Transportation Planning Models The Basis of Traffic Management

Transcription

1 Transportation Planning Models The Basis of Traffic Management Peter Möhl - PTV AG, Germany Prepared for 1st tubs.city Symposium Braunschweig, July 2009

2 Outline 1. Transportation Planning Models 2. Use cases for Traffic Management 3. Conclusions 2

3 Typical Use Cases Modelling of Traffic, Transport and Transportation > Tactical and strategic planning > Analysis, optimization and simulation 3

4 Munich Model: Road Network consists of Links and Nodes Network source: Validate, Intrest, NavTeq, City of Munich 4

5 Junctions are Modelled in Detail Junction Example: Munich Dachauer Str./Max Born Str. 5

6 Junction Detail (2) 6

7 Junction Detail (3) 7

8 Munich Travel Demand Model: Planning Region = 2.5 Mio People in almost 2000 Traffic Anaysis Zones 8

9 Understanding Mobility: The Nature of Travel Demand Base elements of travel > Land use > Acitivities > Transport Means > Infrastructure Trips connect acitivities 100 workers (with car) realize in average per day : 73 x HJH H 1 x HJLH 7 x HJLJH and leave home 80 more times in 45 other activity patterns (Example Grenoble 1998) J H L J H J L H Home J Job L Leisure 9

10 Distribution of trips in space 10

11 Distribution of trips in time 30% 20% 10% 0% HJ Home - job 0h 2h 4h 6h 8h 10h 12h 14h 16h 18h 20h 22h 30% 20% 10% 0% JH Job-home 0h 2h 4h 6h 8h 10h 12h 14h 16h 18h 20h 22h 11

12 Behaviour Oriented Time Distribution can be very Detailed: Private Trips 25% 20% 15% 10% 5% 0% Private trips starting from Home a=doctor, bank etc. b= visits, hospitals k= culture, restaurant s= sport, parks b= service trips Munich Travel Demand Model: survey data from MID

13 Behavior Oriented Time Distribution can be very Detailed: Work Trips 40% 35% 30% 25% 20% 15% 10% AqW AeW AsW AtW WqA WeA WsA WtA 5% 0% Home - Job (Wohnen Arbeit) q=qualifiziert e=einfach/simple s=self employed t=teilzeit/part time Munich Travel Demand Model: survey data from MID

14 Assignment of Trips: Volume/Cap Ratio in Morning Peak 7am-8am 14

15 National Planning Model - VALIDATE Legend <= 25% = free flow <= 60% = normal traffic <= 90% = dense traffic <=110% = congestion >110% = traffic jam Saturation for a typical working day 15

16 Interim Resume > Modelling and simulation are basis for planning and strategic purposes > Higher accuracy through detailed junction modelling > Detailed knowledge about demand > Land use > Trip purposes > Activitiy chains > Distribution in time > Which benefit does this provide for Traffic Management? 16

17 Typical Traffic Management configuration Traffic Management System GUI User Interfaces Administration Parameters Functional Modules Archives Statistics Protocols Measurement Archive Incident Management Strategies Traffic Light Control Sensor Data on location Operational Messages Online Visualization Adaptive Control VMS/DMS GIS Map Lane Control Speed Limit Control Interfaces to field devices Urban Interurban Public Roadworks FCD Environment 17

18 Typical Traffic Management configuration Traffic Management System GUI User Interfaces Administration Parameters Functional Modules Transportation Archives Statistics Protocols Traffic Light Strategies Sensor Data Control on location Planning Model? Adaptive Control VMS/DMS GIS Map Measurement Archive Operational Messages Lane Control Incident Management Online Visualization Speed Limit Control Interfaces to field devices Urban Interurban Public Roadworks FCD Environment 18

19 Transportation Planning Models - inside Traffic Management System GUI User Interfaces Administration Parameters Functional Modules Archives Statistics Protocols Measurement Archive Incident Management Strategies Traffic Light Control Operational Messages Online Visualization Adaptive Control VMS/DMS GIS Map Lane Control Speed Limit Control Interfaces to field devices Urban Interurban Public Roadworks FCD Environment 19

20 Estimation of current traffic state Supply Network Demand OD-matrices Routes Situation Current measurement Traffic incident messages Traffic state 20

21 Estimation of forecast & scenarios Data completition Short-term forecast 15 min Strategy A 15 min Strategy B 21

22 Sensitivity to Incidents Signal Control Variable Message Sign VMS Location Discouraged Destination Zone Recommended 22

23 Example I: Vienna 23

24 Example II: Bavaria 24

25 Services are fed by timeframe dependent data + 25

26 Comparison of approaches and use cases Detectors Detectors and models Floating Car Data/ Floating Mobile Data Local traffic state Broad traffic state Forecast Sensitivity to incidents Scenarios > Hybrid approaches lead to highest value for traffic management 26

27 Mutual Benefit: > Analysis of historical detector data > Aggregated data is basis for > quality assurance > adaptation of transportation planning models 27

28 Conclusion I: Value-added chain of traffic management Traffic Information Traffic Control Online Modeling Historical Analysis Transportation Planning 28

29 Conclusion II: Big picture Scenario management Local solution Scen 1 Scen 2 Local solution Decision Traffic Control Center Online Modeling > Self-fulfilling prophecy? CTS! 8:00 8:15 29

30 Thank you for your attention! Peter Möhl PTV Planung Transport Verkehr AG, D Karlsruhe 30