WASUP Q4 201829 November

Agenda4:30 Welcome Jonathan Butow

4:35 The Power of Analytics and Machine Learning in

Automated Traffic Enforcement David Slack-Smith

5:00 Machine Learning for Safety Peter Condon

5:30 New Capabilities. New Opportunities Jonathan Butow

5:55 Wrap Up Hanlie Erwee

6:10 Networking

The Power of Analytics and Machine Learning in Automated Traffic Enforcement

• Road Safety Council and the Road Safety Commission are responsible for the State’s road safety strategy, Towards Zero 2008 – 2020.

• The strategy aims to reduce those people killed and seriously injured on WA roads by 11,000 by 2020.

• Initiatives and investments are guided by the four cornerstones of safe speeds, safe vehicles, safe road use, safe roads and roadsides.

• Primary source of information is crash data.

Background

• Crash data is often of poor quality, and requires a significant amount of domain expertise to interpret correctly.

• Significant time is spent by analysts manipulating data.

• Furthermore, much of the more specific analysis e.g. vehicle information, requires matching crash records with those held by other agencies.

• Because of this complexity, being able to answer information requirements clearly can be a challenge.

Background

Case Study –Automated Traffic Enforcement

• Automated traffic enforcement is a foundation of the safe speeds and safe road use cornerstones.

• There are a mix of assets deployed under this program, mobile, fixed and red light speed.

• Rigorous and thorough site selection is critical to ensure these assets are deployed as effectively as possible.

• Site selection reports are reviewed and endorsed by a steering committee with members at the executive level.

Automated Traffic Enforcement

• All WA intersections are ranked using a weighting methodology developed by Monash University Accident Research that considered all crash types and severities.

• Crashes are weighted based on severity with all natures included.

• This list is then refined by WA Police and Main Roads WA based on the presence of an existing camera, suitability and feasibility.

Current State

• Minimal data prep outside the platform, aside from one merge

• Data prep inside the platform consisted of some date formatting and then deriving a number of variables within the VA module.

• Next step was to explore the data. We created a simple visualisation to assess the quality of the data and to provide a point of reference for later model building.

Model Building

• The first model we built was a logistic regression model

• We had some initial issues due to missing values being excluded

• The data was partitioned in 40/60 validation/training split.

Model Building

• Next we created a decision tree model, using the same predictors and a random forest model, which was not terrible successful in predicting.

• We also used a gradient boosting model

• Once we had created the models we created our model comparison, using the model comparison object, which selected the logistic regression as the champion model.

Model Building

• Based on the logistic regression model we created a new variable to assess the probability of KSI occurring

• We then ranked intersections based on the sum of these probabilities to find the most ‘risky’ intersections.

• This identified a number of intersections that we had previously not identified.

• A reduction factor based on research from Monash University was applied to the total number of crashes from the new and old lists.

Model Building

• The savings generated by the modelling were the same as the existing method.

• However, the ease of reproduction has already resulted in a significant time saving to the Commission, in addition to being better communicated.

• To better assess risk, the model will be expanded to include volume data and red light contravention to improve accuracy.

Conclusions and Next Steps

Agenda4:30 Welcome Jonathan Butow

4:35 The Power of Analytics and Machine Learning in

Automated Traffic Enforcement David Slack-Smith

5:00 Machine Learning for Safety Peter Condon

5:30 New Capabilities. New Opportunities Jonathan Butow

5:55 Wrap Up Hanlie Erwee

6:10 Networking

Machine Learning for safety

• Build, maintain & operate transmissionand distribution assets: South WestInterconnected Network (SWIN)

• 1.1+ million customers• ~264,000 street lights• ~237,800 solar PV installations*• ~570 battery systems*

~102,000km circuit wire

254,920 km2

The

SWIN

~861,000poles & towers

17,047GWhelectricity transported

* As at 31/5/18

About Western Power

Power Generation Retailer

Western Power

Our Customers

What we do

Network evolution is reliant on community behaviour, technology advancement rates, regulation and policy

Current SWIS model

Future model with small number of islanded

systems

Extreme model without centralised network

Future model with variable network

types

Integrated Network

Fringe Disconnection

Modular Network

Fully Decentralised

BRANCH NETWORK

MESH NETWORK

MICROGRID STAND-ALONE POWER SYSTEM (SPS)KEY

1. Problem definition

2. Key inputs

3. Data not considered

4. Aggregation points

5. Modelling process

6. Partial Dependence plots

Contents

Problem definition

• Western Power has an outstanding safety record

• Understanding incidents is considered an effective way to improve, and we want to consider the data differently

• Project goal is to build a model to predict who is likely to be involved in a near miss each day

