Pavement Performance Prediction of Semarang–Solo Toll Road, Indonesia, using Markov Chain Model

Roads are one of the infrastructures that significantly impact a country's economic growth. The condition of the roads usually decreases due to increasing vehicle volumes. Predicting road conditions is essential for planning future maintenance. This study aims to evaluate the pavement conditions over five years using Markov chain model for five sections of the Semarang-Solo toll road, Indonesia. Two scenarios are selected in the simulation, without-handling and with-handling programs. The results show that the historical data used to compile the transition probability matrix (TPM) from the Markov model greatly influences the simulation results in both scenarios. In addition, the simulation results also indicate that the inner lane for all segments of Semarang - Solo direction is the most crucial because these segments have a relatively high rate of decline in road steadiness and a shorter cycle time for implementing the handling program.