How Likely Are You to Grab a 2026 Spring Festival Train Ticket? A Probabilistic Model

Supply and Technical Limits

2012: 34 transactions/second, 1.2 million tickets/day

2013: 300 transactions/second, 5 million tickets/day

2015: 1 000 transactions/second, 10 million tickets/day

2026 peak: >2 000 transactions/second

Probability Model Construction

Basic Assumptions

Total seats on a popular train: N = 1000 Simultaneous buyers at launch: M = 50 000 System can process 2000 orders per second for the first 60 seconds

User operation delay: t seconds

Case A – Full‑Journey Ticket

Assuming 10 000 users submit full‑journey requests within the first 5 seconds, the system can handle all requests but only 1 000 seats exist.

t=0 s (pre‑fill): 1 000 seats, 10 000 requests → success ≈ 10 %

t=0 s (no pre‑fill): 800 seats, 10 000 requests → success ≈ 8 %

t=1 s: 600 seats, 8 000 requests → success ≈ 7.5 %

t=3 s: 200 seats, 5 000 requests → success ≈ 4 %

t=5 s: 0 seats → success 0 %

Case B – Segment Ticket

At launch segment tickets often show “waitlist” or “no ticket”, making direct success virtually zero; users must rely on the wait‑list system.

Impact of Operation Delay

Side‑by‑side experiment comparing a user who used the pre‑fill feature with one who did not:

Select train – pre‑fill: completed, no pre‑fill: 2 s

Select passenger – pre‑fill: completed, no pre‑fill: 2 s

Select seat – pre‑fill: completed, no pre‑fill: 1 s

Submit order – pre‑fill: 0.5 s, no pre‑fill: 1 s

Total – pre‑fill: 0.5 s, no pre‑fill: 6 s

With a processing capacity of 2 000 orders per second, a 5.5‑second delay puts roughly 11 000 users ahead of you.

Network latency adds about 500 ms (client 100 ms + transmission 100 ms + server queue 300 ms), so a click at 18:00:00.000 reaches the server at ~18:00:00.500.

Pre‑filled success ≈ 10%
No pre‑filled success ≈ 7.8%
Including network latency ≈ 9%

Third‑Party Software Effect

Assuming a 70 % detection rate for third‑party ticket‑snatching tools:

P(third‑party success) = 0.30 × 0.15 + 0.70 × 0.005 = 0.0485 (≈4.85%)
P(official pre‑fill) = 0.09 (≈9%)

Waiting‑List Mathematics

Official data reports a >70 % success rate for wait‑list orders during the 2026 Spring Festival.

Each passenger may submit up to 6 pending orders, each with up to 3 dates, allowing a maximum of 60 “date+train” combinations.

Assuming a single combination has a 35 % success probability (half of the official 70 % average):

1 combination  → 35%
10 combinations → 1‑(1‑0.35)^10 ≈ 98%
30 combinations → 1‑(1‑0.35)^30 ≈ 99.9%

Overall Success Rate Calculation

Combining pre‑fill, direct purchase, and wait‑list strategies yields:

Case A (direct full‑journey): P(A) = 10%
Case B (direct fail, wait‑list succeed): P(B) = 90% × 70% = 63%
Case C (other methods): P(C) ≈ 8%
Total ≈ 81%

Key Factors Influencing Success

Pre‑filling passenger and seat information reduces operation time to ~0.5 s, improving success by ~2 percentage points.

Full‑journey tickets have a markedly higher direct success rate than segment tickets.

Network latency and server queuing add ~0.5 s to the effective request time.

Submitting many wait‑list combinations (up to 60) dramatically raises the overall probability of obtaining a ticket.

Third‑party tools are often throttled (≈70 % detection) and provide limited advantage over official pre‑fill + wait‑list.