LeetCode #1094 — MEDIUM

Car Pooling

Move from brute-force thinking to an efficient approach using array strategy.

The Problem

Problem Statement

There is a car with capacity empty seats. The vehicle only drives east (i.e., it cannot turn around and drive west).

You are given the integer capacity and an array trips where trips[i] = [numPassengers_i, from_i, to_i] indicates that the i^th trip has numPassengers_i passengers and the locations to pick them up and drop them off are from_i and to_i respectively. The locations are given as the number of kilometers due east from the car's initial location.

Return true if it is possible to pick up and drop off all passengers for all the given trips, or false otherwise.

Example 1:

Input: trips = [[2,1,5],[3,3,7]], capacity = 4
Output: false

Example 2:

Input: trips = [[2,1,5],[3,3,7]], capacity = 5
Output: true

Constraints:

1 <= trips.length <= 1000
trips[i].length == 3
1 <= numPassengers_i <= 100
0 <= from_i < to_i <= 1000
1 <= capacity <= 10⁵

Step 01

Brute Force Baseline

Problem summary: There is a car with capacity empty seats. The vehicle only drives east (i.e., it cannot turn around and drive west). You are given the integer capacity and an array trips where trips[i] = [numPassengersi, fromi, toi] indicates that the ith trip has numPassengersi passengers and the locations to pick them up and drop them off are fromi and toi respectively. The locations are given as the number of kilometers due east from the car's initial location. Return true if it is possible to pick up and drop off all passengers for all the given trips, or false otherwise.

Baseline thinking

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

Pattern signal: Array

Example 1

[[2,1,5],[3,3,7]]
4

Example 2

[[2,1,5],[3,3,7]]
5

Core Insight

What unlocks the optimal approach

Sort the pickup and dropoff events by location, then process them in order.

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 #1094: Car Pooling
class Solution {
    public boolean carPooling(int[][] trips, int capacity) {
        int[] d = new int[1001];
        for (var trip : trips) {
            int x = trip[0], f = trip[1], t = trip[2];
            d[f] += x;
            d[t] -= x;
        }
        int s = 0;
        for (int x : d) {
            s += x;
            if (s > capacity) {
                return false;
            }
        }
        return true;
    }
}

// Accepted solution for LeetCode #1094: Car Pooling
func carPooling(trips [][]int, capacity int) bool {
	d := [1001]int{}
	for _, trip := range trips {
		x, f, t := trip[0], trip[1], trip[2]
		d[f] += x
		d[t] -= x
	}
	s := 0
	for _, x := range d {
		s += x
		if s > capacity {
			return false
		}
	}
	return true
}

# Accepted solution for LeetCode #1094: Car Pooling
class Solution:
    def carPooling(self, trips: List[List[int]], capacity: int) -> bool:
        mx = max(e[2] for e in trips)
        d = [0] * (mx + 1)
        for x, f, t in trips:
            d[f] += x
            d[t] -= x
        return all(s <= capacity for s in accumulate(d))

// Accepted solution for LeetCode #1094: Car Pooling
impl Solution {
    pub fn car_pooling(trips: Vec<Vec<i32>>, capacity: i32) -> bool {
        let mx = trips.iter().map(|e| e[2]).max().unwrap_or(0) as usize;
        let mut d = vec![0; mx + 1];
        for trip in &trips {
            let (x, f, t) = (trip[0], trip[1] as usize, trip[2] as usize);
            d[f] += x;
            d[t] -= x;
        }
        d.iter()
            .scan(0, |acc, &x| {
                *acc += x;
                Some(*acc)
            })
            .all(|s| s <= capacity)
    }
}

// Accepted solution for LeetCode #1094: Car Pooling
function carPooling(trips: number[][], capacity: number): boolean {
    const mx = Math.max(...trips.map(([, , t]) => t));
    const d = Array(mx + 1).fill(0);
    for (const [x, f, t] of trips) {
        d[f] += x;
        d[t] -= x;
    }
    let s = 0;
    for (const x of d) {
        s += x;
        if (s > capacity) {
            return false;
        }
    }
    return true;
}

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(n)

Space

O(M)

Approach Breakdown

BRUTE FORCE

O(n²) time

O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED

O(n) time

O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.