UGM modelling in France

Transcription

1 BESTUFS WP3 Round table n 2 Modelling approaches on Urban Goods Transport June, 8th and 9th, 2006 Wildau, Germany UGM modelling in France Jean-Louis Routhier (Laboratoire d Economie des Transports, Lyon, France) Julie Raffaillac (DREIF, France)

2 Source: FDM Matrix TRB Conference Two main UGM modelling approaches Cost modelling To optimise transport costs and facility costs - Vehicle operating costs - Fixed costs - Variable costs - fixed costs Policy oriented modelling - to have a good knowledge of the UGM - to understand trade and transportation policy - to measure the indirect costs of UGM - to feed a global traffic assignment model

3 Two experiences in France A Freight HGV Model in the Region Ile de France Building an O/D matrix to feed a global assignment model MODUS A generation model : FRETURB - Demand generation - Traffic and parking time simulation -O/D distribution -Environmental impact } software } in progress Laboratoire d' Economie des Transports, Lyon

4 Urban Goods French Modelling 1 Freight Modelling in the Region Ile de France to feed a global assignment model adapted from Julie Raffaillac, DREIF

5 1 Aims To refine impacts assessment studies Development of HGV O/D matrix To improve the accuracy of the traffic studies A multi-modal model (Cars, Public Transport, HGV) To enhance traffic management and land use planning A long term (10 to 30 years) strategic model

6 1 HGV Modelling Modelling demand development new facilities/infrastructures Demand O-D matrix of HGV trips Supply multimodal network (Car unit) including HGV characteristics Simulation future terms assignment current situation Evaluation schemes and policy evaluation Analyse diagnosis and indicators (e.g. environmental impact assessment)

7 1 First step: Development of O/D matrix Inputs: traffic surveys data road counts data in Île-de-France 1300 zones «Modus zoning» (cars traffic of DREIF) Land use data Network of Ile de France 7

8 1 The zoning Zoning: 1305 zones

9 1 The zoning and the road network Zoning: 1305 zones Fast lanes in the Ile de France: links modelised

10 1 Assumptions 1 O/D between each zone: Wide zones: distribution of the trips between the zones in proportion to the Industrial and Trade Areas (ITA) OR Dense zones: assignment of the trips between zones through the traffic loading points of the network

11 1 Assumptions 2 Two types of runs: D (60%) direct trips: O and D are distinct O (40%) rounds: O and D are mixed up O/D Pb: Only the first loading point and the last unloading point are identified! The intermediate trips are unknown.

12 1 Some trips are amended: Direct trips if the loaded distance D >> d (observed O D) D= li+d+lj D = α*d O li d Cj lj j Ci D i Rounds Round O/D mixed up 2 criteria: *Distance (O-j) D (more or less 10%) *ITA

13 1 The O/D matrix is overall significant Results i j Fij 2 HGV (morning - evening) matrices HGV Traffic h- 9h F i j F ij 16h-17h go return Morning peak = 11,6% daily traffic Evening peak = 9% daily traffic h-13h 0 Hour

14 1 Step 2: Assignment of the O/D matrix Assignment of the O/D traffic at morning and evening peak hour: according to the «Tribut» method (i.e. the vehicles are assigned first on the road with the lowest generalised cost): car users: am peak hour =17 pm peak hour =11 HGV = 72 method of assignment: the cars are first assigned on the network, then the HGV are assigned on the pre-loaded network. Standard «Flow - speed» curve: a: capacity of the lane, t 0 : no load running time, xi= (number of vehicles loaded at the iteration i)/(a) tchg = t 0 *[(1,1 a * xi ) / (1,1 - xi )], as xi < 1, tchg = t 0 *[(1,1 a ) / 0,1] * xi 2, as xi >= 1

15 1 Result : HGV load at morning peak

16 1 Step 3: Calibration of the O/D matrix Analysis of the hour per hour profiles of traffic Application of ratios to the genuine HGV demand matrix [which is generated for a working day from SITRAM dataset] Assignment of peak matrix on the network Calibration of the assigned traffic on the basis of: a built-up database of various counts (i.e. synthesis of the various data sources towards an homogeneous data base) a defined macro-zones

17 1 Definition of macro-zones

18 1 Analysis of the hour per hour profiles Profil horaires par macrozones (base 100 à 8 heures) Macrozone Macrozone2 Macrozone3 Macrozone Macrozone5 Macrozone6 Macrozone7 Macrozone8 ratios per macro-zones on various periods Macrozone9 Macrozone10 Macrozone11 Macrozone12 Macrozone13 Macrozone14 Macrozone15 Global En % de la matrice journalière M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13 M14 M15 Global Période de pointe du matin VP (7:00 à 9:00) 12.7% 10.4% 11.2% 13.8% 9.9% 11.2% 11.2% 14.6% 14.2% 14.2% 11.4% 11.4% 10.0% 10.5% 11.7% 11.8% Période de pointe du soir VP (17:00-19:00) 9.6% 7.8% 9.1% 8.7% 9.3% 8.6% 8.0% 9.2% 10.7% 6.3% 6.9% 7.8% 7.6% 6.3% 7.9% 8.2% Période creuse VP (10-16H) 25.1% 25.3% 24.4% 26.5% 23.9% 25.7% 26.9% 26.7% 24.5% 26.2% 25.7% 25.5% 27.7% 26.6% 23.9% 25.5% Heure de pointe matin PL (10H - 11H) 6.8% 7.2% 7.5% 7.6% 6.7% 7.9% 8.1% 8.3% 7.6% 8.8% 8.0% 8.0% 8.2% 7.2% 7.0% 7.8% Heure de pointe après-midi PL (14H - 15H) 6.3% 6.8% 6.3% 7.1% 6.1% 6.6% 6.8% 7.1% 6.3% 6.8% 6.5% 6.6% 7.2% 6.7% 6.0% 6.6%

