Commentaires
Diaporama
Plan
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POLYDROM
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C O N T E N T
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1.1 WHY TRANSPORT MODELLING ?
  • Goal: MORE PREDICTIVE POWER
  • This predictive power depends on the quality of:
  • - the input data
  • - the elements of the model
  • - the feedback between the elements of the model
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1.2  SUPPLY FUNCTION
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1.4 SUPPLY AND DEMAND MODEL
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1.5 CONTROL OF DEMAND THROUGH TIME
  • individual transport :    congestion  è  + time       è  + gen. costs
  • public transport :          overload      è  - comfort  è  + gen. costs
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1.6  CONTROL OF DEMAND THROUGH PRICE
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2.1 INTERMODALITY IN POLYDROM
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2.2 INDIVIDUAL TRANSPORT  (iT),
      FUNDAMENTAL DIAGRAM
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2.3 PUBLIC TRANSPORTS (PT),
      HIERARCHY OF SUPPLY
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2.4 PARK AND RIDE  (P+R)
      = CONNECTION OF IT WITH PT
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2.5 PARK AND RIDE  (P+R)
      = CONNECTION OF IT WITH PT
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3.1 CALIBRATION WITH SECTION COUNTS
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3.2 DISTRIBUTION OF SECTION COUNTS
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3.3 COMPARISON OF CASES
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4.1 HOW TO ESTIMATE DEMAND FUNCTIONS ?
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4.2  FORECASTS WITH DEMAND FUNCTIONS
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4.3  TYPICAL APPLICATIONS OF POLYDROM
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5.1 ENVIRONMENT: NOISE EMISSIONS PER LINK
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5.2 ENVIRONMENT:  EMISSIONS OF
ATMOSPHERIC POLLUTANTS
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5.3 ENVIRONMENT:  IMMISSION CONCENTRATIONS
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6.1 WWW.ZUERITRAFFIC.CH 
ACTUAL SITUATION
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6.2 WWW.ZUERITRAFFIC.CH
SHORT TERM FORECASTS
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6.3 WWW.ZUERITRAFFIC.CH 
CITY CENTRE
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6.4 WWW.ZUERITRAFFIC.CH
COMPARISON OF REGIONS
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 6.5 INTELLIGENT SETTING
OF VARIABLE MESSAGE SIGNS
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 6.6 POLYDROM_VMS: Intelligent VMS Settings (I)
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6.7 POLYDROM_VMS: Intelligent VMS Settings (II)
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6.8 TRAFFIC ONLINE CH - 21.02.2004
BERN ó BASEL, left: Q[vhc/h], right: V[km/h]
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6.9 TRAFFIC ONLINE CH
INFORMATION ON TRAVEL TIME