Predict missed-connection risk before checkout

Probabilistic connection reliability for travel platforms

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The Issue

Tight connections
*
delay uncertainty

= missed connections, support load, churn

That's why:

When rail networks are uncertain, digital travel needs a reliability layer.

Trainator prediccts missed-connection risk. Weeks before departure.

Lets's find a reliable journey
with Trainator:

Example Journey : Hamburg Hbf to Paris Est

Select between Option A or B by clicking.

Option A

10:29

Hamburg

ICE73

15:23

Karlsruhe

(0:09)

Make connection:
??%

15:32

Karlsruhe

ICE9572

18:16

Paris

Option B

12:29

Hamburg

ICE927

16:14

Frankfurt

(0:16)

Make connection:
79%

16:30

Frankfurt

EST 9521

20:28

Paris

Trainator

Example only. Times and probabilities are illustrative. Request a sandbox below to validate your routes

This example shows, how Trainator is able to classify connections in order to ensure that users can take an informed choice when booking their journey. Trainators algorithms generate a risk-curve for every arrival and with that is able to compute the likeliness of a delay. Trainator currently generates these curves for up to 14-Days before deparute but is planning on rolling them out to reach 28 days.

Trainator offers

Connection Reliability Insights

Delay Risk Predictions

Real-Time Updates

to enable

Reliability-Aware
Routing & Ranking

Risk-Aware
Ancillaries

Risk-Free
Staff dispatching

and much more...

Curious about use cases, performance, or implementation?

Book a 20-Minute call

Detailed workflows, model performance, use-cases recommandations are shared under NDA

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