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Www.conduits.eu Using Key Performance Indicators for traffic management and Intelligent Transport Systems as a prediction tool Vienna, 23 October 2012.

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Presentation on theme: "Www.conduits.eu Using Key Performance Indicators for traffic management and Intelligent Transport Systems as a prediction tool Vienna, 23 October 2012."— Presentation transcript:

1 www.conduits.eu Using Key Performance Indicators for traffic management and Intelligent Transport Systems as a prediction tool Vienna, 23 October 2012 N. Eden Transportation Research Institute, Technion – Israel Institute of Technology A. Tsakarestos - Technische Universität München I. Kaparias - City University London A. Gal-Tzur-Technion – Israel Institute of Technology P. Schmitz-Brussels-Capital Region S. Hauptmann-Kapsch TrafficCom S. Hoadley-POLIS

2 www.conduits.eu 2 Outline  KPIs Framework  KPIs for Decision Making  Models & Tools  CONDUITS DST  Validation

3 www.conduits.eu 3 Roles of KPI (Cities’ Requirements)  Assess benefits Cost vs. benefit of investment Assess the usefulness of ITS as a whole Identify the limits of ITS  Assist Decision Making  Contract Monitoring  Promote cities’ interests

4 www.conduits.eu 4 KPI’s Categories

5 www.conduits.eu KPIs Data Sources Real Life Measurements Transportation Model KPI Evaluation PastFuturePresent Predictive KPIs

6 www.conduits.eu CONDUITS DST Framework Traffic Efficiency Safety Social Inclusion & Land Use Transportation Modeling Tools VISUM Aimsun… TransCAD… Real Life Applications AVIVIMMunicipal DB SCOOT… Regional DB VISSIM Pollution

7 www.conduits.eu 7 Pollution KPI Where: KPI –Pollution KPI W VT – Vehicle type weighting factor W ET – Emission type weighting factor Q VT,ET – Quantity of emission type per vehicle type

8 www.conduits.eu Predictive Pollution KPI Predictive Pollution KPI 1 St Stage Recommended Tool VISSIM EMI Model External Emission Model

9 www.conduits.eu 9 Emissions Model Types  Average-speed mean travelling speed, VKT  Traffic-situation particular traffic situations (e.g. ‘stop-and-go’)  Traffic-variable traffic flow variables (e.g. average speed, traffic density, queue length, etc.) Validation of road vehicle and traffic emission models – A review and meta-analysis Smit et. Al, Atmospheric Environment, Volume 44, Issue 25, August 2010, Pages 2943–2953

10 www.conduits.eu 10 High Resolution Emissions Model Types  Cycle-variable various driving cycle variables (e.g. idle time, average speed, positive kinetic energy)  Modal engine or vehicle operating o Similar data requirements as Cycle variable Validation of road vehicle and traffic emission models – A review and meta-analysis Smit et. Al, Atmospheric Environment, Volume 44, Issue 25, August 2010, Pages 2943–2953 VERSIT+ (EnviVer) PHEM, AIRE (Transport for Scotland, SIAS,TRL)

11 www.conduits.eu 11 CONDUITS DST Architecture

12 www.conduits.eu 12 Validation  Zurich City personal  Brussels Technische Universität München

13 www.conduits.eu 13 Brussels Indicative Results

14 www.conduits.eu 14 Conclusions  Predictive KPI Framework Development  Support for political decision making  Next Steps CONDUITS Mobility KPI’s Investigation of Road Safety Prediction KPI


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