Volume List  / Volume 11 (3)



DOI: 10.7708/ijtte.2021.11(3).07

11 / 3 / 442 - 453 Pages


Jelena Karapetrovic - Department of Civil Engineering, New Mexico State University, Las Cruces, NM, 88001, USA -

Peter T. Martin - Department of Civil Engineering, New Mexico State University, Las Cruces, NM, 88001, USA -


Urban congestion is getting worse, especially at intersections, where conflicting flows share the same traffic signal cycle. However, traffic demand-responsive control can improve urban traffic operations by directing vehicles to alternative routes with spare capacity. Intersection demand is characterized by left, through and right turning movement (TM) flows from each intersection approach. Information on TMs must be current and consistently reliable for traffic control strategies to be effective. Non-recurring traffic congestion can quickly and unpredictably develop, such as during the Christmas shopping season, causing backups and warranting congestion mitigation. The latest version of the Turning Movement Estimation in Real-Time (TMERT) model, TMERT3, can consistently estimate 5-minute TM flows from 15% of network flow detections. This paper validates TMERT3 by showing it robustly estimates TMs despite widespread detector failure when applied to non-recurrently congested extensive networks. TMERT3 is tested on traffic on the 28-intersection network in Orem/Provo, Utah, USA, during a Christmas shopping season. TMERT3 TM estimates showed to preserve accuracy (Root Mean Square Error- RMSE) even when 20% of the initial detectors fail. So, TMERT3 presented practical applicability in efficiently guiding strategies that mitigate urban traffic congestion.

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Bikowitz, E. W.; Ross, S. P. 1985. Evaluation and Improvement of Inductive Loop Detectors., Transportation Research Record: Journal of the Transportation Research Board 1010: 76–80.


Chen, A.; Chootinan, P.; Ryu, S.; Lee, M.; Recker, W. 2012. An intersection turning movement estimation procedure based on path flow estimator, Journal of Advanced Transportation 46(2): 161–176.


CSI. 2005. Traffic Congestion and Reliability Trends and Advanced Strategies for Congestion Mitigation. Cambridge Systematics Inc., Texas Transportation Institute. 140p.


Ghods, A.H.; Fu, L. 2014. Real-time estimation of turning movement counts at signalized intersections using signal phase information, Transportation Research Part C: Emerging Technologies 47: 128–138.


Google Maps. 2020. Orem/Provo, Utah, USA. Available from Internet: https://www.google.com/maps/@40.2819374,-111.6987692,14z.


Gordon, R.L.; Warren, T. 2005. Traffic Control Systems Handbook. Technical Report No. FHWA-HOP-06-006. Washington, DC. 369p.


HCM 2010. Highway Capacity Manual. Transportation Research Board. Washington, D.C., USA. 1,475p.


Karapetrovic, J.; Martin, P. T. 2020. Estimating intersection turning movement flows with a NETFLO algorithm: weight constraint calibration, Advances in Transportation Studies, an International Journal 52: 73–88.


Karapetrovic, J.; Martin, P. T. 2021. The Turning Movement Estimation In Real-Time (TMERT) Model: Lower Bound Constraint Calibration. In Proceedings of the 12th International Conference on Ambient Systems, Networks and Technologies (ANT 2021), Warsaw, Poland (in print).


Kennington, J.L.; Helgason, R.V. 1980. Algorithms for Network Programming. New York: A Wiley-Interscience Publication, New York, USA. 291p.


Klein, L.A.; Mills, M.K.; Gibson, D.R.P. 2006. Traffic Detector Handbook. Third edition, Volume I, US Department of Transportation, Federal Highway Administrationdoi: FHWA-HRT-06-108 October. 291p.


Lan, C.J. 2001. Adaptive turning flow estimation based on incomplete detector information for advanced traffic management. In Proceedings of the IEEE Conference on Intelligent Transportation Systems - ITSC, 830–835.


Leard, B.; Linn, J.; Munnings, C. 2019. Explaining the Evolution of Passenger Vehicle Miles Traveled in the United States, The Energy Journal 40(1): 25–54.


