Cab, metro or two-wheeler: an NCR commute cost worksheet

Collect your own numbers first

Spend one week measuring before you calculate. For each option you are considering, record:

• Door-to-door time, from locking your room to sitting at your desk. Include the walk or auto to the metro station, security queues and the last walk. Metro trips in NCR often involve an interchange, such as Botanical Garden (Blue and Magenta) or Noida Sector 52 and Sector 51 (Blue and Aqua), and those minutes count.
• Cash cost per trip. Take metro fares from the operator's official fare information or your card's trip history, cab fares from your app's ride history, and two-wheeler costs from your fuel receipts plus parking.
• Fixed monthly costs, such as a two-wheeler EMI, insurance, servicing or a parking pass.
• Usable minutes: how much of the trip you can spend reading notes, watching a recorded class or preparing for an interview. Seated metro time is usually usable; standing in a crowded coach less so; riding a two-wheeler not at all.
• Late arrivals: how many of your trips arrived more than 15 minutes later than planned.

None of the figures in the example below are real fares or costs. They are placeholders to show the method. Replace every one.

The worksheet

Lay it out like this. One column per option, one row per measure.

Then add two derived rows: the cost of each unusable hour, and the monthly cash difference against your cheapest option. Those two rows are what make the choice clear.

The same worksheet in Python

The script takes your inputs and prints a comparison. It uses only the standard library, so it runs anywhere Python is installed.

# Commute comparison. ALL NUMBERS BELOW ARE PLACEHOLDERS, not real fares or costs.
# Replace them with your own measurements before drawing any conclusion.

WORKING_DAYS = 22

options = {
    #               cash per one-way trip, fixed per month, door-to-door min per day, usable min per day
    "Cab":           {"trip": 300, "fixed": 0,    "minutes": 110, "usable": 60},
    "Metro + auto":  {"trip": 70,  "fixed": 0,    "minutes": 130, "usable": 70},
    "Two-wheeler":   {"trip": 45,  "fixed": 2500, "minutes": 90,  "usable": 0},
}

rows = []
for name, o in options.items():
    cash = o["trip"] * 2 * WORKING_DAYS + o["fixed"]
    lost_hours = (o["minutes"] - o["usable"]) * WORKING_DAYS / 60
    rows.append((name, cash, o["minutes"] * WORKING_DAYS / 60, lost_hours))

cheapest = min(r[1] for r in rows)
print(f"{'Option':<14}{'Cash/month':>12}{'Hours/month':>13}{'Unusable h':>12}{'Extra vs cheapest':>19}")
for name, cash, hours, lost in rows:
    print(f"{name:<14}{cash:>12,}{hours:>13.1f}{lost:>12.1f}{cash - cheapest:>19,}")

With the placeholder numbers, the output reads:

Option          Cash/month  Hours/month  Unusable h  Extra vs cheapest
Cab                 13,200         40.3        18.3             10,120
Metro + auto         3,080         47.7        22.0                  0
Two-wheeler          4,480         33.0        33.0              1,400

Read it as a set of trade-offs, not a ranking. In this made-up case, the two-wheeler is the fastest but every minute on it is lost; the metro takes longest but gives the most usable time and costs least; the cab buys some usable time at a large premium. Your real numbers may flip every one of these.

Questions the numbers cannot answer

After the arithmetic, ask these, because they often decide it:

1. 1What happens on a bad day? A two-wheeler in heavy rain or during a GRAP restriction week is a different commute. See the GRAP planning guide.
2. 2What time do you finish? A late logout may fall outside your preferred option's hours or feel less safe on some stretches.
3. 3Can you combine them? Many people take the metro in the morning and a shared cab back, or ride to a station and park.
4. 4Does your employer run transport? Night-shift and some early-shift roles include cabs. Ask before you buy a two-wheeler.

Using the commute for study

If the metro gives you 60 or 70 usable minutes a day, that is a meaningful study budget over a month. Match the task to the conditions: recorded explanations and flashcards work standing up; SQL practice needs a seat and a laptop or at least a phone-friendly environment. Our guide to studying on the Blue and Aqua Lines has a routine built around that.

This worksheet is also a small analytics exercise in its own right: define the measure, collect a week of data, compute, then argue from the result. That define-collect-compute-argue loop is what the Data Analyst programme practises for 3–4 months, mostly in live online classes.