UR:BAN –

Networked

Transportation

System

Good Practice Case of

Industry-led R&D Project

with Good Prospects of

Post-project Exploitation

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. 2008

• Part of the high-tech strategy of the German

government

• Circa EUR 300 million for period 2008-2011

• BMWi share about 52 million to 59 million EUR

per annum for technology development

Sources: www.bmwi.de www.tuvpt.de

• 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

• Stronger concentration of mobility in urban areas

• High multifarious demand leads to conflicts in the limited space

• Transportation processes not optimal

Example Düsseldorf

400.000 Commuters 500.000 Inhabitants

Share of motorised individual transportation:

• 75% der Commuters

• 43% of inner city traffic

• +~100% of delivery traffic

Motivation und Challenge

Current Situation Result

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.

Objective/ Challenge

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

Project Structure UR:BAN - VV

Regional Network • Optimal use of energy by adaptive

driving route guidance

Urban Roads • Electronic Horizon:

Energy-optimised and Traffic-

optimized Driving, Avoid Stopping

Intelligent Intersections • Energy-optimised and Traffic-

optimized Waiting, Starting and

Decision making

StrategicRouting

30min, > 5km

Forward-looking Driving

< 15min, < 5km

Tactical Driving

< 2min, < 1km

Cooperative Infrastructure Networked Transport System

TP – Regional Network

Problem and Objectives

Petrol and Diesel

Propulsion Systems

Hybrid and Electric

Propulsion Systems • Recommend best route for optimal energy use

for different types of powertrains

• consideration of current traffic situation, of

urban strategies and of traffic light cycles

• Objective: efficient traffic flow and reduction of

emissions in urban areas

Speed

Time

Speed

Time

TP – Urban Roads

Drive Adaptive Vehicle Functions

Infrastructure

provides network-

wide preview of traffic

light cycles in entire

network

Processing of

Information

Vehicle Functions &

Assistance Systems

Delay Assistant

Traffic light can

no longer be

reached in green

phase

Phase-optimised

Driving Behaviour Traffic light

changes to red in

5 seconds

“Green Wave” Assistant

Traffic light can

be reached in

green phase

Green Phase Adaptive

Energy Recuperation Traffic light

changes to green

in 10 seconds

• Energy Recuperation

• Gently sail toward red light

• Speed Reduction

• “Sail Through” – light turns green

TP – Urban Roads

Infrastructure Applications

Forecast

Traffic-sensitive

traffic light system

Forecast

Point and duration

of stop

Platoon

Management

• Problem: forecasting traffic light changes must

also consider traffic-sensitive changes

• Solution: applications embedded into

infrastructure capable of forecasting changes in a

dynamic system

• 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

• 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

Urban Roads: Forecasting Traffic

Light Changes Traffic Management

Center

Forecast of

Changes

Cooperative

Toolbox

(TL Data Server)

Service-Provider

Normal weekly cycle Traffic Sensitivity Public Transport Priority

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.)

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

Transportation

processes not

efficiently organized

TP – Intelligent Intersections

Driver Assistance “Intersection Pilot”

Conceptual Approach

Improvement of non-optimal traffic at nodes with

help of information, communication, cooperation

and driver assistance.

“Improved Stopping” & “Improved Waiting” Driving tactics for long red phases & extended start-stop

“Improved Starting” Increased alertness and automatic starting

“Improved Decision Support” Possibilities and obstacles for drivers in current traffic, e.g.

change in direction/turning

“Improved Following” Proactive following via approach strategy based on current

situation

“Improved Entry” Optimized entry into intersection and adaptation

to “green wave”

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

Deployment Guide

What is necessary for establishing operations

in other cities?

Düsseldorf

Braunschweig

Stuttgart

Frankfurt

Berlin

Hamburg

Bremen

Köln

Kassel

Dresden

Nürnberg

München

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

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.

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

Prospects for Exploitation of Results

17

Thanks for your attention!

TÜV Rheinland Consulting GmbH

Am Grauen Stein

51105 Cologne

www.nks-verkehr.eu

info@nks-verkehr.eu

Tel. +49 221 806 4142

Fax +49 221 806 3496

German NCP for Transport

David Doerr