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PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #41

Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The develo

The correct answer is A. 1. Build a tree-based regression model that predicts how many passengers will be picked up at. To optimize shuttle routes based on advance bookings, a regression model predicting passenger counts per stop is needed, followed by an optimization algorithm to define the route.

Submitted by carlos_mx· Apr 18, 2026ML model development

Question

Your organization wants to make its internal shuttle service route more efficient. The shuttles currently stop at all pick-up points across the city every 30 minutes between 7 am and 10 am. The development team has already built an application on Google Kubernetes Engine that requires users to confirm their presence and shuttle station one day in advance. What approach should you take?

Options

  • A
    1. Build a tree-based regression model that predicts how many passengers will be picked up at
  • B
    1. Build a tree-based classification model that predicts whether the shuttle should pick up
  • C
    1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed
  • D
    1. Build a reinforcement learning model with tree-based classification models that predict the

How the community answered

(22 responses)
  • A
    82% (18)
  • B
    9% (2)
  • C
    5% (1)
  • D
    5% (1)

Why each option

To optimize shuttle routes based on advance bookings, a regression model predicting passenger counts per stop is needed, followed by an optimization algorithm to define the route.

A1. Build a tree-based regression model that predicts how many passengers will be picked up atCorrect

Predicting the number of passengers at each shuttle station is a regression problem, as the output is a continuous numerical value. This prediction can then be used by a route optimization algorithm to define the most efficient path based on anticipated demand.

B1. Build a tree-based classification model that predicts whether the shuttle should pick up

A classification model predicts discrete categories (e.g., pick up/don't pick up) rather than the exact number of passengers, which is less useful for fine-grained route efficiency optimization.

C1. Define the optimal route as the shortest route that passes by all shuttle stations with confirmed

Defining the optimal route as simply the shortest route passing all confirmed stations does not utilize the predictive power for demand and might lead to inefficient stops if confirmed stations have very few passengers.

D1. Build a reinforcement learning model with tree-based classification models that predict the

While reinforcement learning could optimize routes, combining it with tree-based classification models is less direct for predicting passenger counts (a continuous value) than a regression approach.

Concept tested: Machine learning model selection for demand prediction and route optimization

Source: https://cloud.google.com/architecture/guidance/route-optimization

Topics

#Regression#Demand Forecasting#Route Optimization#Machine Learning Model Selection

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