Winner of Sydney Data Showdown 2019. Case Study: https://www.contino.io/case-studies/case-study-enabling-realtime-visualisation-and-forecasting-in-transportation Video: https://youtu.be/_ocU9dd9Xec High-velocity transport network in the state of NSW across Bus, Rail, Ferry and Light Rail generates an immense amount of data each day. As a forward-looking government organisation, Transport for NSW is constantly innovating to improve services based on diagnostic insight as well as anticipating taxpayer needs using predictive foresight. Find out how Contino helped TfNSW implement an end to end data and forecasting platform for real-time network traffic predictions within 9 weeks. Using AWS SageMaker, DeepAR, Lambdas and Snowflake.

1. 1
AI Driven Transport
Agency of the Future

2. 2
Enabling “Near Real Time” Opal
Data Lake +
Data Warehouse +
Forecasting
Yun Zhi Lin
Head of Engineering
Contino
Lucas Rafagnin
Principal Consultant
Contino
From the team that brought you
Quantino.ai stock predictions
Winners of Sydney Data Showdown
2019. Case Study here

3. TfNSW Data Driven Innovations and Open Data
3

4. $MM Question:
How many people are on the NSW
Transport Network now and
tomorrow?
4

5. Goals (Objective KPI)
Near real time insights and forecast at 5
minute intervals all 5 transport modes
5
Business Value and KPIs (OGTM)
Tactics
1. Near Real Time Visualisation
2. Forecast based on multiple features
3. Secretary and Executive Visibility
4. Operational Decision making
5. Short delivery timeframe
6. Initially very limited team and budget
Objective
Near real time patronage insights and
foresight to better improve internal and
external customer experience
Mission: data driven organisation leading the development of safe, integrated and efficient transport
systems for the people of NSW
Measures (Tactic KPI)
1. Data updates every 5 minutes
2. 80% for 24/48 hours ahead
3. Mobile Accessible and responsive
4. Operational dashboard
5. Time to value: 1 month
6. Productivity and cost optimisation,
“serverless” where possible

6. 6
Our Challenges
We need
this ASAP Where are all the
data points?
Time to get
strategic! Call
Contino!
Consistency,
Correlation,
Correctness
Security is
not a after
thought!
Visualisation?
Exec vs Ops UX
We need ML
for that!
Experiment
and fail fast!
UX, Business,
Data Science,
Customers
More training!
I can do this
all day!

7. Multi Disciplinary AI Squad
Engineers
Data
Scientists
Innovation
Manager Business
SMEs
Customers
Site Reliability
EngineerLucas

8. Lean Delivery & Client Investment
(Momentum + Outcome Based + Agile Flexibility)
8
$$
$
Value
Time
$$
$$
Phase 1 (3 weeks)
Ingestion Pipeline, Data Lake,
Snowflake DW, Web Visualisations,
Dev/DataOps
Phase 2 (6-9 weeks)
SageMaker Predictions, Tableau
Visuations, Continuous Training
Phase 3
Enhanced ETL, Deeper
data analysis
?? ?? ?? ??
Real Options

9. 9
The Architecture & Value Chain
Starting with Value, then
Constraints and Security

10. ML Algorithms Showdown
10
Linear Regression
Linear Learner
Neural Network
Deep AR
❌ Does not take seasonal patterns
into account
❌ Too sensitive to variances in
patronage and features
❌ Provided low accuracy
✓ Handle seasonal patterns
✓ More data required
😕 Difficult to debug
😕 Confidence intervals are subjective
Correlate ~2 years of travel data with weather, school and public holidays
Output network patronage numbers by mode

11. Travel Pattern
Profiling
Real Time Data (TimeTable + Opal)
Historical Data
KPI Performance and Future Improvements
11
KPIs
✓ Data updates every 5 minutes not due
data source vendor, not our solution
✓ Forecast 24/48 hours ahead with 90+%
accuracy (exceed 80% KPI)
✓ Mobile Accessible and responsive
executive dashboard
✓ Tableau Operational dashboards
✓ Time to value: 3 weeks (exceed 1
month KPI)
✓ Rapid productivity and pay per use
model with fully Serverless
architecture. Including Data Warehouse
The Road Ahead
● Travel patterns (where you going?)
● Station occupancy (crowded tomorrow?)
● Root causes of disruptions/anomalies
● Prescriptive replacement buses
● Data sharing

12. Appendix

13. Near Real Time Network Insights and Prediction
13

14. But there’s something AI/ML can
never match,

15. … And It’s Awesome Contino Stickers and
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