Dynamic Bus Arrival Time Prediction: A temporal difference learning approach

Public transport buses suffer travel time uncertainties owing to diverse factors such as dwell times at bus stops, signals, seasonal variations and fluctuating travel demands etc. Traffic in the developing world in particular is afflicted by additional factors like lack of lane discipline, diverse modes of transport and excess vehicles. The bus travel time prediction problem on account of these factors continues to remain a demanding problem especially in developing countries. The current work proposes a method to address bus travel time prediction in real-time. The central idea of our method is to recast the dynamic prediction problem as a value-function prediction problem under a suitably constructed Markov reward process (MRP). Once recast as an MRP, we explore a family of value-function predictors using temporal-difference (TD) learning for bus prediction. Existing approaches build supervised models either by (a)training based on travel time targets only between successive bus-stops while keeping the no. of models linear in the number of bus-stops OR (b)training a single model which predicts between any two bus-stops while ignoring the huge variation in the travel-time targets during training. Our TD-based approach attempts to strike an optimal balance between the above two class of approaches by training with travel-time targets between any two bus-stops while keeping the number of models (approximately) linear in the number of bus-stops. It also keeps a check on the variation in the travel-time targets. Our extensive experimental results vindicate the efficacy of the proposed method. The method exhibits comparable or superior prediction performance on mid-length and long-length routes compared to the state-of-the art.