The estimation of the saturation flow rate is of utmost importance when defining the signal plan at intersections. Because of the numerous influential factors, the values of which are hard to be determined, the subject problem is to be regarded as an extremely complex one. This research deals with the estimation of a saturation flow rate of a shared lane with permitted left turns. The suggested algorithm is based on the application of the artificial neural networks where the data for training are received by simulation. The results obtained by the neural networks are compared with multiple linear regression and the known HCM 2010 approach for determining the saturated flow of a shared lane. The testing data have shown that the approach based on the artificial neural networks foresaw statistically significantly better values than the ones obtained by multiple linear regression, with an error of 27 veh/h against 49 veh/h. The HCM 2010 approach is significantly worse than the two others included in this research. The ways of the future development of the suggested method could include additional factors, such as the grade of the traffic lane, the proximity of the bus stops, and others.
Highway Capacity Manual. Washington, DC: Transportation Research Board, National Research Council; 2010.
Branston D, Van Zuylen H. The estimation of saturation flow, effective green time and passenger car equivalents at traffic signals by multiple linear regression. Transportation Research. 1978;12(1): 47-53. Available from: doi:10.1016/00411647(78)90107 7 [Accessed 10th Jan 2020].
Kessaci A, Farges JL, Henry JJ. On line estimation of turning movements and saturation flows in PRODYN. Proceedings of the IFAC/IFIP/IFORS Symposium on Control, Computers, Communications in Transportation, 19-21 September 1989, Paris, France. Pergamon; 1989. p. 191-197. Available from: doi:10.1016/B978-0-08-037025-5.50034-7 [Accessed 10th Jan. 2020].
Lin F-B. Saturation flow and capacity of shared permissive left-turn lane. Journal of Transportation Engineering. 1992;118(5): 611-630. Available from: doi:10.1061/(ASCE)0733947X(1992)118:5(611) [Accessed 10th Jan. 2020].
Chang G-L, Chen C-Y, Perez, C. Hybrid model for estimating permitted left-turn saturation flow rate. Transportation Research Record. 1996;1566(1): 54-63. Available from: doi:10.1177/0361198196156600107 [Accessed 10th Jan. 2020].
Glannopoulos GA, Mustafa MAS. Saturation flow and Capacity of shared lanes: Comparative evaluation of estimation methods. Transportation Research Record. 1996;1555(1): 50-58. Available from: doi:10.1177/0361198196155500107 [Accessed 10th Jan. 2020].
Minh CC, Sano K. Analysis of motorcycle effects to saturation flow rate at signalized intersection in developing countries. Journal of the Eastern Asia Society for Transportation Studies. 2003;5: 1211-1222.
Bonneson J, Nevers B, Zegeer J, Nguyen T, Fong T. Guidelines for quantifying the influence of Area Type and other factors on Saturation flow rate. College Station, TX: Texas Transportation Institute; June 2005.
Khosla K, Williams JC. Saturation flow at signalized intersections during longer green time. Transportation Research Record. 2006;1978(1): 61-67. Available from: doi:10.1177/0361198106197800109 [Accessed 10th Jan. 2020].
Radhakrishnan P, Mathew T. Passenger car units and saturation flow models for highly heterogeneous traffic at urban signalised intersections. Transportmetrica. 2011;7(2): 141-162. Available from: doi:
1080/18128600903351001 [Accessed 10th Jan. 2020].
Chen P, Nakamura H, Asano M. Lane Utilization Analysis of Shared Left-turn Lane Based on Saturation Flow Rate Modeling. Procedia-Social and Behavioral Sciences. 2012;43: 178-191. Available from: doi:10.1016/j.sbspro.2012.04.090 [Accessed 10th Jan. 2020].
Chen P, Qi H, Sun J. Investigation of saturation flow on shared right-turn lane at signalized intersections. Transportation Research Record. 2014;2461(1): 66-75. Available from: doi:10.3141/2461-09 [Accessed 10th Jan. 2020].
