation and drainage systems 23(2-3): 97-107.
Alvarez, P.; Hadi, M. 2012. Time-variant travel time distributions and reliability metrics and their utility in reliability assessments, Transportation Research Record: Journal of the Transportation Research Board 2315: 81-88.
Anbaroglu, B.; Heydecker, B.; Cheng, T. 2014. Spatio-temporal clustering for non-recurrent traffic congestion detection on urban road networks, Transportation Research Part C: Emerging Technologies 48(2014): 47-65.
Asgari, H.; Jin, X.; Mohseni, A. 2014. Choice, Frequency, and Engagement – A Framework for Telecommuting Behavior Analysis and Modeling, Transportation Research Record: Journal of the Transportation Research Board 2413: 101-109.
Baratian-Ghorghi, F.; Zhou, H. 2015. Investigating Women’s and Men’s Propensity to Use Traffic Information in a Developing Country, Transportation in Developing Economies 1(1): 11-19.
Chin, S.M.; Franzese, O.; Greene, D.L.; Hwang, H.L.; Gibson, R.C. 2004. Temporary losses of highway capacity and impacts on performance: Phase 2. US - Department of Energy. USA. 131 p.
Chow, A.H.F.; Santacreu, A.; Tsapakis, I.; Tanasaranond, G.; Cheng, T. 2014. Empirical assessment of urban traffic congestion, Journal of Advanced Transportation 48(8): 1000-1016.
Goodwin, L. C. 2002. Weather impacts on arterial traffic flow, Available from internet: https://ops.fhwa.dot.gov.
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