  Traditional vehicle routing algorithms do not consider the changing nature of
traffic. While implementations of Dijkstra's algorithm with varying weights
exist, the weights are often changed after the outcome of algorithm is
executed, which may not always result in the optimal route being chosen. Hence,
this paper proposes a novel vehicle routing algorithm that improves upon
Dijkstra's algorithm using a traffic prediction model based on the traffic flow
in a road network. Here, Dijkstra's algorithm is adapted to be dynamic and time
dependent using traffic flow theory principles during the planning stage
itself. The model provides predicted traffic parameters and travel time across
each edge of the road network at every time instant, leading to better routing
results. The dynamic algorithm proposed here predicts changes in traffic
conditions at each time step of planning to give the optimal forward-looking
path. The proposed algorithm is verified by comparing it with conventional
Dijkstra's algorithm on a graph with randomly simulated traffic, and is shown
to predict the optimal route better with continuously changing traffic.
