Supervisor
Dr. Muhammad Iqbal
Programme
MSc in Data Analytics
Subject
Computer Science
Abstract
Accurate forecasting of pedestrian activity is important for smart city planning, retail analytics, and public service management. This study presents a forecasting framework using Long Short-Term Memory (LSTM) neural networks that integrates multiple data sources, including weather, calendar variables, and engineered temporal features. The model development follows a structured pipeline from baseline univariate models to a fully optimized multivariate model using feature engineering and hyperparameter tuning techniques such as Hyperband and Bayesian optimization. The final model achieved a Mean Absolute Error of 24.05 and explained 97.10% of the variance in unseen data, effectively capturing both regular patterns and short-term fluctuations in pedestrian footfall. The findings highlight the value of multimodal data integration and structured optimization for improving urban time series forecasting.
Date of Award
2025
Full Publication Date
2025
Access Rights
open access
Document Type
Capstone Project
Resource Type
thesis
Recommended Citation
Al Adhami, A.
(2025) Optimizing LSTM Neural Network for Multimodal Multivariate Footfall Prediction. CCT College Dublin.
DOI: https://doi.org/10.63227/652.299.113