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 Outline  KPIs Framework  KPIs for Decision Making  Models & Tools  CONDUITS DST  Validation

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 KPI’s Categories

KPIs Data Sources Real Life Measurements Transportation Model KPI Evaluation PastFuturePresent Predictive KPIs

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 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

Predictive Pollution KPI Predictive Pollution KPI 1 St Stage Recommended Tool VISSIM EMI Model External Emission Model

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 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 CONDUITS DST Architecture

12 Validation  Zurich City personal  Brussels Technische Universität München

13 Brussels Indicative Results

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