Monte Carlo Commute Time Simulator (Chart.js - Stable, Cleaned)

Monte Carlo Commute Time Simulator

Problem Context

The Problem: Calculate the time it takes to commute from home to the office.

We are trying to answer:

How much time do I need to allow in order to have 75% confidence that I will arrive on time?
How much time should I allow in order to have 99.5% confidence that I will arrive on time (on days that start with an important meeting)?

The Situation:

Drive 2 miles on a highway (Segment 1): 90% probability of averaging 65 MPH, 10% probability of averaging 20 MPH due to a traffic jam.
Intersection: Encounter a traffic light that is red for 90 seconds and green for 30 seconds.
Travel 2 more miles on a surface street (Segment 2): 70% probability of averaging 30 MPH, 10% probability of 20 MPH, 10% probability of 40 MPH, and 10% probability of a traffic jam taking 30 minutes for this segment.

For demonstration purposes only.

Number of Iterations (100-50k):

Highway Segment (2 miles)

Fast Probability (%):

Fast Speed (MPH):

Slow Speed (MPH):

Traffic Light

Red Time (sec):

Green Time (sec):

Surface Street (2 miles)

Normal Prob (%):

Normal Speed (MPH):

Slow Prob (%):

Slow Speed (MPH):

Fast Prob (%):

Fast Speed (MPH):

Traffic Jam Prob (%):

Traffic Jam Time (min):

This Monte Carlo simulation demonstrates:

How to model uncertainty in a commute using probability distributions
The importance of understanding tail events in risk management
The difference between planning for regular days (75% confidence) vs. critical days (99.5%)
How to identify which segment contributes most to the overall variability

Run the simulation to see results