Ride Matching Algorithm using Priority Queues

Data Structures

• Priority Queues: Utilized as min-heaps for efficient retrieval of the nearest available driver or rider.

Challenges

• Efficiency: Matching users with drivers in real-time to minimize wait times.
• Scalability: Handling a large number of simultaneous requests.

Benefits

• Reduced Wait Times: Users are matched with drivers efficiently.
• Improved User Satisfaction: Faster response times enhance user experience.

Algorithm

• Heap Implementation: Using priority_queue in C++ for min-heap operations.
• Matching Strategy: Match the closest available driver to a rider based on priority.

Example Code (C++)

RideMatching.cpp

Time Complexity:

• addDriver Operation (addDriver function):
  • Time Complexity: O(log D), where D is the number of drivers in the priority queue.
  • Explanation: Adding a driver to the priority queue (drivers) involves inserting the driver into the heap, which has a time complexity of O(log D).
• addRider Operation (addRider function):
  • Time Complexity: O(log R), where R is the number of riders in the priority queue.
  • Explanation: Adding a rider to the priority queue (riders) involves inserting the rider into the heap, which has a time complexity of O(log R).
• matchRide Operation (matchRide function):
  • Time Complexity: O(log D + log R)
  • Explanation: Finding a match between the top driver and top rider involves retrieving and removing elements from the priority queues (drivers and riders). Both operations have a time complexity of O(log D) and O(log R) respectively, resulting in a combined time complexity of O(log D + log R) for matchRide.

Space Complexity:

• Space Complexity: O(D + R)
  • Explanation: The space complexity is O(D + R), where D is the number of drivers and R is the number of riders currently in their respective priority queues (drivers and riders). Each driver and rider object takes up space in its priority queue.