Road navigation systems are important systems for pedestrians, drivers, and autonomous vehicles. Routes provided by such systems can be unintuitive, and may not contribute to an improvement of users' mental models of maps and traffic. Automatically-generated explanations have the potential to solve these problems. Towards this goal, in this paper we propose algorithms for the generation of explanations for routes, based on properties of the road networks and traffic. We use a combination of inverse optimization and diverse shortest path algorithms to provide optimal explanations to questions of the type "why is path A fastest, rather than path B (which the user provides)?", and "why does the fastest path not go through waypoint W (which the user provides)?". The explanations reveal properties of the map—such as speed limits, congestion and road closure—that are not compatible with users' expectations, and the knowledge of which would make users prefer the system's path. We demonstrate the explanation algorithms on real map and traffic data, and conduct an evaluation of the properties of the algorithms.
|Title of host publication||IEEE International Conference on Intelligent Transportation Systems|
|Publication status||Accepted/In press - 2023|