Volume List  / Volume 7 (1)



DOI: 10.7708/ijtte.2017.7(1).09

7 / 1 / 117-133 Pages


Ali Soltani-Sobh - Department of Transportation City of Miami Beach, FL, USA -

Marija Ostojic - Northwestern University, Evanston, IL, USA -

Aleksandar Stevanovic - Department of Civil, Environmental, and Geomatics Engineering, Florida Atlantic University, FL, USA -

Jiaqi Ma - Transportation Solutions Division, Leidos, Inc., VA, USA -

David K. Hale - Transportation Solutions Division, Leidos, Inc., VA, USA -


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.

Download Article

Number of downloads: 1678


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.


Hasan, S.; Choudhury, C.F.; Ben-Akiva, M.E.; Emmonds, A. 2011. Modeling of travel time variations on urban links in London, Transportation Research Record: Journal of the Transportation Research Board 2260: 1-7.


Kwon, J.; Barkley, T.; Hranac, R.; Petty, K.; Compin, N. 2011. Decomposition of travel time reliability into various sources: incidents, weather, work zones, special events, and base capacity, Transportation Research Record: Journal of the Transportation Research Board 2229: 28-33.


Kwon, J.; Mauch, M.; Varaiya, P. 2006. Components of congestion: Delay from incidents, special events, lane closures, weather, potential ramp metering gain, and excess demand, Transportation Research Record: Journal of the Transportation Research Board 1959: 84-91.


Lin, W.H.; Kulkarni, A.; Mirchandani, P. 2004. Short-Term Arterial Travel Time Prediction for Advanced Traveler Information Systems, Journal of Intelligent Transportation Systems: Technology, Planning, and Operations 8(3): 143-154.


Malinovskiy, Y.; Wu, Y.J.; Wang, Y.; Lee, U.K. 2010. Field experiments on bluetooth-based travel time data collection. In Proceeding of the Transportation Research Board 89th Annual Meetingm, No. 10-3134.


Palacharla, P.V.; Nelson, P.C. 1999. Application of Fuzzy Logic and Neural Networks for Dynamic Travel Time Estimation, International Transactions in Operational Research 6(1): 145-160.


Skabardonis, A.; Varaiya, P.; Petty, K. 2003. Measuring recurrent and non-recurrent traffic congestion, Transportation Research Record: Journal of the Transportation Research Board 1856: 118-124.


Soltani-Sobh, A.; Heaslip, K.; Bosworth, R.; Barnes, R.; Song, Z. 2016. Do Natural Gas Vehicles Change Vehicle Miles Traveled (VMT)? An Aggregate Time Series Analysis, In Proceeding of the Transportation Research Board 95th Annual Meeting, No. 16-0192.


Hojati, A.T.; Ferreira, L.; Washington, S.; Charles, P.; Shobeirinejad, A. 2016. Modelling the impact of traffic incidents on travel time reliability, Transportation Research Part C: Emerging Technologies, 65: 49-60.


Taylor, M.A.P.; Somenahalli, S. 2014. Urban Arterial Road Travel Time Variability Modeling Using Burr Regression. In Proceeding of the Transportation Research Board 93rd Annual Meeting, No. 14-5654.