Time-Dependent Route Planning for Truck Drivers

We study the problem of computing time-dependent shortest routes for truck drivers. In contrast to conventional route planning, truck drivers have to obey government regulations that impose limits on nonstop driving times. Therefore, route planners must plan break periods in advance and select suitable parking lots. To ensure that maximum driving times are not exceeded, predictable congestion due to, e.g., peak hours should also be taken into account. Therefore, we introduce the truck driver routing problem in time-dependent road networks. It turns out that the combination of time-dependent driving times with constraints imposed by drivers' working hours requires computation of multiple time-dependent profiles for optimal solutions. Although conceptually simple, profile search is expensive. We greatly reduce (empirical) running times by calculating bounds on arrival and departure times during additional search phases to only query partial profiles and only to a fraction of the parking lots. Carefully integrating this approach with a one-to-many extension of time-dependent contraction hierarchies makes our approach practical. For even faster queries, we also propose a heuristic variant that works very well in practice. Excellent performance of our algorithms is demonstrated on a recent real-world instance of Germany that is much harder than time-dependent instances considered in previous works.