Modelli per la simulazione della mobilità per acquisti

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1 Corso di LOGISTICA TERRITORIALE DOCENTE prof. ing. Agostino Nuzzolo Modelli per la simulazione della mobilità per acquisti 1

2 Flussi e modelli merci Definizioni Zona di produzione (f) Produttore Zona di acquisizione (o) Zona di vendita al dettaglio (d) Zona di consumo (e) Flussi di merce (modelli I-O) Veicoli commerciali (modelli per la simulazione della scelta modale e di logistics chain) Mercato Generale Centro di Distribuzione Magazzino all ingrosso Flussi di merce (modelli merci a scala metropolitana/urbana) Vendita al dettaglio Grande Distribuzione Organizzata Flussi di veicoli (consumatori) Residenza Veicoli commerciali Flussi di merce Attività di servizio 2

3 Flussi e modelli merci Definizioni Zona di produzione (f) Produttore Flussi di merce (modelli I-O) Veicoli commerciali (modelli per la simulazione della scelta modale e di logistics chain) Mercato Generale Modelli merci a scala urbana/metropolitana Zona di acquisizione (o) Centro di Distribuzione Magazzino all ingrosso Flussi di merce (modelli merci a scala metropolitana/urbana) Veicoli commerciali Zona di vendita al dettaglio (d) Vendita al dettaglio Grande Distribuzione Organizzata Flussi di merce Zona di consumo (e) Modelli passeggeri a scala urbana /metropolitana Flussi di veicoli (consumatori) Attività di servizio Residenza 3

4 Zonizzazione Zona di traffico: porzione di territorio con caratteristiche omogenee rispetto alle attività, all accessibilità, alle infrastrutture ed ai servizi di trasporto. Centroide: punto del territorio rappresentativo del baricentro delle origine e delle destinazione di una zona di traffico. Centroidi interni Centroidi di cordone Centroidi esterni Centroidi esterni area di studio Cordone 7 4

5 Le matrici Origine-Destinazione Hanno un numero di righe e di colonne pari al numero di zone. Il generico elemento D od fornisce il numero degli spostamenti che hanno origine nella zona o e destinazione nella zona d nel periodo di riferimento considerato. Spostamenti intrazonali Spostamenti interni Spostamenti di scambio Spostamenti di attraversamento O/D spostamenti di scambio esterno-interno spostamenti di scambio interno-esterno spostamenti di attraversamento = spostamenti intrazonali 5

6 Livelli di zonizzazione e matrici OD livello regionale, le zone di traffico coincidono con le regioni e le matrici OD sono interregionali livello provinciale, ciascuna zona di traffico coincide con una provincia e le matrici OD sono interprovinciali (o intraregionali) livello comunale(metropolitano), ciascuna zona di traffico è rappresentata da una area sub-comunale (o da uno o più comuni ) e le matrici OD sono matrici OD intracomunali (intercomunali) 6

7 Urban freight trips structure truck flows (on-line delivering) warehouse/courier location end consumer location passenger flows (store shopping) truck flows (shop restocking) passenger flows (store shopping) retailer location goods flow shopping trip flow 7

8 Urban freight modelling framework SHOPPING Purchase SHOPPING Trip RESTOCKING - Quantity Type and location of shop Shopping trip O-D matrices RESTOCKING - Delivery RESTOCKING Tour-Vehicle Produced Trips Mode choice Retail Activities Produced in-store Purchases Trip Generation Mode O-D matrices ATTRACTION TRANSPORT SERVICE DEPARTURE TIME Purchase Generation Inhabitants, Visitors Quantity purchase model Attracted quantities Quantity O-D flows for transport services TRIP ORDER and VEHICLE TYPE Produced on-line Purchases Warehouses and Distribution Activities ACQUISITION SHIPMENT SIZE DELIVERY LOCATION ACQUISITION AND COURIER SERVICE Quantity O-D matrices Delivery O-D matrices Delivery Tours Freight Vehicle O-D matrices model data 8

9 Urban freight modelling framework SHOPPING Purchase SHOPPING Trip Type and location of shop Shopping trip O-D matrices Produced Trips Mode choice Produced in-store Purchases Trip Generation Mode O-D matrices Purchase Generation Inhabitants, Visitors Quantity purchase model Produced on-line Purchases Warehouses and Distribution Activities ACQUISITION AND COURIER SERVICE 9

