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Typical optimizations for car routing are shortest route, fastest route, cheapest route and with constraints for specific waypoints. The rise of e-mobility poses new challenges to route planning, e.g. sparse charging infrastructure, limited range, and long charging have to be taken into account and offer room for optimization. [19]
The various problems, algorithms, and tools of cost distance analysis operate over an unconstrained two-dimensional space, meaning that a path could be of any shape. Similar cost optimization problems can also arise in a constrained space, especially a one-dimensional linear network such as a road or telecommunications network.
GraphHopper is an open-source routing library and server written in Java and provides a routing API over HTTP. [1] It runs on the server, desktop, Android, iOS or Raspberry Pi. [2] [3] By default OpenStreetMap data for the road network and elevation data from the Shuttle Radar Topography Mission is used.
Route planning: calculates optimized route based on user needs, such as starting point, destination, and travelling mean, et cetera. This functionality could be extended to driving assistant. Tracking: shows where the tracked object have been. (This functionality relies past positioning data to be stored, so not just the "software" technically).
The above method is primitive real-time signal optimization at best. This method will optimize one traffic signal at a time. However, in the real world, a motorist's commute involves driving through multiple signals. Thus, multiple traffic signals need to be collectively synchronized in order to be effective.
SUMO has also been proposed as a toolchain component for the development and validation of automated driving functions via various X-in-the-Loop and digital twin approaches. [3] [4] SUMO is used for research purposes like traffic forecasting, evaluation of traffic lights, route selection, or in the field of vehicular communication systems. SUMO ...
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