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, Ian Jefferson

Research output: Contribution to journalArticlepeer-review

45 Downloads (Pure)

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.

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

Keywords

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

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Management, Monitoring, Policy and Law

Fingerprint

Dive into the research topics of 'Neural network approach to modelling transport system resilience for major cities: case studies of Lagos and Kano (Nigeria)'. Together they form a unique fingerprint.

Cite this