routeplanning

Route planning is the process of determining a path from a starting point to a destination within a network, with the aim of optimizing one or more objectives such as travel time, distance, cost, or reliability. In road networks, nodes represent intersections and edges represent road segments, with weights encoding the travel cost or duration. Weights may be static or time-dependent to reflect traffic conditions, incidents, or dynamic pricing.

Algorithms for route planning fall into several categories. Classical shortest-path algorithms include Dijkstra's algorithm and Bellman–Ford, which find optimal routes in graphs with nonnegative or general weights, respectively. A* augments these searches with heuristics to speed up finding a path to a target. Bidirectional search starts from both ends to reduce exploration. For large-scale networks, speedups such as contraction hierarchies, landmark-based methods (ALT), and hub labeling are commonly used to enable fast queries on offline or semi-online data.

Multi-criteria and time-dependent planning: In practice, travelers may optimize multiple criteria (time, distance, number of transfers). Multi-objective or Pareto-optimal approaches can be used, sometimes combined with user preferences. Time-dependent networks model how edge weights vary by departure time; stochastic or robust models handle uncertainty in traffic.

Data and tools: Route planning relies on road-network data from GIS sources, OpenStreetMap, or commercial providers, together with real-time traffic feeds and historical patterns. Routing engines power consumer apps, logistics platforms, and autonomous systems.

Applications and challenges: Applications include navigation, fleet optimization, and emergency response. Challenges include data quality, real-time updates, scale, dynamic changes, multi-criteria decision making, energy or environmental constraints, and privacy concerns.