Bagheri E, Mehran B, Hellinga B. Real-Time Estimation of Saturation Flow Rates for Dynamic Traffic Signal Control Using Connected-Vehicle Data. Transportation Research Record. 2015;2487(1): 69-77. Available from: doi:10.3141/2487-06 [Accessed 10th Jan. 2020].
Wang L, Wang Y, Bie Y. Automatic Estimation Method for Intersection Saturation Flow Rate Based on Video Detector Data. Journal of Advanced Transportation. 2018;2018: 1-9. Available from: doi:10.1155/2018/8353084 [Accessed 10th Jan. 2020].
Ma W, Liu Y, Zhao J, Wu N. Increasing the capacity of signalized intersections with left-turn waiting areas. Transportation Research Part A: Policy and Practice. 2017;105: 181-196. Available from: doi:10.1016/j.tra.
08.021 [Accessed 10th Jan. 2020].
Zhao J, Yu J, Zhou X. Saturation flow models of exit lanes for left-turn intersections. Journal of Transportation Engineering Part A: Systems. 2018;145(3): 04018090.Available from: doi:10.1061/JTEPBS.0000204 [Acce-ssed 10th Jan. 2020].
Liu X, Lai L, Kong Y, Le Vine S. Protected turning movements of non-cooperative automated vehicles: Geometrics, trajectories, and saturation flow. Journal of Advanced Transportation. 2018;2018: 1-12. Available from: doi:10.1155/2018/1879518 [Accessed 10th Jan. 2020].
Bester CJ, Meyers WL. Saturation flow rates. Proceedings of 26th Annual Southern African Transport Conference, 9-12 July 2007, Pretoria, South Africa.
Minh C-C, Sano K. Analysis of motorcycle effects to saturation flow rate at signalized intersection in developing countries. Journal of the Eastern Asia Society for Transportation Studies. 2003;5: 1211-1222.
Dreo J, Petrowski A, Siarry P, Taillard E. Metaheuristics for hard optimization: Methods and case studies. Springer Science and Business Media; Jan. 2006.
Biratarri M, KacprzykJ. Tuning metaheuristics: A machine learning perspective. Berlin: Springer; Apr. 2009.
Shi W-M, Shen Q, Kong W, Ye B-X. QSAR analysis of tyrosine kinase inhibitor using modified ant colony optimization and multiple linear regression. European Journal of Medicinal Chemistry. 2007;42(1): 81-86. Available from: doi:10.1016/j.ejmech.2006.08.001 [Accessed 10th Jan. 2020].
Yuan M, Ekici A, Lu Z, Monteiro R. Dimension reduction and coefficient estimation in multivariate linear regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology). 2007;69(3): 329-346. Available from: doi:10.1111/j.1467-9868.2007.00591.x [Accessed 10th Jan. 2020].
Lu Z, Monteiro RD, Yuan M. Convex optimization methods for dimension reduction and coefficient estimation in multivariate linear regression. Mathematical Programming. 2012;131(1-2): 163-194. Available from: doi:10.1007/s10107-010-0350-1 [Accessed 10th Jan. 2020].
Luk K-C, Ball JE, Sharma A. An application of artificial neural networks for rainfall forecasting. Mathematical and Computer Modelling. 2001;33(6-7): 683-693. Available from: doi:10.1016/S0895-7177(00)00272-7 [Acce-
ssed 10th Jan. 2020].
Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. Technical Report, California Univ San Diego La Jolla Inst for Cognitive Science, 1985.
Da Silva IN, Spatti DH, Flauzino RA, Bartocci Liboni LH, dos Reis Alves SF. Artificial neural networks. Cham: Springer International Publishing; 2017.
Guest Editor: Eleonora Papadimitriou, PhD
Editors: Dario Babić, PhD; Marko Matulin, PhD; Marko Ševrović, PhD.
The mission of our journal is to provide high quality communication of research results, which is crucial for the effectiveness of scientific research, the promotion of scientific information, new ideas, and original research in the field of traffic and transportation engineering.
1848-4069
0353-5320
Proud publisher
Accelerating Discoveries in Traffic Science |
2024 © Promet - Traffic&Transportation journal
