TMCpro: Presence and Future of Real Time Traffic Information

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Information about TMCpro: Presence and Future of Real Time Traffic Information
Technology

Published on November 4, 2008

Author: TMCpro

Source: slideshare.net

Description

Präsentation von Dr. Ulrich Fastenrath, Head of Product Development T-Systems Traffic GmbH, zu TMCpro, Floating Phone Data und der Zukunft von Verkehrsinformationsdiensten

TMCpro - Presence and Future of Real Time Traffic Information Dr. Ulrich Fastenrath T-Systems – Systems Integration DDG Gesellschaft für Verkehrsdaten mbH

System overview Sat-Uplink Leased lines Playout Center Traffic Data ISDN Administrator page 1

The TMCpro approach to quality Quality Traffic Modelling and 100% Forecasting Raw Content Service Terminal User Data Provider Provider Device page 2

Producing Numerical Data with Sensors Stationary data collection systems improve the quality of traffic information. GSM DDG 4.000 sensors Sensor > 5.500 loops • measures traffic flow and average speed • distinguishes cars from trucks Detected network contains • reports programmable events >90% of all incidents page 3

From Traffic Data to Traffic Information LMSt VIZ / VRZ SES FCD Data sources Data Communication interfaces, Data preprocessor collection (Plausibility checks, Aggregation, Localization) Product ∂ρ Traffic ) ∂( ρV analysis, Generation of traffic reports, + = ν rmp , generation ∂t ∂x Calculation of travel times, Historical time series, Disturbance development forecasts, ν ∂V ∂V 1 ∂P( ρ ) 1 Traffic data +V =− Short term predictions, rmp ⋅ (Vrmp − V ). + ⋅ (Ve − V ) + management ∂t ρ ∂x τ (ρ) ∂x automated consistency checks, ρ center Customer specific features Traffic information (Customer interface) page 4

Traffic does not behave as it is supposed to Extrapolation characteristic, 2 lanes 0,8 0,7 0,6 0,5 Gamma 0,4 0,3 0,2 0,1 0 0 10 20 30 40 50 60 70 80 90 100 k [Fzg./km] gamma_s60 std_gamma_s60 std_gamma_s300 gamma_c std_gamma_c gamma_s300 page 5

Calibrating the free Velocity 150 140 130 120 free velocity [km/h] 110 100 90 80 70 60 50 02:00 04:00 06:00 08:00 10:00 12:00 14:00 16:00 18:00 20:00 22:00 Time page 6

Coming to terms with the past Aggregation Interval Time Diagonal System Telegram #3 Time Telegram #2 Telegram #1 Data Time page 7

Go with no flow? Classification of zero flux Classification of zero flux (inductive loops) (infrared detectors) 100 100 80 80 frac frac SV 60 SV 60 tion tion FV [%] FV [%] KA 40 KA 40 SV meas 20 20 0 0 0 2 4 6 8 10 12 14 16 18 20 22 24 0 2 4 6 8 10 12 14 16 18 20 22 24 Time of the day Time of the day 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) page 8

Is the traffic still there when nobody looks? 150 velocity [km/h] 100 MQ LOK HIL GKT 50 0 07:00 08:00 09:00 10:00 11:00 time 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. page 9

Detection of disturbed traffic states by DDG infrastructure page 10

Detection of disturbed traffic states by DDG infrastructure II page 11

A Scheme for measuring Product Quality Reference: BMW AG, Dr. Klaus Bogenberger, „Qualität von Verkehrsinformationen“, Straßenverkehrstechnik 10/2003 customers‘ expectation page 12

Visualisation of Complex Dynamic Systems page 13

Road Weather and Road Conditions Road detector system for icy conditions TMC-Code Meaning 1002 Danger of aquaplaning 1003 Slippery road 1019 Slippery road due to frost 1009 Freezing rain 1008 Black ice 1011 Slush 1112 Rain 1109 Heavy rain 1104 Snowfall 1101 Heavy snowfall 1107 sleet page 14

