TMCpro: Presence and Future of Real Time Traffic Information

TMCpro

- Presence and Future of Real Time Traffic Information

Dr. Ulrich FastenrathT-Systems –

Systems Integration

DDG Gesellschaft für Verkehrsdaten mbH

System overview

Administrator

Playout

Center

Sat-Uplink

Leased

lines

Traffic Data

ISDN

Quality

Raw Data

Content Provider

Service Provider

Terminal Device

User

100%

Traffic Modelling and

Forecasting

The TMCpro

approach to quality

Producing

Numerical

Data with

SensorsStationary

data

collection

systems improve

the quality of traffic information.

Sensor• measures traffic flow and average speed

• distinguishes cars from trucks

• reports programmable events

> 5.500 loops

DDGGSM

4.000 sensors

Detected network contains >90% of all incidents

Traffic datamanagement

center

Traffic information (Customer interface)

Product

generation Traffic analysis, Generation of traffic reports,

Calculation of travel times, Historical time series, Disturbance development forecasts,

Short term predictions,automated consistency checks,

Customer specific features

Datacollection

Communication interfaces, Data preprocessor (Plausibility checks, Aggregation, Localization)

Data sourcesLMSt VIZ / VRZ SES FCD

( )

).()()(

1)(1

,

VVVVx

PxVV

tV

xV

t

rmprmp

e

rmp

−⋅+−⋅+∂

∂−=

∂∂

+∂∂

=∂

∂+

∂∂

ρν

ρτρ

ρ

νρρ

From Traffic Data to Traffic Information

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,8

0 10 20 30 40 50 60 70 80 90 100

k [Fzg./km]

Gam

ma

gamma_s60 std_gamma_s60 std_gamma_s300 gamma_c std_gamma_c gamma_s300

Extrapolation characteristic, 2 lanes

Traffic does not behave as it is supposed to

50

60

70

80

90

100

110

120

130

140

150

02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00

Time

free

vel

ocity

[km

/h]

Calibrating the free Velocity

Telegram #1

Aggregation Interval

Telegram #2

Time Diagonal

Telegram #3

Data Time

System Time

Coming to terms with the past

Classification of zero flux(inductive loops)

0

20

40

60

80

100

0 2 4 6 8 10 12 14 16 18 20 22 24

Time of the day

fraction[%]

SV

FV

KA

Classification of zero flux(infrared detectors)

0

20

40

60

80

100

0 2 4 6 8 10 12 14 16 18 20 22 24

Time of the day

fraction[%]

SV

FV

KA

SV meas

Shown in red is the fraction of all zero flux situations which were due to stationary traffic(data are from 15.05.2002 15:00 - 20.05.2002 06:40)

Go with no flow?

0

50

100

150

07:00 08:00 09:00 10:00 11:00

time

velo

city

[km

/h]

MQLOKHILGKT

Passage of shock fronts at a virtual detector: the test position is 2284 m away from the upstream detector and 3581 m away from the downstream detector.

Is the traffic still there when nobody looks?

Detection of disturbed traffic states by DDG infrastructure

Detection of disturbed traffic states by DDG infrastructure II

Reference: BMW AG, Dr. Klaus Bogenberger, „Qualität von Verkehrsinformationen“, Straßenverkehrstechnik 10/2003

customers‘ expectation

A Scheme for measuring Product Quality

Visualisation

of Complex Dynamic Systems

Road Weather

and Road Conditions

TMC-Code Meaning1002 Danger of aquaplaning1003 Slippery road1019 Slippery road due to frost1009 Freezing rain1008 Black ice1011 Slush1112 Rain1109 Heavy rain1104 Snowfall1101 Heavy snowfall1107 sleet

Road detector system for icy conditions

Meteorological input data

Precipitation radar images

Convert TMC-Codes into weather messages

Example

for

a TMC Message

„Danger

of Aquaplaning“

The product feature „DDG road weather“

Navigation in Space and Time

Some Varieties of Traffic Forecast

?

