Neural network approach to modelling transport system resilience for major cities: case studies of Lagos and Kano (Nigeria)

Research output: Contribution to journalArticlepeer-review

Authors

External organisations

  • Ahmadu Bello University

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 rele-vant design standards obtained from the literature. A multi-layer feed-forward and back-propaga-tion model involving input–output and curve fitting (nftool) in the MATLAB R2019b software wiz-ard was used. Three algorithms—including Levenberg–Marquardt (LM), Bayesian Regularization (BR), and a Scaled Conjugate Gradient (SCG)—were selected for the simulation. LM converged eas-ily 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.

Bibliographic note

Funding Information: Funding: This research was funded by the Petroleum Trust Development Fund (PTDF), Nigeria (https://ptdf.gov.ng/), which sponsored the doctoral study fellowship of Suleiman Hassan Otuoze. The APC was funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant EP/J017698/1 (Transforming the Engineering of Cities to Deliver Societal and Planetary Wellbeing) for Dexter V.L. Hunt. Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Details

Original languageEnglish
Article number1371
Number of pages20
JournalSustainability (Switzerland)
Volume13
Issue number3
Publication statusPublished - 28 Jan 2021

Keywords

  • Artificial neural network, Critical infrastructure, Modelling, Resilience, Sustainability transport, Traffic congestion, Urbanization