Towards building a bus travel time prediction model for Metro Manila

Land Transportation Sector is one of the key sectors in the Philippine economy particularly in Metro Manila. With the rapid urbanization of the Philippines, the urban transport infrastructure is expected to experience pressures posing a major risk of urban transport degradation resulting into longer travel times, economic and productivity losses. In light of this, the Land Transportation Franchising and Regulatory Board (LTFRB) along with DOST-ASTI has initiated a project on implementing a bus management system for Public Utility Vehicles utilizing real time GPS location data. This study takes on establishing a travel time prediction for the buses given a specific route. The travel time estimation was performed using Extremely Randomize Trees, a supervised machine learning algorithm. The resulting prediction set had a correlation of determination score indicative of a good predictive performance for travel time prediction.