  Conventional route planning services typically offer the same routes to all
drivers, focusing primarily on a few standardized factors such as travel
distance or time, overlooking individual driver preferences. With the inception
of autonomous vehicles expected in the coming years, where vehicles will rely
on routes decided by such planners, there arises a need to incorporate the
specific preferences of each driver, ensuring personalized navigation
experiences. In this work, we propose a novel approach based on graph neural
networks (GNNs) and deep reinforcement learning (DRL), aimed at customizing
routes to suit individual preferences. By analyzing the historical trajectories
of individual drivers, we classify their driving behavior and associate it with
relevant road attributes as indicators of driver preferences. The GNN is
capable of representing the road network as graph-structured data effectively,
while DRL is capable of making decisions utilizing reward mechanisms to
optimize route selection with factors such as travel costs, congestion level,
and driver satisfaction. We evaluate our proposed GNN-based DRL framework using
a real-world road network and demonstrate its ability to accommodate driver
preferences, offering a range of route options tailored to individual drivers.
The results indicate that our framework can select routes that accommodate
driver's preferences with up to a 17% improvement compared to a generic route
planner, and reduce the travel time by 33% (afternoon) and 46% (evening)
relatively to the shortest distance-based approach.
