Visual Analytics for Improved Management of Transportation Operations Jesus A. Martinez Southwest Research Institute Transpo October 2012

Agenda What is visual analytics? Sources of data Putting the data to use Scenario I – Incident Root Cause Analysis Scenario II – Tolling Customer Relationship Management Advice for Developing ITS Visualizations

A Picture is Worth More than a Thousand Words Disparate systems have been generating massive quantities of data for years and years One Sunguide® ATMS database has 250+ GB and is rapidly growing Challenge is to visually summarize the data in a way that has meaning and is able to drive decisions

Growing Industry Adoption Health Information Exchange –Identify high risk populations –Target cost saving interventions –Visualize city wide patient movement Process Analytics –Manufacturing and chemical –Analyze process variables –Improve performance and simplify reporting PictureItSettled® - Negotiation Decision Support –Analyze 1000s of historical cases –Generate informed negotiation strategy –Predict negotiation outcomes

PictureItSettled PictureItSettled® 1. Plan Negotiation 2. Predict Outcomes

The Need for Visual Analytics in the ITS Industry Avg Travel Time Road 110 Road 220 RoadDateTimeTravel Time Volume Road 110/12/20029:15am1015 Road 210/12/20029:15am2017 Road 110/12/20029:16am1022 Road 210/12/20029:16am2021 ………… Road 110/12/20129:15am1030 Road 210/12/20129:15am2032 ? Example visualization: Road conditions at one point in time: Road conditions for the past 10 years: Millions of rows

Consolidate Multiple Data Sources Data in disparate systems, transformed into a common format Data Warehouse Weather Police Accident Reports Construction Traffic, DMS, Road Ranger Etc. Tolling Systems

Using Visual Analytic in ITS Management Scenario I Incident Root Cause Analysis Scenario II Tolling Customer Relationship Management

Scenario I - Incident Root Cause Analysis High rate of incidents at single location Cause is not obvious from looking at incident reports Assume something else is going on Use power of combined data to expose real problem

Step 1 – View Incident Heat Map

Step 2 – Select Target Hotspot

Step 3 – View Incident Details

Step 4 – View Problem Area Timeline Option 1: Manually add/remove sources to explore correlation

Step 4 – View Problem Area Timeline Option 2: Allow the computer to search for patterns automatically Considers all available data sources Search for patterns in the data Common patterns bubble to the top Event PatternFrequency High Congestion, Morning70% High Congestion, Rainfall55% Icy, Football game30% ……

Using Visual Analytic in ITS Management Scenario I Incident Root Cause Analysis Scenario II Tolling Customer Relationship Management

Scenario II – Tolling Customer Relationship Management Web or tablet based dashboard Shows key performance indicators Number of tag reads Revenue Combine with data mining Customer classification Customer attrition prediction Marketing efficiency

Scenario II – Tolling Customer Relationship Management Data mine tag reads to determine type of customer Commuter Commercial Single Trip Target ad campaign to underperforming groups

Scenario II – Tolling Customer Relationship Management Predict customer attrition Identify customers that may discontinue use of the toll road Send individual promotional materials

Scenario II – Tolling Customer Relationship Management Are the promotions sent out actually working? How many promotions should be sent out? What population should be targeted for maximum effect?

Advice for Developing ITS Visualizations Involve stakeholders at all levels from the beginning Explain the role and value of visual analytics Know your data Location – how to access? Quality – how much preprocessing? Quantity – on what scale? Go for quick wins Identify problems that can be measured Target visual analytics to cause a measureable improvement Quantifiable value of visual analytics will drive further investment

Questions?