Prediction of Travel Time over Unstable Intervals Between Adjacent Bus Stops Using Historical Travel Time in Both the Previous and Current Time Periods

Travel time prediction is an important issue for many people who want to know their departure time from an origin and arrival time at a destination in order to make decisions (e.g., postpone departure time at certain hours) and to reduce their waiting time at bus stops. Accurate predictions of bus travel time are necessary to know whether the travel time over target intervals between pairs of adjacent bus stops is stable or not. For this purpose, at first, we classified intervals between adjacent bus stops into two classes: stable and unstable. Next, we identified two statistically significant factors: variations of travel time in the same time periods over days and correlation of travel time between eight time-periods, which influence the bus travel time in the current time-period over unstable intervals. Then, we developed nonlinear dynamical models for predicting bus travel time over each unstable interval between adjacent bus stops for 7 time periods in a day. The proposed method basically utilizes time series methods based on Artificial Neural Network (ANN), Support Vector Machine Regression (SVR) and Random Forest (RF). We conducted experiments using bus probe data collected from November 21st to December 20th, 2013 and provided by Nishitetsu Bus Company, Fukuoka, Japan. In addition, to evaluate our proposed approach, we conducted a comparison experiment between our proposed model and the model in our previous study. Experimental results show that our proposed models can effectively improve the previous study model on the prediction accuracy of travel times over unstable intervals.