Improving Traffic in Oulu Team BlueJarkko Vatjus-Anttila, Matti Pouke, Nervo Verdezoto, Matteo Picozzi
Introduction● We decided to pick the vehicular traffic data and Wifi/BT access data as the basis of our teamwork.● The key questions for the work: ● Who: City traffic-related decision-makers and planners. ● What: A tool, which is able to show temporal information about both vehicular and pedestrian traffic. ● Where: City of Oulu, perhaps nation wide audience ● Why: To help understand temporal traffic phenomena better, and to help decision makers to detect problematic patterns, which should be taken into account in city planning. ● How: With a combination of data mining and web-based viewing tool, which allow to inspect temporal traffic data as charts, as well as fit into the city map.
Thinking of the Masterminds
Interviews● We went and visited the center of traffic control authorities, asked a few questions about the current solutions, and finally showed off a demo or ours● We got three different views on the topic: ● Traffic management employees ● Local traffic police ● City planner
How does the old system look like?Black&White map, with green intersections(color has no meaning here) How to get intersection history data: 1) RMB select one intersection 2) Select ”properties” 3) Select time/date, and export 4) Import to Excel 5) Draw a graph! 6) Goto 1) if other intersections are needed History data exists, but no statistical analysis nor graphical representation of it.
Inteview Results: Generic comments: Oulu traffic Oulu traffic police officer Oulu city planning management:● They have realtime ● Real-time calculation and ● Most important thing for information of the overall prediction of the traffic city planners is the traffic system waves would be very pedestrian traffic valuable information for estimation, because a lot of● But they do not have any Police group management designs and decisions are statistics. They have only made based on this. excel exports and manual ● An ability to place units processing ahead where the masses ● However, this info is not at are traveling, would be the moment available real-● Traffic light re-programming AWESOME time, and hence this new is slow, since data not system would be easily available ● Pedestrian traffic found AWESOME! very valuable and● Graphical, realtime, interesting. Especially ● Nowadays, they put statistical analysis of the when delivered realtime. someone to stay at one traffic is AWESOME, which intersection and count they have been requesting ● Overall, they were amount of people for some time already impressed of a demo done manually... in less than a week. It was● Alas, it is too expensive, much better than any of the they have been told :| recent ”demos” that they have seen.
Inteview Results: Improvement ideas Oulu traffic Oulu traffic police officer Oulu city planning management:● A series of traffic posts and ● Statistical traffic volume ● Pedestrian movements their cumulative traffic over combined to accident realtime time is very valuable for database with location traffic light reprogrammers. information is a wanted ● Ability to offer optimal solution places for companies to ● Programming of ”green have their outlets, based waves” especially ● Statistical estimation of on the amount of problematic at the traffic for areas where there pedestrian traffic in moment. are no sensors would be different places. very useful.● These exists a static model of oulu with adjusted traffic ● Separation of traffic capacity per road. It could be between light and heavy combined with the statistical vehicles is desired. model of the esitmated traffic data ● Reading back sensor data from Nokia Maps, would● The sensor data that was enlarge the sensor given to us does not match coverage. the capacity nor organization of the intersections. This would be needed for ”final” version.
Interview Results: Typical use-cases● Traffic planning: re-programming of traffic lights ● Now: export number of slices of history data for given intersections. Import them to excel. Calculate statistics. Calculate new optimal timings. Re-program the lights. They can do 20 lights at a time now. ● With our tool: They can select the time frame of interest. They can see staistics immediately for day/night periods separately and evaluate the reprogramming need immediately.● Police: moving of units in the field ● Now: Midsummer comes and historycally, police knows when, how and to where people are leaving the city. If the guess fails, re-placement of units in the field needs to be done. ● With our tool: From realtime data traffic vectors are calculated and they see all the time the ”traffic wave” as how it exists. They can immediately react to the change of mass. The most important factor is time.
Conclusions● We apply information visualisation techniques to get insights about traffic in Oulu.● Based on the interview feedback we validated our hypothesis and ideas.● During the project we learned a lot about data mining, data processing and information visualisation techniques.