Prediction model for bus inter-stop travel time considering the impacts of signalized intersections
mode - bus, place - asia, planning - methods
Inter-stop travel time, bus operation, prediction model, signalized intersection, regression analysis
Since bus inter-stop travel times (BISTTs) are significant components of the route travel time, providing accurate BISTTs can effectively improve the route travel time prediction accuracy. In this paper we propose a novel prediction model for BISTTs, which takes five factors as input variables: stop distance, historical inter-stop travel times, number of intersections, intersection traffic volumes, and intersection signal timing schemes. The Lagrange Multiplier (LM) test is conducted to check autocorrelation in the model residuals. Cases of two bus routes in Harbin have been studied based on field data. Mean average errors for both bus routes are smaller than 8%, indicating the high prediction accuracy in predicting BISTTs. Sensitivity analysis is conducted and results show that excluding any one of those five factors would cause the failure of the model in the LM test, indicating that the presence of all factors is required to maintain the validity of this model.
Permission to publish the abstract has been given by Taylor&Francis, copyright remains with them.
Qi, W., Wang, Y., Bie, Y., & Ren, J. (2021). Prediction model for bus inter-stop travel time considering the impacts of signalized intersections. Transportmetrica A: Transport Science, Vol. 17(2), pp. 171-189.