LeetCode #2651 — EASY

Calculate Delayed Arrival Time

Build confidence with an intuition-first walkthrough focused on math fundamentals.

The Problem

Problem Statement

You are given a positive integer arrivalTime denoting the arrival time of a train in hours, and another positive integer delayedTime denoting the amount of delay in hours.

Return the time when the train will arrive at the station.

Note that the time in this problem is in 24-hours format.

Example 1:

Input: arrivalTime = 15, delayedTime = 5 
Output: 20 
Explanation: Arrival time of the train was 15:00 hours. It is delayed by 5 hours. Now it will reach at 15+5 = 20 (20:00 hours).

Example 2:

Input: arrivalTime = 13, delayedTime = 11
Output: 0
Explanation: Arrival time of the train was 13:00 hours. It is delayed by 11 hours. Now it will reach at 13+11=24 (Which is denoted by 00:00 in 24 hours format so return 0).

Constraints:

1 <= arrivaltime < 24
1 <= delayedTime <= 24

Step 01

Brute Force Baseline

Problem summary: You are given a positive integer arrivalTime denoting the arrival time of a train in hours, and another positive integer delayedTime denoting the amount of delay in hours. Return the time when the train will arrive at the station. Note that the time in this problem is in 24-hours format.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Math

Example 1

15
5

Example 2

13
11

Step 02

Core Insight

What unlocks the optimal approach

Use the modulo operator to handle the case when the arrival time plus the delayed time goes beyond 24 hours.
If the arrival time plus the delayed time is greater than or equal to 24, you can also subtract 24 to get the time in the 24-hour format.

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #2651: Calculate Delayed Arrival Time
class Solution {
    public int findDelayedArrivalTime(int arrivalTime, int delayedTime) {
        return (arrivalTime + delayedTime) % 24;
    }
}

// Accepted solution for LeetCode #2651: Calculate Delayed Arrival Time
func findDelayedArrivalTime(arrivalTime int, delayedTime int) int {
	return (arrivalTime + delayedTime) % 24
}

# Accepted solution for LeetCode #2651: Calculate Delayed Arrival Time
class Solution:
    def findDelayedArrivalTime(self, arrivalTime: int, delayedTime: int) -> int:
        return (arrivalTime + delayedTime) % 24

// Accepted solution for LeetCode #2651: Calculate Delayed Arrival Time
impl Solution {
    pub fn find_delayed_arrival_time(arrival_time: i32, delayed_time: i32) -> i32 {
        (arrival_time + delayed_time) % 24
    }
}

// Accepted solution for LeetCode #2651: Calculate Delayed Arrival Time
function findDelayedArrivalTime(arrivalTime: number, delayedTime: number): number {
    return (arrivalTime + delayedTime) % 24;
}

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(log n)

Space

O(1)

Approach Breakdown

ITERATIVE

O(n) time

O(1) space

Simulate the process step by step — multiply n times, check each number up to n, or iterate through all possibilities. Each step is O(1), but doing it n times gives O(n). No extra space needed since we just track running state.

MATH INSIGHT

O(log n) time

O(1) space

Math problems often have a closed-form or O(log n) solution hidden behind an O(n) simulation. Modular arithmetic, fast exponentiation (repeated squaring), GCD (Euclidean algorithm), and number theory properties can dramatically reduce complexity.

Shortcut: Look for mathematical properties that eliminate iteration. Repeated squaring → O(log n). Modular arithmetic avoids overflow.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Overflow in intermediate arithmetic

Wrong move: Temporary multiplications exceed integer bounds.

Usually fails on: Large inputs wrap around unexpectedly.

Fix: Use wider types, modular arithmetic, or rearranged operations.