19 1 Before Before calibration calibration After After calibration calibration

20 Urban Goods Modelling in France 2 The FRETURB model Laboratoire d'economie des Transports, Lyon

21 Urban freight transport : what to take in account? abroad Intermediate goods industry Working sites Network management Consumer goods Processing industry Pick-ups / deliveries Logistics (Consolidation - breakdown) Waste processing Urban management Dense urban area CONSUMERS Purchase Wholesale Waste collection small Retail supermarkets Outskirts Laboratoire d'economie des Transports, Lyon

22 Laboratoire d'economie des Transports, Lyon 2 methodological commitment The specificity of UGM Regional and National goods transport : 1 truck = x tons Output region : i - The O/D matrix of goods is equivalent with the O/D matrix of vehicles - The interurban transport is generally made in direct trips - There is an isometry between Véh.km and T.km The gravity model is efficient : Tij = K Ti. T. j f ( cij) Input region j

23 Laboratoire d'economie des Transports, Lyon 2 methodological commitment of FRETURB Inside the Town : the organisation in tours is dominant - The O/D matrix of goods is different from the O/D matrix of vehicles - vehicles have very different sizes and freight volume - Packaging are very different One quantity of goods may be delivered by different types of vehicles, of way of organisation etc. The choice of the size of vehicle, of the organisation, of the management are determined by factors exogenous to transport. The gravity model is failing

24 aboratoire d'economie des Transports, Lyon 2 The methodological assumptions for modelling UGM To use the most efficient unit of observation In order to take into account the rounds In order to have a good knowledge of the generators - activity, - Environment, -logistic organisation -deliveries and pick-ups during a week In order to have a good description of the deliveries and pick-ups - type of vehicle, - weight, packaging, type of products, - round or direct trip? The action of delivering or pick-up (one vehicle, one place, one operator) To mix the data sources and to integrate the different space and time schemes

25 Laboratoire d'economie des Transports, Lyon Which data base for modelling UGM? 2 coupled large specific surveys towards: 4500 establishments to have a good knowledge of generators - activity, - Environment, - logistic organisation (own account, third party) - deliveries and pick-ups during a week 2200 drivers delivering the generators to have a thorough description of delivery/pick-ups organisation - type of vehicle, - weight, packaging, type of products, -round or direct trip? -Routing and scheduling

26 Laboratoire d'economie des Transports, Lyon 2 FRETURB : a model built on empirical data Basis: results of specific UGM surveys Indicators on the logistic behaviour of various types of activities Input: Local data: Household trips survey Input: Local data Establishment register (SIRENE) Geographical data (zoning) FRETURB Model Output: Assessment of: - Demand of goods transport - Road occupancy - Energy consumption - Pollution

27 2 A urban freight simulation model : FRETURB Activities location Generation per zone of: pick-ups household de livraisons et d enlèvements and deliveries purchasing trips Urban planning Regulation Urban logistics organisation Business logistics Road occupancy Occupation de la voirie On road parking duration per area (car unit x hours) Road occupancy by running vehicles per area (car unit x km) hourly occupancy of the road (road traffic and parking) Road congestion Energy / Environmental nuisance Control variable Sensitivity variable Processing module Output

28 2 Description of the routes Three types of stops Two stops of direct trips (TD) loading In a round: unloading The ordinary delivery stops (s) The main loading/unloading point (P) Starting/ending trip Connecting leg Laboratoire d'economie des Transports, Lyon

29 2 1 : generation The characteristics of the generation of deliveries and pick-ups n e : the number of deliveries and pick-ups of an establishment depends on: a: the type of activity* (acc. to the NACE classification) p: the nature of the premises (size of store, depot, office ) j: the size of the establishment (number of jobs) n e = ƒ (a, p, j) Laboratoire d'economie des Transports, Lyon

30 2 1 : generation of deliveries and pick-ups number of deliveries/pick-ups per estab. per week Wholesale (non alim.) Wholesale (alim.) industry (base) Services offices Number of jobs per establishement The curves are calculated for 45 types of activities N z : number of deliveries and pick-up in a zone z Laboratoire d'economie des Transports, Lyon N z =Σn e e z

31 2 2 : road occupancy - traffic generation length of a trip between two ordinary stops v: type of vehicle Own account: Forwarder consignee m: mode of management Third party round Direct trip mode of organisation round n: number of stops l: length of a trip between two stops l= ƒ(v,m,n,δ) n: number of stops L l l δ: density of the zone L l Laboratoire d'economie des Transports, Lyon

