1. SELF-ADAPTING FLOW STATUS FORECASTS Satu Innamaa VTT Technical Research Centre of Finland

2. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 20052 CONTENTS Background Purpose Study site Model Results Discussion Conclusions

3. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 20053 BACKGROUND Stationary prediction models require man-made re-calibration and collection of databases. The ability to learn while working online could improve the performance of the model. Many self-adapting traffic prediction models are based on a constantly increasing database. If all the samples are stored, the database grows fast and becomes too large to be used online. If only samples different from the samples in the database are stored, the database becomes skewed. There is a need for a prediction method capable of adapting itself but without storing all the samples in a database.

4. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 20054 PURPOSE OF THE STUDY The purpose of this study was to develop a method for making a self-adapting short-term prediction model for the flow status. Specifically, the objective was to find a method that could predict the flow status on a satisfactory level would learn by itself during online operation would also be practical for long-term online use. The method was tested on the busiest ring road around Helsinki.

5. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 20055 STUDY SITE

6. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 20056 PRINCIPLES OF THE MODEL

7. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 20057 UPDATING PRINCIPLE

8. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 20058 MODEL FOR THE TEST ROAD A model was made for the test road according to these principles. The outcome of the traffic situation was described with 5 traffic flow status classes. Flow status classes for vehicles entering the links within the next 15 minutes were predicted on the basis of weather and road condition and travel time information. The forecast was given for 5-minute periods at 5-minute intervals. Flow statusTravel speed / free speed (%) free-flowing traffic >90 heavy traffic75-90 slow traffic25-75 queuing traffic 10-25 stopped traffic <10

9. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 20059 DETAILS OF THE MODEL A model including 3 SOMs and 9 outcome distribution tables was made for each road section. Each model received as input the 3 last 5-minute median travel times of the preceding road section, the road section in question, and the following road section. A distribution of the flow status outcome classes was made for each map unit for each weather and road condition class on the basis of the samples that were closest to the map unit. A SOM, along with the outcome distributions, formed the prediction model.

10. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 200510 MODEL FOR ONLINE TRIAL

11. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 200511 RESULTS FROM THE TRIAL (1/2)

12. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 200512 RESULTS FROM THE TRIAL (2/2) Proportion of correct forecasts was >95% most of the time. Weekly rhythm was observed. The weekend traffic flow was mostly free-flowing and those forecasts succeeded better than during the working week. The prediction period had little effect on the performance of the model, while the differences between the road sections were more substantial. Nevertheless, the self-adaptation process increased the percentage of correct forecasts by 0.2 %-points per week, on average during the first 9 weeks of the trial – the average increase being 1.8 %-points per week during the first 3 weeks.

13. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 200513 DISCUSSION (1/2) Principles were developed for a self-adapting model and for the prediction of the flow status. The structure of the model made it possible for the model to learn by itself without the need to save all the data. Consequently, it made long-term online use possible, contrary to some earlier studies. The performance of the model could be considered satisfactory in relation to the coarseness of the monitoring system.

14. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 200514 DISCUSSION (2/2) The self-adapting principle improved the performance of the model as the percentage of correct forecasts increased on the average by 0.0-0.5 percentage points per week during the first 9 weeks of the trial – the average increase being more substantial during the first 3 weeks. The principles of the model can also be applied in other locations. Frequently, the practical limitations of the data collection are set by the monitoring equipment available on each site. The input area is dependent on the location and of the characteristics of the traffic flow.

15. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 200515 CONCLUSIONS If the flow status outcome classes are well separated into clusters, a model based on these principles should be able to predict even impacts of incidents better and better over time. An important aspect is that there is no need to save all the data into databases, which also makes long-term online use possible and practical. If the circumstances on the road change, the outcome distributions can be initialized again to allow the model to learn the new traffic situations by itself.

16. VTT BUILDING AND TRANSPORT Copyright © VTT Satu Innamaa, VIKING Workshop in Copenhagen 5-6 October 200516 BEST PRACTICES What best practice cases can be identified? Forecasts of the traffic situation are essential both for traffic management and information applications. Prediction models are practicle only when they are self-adapting. What specific aspects can be regarded as best practice? The whole case Are the best practices to the country in question, to a certain region or globally? Globally