Simulation-based regression models to estimate bus routes and network travel times

This paper presents an approach to estimate bus route and network travel times using micro-simulation. This can be used in predicting the effectiveness of bus route designs using some network traffic measures or indicators. The used indicators are average network traffic intensity, posted speeds, route length, frequency of bus operation, and average passenger loadings (boarding and alighting). Regression models are calibrated to predict both route and overall network travel times. The prediction errors of these models were investigated and analyzed, and regression models were validated. Results indicated the validity of the calibrated regression models. Conclusions are made on how the devised models can be validated in reality and used for route planning purposes to determine best operating conditions such as the frequency.