2.05 - Innovation Lab - KULeuven - Open Traffic Center

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9. Dynamic Traffic Assignments

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17. Double loop detector data (in Flanders → Open data)

7. Forecast outcome of scenario’s

3. OpenTrafficCenter : Visualizing traffic within clicks

4. Automatic identification and retrieval of open data traffic measurements along the path between points

14. Availability of open data for transportation analysis

19. Reduce set - up effort

6. Automated set - up of dynamic traffic simulation

22. Nobody trusts a traffic model only modellers do Everybody trusts data only data scientists don’t

5. Realtime: Traffic state estimation • Data filtering and interpolation ( Treiber & Helbing - 2002)

1. A real - time first - order model of highway flows based on open source count data Willem Himpe, M.J. Chris Tampère

12. Dynamic network loading Dynamic shortest paths Equilibrium loading → choices 3 STEPS of a Dynamic Traffic Assignment

23. Only dynamic traffic assignment model in the world with such accurate outputs Model results Measurements

20. Automatically build any sub - network • Capacities and FD calibration o Semi - automated procedure o Focus on important features • Demand generation o Identify origins & destinations (on/off ramps) o Splitting rates at diverges o Automatic checks based on conservation of flow

18. • Characteristics o Volume: > 1.5 year o Variety: Speed / Flow / Class ↔ Events / VMS o Velocity: Realtime (minutes) o Veracity: detector errors • Open source technologies o PostgreSQL o Timescale o ... Big data analysis

27. Thank you Willem Himpe, M.J. Chris Tampère Financial support is acknowledged : • “Impulsfinanciering” by Faculty of Engineering Science KU Leuven • INEA - CEF project CONCORDA (TRAN/M2016/1364071) You can look at practically any part of anything manmade around you and think ‘some engineer was frustrated while designing this.’ It’s a little human connection. (http://xkcd.com/277) willem.himpe {at} kuleuven.be

15. Network preparation • GRB o Geographic reference map o Midscale level o All administrative roads of Flanders o Undirected graph • OSM – [highway] tags o Road characteristics o Controlled intersection o Overlap and non - connectivity • OSMNX vs PostGIS + PGRouting

8. Our mission Promote the use of ( dynamic ) traffic models o Educate Most gains are in the algorithms o Evidence based on observations Real - world networks Opportunities for open data o Reduce set - up time Large networks require huge efforts • Computation time • Calibration effort

13. Socio - demographic level Decisional level operational level Evaluation level Demand for mobility Network topology Activities Population Destination choice Departure time choice Modal choice Route choice Dep. time adjustments Trip chaining Scheduling Multimodal Network loading External factors Accidents, special events Congestion levels Network Capacity Accessibility Connectivity Information systems Traffic management systems Acquired information Acquired experience Route advices Rerouting Generalized costs (time, reliability , safety accessibility, environmental impact) Transport Demand Management Route flows Change of destination

16. Challenges for open data - based networks • Non - uniform representation o Data format (labels) o Connectivity o Aggregation level o Mini - network at intersections • Missing information o Roads • Lanes • Speed limit • Classification • Accessibility o Intersections • Turn restrictions • Signal settings • Turning lanes

24. Seamless virtual world Historical Data Real time Interactions Future predictions Digital description of real world • Reoccurring congestion • Bottleneck analysis • Activity patterns • Demand evolution • Ex post investment analysis • ... • Traffic state estimation • Operational feedback • Incident detection • Accident management • Route guidance • Mobility as a Service • ... • Infrastructural assessment • Evaluation new technology • Demographic trends • Ex ante policy evaluation • ... 24

11. Traffic Models • Netwerk effects in large regions • Dynamics of the peak hour • Congestion! • Type & location of bottleneck • Amount, length, time loss of spillback • Route choice: shortcuts • Departure time choice : widening of the peak • Applications • Planning of maintenance • Congestion pricing • Introduction of automated vehicles 19.474 Links 11.539 Nodes 1.061 Zones 31.520.188 Routes Leuven Mechelen Aalst Halle Brussels Ninove

21. ∆ 흉 = ퟏ 퐦퐢퐧 ( ~ ퟏ퐬퐞퐜 ) ∆ 흉 = ퟏퟎ 퐦퐢퐧 ( ퟏퟎ퐱 퐟퐚퐬퐭퐞퐫 ) Efficient simulations: Iterative – Link Transmission Model (I - LTM) • Aggregation of vehicles o Kinematic Wave Theory (like a gas or fluid ) o 2.56 10 10 vs 9.69 10 7 computations in large network • Aggregation of links o Analytic formulation within a link o Numeric scheme at nodes (= intersections) • Aggregation of time o Implicit formulation requires iterative solution scheme o From cursed to blessed → warm start

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