  With the rise of big data technologies, many smart transportation
applications have been rapidly developed in recent years including bus arrival
time predictions. This type of applications help passengers to plan trips more
efficiently without wasting unpredictable amount of waiting time at bus stops.
Many studies focus on improving the prediction accuracy of various machine
learning and statistical models, while much less work demonstrate their
applicability of being deployed and used in realistic urban settings. This
paper tries to fill this gap by proposing a general and practical evaluation
framework for analysing various widely used prediction models (i.e. delay,
k-nearest-neighbour, kernel regression, additive model, and recurrent neural
network using long short term memory) for bus arrival time. In particular, this
framework contains a raw bus GPS data pre-processing method that needs much
less number of input data points while still maintain satisfactory prediction
results. This pre-processing method enables various models to predict arrival
time at bus stops only, by using a KD-tree based nearest point search method.
Based on this framework, using raw bus GPS dataset in different scales from the
city of Dublin, Ireland, we also present preliminary results for city managers
by analysing the practical strengths and weaknesses in both training and
predicting stages of commonly used prediction models.