Martin, P. T. 1997. Turning Movement Estimation in Real-Time, Journal of Transportation Engineering 123(4): 252–260.


Martin, P. T.; Feng, Y.; Wang, X. 2003. Detector Technology Evaluation. Report No. MPC Report No. 03-154. Fargo, ND. 140p.


Mirchandani, P.B.; Nobe, S.A.; Wu, W.W. 2001. Online Turning Proportion Estimation in Real-Time Traffic-Adaptive Signal Control, Transportation Research Record: Journal of the Transportation Research Board 1748(1): 80–86.


Mozolin, M.; Thill, J.C.; Usery, E.L. 2000. Trip distribution forecasting with multilayer perceptron neural networks: A critical evaluation, Transportation Research Part B: Methodological 34(1): 53–73.


Nakatsuji ,T.; Nakano, K.; Nanthawichit, C.; Suzuki, H. 2004. Estimation of Turning Movements at Intersections: Joint Trip Distribution and Traffic Assignment Program Combined with a Genetic Algorithm, Transportation Research Record: Journal of the Transportation Research Board 1882(1): 53–60.


NCHRP. 2008. Task 63: Effective Strategies for Congestion Management, National Cooperative Highway Research Program (NCHRP) Project 20-24. Cambridge Systematics, Inc. Cambridge, MA. 214p.


Rakha, H.; Tawfik, A. 2009. Traffic Networks: Dynamic Traffic Routing, Assignment, and Assessment. Report No. FHWA-HOP-09-015. Washington, DC: US Department of Transportation, Federal Highway Administration. 22 p.


Rao, A.M.; Rao, K.R. 2012. Measuring urban traffic congestion-a review, International Journal for Traffic & Transport Engineering 2(4): 286-305.


Riouali, Y.; Benhlima, L.; Bah, S. 2019. An Integrated Turning Movements Estimation to Petri Net Based Road Traffic Modeling, Journal of Sensor and Actuator Networks 8(3): 1-18.


Schrank, D.; Eisele, B.; Lomax, T. 2019. 2019 Urban Mobility Report. Texas A&M Transportation Institute, The Texas A&M University System. 50p.


Shirazi, M.S.; Morris, B.T. 2016. Vision-Based Turning Movement Monitoring: Count, Speed & Waiting Time Estimation, IEEE Intelligent Transportation Systems Magazine 8(1): 23–34.


Tuydes-Yaman, H.; Altintasi, O.; Sendil, N. 2015. Better estimation of origin–destination matrix using automated intersection movement count data, Canadian Journal of Civil Engineering 42(7): 490–502.


UDOT. 2020. AADT (Open Data) | UDOT Open Data. Utah Department of Transportation (UDOT). Available from Internet: http://data-uplan.opendata.arcgis.com/datasets/c2c6fe2c52b141b6afb4374d5825c611_0. (Accessed: 17 August 2020).


US Census Bureau. 2020. QuickFacts: Provo City, Utah; Orem city, Utah; United States. Available from Internet: https://www.census.gov/quickfacts/fact/table/provocityutah,oremcityutah,US/PST045219. (Accessed: August 17 2020).


Vespa, J.; Medina, L.; Armstrong, D. 2020. Demographic turning points for the United States: Population projections for 2020 to 2060. Current Population Reports No. P25-1144. 15p.


Walther, B. A.; Moore, J. L. 2005. The concepts of bias, precision and accuracy, and their use in testing the performance of species richness estimators, with a literature review of estimator performance, Ecography 28(6): 815–829.


Wu, J. H.; Thnay, C. 2001. An O–D based method for estimating link and turning volume based on counts, ITE District 6: 8–11.


Yang, Z.; Pun-Cheng, L.S.C. 2017. Vehicle detection in intelligent transportation systems and its applications under varying environments: A review, Image and Vision Computing 69: 143–154.


Zhai, W.X.; Ardian, D. 2020. Traffic flow control of the intersection in urban traffic system under the environment of internet of vehicles, Advances in transportation studies (Special Issue 1): 31–40.