Impact of Horizontal Curves and Percentage of Heavy Vehicles on Right Lane Capacity at Multi-lane Highways

  • Ahmed Mohamed Semeida Assistant Professor of Highways and Traffic Engineering, Department of Civil Engineering, Faculty of Engineering, Port-Said University
Keywords: multi-lane highway, road geometric properties, traffic characteristics, right lane capacity, artificial neural networks,


In the present research, the influence of road geometric properties and traffic characteristics on the right lane capacity value is explored for horizontal curves. The non-traditional procedure (artificial neural networks - ANNs), is adopted for modelling. The research utilizes 78 horizontal curves that provide the traffic and road geometry data, of which55 curves are classified as four-lane and the rest as six-lane ones. Two types of models are introduced to explore the right lane capacity as capacity at curves, and the capacity loss between curves and tangents. The results show that, for horizontal curves, the most effective variables affecting both road types are the percentage of heavy vehicles in traffic composition (HV) followed by radius of curve (R), and the lane width (LW). Furthermore, the capacity loss is also highly affected by R followed by HV. The derived outcomes present a remarkable move towards the beginning of an Egyptian highway design guide.

Author Biography

Ahmed Mohamed Semeida, Assistant Professor of Highways and Traffic Engineering, Department of Civil Engineering, Faculty of Engineering, Port-Said University

Civil Engineering

Highways and Traffic Engineering


Kerner BS. Three phase Traffic Theory and Highway Capacity. Physica A. 2004;333:379-450. doi: 10.1016/j.physa.2003.10.017

Transportation Research Board. Highway Capacity Manual. 4th ed. Washington, DC: Transportation Research Board, National Research Council; 2010.

Minderhoud M, Botma H, Bovy P. Assessment of roadway capacity estimation methods. Transport Res Rec. 1997;1572:59-67. doi: 10.3141/1572-08

Hashim IH, Abdel-Wahed TA. Effect of Highway Geometric Characteristics on Capacity Loss. Transport Sys Eng & Inf Tech. 2012;12(5):65-75. doi: 10.1016/S1570-6672(11)60223-7

Iwasaki M. Empirical Analysis of Congested Traffic Flow Characteristics and Free Speed Affected by Geometric Factors on an Intercity Expressway. Transport Res Rec. 1997;1320:242-250. Available from:

Ibrahim AT, Hall FL. Effect of Adverse Weather Conditions on Speed-Flow- Occupancy Relationships. Transport Res Rec. 1994;1457:184-191. Available from:

Shankar V, Mannering F. Modeling the Endogeneity of Lane-Mean Speeds and Lane- Speed Deviations: A Structural Equations Approach. Trans Res Part A. 1998;32: 311-322. doi: 10.1016/S0965-8564(98)00003-2

Bang KL. Indonesian Highway Capacity Manual. Department of Public Works.Jakarta, Indonesia: Directorate General Highways; 1997.

Yang X, Zhang N. The marginal decrease of lane capacity with the number of lanes on highway. Proceedings of the Eastern Asia Society for Transportation Studies. 2005;5:739-749. Available from:

Ben-Edigbe J, Ferguson N. Extent of capacity loss resulting from pavement distress. Proceedings of the Institution of Civil Engineers: Transport. 2005;158:27-32. Available from:

Velmurugan S, Madhu E, Ravinder K, Sitaramanjaneyulu K, Gangopadhyay S. Critical evaluation of roadway capacity of multi-lane high speed corridors under heterogeneous traffic conditions through traditional and microscopic simulation models. Ind. Roads Cong. 2010;556: 235-264. Available from:

Arasan V, Arkatkar S. Derivation of Capacity Standards for Intercity Roads Carrying Heterogeneous Traffic using Computer Simulation. Procedia. 2011;16:218-229. doi: 10.1016/j.sbspro.2011.04.444

García A, Torres AJ, Romero MA, Moreno AT. Traffic Microsimulation Study to Evaluate the Effect of Type and Spacing of Traffic Calming Devices on Capacity. Procedia. 2011;16:270-281. doi: 10.1016/j.sbspro.2011.04.449

Semeida AM. New Models to Evaluate the Level of Service and Capacity for Rural Multi-Lane Highways in Egypt. Alex Eng J. 2013;52(3):455-466. doi: org/10.1016/j.aej.2013.04.003

Semeida AM. Derivation of level of service by artificial neural networks at horizontal curves: a case study in Egypt. Eur Trans Res Rev. 2015;7(1):1-12. doi: 10.1007/s12544-014-0152-2

General Authority of Roads, Bridges and Land Transport. Cairo, Egypt: "GARBLT", System of Traffic counting data; 2009.

Van Arem B, van der Vlist MJM, de Ruiter JCC, et al. Design of the Procedures for Current Capacity Estimation and Travel Time and Congestion Monitoring. DRIVE-11, Project V2044. Commission of the European Communities; 1994.

Minderhoud M, Botma H, Bovy P. Roadway capacity using the product-limit approach. 77th Annual Meeting of the Transportation Research Board. Washington D.C; 1998.

Semeida AM, El-Shabrawy M. Impact of multi-lane pavement condition on passenger car traffic. Građevinar. 2016;68(8):635-644. doi: 10.14256/JCE.1466.2015

"NeuroSolutions 7".Gainesville, Florida: NeuroDimension, Inc.; 2014.

Tarefder RA, White L, Zaman M. Neural Network Model for Asphalt Concrete Permeability.Mat Civ Eng. 2005;17:19-27. Available from:

How to Cite
Semeida AM. Impact of Horizontal Curves and Percentage of Heavy Vehicles on Right Lane Capacity at Multi-lane Highways. PROMET [Internet]. 2017Jun.27 [cited 2020Feb.17];29(3):299-0. Available from: