Neural Network Approach to Modelling Transport System Resilience for Major Cities: Case Studies of Lagos and Kano (Nigeria)

Suleiman Hassan Otuoze, Dexter V. L. Hunt and Ian Jefferson
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Suleiman Hassan Otuoze: Department of Civil Engineering, University of Birmingham, Edgbaston B15 2TT, UK
Dexter V. L. Hunt: Department of Civil Engineering, University of Birmingham, Edgbaston B15 2TT, UK
Ian Jefferson: Department of Civil Engineering, University of Birmingham, Edgbaston B15 2TT, UK

Sustainability, 2021, vol. 13, issue 3, 1-20

Abstract: Congestion has become part of everyday urban life, and resilience is very crucial to traffic vulnerability and sustainable urban mobility. This research employed a neural network as an adaptive artificially-intelligent application to study the complex domains of traffic vulnerability and the resilience of the transport system in Nigerian cities (Kano and Lagos). The input criteria to train and check the models for the neural resilience network are the demographic variables, the geospatial data, traffic parameters, and infrastructure inventories. The training targets were set as congestion elements (traffic volume, saturation degree and congestion indices), which are in line with the relevant design standards obtained from the literature. A multi-layer feed-forward and back-propagation model involving input–output and curve fitting (nftool) in the MATLAB R2019b software wizard was used. Three algorithms—including Levenberg–Marquardt (LM), Bayesian Regularization (BR), and a Scaled Conjugate Gradient (SCG)—were selected for the simulation. LM converged easily with the Mean Squared Error (MSE) (2.675 × 10 −3 ) and regression coefficient (R) (1.0) for the city of Lagos. Furthermore, the LM algorithm provided a better fit for the model training and for the overall validation of the Kano network analysis with MSE (4.424 × 10 −1 ) and R (1.0). The model offers a modern method for the simulation of urban traffic and discrete congestion prediction.

Keywords: traffic congestion; critical infrastructure; urbanization; resilience; sustainability transport; modelling; artificial neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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