32 2 2 : road occupancy - traffic generation length of a trip between two ordinary stops l v,m,δ = α*log(n)+ β α < Length of the trip leg (km)) Articulated trucks rigid lorries LGV <3,5 T Length of the trip leg (km) rigid lorries LGV <3,5 t Number of stops third party Number of stops Own account (consignee) Laboratoire d'economie des Transports, Lyon

33 2 2 : road occupancy - traffic generation length of a starting/ending trip L v: type of vehicle L Own account: Forwarder consignee d z : distance from the Centre m: mode of management Third party a: activity L: length of starting/ending trip L= ƒ (v,a,m,d z ) L v,a,m = α* d z +β D z : distance (veh.km) generated by the activity of the zone z D z = Σ(l)+ Σ(L) z z Laboratoire d'economie des Transports, Lyon Unit : vehicles*km (car unit )

34 2 3 : Road occupancy - parking time The characteristics three types of stops: TD, s, P: activity delivered n: number of stops δ z : density of the zone Type and packaging of the products v: size of vehicle t: time of a stop for delivering easiness for loading / unloading. parking facilities P s T dp,z : total time for double parking in a zone Laboratoire d'economie des Transports, Lyon

35 2 3 : road occupancy - parking time stop duration (mn) articulated vehicle rigid lorries LGV Number of stops Laboratoire d'economie des Transports, Lyon t = ƒ(n, v) %dp z = ƒ (δ z ) T } dpz : total time for double parking in z T dp,z = Σ (t i * %dp z ) i Unit : vehicles*hour (car unit )

36 : Hourly occupancy of the roads dépends on the type of vehicle and on the activity delivered 6% 5% 4% 3% 2% 1% 0% Hourly breackdown of the deliveries LGV <3,5t. 6% 5% 4% 0% 2,00% 1% Hourly breackdown of the deliveries Rigid lorries % deliveries VUL % deliveries rigid lorries 0% hour hour 6% 5% 4% 3% 2% 0% 0% Hourly breackdown of the deliveries Articulated trucks % de livraisons articulés hour Laboratoire d'economie des Transports, Lyon

37 2 5 : distribution of the traffic The aim is to build a non oriented distribution matrix It is necessary to calculate: 5.1. the distances d(zi,zj) 5.2. the average speeds on each section (zi,zj) 5.3. the choice of the route Laboratoire d'economie des Transports, Lyon

38 The distances d(z i,z j ) Distance between two adjacent zones The «as the crow flies» distance is corrected as following: if cfd= 0 thendr= 0 if cfd > 20 km then dr = cfd * 1.1 otherwise: dr = dvo * (1.1 +(0.3 * exp (-cfd / 20))) rectilinear distance (dr), «as the crow flies» distance (cfd) Laboratoire d'economie des Transports, Lyon

39 Calculation of the speed Speed depends on: density indicator type of road it is used for route choice density indicator ij ij = ((Pop ori + Pop dest ) + (Nb deliv./pick-ups ori + Nb deliv./pick-ups dest )) (Surf ori + Surf dest ) ij < 2000 : 30 km/h 2000<= ij < 8000 : 20 km/h ij >= 8000 : 10 km/h type of road smallroads: speed * 1 highways : speed * 2.5 fast lane : vitesse * 1.5 Laboratoire d'economie des Transports, Lyon

40 Choice of the route - Use of the (Dijkstra) algorithm - selection of the neigthbouring zones, - building of a table of the go through zones - drawing of the «best» route in order to build the total distance between Z i and Z j Laboratoire d'economie des Transports, Lyon

41 2 5.4 distribution a typology of the routes is built according to: the zone type of trip: TD, s, P own account (forwarder or consignee), third party Type of vehicle, number of stops more than 3000 different types for 46 zones), a distance d for each type of trip (see part 3) zones destination possible two ways of distribution: - the closeness of the zone to d - the weight of the different activities in the destination zone Laboratoire d'economie des Transports, Lyon

42 2 5 : Air pollution Modelling Local Local data data :: segments segments of of activities activities generating generating urban urban traffic traffic Traffic generation Pick-ups / deliveries Urban management traffic UGM Purchasing trips Freight Freight through through traffic traffic Other Other motorised motorised traffic traffic Network Network Traffic assignment Energy Energy balance balance Fuel Fuel consumption consumption Standard Standard data data Emissions modelling Pollutants Pollutants emissions emissions Meteorology Meteorology Topography Topography Laboratoire d'economie des Transports, Lyon Dispersion modelling Air Air concentration concentration

43 2 Summary of the results of the model description of the demand (with an explanation by the logistics behaviour), occupancy of the roads by running and parking vehicles, hourly profile of the traffic (peak hours) distribution O/D of the traffic energy consumption environmental balance (CO2, NOx, SO2, PP) simulation of sustainable policy: - to limit urban spread - to bring services and trade closer to the consumer - to relocate the warehouses (UDC, ) software (V2) in house in progress Laboratoire d'economie des Transports, Lyon

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