  Providing real time information about the arrival time of the transit buses
has become inevitable in urban areas to make the system more user-friendly and
advantageous over various other transportation modes. However, accurate
prediction of arrival time of buses is still a challenging problem in
dynamically varying traffic conditions especially under heterogeneous traffic
condition without lane discipline. One broad approach researchers have adopted
over the years is to segment the entire bus route into segments and work with
these segment travel times as the data input (from GPS traces) for prediction.
This paper adopts this approach and proposes predictive modelling approaches
which fully exploit the temporal correlations in the bus GPS data.
Specifically, we propose two approaches: (a) classical time-series approach
employing a seasonal AR model (b)unconventional linear, non-stationary AR
approach. The second approach is a novel technique and exploits the notion of
partial correlation for learning from data. A detailed analysis of the marginal
distributions of the data from Indian conditions (used here), revealed a
predominantly log-normal behavior. This aspect was incorporated into the above
proposed predictive models and statistically optimal prediction schemes in the
lognormal sense are utilized for all predictions. Both the above temporal
predictive modeling approaches predict ahead in time at each segment
independently. For real-time bus travel time prediction, one however needs to
predict across multiple segments ahead in space. Towards a complete solution,
the study also proposes an intelligent procedure to perform (real-time)
multi-section ahead travel-time predictions based on either of the above
proposed temporal models. Results showed a clear improvement in prediction
accuracy using the proposed methods,