?Growth Rate

Pre- Warning

!q

Duration

Bottlenecks

link

AB

Qarr

(A)

Qarr

(B)

recoveryrecovery

breakdownspillover

active

inactive

blocked

activity of bottlenecks

9576 bottlenecks analysed 2843 bottlenecks

considered relevant for pre-warnings

Breakdown Frequencies at Bottlenecks

Reference: Brilon, W.; Zurlinden, H.: Kapazität von Straßen als Zufallsgröße, Straßenverkehrstechnik 4/2004, S. 164-172

Flow rate (q), probability of breakdown (Pbd) and of congestion (Pc) at site Düsseldorf Mörsenbroich located along the highway A52

0%

20%

40%

60%

80%

100%

0 2 4 6 8 10 12 14 16 18 20 22 24time of day 05.07.2004 [h]

prob

abili

ty [%

]

0

300

600

900

1200

flow

rate

[vph

pl]

P_bd(+15 min)P_cq

Breakdown of traffic flow is a stochastic event, whereby probabilities of breakdown are associated with specific flow rates.

Breakdown Probabilities at Bottlenecks

Störfallmodell

Konkrete Realisierung einer Verkehrsstörung

0

400

800

1200

1600

2000

3 4 5 6 7 8 9 10 11 12 13 14 15 16Tageszeit [H]

Ver

kehr

sstä

rke

[Fz/

h/S

pur]

0

4

8

12

16

20

Ver

wei

ldau

er [m

in]

Q-IN Kapazität Cv Q-OUT Tv

breakdown

recovery

Delay caused by breakdown of traffic flow at different times Tbd

0

10

20

30

40

50

5 6 7 8 9 10 11entry time [h]

Del

ay [m

in] T_bd=5,75

T_bd=6,5T_bd=7,5T_bd=8,0

Delay Times at Bottlenecks

Pre-Warnings: Example 1

Pre-Warnings: Example

Pre-Warnings: Example 3

0%

20%

40%

60%

80%

100%

0% 20% 40% 60% 80% 100%

FPR [%]

TPR

[%]

0

50

100

150

200

250

300

VWZ

[min

] ROC

K

L

VWZ

Quality of Pre-Warnings

23,000 kmmotorways

1,280,000 kmin total

106,000 kmhighways

Motorways are not enough

Do-iTDo-iT

is

part

of the

research

and development

initiative „Verkehrsmanagement 2010“

sponsored

by

BMWi

Do-iT: The

Project

DDG Gesellschaft für Verkehrsdaten mbH

Partners:

Landeshauptstadt Stuttgart

Innenministerium Baden-Württemberg

Stadt Karlsruhe

====!"§==Mobile=Associated: T-Mobile Deutschland GmbH

Universität Stuttgart, represented byInstitut für Anwendungen der Geodäsie im Bauwesen andLehrstuhl für Verkehrsplanung und Verkehrsleittechnik

Data provision for public and private

applications

Floating Phone Data: Functional PrincipleDo-iT

FPD-Server

BTSBSC

MSC

BTS

A interface

A-bis interface

MS

Floating Phone Data

Network Probes

Identification of Active Road Users

Mobile Phone Positioning

Map-Matching & Trajectory Generation

BTS

BSC

Reference: IAGB University of Stuttgart

A linkA-bis link

BTS BSC MSC

BTS

MS

All mobiles:• Localisation Updates (in particular at LA updates)

Active mobiles only:• Handover events• Measurement Reports (~ 2 Hz)

(LAC1) -> (LAC2,CI2)

(CI1) -> (CI2)

CI,TA (=distance)Field strength

Master data needed forinterpretation:

Cell geometry

Topology data(=antenna locations)

Best server plots

Temporary Mobile Subscriber ID

Establishing

the

Data BasisDo-iT

Network

covered

and Applications

C-NetCity of Karlsruhe

C-NetCity of Stuttgart

A-Netmotorways

B-Netfederal highways

U-Netdiversion routes

AK Walldorf

AK Weinsberg

Applications

Innenministerium BW (A-Net, U-Net):• Dynamic Network Control• Traffic State of U-Net

