Modelling in the Netherlands BESTUFS II Berlin, 8-9 june Johan Visser OTB Research Institute Delft University of Technology the Netherlands

Transcription

1 Modelling in the Netherlands BESTUFS II Berlin, 8-9 june 2006 Johan Visser OTB Research Institute Delft University of Technology the Netherlands 1

2 Overview of Presentation Issues lead to models The Dutch way of doing things National modeling framework Local traffic models Specific issue-modeling 2

3 Introduction: Issues lead to models! 1980ies: growth & air quality Fast growth of transit freight traffic through NL Focus: high way use and multimodality Local air quality in cities Focus: local traffic models + air quality maps 90ies: Accessibility issues Local accessibility (concern about closing the city for freight) Focus: new logistic concepts, like UDC and underground logistics Connectivity of industrial locations Focus: heavy traffic and multimodal terminals New Millennium: Local air quality! Implementation of EU air quality rules(pm10 & NO2) block urban development projects Focus: Emissions of heavy vehicles (trucks) Issues led to models 3

4 The Dutch way of doing things Planning process: centralised on national level Key player: nat. Dept of Transport (Min Verkeer en Waterstaat) Rijkswaterstaat (national highway authority) Platform Urban Distribution (until 2003) Regional authorities: no interest in freight Local authorities: independent but depend on national funding, No interest in freight issues 4

5 Modeling Framework: well developed, but urban freight is missing modules results transport scale Economic relations Euro national TEM + SMILE SMILE LMS Goods flows In tonnes Freight transport on roads in tonnes Freight traffic in trips and kms National truck matrix VAM NSTR-groups Relations between 100 (nuts 3) regions Relations between 400 regions regional NRM RGM Regional Truck matrix Callibrated reg truck matrix Truck flows on network Trips per day period Relations between regions Assignment to regional network 5

6 Regional models Freight: own O/D-matrix Desaggregation of national O/D-matrix Bias of 3-180% with actual traffic counts Need for including accurate prod/attract model To create synthetic freight vehicle matrix Only includes major routes through and around cities Recent progress: network analysis, combining local and regional models and expertise 6

7 Local traffic models: OD-matrix techniques Since 80ies (> inhab.) Static but more and more dynamic Includes emissions and noise model Freight: Heavy vehicles as subcategory (incl buses) sometimes own OD-matrix Examples: Omnitrans (BGC), Questor (DHV) Freight included in Leiden, Groningen Dept of VROM (publ. CROW) - Guideline urban freight model - Simple, do it yourself method - Hardly used 7

8 Experimental models: evaluating logistic concepts FTAM-project (TU-Delft): UFT-model GIS-based stochastic simulation model GoodTrip Model OLS-modeling (TUD/TNO/DHV) Good flow analysis in Utrecht Network simulation of OLS Citybox (TUD/TNO) 2002-? 8

9 Goodtrip model Supply chain based model For evaluating logistic concepts Urban distribution centre Undergrou nd logistic systems 9

10 Goodtrip model Stoachistic generation of trip ends and trips Attraction constrained distribution model Distinguishes different supply chains O/D-matrix, charact round trips, load factor, number of stops per trip GIS for route choice and link intensities rondritten en netwerkintensiteiten per modaliteit 10

11 Step 1: Transport Demand establishment r selling floor space, A r (gross/net) [m 2 ] employees, L r [#] deliveries per establishment*, F r g,p [#] delivery size, q r g,p [m 3 ] turnover per floor space*, TO r a [EUR/m 2 ] turnover per employee*, TO r l [EUR] value density, VD r g [EUR/m 3 ] volume per floor space*, q r g,a [m 3 /m 2 ] volume per employee*, q r l,a [m 3 ] method I method IIa method IIb method IIIa method IIIb * per time-unit (day,week, year) volume* ( Q ) ) r G [m 3, tons, # units, EUR] key: input variable note: volume can be expressed in volume [m 3 ], mass [metric tons], load units [# units], or monetary value [EUR] output variable operator method 11

12 Step 2: Traffic generation volume* Q [m 3,ton, # units, EUR] delivery size, q [m 3, ton, # units, EUR] number of deliveries** F delivery [#] deliveries per tour F stops [#] number of tours F tours [#] kilometres per tour d tour [km] kilometres per delivery D delivery [km] method method I II traffic performance (loaded) P loaded [km] empty trips factor f [.] * per time-unit (day,week, year) ** per area note: volume can be expressed in metric volume [m 3 ], mass [metric tons], load units [# units], or monetary value [EUR] traffic performance P traffic [km] transport performance P transport [tonkm] key: input variable output / intermediate variable operator method additional calculation 12

13 Step 3: Trip generation Model Urban Freight Transport Deliveries / km2 Number 0-18 Poor models for UDC-feasibility study Lack of representative data How to model multi-stop trips Testing of a stochastic model for UFT 1. Number of deliveries per dest. 2. Number of trips per time period 3. Route choice per trip 4. calculation link use Delft OTB 1999 / eritkm2.prj NO2P98,00 to 103,90 103,90 to 106,10 106,10 to 106,71 106,71 to 118,68 Model Urban Freight Transport Link intensity Delft Number of trips Out of Range E Number of stops per trip Number of trips Average travel time [minutes] Average travel time per stop [minutes/stop] average cm OTB 1999 / efrqb.prj NO2P

14 Environmental modeling Standardised models Developed by TNO Under auspicien of NMP CAR air quality SRM traffic noise calculation Emission parameters Including valuation of impacts (for cba-purpose) 14

15 City box (1) real input based simulation model Large database, data from 18 retail companies with 1400 shops. Shops receive about 6.67 million load units per year and as an average every shop receives 91 load units per week. Data contained: customer identity (name, address, postal code, location) drop characteristics (volume, mass and number of load units per routing) present-day scheduling program, data of the vehicle fleet (numbers, lifetime, capacity) Key Performance Indicators (load units per drop, cost per delivery, cost per load-unit, cost per km., cost per hour, cost per vehicle etc.) 15

16 Citybox (2) Modeling is based on earlier experiences with modelling of UDC and OLS. RESPONSE, simulation model for simulating the freight movements (trips) CiDiS, simulation model to analyse the influence on the service quality and cost of: Planning algorithms Stochastic behavior of traffic and terminal operation, incl arrival of long haul trucks Environmental models 16

17 bevoorradingsprofielen Qualitative model With statistical data To evaluate accessibility and environmental issues 17

18 Conclusions From traffic models to aggregated simulation models Poor data quality poor modeling results Modeling: high percentage unexplained Synthetic OD-matrix required Unreliable trip generation data (see example) Understanding the data perhaps more important than the modeling It s a bit quiet in area of modeling in NL! Number of vehicles versus size industrial area 18