Meteorological input data page 15

Precipitation radar images page 16

Convert TMC-Codes into weather messages page 17

Example for a TMC Message „Danger of Aquaplaning“ page 18

The product feature „DDG road weather“ page 19

Navigation in Space and Time page 20

Some Varieties of Traffic Forecast Growth Rate Duration ? ? q ! Pre- Warning page 21

Bottlenecks A Qarr(A) B Qarr(B) link active blocked 9576 bottlenecks analysed spillover 2843 bottlenecks breakdown considered relevant for pre-warnings recovery recovery inactive activity of bottlenecks page 22

Breakdown Frequencies at Bottlenecks Reference: Brilon, W.; Zurlinden, H.: Kapazität von Straßen als Zufallsgröße, Straßenverkehrstechnik 4/2004, S. 164-172 page 23

Breakdown Probabilities at Bottlenecks Flow rate (q), probability of breakdown (Pbd) and of congestion (Pc ) at site Düsseldorf Mörsenbroich located along the highway A52 100% 1200 80% 900 flow rate [vphpl] probability [%] 60% P_bd(+15 min) 600 P_c 40% q 300 20% 0% 0 0 2 4 6 8 10 12 14 16 18 20 22 24 time of day 05.07.2004 [h] Breakdown of traffic flow is a stochastic event, whereby probabilities of breakdown are associated with specific flow rates. page 24

Störfallmodell Konkrete Realisierung einer Verkehrsstörung breakdown 20 2000 recovery 16 Verweildauer [min] 1600 Verkehrsstärke [Fz/h/Spur] 12 1200 800 8 400 4 0 0 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Tageszeit [H] Q-IN Kapazität Cv Q-OUT Tv page 25

Delay Times at Bottlenecks Delay caused by breakdown of traffic flow at different times T bd 50 40 T_bd=5,75 Delay [min] 30 T_bd=6,5 20 T_bd=7,5 T_bd=8,0 10 0 5 6 7 8 9 10 11 entry time [h] page 26

Pre-Warnings: Example 1 page 27

Pre-Warnings: Example page 28

Pre-Warnings: Example 3 page 29

Quality of Pre-Warnings 100% 300 80% 250 200 ROC VWZ [min] TPR [% ] 60% K 150 40% L 100 VWZ 20% 50 0% 0 0% 20% 40% 60% 80% 100% FPR [%] page 30

Motorways are not enough 1,280,000 km in total 23,000 km 106,000 km motorways highways page 31

Do-iT: The Project Do-iT is part of the research and development Do-iT initiative „Verkehrsmanagement 2010“ sponsored by BMWi Partners: Innenministerium Baden-Württemberg Landeshauptstadt Stuttgart Stadt Karlsruhe Universität Stuttgart, represented by Institut für Anwendungen der Geodäsie im Bauwesen and Lehrstuhl für Verkehrsplanung und Verkehrsleittechnik Associated: T-Mobile Deutschland GmbH ====!quot;§==Mobile= DDG Gesellschaft für Verkehrsdaten mbH page 32

Floating Phone Data: Functional Principle Do-iT A-bis A interface interface BTS BSC BTS MSC BSC MS BTS Network Probes Mobile Phone Positioning Identification of Active Road Users Data provision for public and private Floating Phone Data Map-Matching & applications Trajectory Generation FPD-Server Reference:33 page IAGB University of Stuttgart

Establishing the Data Basis Do-iT BTS BTS BSC MSC MS All mobiles: A-bis link A link • Localisation Updates (in particular at LA updates) (LAC1) -> (LAC2,CI2) Active mobiles only: (CI1) -> (CI2) • Handover events • Measurement Reports (~ 2 Hz) CI,TA (=distance) Field strength Temporary Mobile Subscriber ID Master data needed for Cell geometry interpretation: Topology data (=antenna locations) Best server plots page 34

