German modelling, example VISEVA-W

Transcription

1 German modelling, example VISEVA-W Jörg Uhlig Wildau

2 Overview > Traffic demand model > Structure of the regarded area > Structural and Behavioural data > VISEVA-W user interface > Calibration of the traffic demand model > Results of the traffic demand model > Summary 2

3 Traffic demand model Demand model Network model Calibration Analysis Prognosis Database for emission- and immission models 3

4 Traffic demand model C C C C Consignee 3 C Consignee 2 Consignee 1 o d Consignee... Consignee... trip 1 S Sender Consignee n-1 trip n E S E S starting trip C connecting trip E ending trip 4

5 Traffic demand model Trip generation Input data: Structure and behavioural data Results: Traffic volume of every traffic zone Trip assessment Input data : Indicator matrices (travel time, travel costs) and parameters of the evaluation function Results: Assessment matrices for the relations S, E, C, O, D Trip Distribution (iterative procedure) Input data : Origin and destination traffic of every traffic zone, assessment matrices Results: Traffic Flow Matrices for all commercial traffic classes and commercial transport vehicle types 5

6 Traffic demand model Internal traffic of the regarded area Origin, destination and transit traffic Car (private) Car (commercial) Car traffic Lorry small (<3,5 t weight) Lorry medium (<7,5 t weight) Lorry big (>7,5 t weight) Lorry traffic Traffic master plan region Stuttgart BVWP 6

7 Structure of the regarded area Network model of the regarded area 7

8 Structure of the regarded area > Nodes > Links with a total length of km > 792 traffic zones > connectors > 315 points of traffic census > data of the public transport (e.g. stations, lines, timetables) 8

9 Structure of the regarded area Network model with traffic zones 9

10 Structure of the regarded area Connectors of the traffic zones 10

11 Structure of the regarded area Longitudinal inclination 11

12 Structure and behavioural data Behavioural data from the National automobile traffic survey in Germany Kraftfahrzeugverkehr in Deutschland (KiD 2002) > Number of driver per employees > Number of tours per driver > Number of destinations per tour > Attraction rate of the destination side (Number of trips per employees) 12

13 Structure and behavioural data > Car > Lorry small (< 3.5 t over all weight) > Lorry medium (< 7.5 t over all weight) > Lorry big (> 7.5 t over all weight) 13

14 Structure and behavioural data 14

15 Structure and behavioural data Data in thousands Inhabitants Employees

16 Structure and behavioural data Problem: > The traffic demand calculation needs for every traffic zone detailed data for the number of employees in the industry sectors > The federal statistic provides only data on municipal or higher level with different structures of the industry sectors Solution: > Use of the database of the Chamber of Industry and Commerce and the Chamber of Handicrafts Stuttgart > Determination of the number of employees per industry sector in every traffic zone 16

17 Employees per industry sector Data in thousands Producing sector Trade Traffic and Accomodation Other Services

18 VISEVA-W user interface Structure data Commercial traffic classes File register Indicator matrices car Indicator mat. lorry < 3,5 t Indicator mat. lorry < 7,5 t Indicator mat. lorry > 7,5 t Assessment matrices Traffic flow matrices by commercial traffic classes Traffic flow matrices by vehicle type Traffic demand calculation with VISEVA-W 18

19 VISEVA-W user interface Traffic volume per zone Parameter of the evaluation function Commercial traffic class file 19

20 VISEVA-W user interface Parameter of the evaluation function Curve of the evaluation function 20

21 VISEVA-W user interface Indicator matrices of the vehicle types (4 vehicle types x 2 indicators = 8) 21

22 VISEVA-W user interface Relations of assessment Assessment matrices for each commercial traffic class (4 industry sectors x 4 vehicle types = 16) 22

23 VISEVA-W user interface Relations of demand Traffic flow matrices for each commercial traffic class (4 industry sectors x 4 vehicle types = 16) 23

24 VISEVA-W user interface Traffic flow matrices for each vehicle type 24

25 Calibration of the traffic demand model % % -90% % -80% % -70% % -60% % -50% % -40% % -30% % -20% % -10%... 0% 0%... 10% 10%... 20% 20%... 30% 30%... 40% 40%... 50% 50%... 60% 60%... 70% 70%... 80% 80%... 90% 90% % 100% % 110% % 120% % Anzahl Messquerschnitte 130% % 140% % Abweichung zwischen Modell und Zählwerten - gewichtet mit der gezählten Verkehrsstärke 1 0,8 Abweichung Sum m enkurve Calibration with traffic census data 0,6 0,4 0,2 0

26 Calibration of the traffic demand model 0% 0% 0% -2% -2% 3% Calibration with traffic census data -1% 0% -3% 2% 1% 9% Vehicles Car Lorry 26

27 Results of the traffic model Data in thousands Car Lorry < 3,5t Lorry < 7,5t Lorry > 7,5t

28 Results of the traffic model Car Lorry < 3,5t Lorry < 7,5t Lorry > 7,5t Data in thousands Producing sector Trade Traffic and Accomodation Other Services Lorry > 7,5t Lorry < 7,5t Lorry < 3,5t Car

29 Results of the traffic model Internal traffic 67,2% Origin, destination, transit traffic 32,8% Private traffic 76,1% Commercial traffic 23,9% Prognosis Traffic demand in trips per day 29

30 Results of the traffic model Internal traffic 53,2% Origin, destination, transit traffic 46,8% Private traffic 68,6% Commercial traffic 31,4% Prognosis Traffic demand in driven vehicle-km per day 30

31 Results of the traffic model Total number of vehicles Cars Small delivery vehicle Medium delivery vehicle HGV Link number Link type Car/Lorry Results of assignment: Traffic volumes 31

32 Results of the traffic model Results of assignment: Flow bundle 32

33 Results of the traffic model Volume capacity ratio of links > % 33

34 Summary > The result of the project is a complete reflection of the urban traffic demand (commercial, freight and passenger traffic). > The effort for data collection and processing depends on the availability of behavioural data and the definition of commercial traffic classes (VISEVA supports individual definitions of commercial traffic classes). > For the commercial and freight traffic model of the region Stuttgart data from the KiD-survey was used for the first time. Our experience is that this database is suitable for the demand calculation. > The assignment on the network provides traffic flows for commercial and freight vehicle types and passenger cars. > Based on this model detailed analyses of the commercial and freight traffic in connection with passenger traffic are possible and data for emission and immission models can be derived. 34

35 Thank you for your attention. PTV - traffic mobility logistics PTV Planung Transport Verkehr AG, Dresden 35

36 Traffic model VISEVA-Calculation of demand matrices Project for private passenger traffic Project for urban commercial transport VISEVA-W Trip matrices Trip matrices VISUM-Assignment Version for the total traffic Additional trip matrices Indicator matrices Indicator matrices 36