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Route-Planning

Description

A route planning system is akin to what you see in Google Maps; the objective is to find the optimal path between two locations, and to allow for more complex queries. For instance, not only can you find the shortest path from the Psychiatry Parking Lot to the Stadium, but you can also ask to stop by a bookstore and a restaurant along the way, allowing for some very interesting results. Answers can be found in submission.py

Shortest Paths

Uniform Cost Search (UCS) is used to compute the optimal path between the start and end tags. The realization of this problem can be found in the ShortestPathProblem class. To see the results, follow the following procedure:

  • Run python mapUtil.py > readableStanfordMap.txt to generate a text file of the possible locations and their tags.
  • Choose a starting location and an end tag, and implement the getStanfordShortestPathProblem function accordingly.
  • Run python grader.py 1b-custom to generate path.json.
  • Run python visualization.py. A sample of the results is shown below:

image

Unordered Waypoints

The state definition is adjusted to allow for unordered waypoints, which can be seen in the WaypointsShortestPathProblem class. To see the results, follow the following procedure:

  • Run python mapUtil.py > readableStanfordMap.txt to generate a text file of the possible locations and their tags.
  • Choose a starting location, an end tag, and a set of intermediary stops, then implement the getStanfordWaypointsShortestPathProblem function accordingly.
  • Run python grader.py 2c-custom to generate path.json.
  • Run python visualization.py. A sample of the results is shown below: image

Speeding up With A*

Two heuristics are implemented for use in the A* algorithm to speed up the process and allow for extremely quick queries. The heuristics are as follows:

  • StraightLineHeuristic computes the straight line distance between the startTag and closest location having the endTag.
  • NoWaypointsHeuristic computes the shortest path while ignoring the intermediary waypoints, akin to the first part. The NoWaypointsHeuristic, in particular, seems to speed up the computation significantly.