Network covered and Applications Do-iT Applications A-Net U-Net motorways Innenministerium BW (A-Net, U-Net): diversion routes AK Walldorf • Dynamic Network Control • Traffic State of U-Net AK Weinsberg Cities of Stuttgart and Karlsruhe B-Net (C-Net, urban U-Net): federal highways • Improvement of knowledge about current traffic situation • Estimation of Travel Times C-Net • direct measurement of the City of Karlsruhe impact of network control • improvement of control strategies C-Net DDG (all networks): City of Stuttgart • Navigate, TMCpro page 35

Measurement of Traffic Flow Do-iT Comparison of Location Area Updates and traffic flow as measured by stationary sensor 4000 3500 3000 Rate [events/h] 2500 Q-SES 2000 LA boundary LAC-Updates 1500 LA 2 1000 LA 1 500 CI 2 0 0 3 6 9 12 15 18 21 0 time of day [HH] Cell boundary Flow of mobiles ≠ traffic flow Frequency of transitions Superposition of more than one traffic flow LA1 → (LA2,CI2) page 36

Network covered at Abis Level Do-iT page 37

Example at A Level: Free Traffic Do-iT LAC CI Azimut Time 22111 28961 22111 12228 28939 12228 120 06:28:32 17980 28939 17980 45 06:28:40 28939 18002 225* 06:29:46 28939 15639 45* 06:30:24 18002 28939 32092 240 06:31:17 15639 28939 54179 300 06:31:38 28939 54178 160 06:32:24 28950 3413 06:33:39 32092 * = Tunnel 9 17 54 54178 3413 page 38

Projektnetz Do-iT page 39

Do-iT Example for Incident Detection Stationary Sensors NET-FCD 3413(28950)->55508(28682) 60 55 50 45 40 travel time [m] 35 30 25 20 15 10 5 0 0 4 8 12 16 20 24 arrival time [h] page 40

A Truck Accident observed by Floating Phones Do-iT page 41

Waves of Holiday Traffic, southbound Do-iT page 42

It can always get worse. Do-iT page 43

The Traffic Jam is no Respecter of Persons. Do-iT page 44

Early Example from a Trunk Road Do-iT page 45

TMCpro: Neue Inhalte für die dynamische Navigation. Baulich getrennte Fahrspuren. Do-iT page 46

TMCpro: Neue Inhalte für die dynamische Navigation. Drei-Phasen-Theorie. Do-iT page 47

TMCpro: Neue Inhalte für die dynamische Navigation. Synchronisierter Verkehr. Do-iT page 48

TMCpro: Neue Inhalte für die dynamische Navigation. Rückreisewellen. Do-iT page 49

TMCpro: Neue Inhalte für die dynamische Navigation. Auch das gibt es. Do-iT page 50

Travel Time Data for a Rail Transit Mode 12.07.2007 900 600 Reisezeit [s] Rohdaten Züge 300 0 5 6 7 8 9 10 11 12 Austrittszeit TA [h] page 51

Trains Identified 900 600 Reisezeit [s] S4, S41 10.7. 11.7. 12.7. 300 ICE 78 ICE 604 ICE 778 ICE 976 ICE 602 ICE 372 ICE 278 ICE 76 ICE 600 0 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] page 52

Enrichment of stationary Infrastructure by the Mobile Network A8-OW (stk_id=5) TMC-LC LUP + Call LATT- LUP Messung SES/VIZ AK AS AD AS AS Stuttgart Leonberg Heimsheim Pforzheim O page 53 N W

Probability Density of Cells along a Motorway O->W 2/3 1/3 page 54

Do-iT Example for Incident Localisation Floating Phones Stationary Sensors Incident indication time of day location page 55

Sources for Traffic Data in Germany in the Course of Time stationary detection Cities traffic management systems centers convergence FCD diverse FCD species zone motor Net-FCD ways limited installations rollout net-FCD high SES of DDG ways Regional TICs GATS-FCD 1990 2000 2010 2020 page 56

Sensors, Floating Cars and Floating Phones Do-iT Floating Phones Police Loops Sensors Floating Cars Data Collection Traffic Modelling Editorial Traffic Forecast Team Traffic information page 57

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