Cities of Stuttgart and Karlsruhe (C-Net, urban U-Net):• Improvement of knowledge about current traffic situation• Estimation of Travel Times• direct measurement of the impact of network control• improvement of control strategies

DDG (all networks):• Navigate, TMCpro

Do-iT

Comparison of Location Area Updatesand traffic flow as measured by stationary sensor

0

500

1000

1500

2000

2500

3000

3500

4000

0 3 6 9 12 15 18 21 0

time of day [HH]

Rat

e [e

vent

s/h]

Q-SESLAC-Updates

Flow of mobiles ≠

traffic flowSuperposition of more than one traffic flow

LA boundary

Cell boundary

LA 1LA 2

CI 2

Frequency of transitions LA1 → (LA2,CI2)

Measurement

of Traffic

FlowDo-iT

Network

covered

at Abis LevelDo-iT

2211112228

17980

1800215639

32092

5417

9

54178

3413

LAC CI Azimut Time28961 2211128939 12228 120 06:28:3228939 17980 45 06:28:4028939 18002 225* 06:29:4628939 15639 45* 06:30:2428939 32092 240 06:31:1728939 54179 300 06:31:3828939 54178 160 06:32:2428950 3413 06:33:39

* = Tunnel

Example

at A Level: Free TrafficDo-iT

Do-iTProjektnetz

Example

for

Incident

DetectionDo-iT

3413(28950)->55508(28682)

0

5

10

15

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30

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40

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50

55

60

0 4 8 12 16 20 24

arrival time [h]

trave

l tim

e [m

]

NET-FCDStationary

Sensors

A Truck Accident

observed

by

Floating Phones

Do-iT

Waves

of Holiday Traffic, southbound

Do-iT

It

can

always

get

worse.

Do-iT

The

Traffic

Jam is

no Respecter

of Persons.

Do-iT

Early

Example

from

a Trunk Road

Do-iT

TMCpro: Neue Inhalte für die dynamische Navigation. Baulich getrennte Fahrspuren.

Do-iT

TMCpro: Neue Inhalte für die dynamische Navigation. Drei-Phasen-Theorie.

Do-iT

TMCpro: Neue Inhalte für die dynamische Navigation. Synchronisierter Verkehr.

Do-iT

TMCpro: Neue Inhalte für die dynamische Navigation. Rückreisewellen.

Do-iT

TMCpro: Neue Inhalte für die dynamische Navigation. Auch das gibt es.

Do-iT

12.07.2007

0

300

600

900

5 6 7 8 9 10 11 12

Austrittszeit TA [h]

Rei

seze

it [s

]

RohdatenZüge

Travel Time Data for

a Rail

Transit Mode

0

300

600

900

6,0 6,5 7,0 7,5 8,0 8,5 9,0 9,5 10,0 10,5 11,0 11,5 12,0

Austrittszeit TA [h]

Rei

seze

it [s

]

10.7.11.7.12.7.

ICE 778 ICE 604

ICE 976

ICE 78

ICE 602 ICE 278 ICE 76

ICE 600

ICE 372

S4, S41

Trains

Identified

A8-OW (stk_id=5)

TMC-LC

SES/VIZ

LUP

LUP + Call

AK Stuttgart

AS AD Leonberg

AS Heimsheim

AS PforzheimO N W

LATT-Messung

Enrichment

of stationary

Infrastructure

by

the Mobile Network

1/3

2/3

O->W

Probability

Density

of Cells

along

a Motorway

Example

for

Incident

LocalisationDo-iT

Incident indicationFloating Phones

locationtim

e of

day

Stationary

Sensors

Sources for Traffic Data in Germany in the Course of Time

1990 20102000 2020

high ways

motor ways

Citiesstationary detection systems

FCD

Net-FCD

stationary

traffic management centers

GATS-FCD

convergence zone

Regional TICs

limited installations

rollout net-FCD

diverse FCD species

SES of DDG

Sensors, Floating Cars and Floating Phones

Editorial Team

Traffic information

Traffic ModellingTraffic Forecast

Data Collection

Police Loops Sensors Floating Cars

Do-iT

Floating Phones

TMCpro: Presence and Future of Real Time Traffic Information

Technology

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