1. Application of GPS

2. Simple beginnings
We started with the basic premise of measuring legs and dwells
We started with the principle of using all results within a SD of the mean

3. Data example
3866 different movements in a month in august from 1280 in January

4. First evolutions
What more can we do with GPS If we can see where the norm, we can see where the Network fails to deliver If we can see the norm, we can see where things change. If we can measure the norm we can tell the impact of change

5. Impact of a speed restriction

6. Carrying the legacy

7. Improving Right time (to 10 sec’s)
We know the exact time a train arrives
We know the exact time a train departs
We know the exact time a train passes
We do challenge manual times
We don’t challenge Auto times, we do challenge the associated attribution

8. Picking up cautionary aspects

9. SRT measurement

10. Dwells, Peak and Off peak

11. Berth offset variability
TRUST reported a two minute dwell at a station. ARS logs show that CF1812 signal was not at red and the route from CF1806 was requested at 07:29:13,when 2K12 stepped from GW1816 to GW1814.The train ahead(1F62) was in berth CF804 at that time, so CF1806 should have gone to green. The initial step from GW approach to GW1818 took place at 07:25:49 with ARS reporting 2K12 had “sufficient route up to 1806” at that time

12. AEGIS- map the assets

13. AEGIS – pattern identification

14. Correcting what we find
When we find an SRT issue we open up a can of worms
Why is it deficient
What's the solution
Value vs journey time
Resource impact/plan capability

15. Next stage Automated notification of change
CIF/GENIUS headcode mapping
Measurement of variance before/after RHTT
Leg linking
MySQL direct DB query interface