1. The Science of Prediction Location Intelligence Conference April 4, 2006 How Next Generation Traffic Services Will Impact Business Dr. Oliver Downs, Chief Scientist

2. Traffic Congestion Impacts Business Productivity For Urban Areas Traffic Increases Represent Major Business Hurdle Congestion has real costs and affects business productivity –Hard costs Lost labor time from employees spending hours in traffic Extra time for pick-up and delivery/reduced production time Extra vehicles to meet “just-in-time” demands of customers and scheduling problems caused by longer delivery times –Soft costs Business credibility During the last decade in the nation’s 68 largest urban areas: –Time wasted due to traffic rose from 1.9 to 4.5 billion hours –Heavily congested roads increased from 14% to 36% –Congested roads increased from 21% to 28% –Uncongested roads decreased from 65% to 36%

3. Traffic Information is evolving to greater levels of customer value Of Traffic Information The Evolution Flow Data –Real-time speed information –Useful for digital consumption Prediction & Relevance –System-wide analysis of traffic –Useful for planning, re-routing, analysis and decisions Incident Data –Identifying trouble spots for consumers –“Nuggets of usefulness”

4. Aggregate traffic-related content from public and private sources Enhance real-time data using proprietary error detection and correction. Utilize sophisticated Bayesian modeling for prediction. Distribute to customers via XML services What We Do

5. Features: Estimates of traffic flow patterns every 15 minutes for up to one year in the future Amount of time expected for congestion to start or clear at Inrix Traffic Segments Drive time predictions for key routes in all metropolitan markets Proprietary error detection and correction of individual real-time traffic sensors significantly increases the quality of the flow reporting Traffic Service Real-Time Flow Prediction Inrix indicates the most likely duration of a current jammed segment of the highway, making it easy to calculate route times Inrix provides the time until the adjacent road segment will most likely become jammed.

6. The Bayesian approach to predictive modeling allows us to learn the parameters θ of an appropriate probabilistic generative model from a set of data, Bayes’ rule And Prediction Bayesian Reasoning To do so, we define variables in the model that correspond to the unknown parameters θ, assign priors to these variables based on our background knowledge ε and use Bayes' rule to update our beliefs about these parameters given observed data: We can then average over the learned distributions of θ to make predictions

7. Inrix Metadata –Current/ Historical Traffic –Time of Day –Day of Week –Weather –Sporting Events –Season –School Schedules –Holiday Status –Incident/ Construction Reports –Express Lane Direction A Bayesian network is a graphical model that can be used to uncover causal relationships between a large number of variables Inrix utilizes sophisticated Bayesian modeling incorporating “metadata” – attributes that we would expect to influence the observed sensor data both now and some time in the future Leveraging Bayesian Networks Prediction and Forecasting

8. The set of conditionally independent causal relationships combine to describe a series of rules that are probabilistically predictive of a given outcome of the target variables, a decision tree Each end leaf of the tree, describes a particular rule that a current traffic situation may follow, and a probability distribution over the predicted outcome “Color 109 = False and Color 118 not = False and Black Start 24 = 'None' and Pct b15 75 not = '50-75' and Color 132 = False and Black Start 144 not = 'None' and Color 119 = False and Pct b15 132 = '30-50‘” Rules Underlying the Causal Relationships Prediction and Forecasting

9. Bayesian modeling incorporating contextual “metadata” provides significantly higher quality/accuracy than simple marginal models based upon historical traffic Why Only Bayesian Models Provide Accurate Forecasts Bayesian vs. Marginal Models Rolling Stones Concert, 7.30pm at Key Area in Seattle 10/30/2005. Effect of Bayesian incorporation of metadata into forecast 48 hours ahead, for 7pm 10/30/2005. Historical Averaging Traffic is normally leaving Seattle at 7pm on a Sunday evening Bayesian Analysis Model Due to Rolling Stones Concert at Key Arena at 7.30pm we predict heavier than normal traffic near the I-5 exits entering downtown Seattle at 7pm

10. Providing intra day and predicted travel times and travel speeds for key routes Delivering time dependent, traffic influenced routing and dynamic rerouting with turn-by-turn navigation Generating 2D and 3D traffic speed maps showing real-time, predicted & forecasted traffic hot spots Use Cases Example Customer Illustrating expected times for congestion to clear Providing 1-day, 5-day or 10-day metropolitan traffic forecasts Analyzing traffic congestion bottlenecks for transportation and site planning Highlighting real-time traffic alerts on a map Developing highly personalized traffic reports and SMS alerts