The waiting mechanic is the part that trips people up.
Model the train schedule as a graph where stations are nodes and each train creates a path of directed edges between consecutive stations. Then perform a graph traversal (BFS/DFS) from the start station, allowing transfers at shared stations, to check if the end station is reachable.
Pro tip: Clarify whether the schedule is static or dynamic, and discuss how to handle large-scale data with indexing or parallel processing, showing awareness of real-world ML system constraints.
Confirm that passengers can wait at stations and transfer between any trains that share a station, regardless of arrival times. Ask about input size, time constraints, and whether the schedule is fixed.
Represent each station as a node. For each train, add directed edges from each station to the next station in its list. This captures the possible movements along a single train.
Since passengers can transfer at shared stations, the graph naturally allows this because all trains visiting a station connect to the same node. Waiting is implicitly allowed as there are no time constraints on edges.
Use BFS or DFS starting from the start station to determine if the end station is reachable. BFS is preferred for shortest path or if the graph is large, but DFS is simpler for reachability.
Discuss time and space complexity: O(V+E) for traversal, where V is number of stations and E is total number of consecutive station pairs across all trains. Mention potential optimizations like early termination or bidirectional search.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.