Development of MLR, ANN and ANFIS Models for Estimation of PCUs at Different Levels of Service

Document Type : Regular Article

Authors

1 Research Scholar, Department of Civil Engineering, National Institute of Technology, Warangal, India

2 Assistant Professor, Department of Civil Engineering, National Institute of Technology, Warangal, India

Abstract

Passenger car unit (PCU) of a vehicle type depends on vehicular characteristics, stream characteristics, roadway characteristics, environmental factors, climate conditions and control conditions. Keeping in view various factors affecting PCU, a model was developed taking a volume to capacity ratio and percentage share of particular vehicle type as independent parameters. A microscopic traffic simulation model VISSIM has been used in present study for generating traffic flow data which some time very difficult to obtain from field survey. A comparison study was carried out with the purpose of verifying when the adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN) and multiple linear regression (MLR) models are appropriate for prediction of PCUs of different vehicle types. From the results observed that ANFIS model estimates were closer to the corresponding simulated PCU values compared to MLR and ANN models. It is concluded that the ANFIS model showed greater potential in predicting PCUs from v/c ratio and proportional share for all type of vehicles whereas MLR and ANN models did not perform well.

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References

[1]     Anand S, Sekhar SVC, Karim MR. Development of Passenger Car Unit (PCU) values for Malaysia. J East Asia Soc Transp Stud 1999;3:73–80.
[2]     Tracey JA, Zhu J, Crooks KR. Modeling and inference of animal movement using artificial neural networks. Environ Ecol Stat 2011;18:393–410. doi:10.1007/s10651-010-0138-8.
[3]     Moghaddamnia A, Ghafari Gousheh M, Piri J, Amin S, Han D. Evaporation estimation using artificial neural networks and adaptive neuro-fuzzy inference system techniques. Adv Water Resour 2009;32:88–97. doi:10.1016/j.advwatres.2008.10.005.
[4]     Jang J-SR. ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 1993;23:665–85. doi:10.1109/21.256541.
[5]     Lin CT, Lee CSG. Neural fuzzy system. Prentice Hall, Englewood Cliffs, 1995.
[6]     Tay J-H, Zhang X. Neural Fuzzy Modeling of Anaerobic Biological Wastewater Treatment Systems. J Environ Eng 1999;125:1149–59. doi:10.1061/(ASCE)0733-9372(1999)125:12(1149).
[7]     Azamathulla HM, Chang CK, Ab. Ghani A, Ariffin J, Zakaria NA, Abu Hasan Z. An ANFIS-based approach for predicting the bed load for moderately sized rivers. J Hydro-Environment Res 2009;3:35–44. doi:10.1016/j.jher.2008.10.003.
[8]     Keller EL, Saklas JG. Passenger Car Equivalents From Network Simulation. J Transp Eng 1984;110:397–411. doi:10.1061/(ASCE)0733-947X(1984)110:4(397).
[9]     Ramanayya T V. Highway capacity under mixed traffic conditions. Traffic Eng Control 1988;29:284–7.
[10]    Fan HSL. Passenger car equivalents for vehicles on Singapore expressways. Transp Res Part A Gen 1990;24:391–6. doi:10.1016/0191-2607(90)90051-7.
[11]    Elefteriadou L, Torbic D, Webster N. Development of passenger car equivalents for freeways, two-lane highways, and arterials. Transp Res Rec J Transp Res Board 1997:51–8.
[12]    Chandra S, Kumar U. Effect of Lane Width on Capacity under Mixed Traffic Conditions in India. J Transp Eng 2003;129:155–60. doi:10.1061/(ASCE)0733-947X(2003)129:2(155).
[13]    Arasan VT, Arkatkar SS. Microsimulation Study of Effect of Volume and Road Width on PCU of Vehicles under Heterogeneous Traffic. J Transp Eng 2010;136:1110–9. doi:10.1061/(ASCE)TE.1943-5436.0000176.
[14]    Bains MS, Ponnu B, Arkatkar SS. Modeling of Traffic Flow on Indian Expressways using Simulation Technique. Procedia - Soc Behav Sci 2012;43:475–93. doi:10.1016/j.sbspro.2012.04.121.
[15]    Mehar A, Chandra S, Velmurugan S. Passenger Car Units at Different Levels of Service for Capacity Analysis of Multilane Interurban Highways in India. J Transp Eng 2014;140:81–8. doi:10.1061/(ASCE)TE.1943-5436.0000615.
[16]    Chandra S, Mehar A, Velmurugan S. Effect of traffic composition on capacity of multilane highways. KSCE J Civ Eng 2016;20:2033–40. doi:10.1007/s12205-015-0479-9.
[17]    Khademi F, Akbari M, Nikoo M. Displacement determination of concrete reinforcement building using data-driven models. Int J Sustain Built Environ 2017;6:400–11. doi:10.1016/j.ijsbe.2017.07.002.
[18]    Jiang G, Keller J, Bond PL, Yuan Z. Predicting concrete corrosion of sewers using artificial neural network. Water Res 2016;92:52–60. doi:10.1016/j.watres.2016.01.029.
[19]    Mashhadban H, Kutanaei SS, Sayarinejad MA. Prediction and modeling of mechanical properties in fiber reinforced self-compacting concrete using particle swarm optimization algorithm and artificial neural network. Constr Build Mater 2016;119:277–87. doi:10.1016/j.conbuildmat.2016.05.034.
[20]    Zhou Q, Wang F, Zhu F. Estimation of compressive strength of hollow concrete masonry prisms using artificial neural networks and adaptive neuro-fuzzy inference systems. Constr Build Mater 2016;125:417–26. doi:10.1016/j.conbuildmat.2016.08.064.
[21]    Behfarnia K, Khademi F. A comprehensive study on the concrete compressive strength estimation using artificial neural network and adaptive neuro-fuzzy inference system. Int J Optim Civ Eng 2017;7:71–80.
[22]    Biswas S, Chandra S, Ghosh I. Estimation of Vehicular Speed and Passenger Car Equivalent Under Mixed Traffic Condition Using Artificial Neural Network. Arab J Sci Eng 2017;42:4099–110. doi:10.1007/s13369-017-2597-9.
[23]    Guisan A, Edwards TC, Hastie T. Generalized linear and generalized additive models in studies of species distributions: setting the scene. Ecol Modell 2002;157:89–100. doi:10.1016/S0304-3800(02)00204-1.
[24]    Khademi F, Akbari M, Jamal SM, Nikoo M. Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Front Struct Civ Eng 2017;11:90–9. doi:10.1007/s11709-016-0363-9.
[25]    Khademi F, Jamal SM, Deshpande N, Londhe S. Predicting strength of recycled aggregate concrete using Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System and Multiple Linear Regression. Int J Sustain Built Environ 2016;5:355–69. doi:10.1016/j.ijsbe.2016.09.003.
[26]    Mehar A, Chandra S, Velmurugan S. Highway capacity through vissim calibrated for mixed traffic conditions. KSCE J Civ Eng 2014;18:639–45. doi:10.1007/s12205-014-0440-3.

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History

  • Receive Date: 26 July 2017
  • Revise Date: 06 September 2017
  • Accept Date: 08 September 2017

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