Calculating route distance(driving as mode of transit) instead of Haversine distance

calculate_route_distance.py

@@ -0,0 +1,110 @@
+import requests
+from typing import Dict, List
+import csv
+import random
+import math
+
+distanceMatrixAPIKey = "SnWswgHwafCJDGDCYI6esdDC1iX2nBt3C0UWt7y1K4qiHeFpaim75dVAQTZD0Dfa"  # demo api key
+PREF_DISTANCE_THRESHOLD = 2
+
+
+def read_tsv(file_path: str) -> List[Dict[str, str]]:  # read the tsv file
+    """
+    Function to read the tsv file for school.tsv and centers.tsv
+    Return a list of schools/centers as dicts.
+    """
+    data = []
+    with open(file_path, 'r', newline='', encoding='utf-8') as file:
+        reader = csv.DictReader(file, delimiter='\t')
+        for row in reader:
+            data.append(dict(row))
+    return data
+
+
+def school_sort_key(s):
+    # intent: allocate students from schools with large students count first
+    # to avoid excessive fragmentation
+    return (-1 if int(s['count']) > 500 else 1) * random.uniform(1, 100)
+
+
+def append_row_to_tsv(row_data):  # append the row to the tsv file
+    # Open the TSV file in append mode and create a csv writer object
+    file_path = "./results/distance.tsv"
+    with open(file_path, 'a', newline='', encoding='utf-8') as tsv_file:
+        writer = csv.writer(tsv_file, delimiter='\t')
+
+        # Write the row data to the TSV file
+        writer.writerow(row_data)
+
+
+def haversine_distance(lat1, lon1, lat2, lon2):  # calculate haversine distance
+    """
+    Calculate the great circle distance between two points
+    on the earth specified in decimal degrees
+    - Reference: https://en.wikipedia.org/wiki/Haversine_formula
+    """
+    # Convert decimal degrees to radians
+    lat1, lon1, lat2, lon2 = map(math.radians, [lat1, lon1, lat2, lon2])
+
+    # Haversine formula
+    dlon = lon2 - lon1
+    dlat = lat2 - lat1
+    a = math.sin(dlat/2)**2 + math.cos(lat1) * \
+        math.cos(lat2) * math.sin(dlon/2)**2
+    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))
+    radius_earth = 6371    # Average Radius of Earth in km
+    distance = radius_earth * c
+    return distance
+
+
+# calculate route distance using distancematrix api, considers driving as means of transit
+def calculate_route_distance(lat1, lon1, lat2, lon2):
+    """
+    Calculate the best route distance between two points in driving mode in meters
+    - Reference: https://distancematrix.ai/
+    """
+
+    # distancematrix.ai url
+    url = f"https://api-v2.distancematrix.ai/maps/api/distancematrix/json?origins={
+        lat1},{lon1}&destinations={lat2},{lon2}&key={distanceMatrixAPIKey}"
+
+    try:
+        response = requests.get(url)
+        if "limit exceeded" in response.text:
+            print("API limit exceeded")
+            exit(1)
+        else:
+            distance = response.json(
+                # converting to km
+            )['rows'][0]['elements'][0]['distance']['value'] / 1000
+
+        return distance
+    except Exception as e:
+        print(e)
+        return 99999999  # 8(9s) to indicate error
+
+
+schools = sorted(
+    read_tsv("./sample_data/schools_grade12_2081.tsv"), key=school_sort_key)
+centers = read_tsv("./sample_data/centers_grade12_2081.tsv")
+
+for s in schools:
+    school_lat = s.get('lat')
+    school_long = s.get('long')
+    if len(school_lat) == 0 or len(school_long) == 0:
+        continue
+
+    for c in centers:
+        if s['scode'] == c['cscode']:
+            distance = 0
+            append_row_to_tsv([s['scode'], c['cscode'], distance])
+            continue
+
+        haversine_distance = haversine_distance(float(school_lat), float(  # calculating haversine distance to filter out the centers that are far away
+            school_long), float(c.get('lat')), float(c.get('long')))
+        if haversine_distance <= PREF_DISTANCE_THRESHOLD:
+            distance = calculate_route_distance(float(school_lat), float(  # calculating route distance for the centers that are near
+                school_long), float(c.get('lat')), float(c.get('long')))
+
+            append_row_to_tsv([s['scode'], s['name-address'],  # appending the row to the TSV file
+                               c['cscode'], c['name'], distance])