Using a Linear Regression Approach to Sequential Interindustry Model for Time-Lagged Economic Impact Analysis

The input-output (IO) model is a powerful economic tool with many extended applications. However, one of the widely criticized drawbacks is its rather lengthy time lag in data preparation, making it impossible to apply IO in high-resolution time-series analysis. The conventional IO model is thus unfortunately unsuited for time-series analysis. In this study, we present an innovative algorithm that integrates linear regression techniques into a derivative of the IO method, the Sequential Interindustry Model (SIM), to overcome the inherent shortcomings of statistical lags in conventional IO studies. The regressed relationship can thus be used to predict, in the short term, the accumulated chronological impacts induced by fluctuations in sectorial economic demands under disequilibrium conditions. A simulated calculation is presented to serve as an illustration and verification of the new method. In the future, this application can be extended beyond economic studies to broader problems of system analysis

Capacity Prediction Model Based on Limited Priority Gap-Acceptance Theory at Multilane Roundabouts

Capacity is an important design parameter for roundabouts, and it is the premise of computing their delay and queue. Roundabout capacity has been studied for decades, and empirical regression model and gap-acceptance model are the two main methods to predict it. Based on gap-acceptance theory, by considering the effect of limited priority, especially the relationship between limited priority factor and critical gap, a modified model was built to predict the roundabout capacity. We then compare the results between Raff’s method and maximum likelihood estimation (MLE) method, and the MLE method was used to predict the critical gaps. Finally, the predicted capacities from different models were compared, with the observed capacity by field surveys, which verifies the performance of the proposed model