UR:BAN Networked Transportation System. Good Practice Case of Industry-led R&D Project with Good Prospects of Post-project Exploitation

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1 UR:BAN Networked Transportation System Good Practice Case of Industry-led R&D Project with Good Prospects of Post-project Exploitation

2 Programme Mobility and Transportation Technologies "Mobility and Transport Technologies The third Transport research program of the German Federal Government: Coordinated by ministry of Economics and Technology (BMWi) Other ministries involved: transport, research, environment, agriculture adopted by the federal government in Feb Part of the high-tech strategy of the German government Circa EUR 300 million for period BMWi share about 52 million to 59 million EUR per annum for technology development Sources:

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4 Motivation und Challenge Current Situation progressive urbanization leads to more and more people living in urban areas Motorized individual transportation bears the brunt of personal mobility and delivery of goods Objective/ Challenge Result Stronger concentration of mobility in urban areas High multifarious demand leads to conflicts in the limited space Transportation processes not optimal Example Düsseldorf Commuters Inhabitants Share of motorised individual transportation: 75% der Commuters 43% of inner city traffic +~100% of delivery traffic Development of intelligent infrastructure and intelligent network with vehicles of different drive systems for an energy optimal transport system. The aim is to optimize the traffic efficiency and reduce emissions in urban areas.

5 Partners in Project UR:BAN OEMs Adam Opel AG Audi AG BMW AG Daimler AG MAN Truck & bus AG Volkswagen AG Automotive Suppliers Robert Bosch GmbH Continental Automotive GmbH Continental Safety Engineering International Continental Teves AG & Co. ohg Research Centers Federal Highway Research Institute German Centre for Aerospace ev Fraunhofer Society Universities University of Applied Sciences of the Saarland Institute of Automotive Engineering of the RWTH Aachen TU Braunschweig TU Chemnitz TU Munich University of the Federal Armed Forces Munich The universities of Duisburg-Essen, Kassel and Würzburg Enterprises TomTom Development Germany GmbH ifak Magdeburg ev TRANSVER GmbH Cities City of Dusseldorf City of Kassel City of Braunschweig

6 Project Structure UR:BAN - VV Cooperative Infrastructure Networked Transport System Regional Network Optimal use of energy by adaptive driving route guidance StrategicRouting 30min, > 5km Urban Roads Electronic Horizon: Energy-optimised and Trafficoptimized Driving, Avoid Stopping Intelligent Intersections Energy-optimised and Trafficoptimized Waiting, Starting and Decision making Forward-looking Driving < 15min, < 5km Tactical Driving < 2min, < 1km

7 TP Regional Network Problem and Objectives Petrol and Diesel Propulsion Systems Speed Time Hybrid and Electric Propulsion Systems Recommend best route for optimal energy use Speed for different types of powertrains consideration of current traffic situation, of urban strategies and of traffic light cycles Time Objective: efficient traffic flow and reduction of emissions in urban areas

8 TP Urban Roads Drive Adaptive Vehicle Functions Infrastructure provides networkwide preview of traffic light cycles in entire network Traffic light changes to red in 5 seconds Processing of Information Traffic light can no longer be reached in green phase Vehicle Functions & Assistance Systems Delay Assistant Phase-optimised Driving Behaviour Energy Recuperation Gently sail toward red light Traffic light changes to green in 10 seconds Traffic light can be reached in green phase Green Wave Assistant Green Phase Adaptive Energy Recuperation Speed Reduction Sail Through light turns green

9 TP Urban Roads Infrastructure Applications Forecast Traffic-sensitive traffic light system Problem: forecasting traffic light changes must also consider traffic-sensitive changes Solution: applications embedded into infrastructure capable of forecasting changes in a dynamic system Forecast Point and duration of stop Where will the vehicle stop? Development and integration of applications for estimating the point of stopping and duration of stop by determining the length of cues on the basis of current traffic situation and local sensor data Platoon Management Problem: Trucks approach more slowly Truck convoys are torn apart by green waves that are optimised for car traffic higher emissions Solution: Process for adapting traffic light cycles on particular arteries to truck platoons

10 Traffic Management Center Forecast of Changes Urban Roads: Forecasting Traffic Light Changes Cooperative Toolbox (TL Data Server) Service-Provider Normal weekly cycle Traffic Sensitivity Public Transport Priority 10

11 TP Intelligent Intersections Problem and Objectives Problem of inner-city intersections: Limitations of capacity High user demand for use of limited infrastructure Inevitable and unpredictable disturbances (cues, emergency vehicles, road closures etc.) Transportation processes not efficiently organized Objective is tactical driving for greater traffic and energy efficiency: e.g. Reduction of emissions to achieve levels characteristic of normal sections of network Increase of capacity via traffic flow optimization in the context of available and optimized release times Communication and cooperation to balance the multiple demands for use on the part of motorists, cyclists, public transport, special vehicles, and other users Differences (to other sub-projects) Optimization of stopping and starting operations at intersections with the help of driver assistance systems and recommendations for the driver Additional consideration of the current situation, e.g. ambulence traffic, road closures and lane-specific information

12 TP Intelligent Intersections Driver Assistance Intersection Pilot Improved Stopping & Improved Waiting Driving tactics for long red phases & extended start-stop Improved Entry Optimized entry into intersection and adaptation to green wave Improved Decision Support Possibilities and obstacles for drivers in current traffic, e.g. change in direction/turning Improved Starting Increased alertness and automatic starting Improved Following Proactive following via approach strategy based on current situation Conceptual Approach Improvement of non-optimal traffic at nodes with help of information, communication, cooperation and driver assistance.

13 Effect of Sub-Projects Reduction of Emissions + Rise in Efficiency Regional Network Drive Adapted Guidance Powertrain-specific routing based on current traffic situation, city strategies and traffic light cycles Urban Roads Driver Assistance Green wave and proximity assistance Enhancement of infrastructure to facilitate provision of traffic data and phase adaptation Intelligent Intersections Intersection Pilot Cooperative driver assistance for more efficient manoevering with respect to energy consumption and traffic flow

14 Deployment Guide What is necessary for establishing operations in other cities? Hamburg Bremen Berlin Braunschweig Düsseldorf Kassel Köln Dresden Frankfurt Nürnberg Stuttgart Description of the transportation telematic infrastructure in Germany Clustering of different stages of development Development of clustered reference architectures Viable and practical test and demonstration concepts Transferability study München

15 Prospects for Exploitation of Results Scientific and Technical Prospects of Exploitation: Builds on firm foundation laid by previous national and international R&D projects in the area of collaborative technologies (AKTIV-VM, SIM-TD) Makes use of existing technologies, but expands their application to includenew aspects: Consideration of special requirements of application in the urban context Consideration of optimisation of vehicle energy consumption in urban traffic management strategies (not just speed and distance) Inclusion of test fields ensures that developed solutions are analysed and optimised under real conditions a first step towards even wider implementation of the systems.

16 Prospects for Exploitation of Results Economic Prospects of Exploitation: Profile of R&D consortium: includes all relevant actors R&D phase will be followed by a market evaluation to assess the conditions for economic eploitation/commercialisation Hope to roll out technologies for application in further cities in Germany Favorable policy drivers: EU policies concerning spread of sustainable transportation technologies and increasing problems in urban areas associated with high CO2 and particulate emissions will pressure municipalities to provide for more sustainable transportation systems

17 Thanks for your attention! German NCP for Transport TÜV Rheinland Consulting GmbH Am Grauen Stein Cologne Tel Fax David Doerr 17

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