  This paper presents an effective framework for estimating time of arrival of
vehicles (buses) in an Intelligent Transit Management System (ITMS) having
sparse position updates. Our contributions towards this is firstly in
implementing a constrained optimization based road linestring segmenting
framework ensuring ideal segment lengths and segments with sufficient density
of vehicle position measurements which will result in valid statistics for
scenarios involving sparse position measurements. Over this we propose a
comprehensive approach for predicting traffic delays and estimated time of
vehicle arrival addressing both the spatial and temporal dependencies of
traffic. The traffic delay model is built on top of the T-GCN architecture on
which we optimally augment an adjacency matrix which models a complexly
connected road network considering the degree of influence between road
segments, enabling the traffic delay model to look beyond physical road
connectivity in predicting traffic delays and therefore producing better
estimates of arrival times to points along the designated route of the
vehicles.