• Information will be used to our existing safety strategy

• Incidents

• Timesheets

• Weather

• Position history

• Date

Key inputs

• Contractor and office based staff near misses

• Personal details

• Telematics

Data not considered

• Individuals are too unpredictable with available data

• Work locations (depots) are too coarse to be useful

• Team (primary reporting code) provide a natural aggregation point for people who typically work closely together

Aggregation points

• Random Forest was used due to past performance on similar models

• SAS Enterprise Miner automatic variable selection

• Receiver Operator Characteristic (ROC) curve on validation data used to select champion model

• Test dataset used to validate performance of champion model

• Champion model retrained on full dataset

Modelling process

• Completely black box models aren’t acceptable

• Partial Dependence plots iteratively rescore model with a single variable set to each possible value

• Results shows the influence of single variable on the outcome

Partial Dependence plots

• Frontline leaders need more detailed explanations than Partial Dependence plots can provide

• LIME can explain the results of any predictive model

• Iteratively make small changes to part of the input, rescoring the model, and fitting a linear model on the results

Local Interpretable Model-Agnostic Explanations (LIME)

Head office363 Wellington StreetPerth, WA 6000westernpower.com.au

Agenda

4:30 Welcome Jonathan Butow

4:35 The Power of Analytics and Machine Learning in

Automated Traffic Enforcement David Slack-Smith

5:00 Machine Learning for Safety Peter Condon

5:30 New Capabilities. New Opportunities Jonathan Butow

5:55 Wrap Up Hanlie Erwee

6:10 Networking

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New Capabilities. New OpportunitiesNew SAS and what it means for you?

Jonathan Butow

Advanced Analytics Innovation

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• Thank you

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New Dynamic Market Place

1976 2018

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1997

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2005

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Insight Discovery& Model Build

Model Governance& Deployment

Cloud NativeDeployment &Architecture

Support forModernisation

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Support for modern ML/DL techniques

• Decision Trees• Generalized Linear Models

• K-means, K-modes, K-Prototype Clustering• Linear Regression• Logistic Regression• Nonlinear Regression

• Generalized Additive model• Non-parametric Logistic Regressions• Ordinary Least Squares Regression• Partial Least Squares Regression• Principal Component Analysis

• Quantile Regression• Factorization Machines• Gradient Boosting• Random Forest

• Support Vector Machines• Convolutional Neural Networks• Recurrent Neural Networks

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Model Interpretability

• Partial Dependence (PD)

• Individual Conditional Expectation (ICE)

• Local Interpretable Model-agnostic Explanations (LIME)

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Model Deployment and Publishing

Building models is academic.Deploying them is economics.

Gartner 2018

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Automated Model Deployment Options

BATCH SCORING REAL/NEAR-REAL TIME SCORING

Base SAS

In-Database

CAS API

Event Stream Processing

CAS API

MAS/REST API

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SAS® CloudDescriptions

Managed Services

Your software or infrastructure can be hosted or remotely managed by SAS experts 24/7.

Results-as-a-Service

Give us your data and problem, and we give you the answers on which you can take action.

Software-as-a-Service

Off-the-shelf offerings designed to scale and fit for purpose. Sign up, log in, and get to work. Can be modified to your future needs.

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• General policy on virtualized environments

• Connectivity to cloud native data repositories (S3, SQL Server on Azure)

• SAS Analytics for Containers• SAS Analytics for Containers on

SAS Viya*• SAS support of Docker Containers

and Kubernetes Orchestration• SAS support of Cloud Foundry

• Amazon: AWS Quick Start*• Microsoft Azure• Oracle Cloud• Google Cloud Platform• Managed Analytic Service

Providers…

SAS® on Cloud ProvidersExample Offerings

SAS® on Cloud Providers

General Support for Cloud Providers

Cloud Deployment Patterns Cloud-Specific Offerings

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r e se r v e d.

New Opportunities

Modernise your current SAS implementation to appeal to broader audience

Tackle new analytics requirements using SAS Deep learning/Gradient Boosting algorithms

Get to production and deploy your model quicker by leveraging automated REST API model deployment

Reduce overall project complexity and time to market by leveraging SAS cloud solutions and support

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Agenda

4:30 Welcome Jonathan Butow

4:35 The Power of Analytics and Machine Learning in

Automated Traffic Enforcement David Slack-Smith

5:00 Machine Learning for Safety Peter Condon

5:30 New Capabilities. New Opportunities Jonathan Butow

5:55 Wrap Up Hanlie Erwee

6:10 Networking

Wrap Up

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Best Presentation Award Winner Q2 2018

Dean HiniSAS Melbourne User Group

(SMUG)

Wrap Up• Thank you

• Presenters• Committee• Audience

• Survey – Please complete & hand back for Lucky Draw

• Another Survey coming your way!• Mid December till end January 2019• Revamping user groups – we need your input• Win one of 4 Google Home Hubs

• Presentations available on User Group website

• Lucky Draw

Lucky Draw

Please join us for snacks and drinks