Evaluating Ridership Forecast Accuracy in Indian Urban Transit Projects Using MAPE and Regression Analysis

Urban transport systems in India are undergoing rapid transformation, with significant investments in metro rail, Bus Rapid Transit (BRT), and related infrastructure. Despite these investments, the performance of several systems has consistently fallen short of expectations, resulting in considerable economic, social, and financial implications. A key contributing factor is the persistent overestimation of ridership forecasts in Detailed Project Reports (DPRs), which undermines the long-term viability of these projects.

This study critically evaluates the performance of operational metro, BRT, and Intermediate Public Transport (IPT) corridors using a Projected vs. Achieved Performance framework. The magnitude of forecasting error is first quantified using the Mean Absolute Percentage Error (MAPE), which reveals an average forecast bias exceeding 1500% in newly operational metro systems, highlighting the scale of the planning discrepancy.

To investigate the underlying causes of this bias, a Multiple Linear Regression (MLR) model is employed to examine the relationship between achieved ridership and key planning variables commonly used in DPR forecasts, including income levels, network expansion, and modal competition. The results indicate that the forecasting bias is significantly associated with an overestimation of the effects of network expansion and a systematic underestimation of competition from Intermediate Public Transport (IPT).

A comparative analysis of high-error and low-error projects further provides insights into the structural limitations of existing forecasting practices. Based on these findings, this study proposes data-driven recommendations for adopting more robust, econometrically grounded, and locally adaptive forecasting methodologies, which are essential for improving the sustainability and planning effectiveness of future urban transit investments in India.