Title: STARIMA-based Traffic Prediction with Time-varying Lags,
Abstract: Based on the observation that the correlation between observed traffic at two
measurement points or traffic stations may be time-varying, attributable to the
time-varying speed which subsequently causes variations in the time required to
travel between the two points, in this paper, we develop a modified Space-Time
Autoregressive Integrated Moving Average (STARIMA) model with time-varying lags
for short-term traffic flow prediction. Particularly, the temporal lags in the
modified STARIMA change with the time-varying speed at different time of the
day or equivalently change with the (time-varying) time required to travel
between two measurement points. Firstly, a technique is developed to evaluate
the temporal lag in the STARIMA model, where the temporal lag is formulated as
a function of the spatial lag (spatial distance) and the average speed.
Secondly, an unsupervised classification algorithm based on ISODATA algorithm
is designed to classify different time periods of the day according to the
variation of the speed. The classification helps to determine the appropriate
time lag to use in the STARIMA model. Finally, a STARIMA-based model with
time-varying lags is developed for short-term traffic prediction. Experimental
results using real traffic data show that the developed STARIMA-based model
with time-varying lags has superior accuracy compared with its counterpart
developed using the traditional cross-correlation function and without
employing time-varying lags.