Big Data Analytics for Predictive Modeling of Indian Public Transit Passenger Demand Patterns

Abstract

Big Data Analytics for Predictive Modeling of Indian Public Transit Passenger Demand Patterns synthesizes a multifaceted framework that amalgamates advanced data processing techniques, statistical learning theory, and linear algebra–based modeling to forecast transit usage dynamics. This study employs an integrative approach whereby heterogeneous datasets, acquired from automated fare collection systems, sensor networks, and mobile applications, are preprocessed, harmonized, and subsequently analyzed via both classical econometric models and modern machine learning algorithms. Emphasis is placed on the development and refinement of complex linear algebra models that leverage matrix decompositions and eigenvalue analyses to capture the intrinsic structure of high-dimensional data. A novel linear lagbera modeling approach is introduced to incorporate temporally lagged variables, thereby encapsulating delayed effects and intertemporal dependencies that are critical for accurately forecasting passenger demand. The methodology is underpinned by rigorous mathematical formulations—including singular value decomposition (SVD), principal component analysis (PCA), and regularized regression techniques—to mitigate noise and enhance model interpretability. Simulation experiments and theoretical analyses substantiate the performance improvements attributable to the integration of advanced linear algebraic constructs. The resultant models demonstrate substantial predictive accuracy, computational efficiency, and resilience to data heterogeneity. By bridging the methodological gap between traditional time-series forecasting and state-of-the-art linear algebraic frameworks, the research offers substantive insights for optimizing transit planning, resource allocation, and strategic decision-making in rapidly evolving urban transit networks.