Orlando Bus Stops Hash Table Implementation

Overview

This script implements a custom hash table using linear probing and dynamic resizing. It processes data from a JSON file containing bus stops in Orlando, identified by unique stop codes and names. The implementation demonstrates:

• Hashing with collision resolution via linear probing.
• Dynamic resizing when the load factor exceeds 50%.
• Insertion and search operations on the hash table.

Additionally, the script compares hash table performance with a Python dictionary for data loading.

---

Features

1. Hash Table Implementation:
   • Linear Probing: Handles collisions by searching for the next available bucket.
   • Dynamic Resizing: Automatically doubles the table size when the load factor exceeds 50%.
2. Custom Hash Function: Generates bucket indices based on string keys.
3. JSON Parsing: Reads and decodes bus stop data into Python objects.
4. Performance Metrics: Measures insertion and search times.

---

Output

• Total bus stops processed and inserted.
• Number of collisions during insertion.
• Results of searching for specific stop codes.
• Comparison of hash table vs dictionary data loading performance.

---

Performance Highlights

• Dynamic resizing ensures the hash table maintains efficiency even with high load factors.
• Linear probing resolves collisions but may increase search time under high load.

---