10 Urban freight modelling framework Purchase generation ACQ =n m i, s, h i i, s, h o. o ACQ o i,s,h is the number of purchases of goods type s made by end consumers belonging to the category i and living in zone o, through the shopping mode h (i.e. in store or on line) and spending more than 30,00; n oi is the number of end consumers belonging to the category i and resident in zone o; m i,s,h is the average number of purchases of goods type s, made using the shopping mode h (and spending more than 30,00) by end consumer belonging to the category i; 10

11 Urban freight modelling framework Average number of purchases i, s, h i, s, h m y p y y m o i,h [s] is the average number of purchases of goods type s, made using the shopping mode h (and spending more than 30,00) by end consumer belonging to the category i; p i,s,h [y] is the probability to made y purchases of goods type s (and spending more than 30,00) by end consumer belonging to category i using the shopping mode h; it is obtained by a purchase choice model. 11

12 Example of purchase choice model 69% ha effettuato almeno un acquisto 80% solo in store 20% sia in store che on line in store e on line; 14% Acquisti on line un acquisto (89%) due acquisti(10%) tre acquisti (1%) nessun acquisto; 31% solo in store; 55% Due acquisti 10% Tre acquisti 1% Un acquisto 89% 12

13 Example of purchase choice model Average weekly purchases and purchases made per trips less than 19 years old between years old between years old more than 65 years in-store on-line total purchases per trips all employed housewife student other in-store on-line total purchases per trips

14 Urban freight modelling framework Example of purchase choice tree (max 2 purchases) 1 purchases 2 purchases no purchase 0 on-line and 1 in-store 1 on-line and 0 in-store 0 on-line and 2 in-store 1 on-line and 1 in-store 2 on-line and 0 in-store 14

15 Urban freight modelling framework Purchase choice tree 1 purchases 2 purchases Based on the survey analysis no statistically significant number of users makes only one on-line purchase no purchase 0 on-line and 1 in-store 1 on-line and 0 in-store 0 on-line and 2 in-store 1 on-line and 1 in-store 2 on-line and 0 in-store to make purchases (acq) no purchase alt 1 0 on-line and 1 in-store more than 1 on-line and/or more than 1 in-store alt 2 alt 3 15

16 Purchase choice logit model [1/3] The systematic utilities of each elementary alternative have been expressed as a linear combination of the following attributes: Demographic fem is a dummy variable equal to 1 if the end consumer is female, 0 otherwise; male is a dummy variable equal to 1 if the end consumer is male, 0 otherwise; young is a dummy variable equal to 1 if the end consumer is between 14 and 19 years old, 0 otherwise; medium is a dummy variable equal to 1 if the end consumer is between 20 and 44 years old, 0 otherwise; high is a dummy variable equal to 1 if the end consumer is between 45 and 65 years old, 0 otherwise; comp is the number of household members; Economic student is a dummy variable equal to 1 if the end consumer is student, 0 otherwise; employee is a dummy variable equal to 1 if the end consumer is employed, 0 otherwise; housewife is a dummy variable equal to 1 if the end consumer is a housewife, 0 otherwise. 16

17 Purchase choice model Nested logit model [2/2] i p k p i x / k exp( Y ) h k exp( Y ) h exp( V ) j x exp( V ) j Probability to do choice k (e.g., to purchase) Probability to choose alternative x (e.g., purchase both in-store or on-line) having choosen the nest k (e.g., to purchase) Y k = ln ( θexp(v x θ) x I k ) Logsum variable V x = z β x,z T x,z Systematic utility function b x,z, model parameter T x,z, attributes of alternative x

18 Purchase choice model [2/3] 18

19 Purchase choice model [3/3] 19

20 Urban freight modelling framework SHOPPING Purchase SHOPPING Trip RESTOCKING - Quantity Type and location of shop Shopping trip O-D matrices RESTOCKING - Delivery RESTOCKING Tour-Vehicle Produced Trips Mode choice Retail Activities Produced in-store Purchases Trip Generation Mode O-D matrices ATTRACTION TRANSPORT SERVICE DEPARTURE TIME Purchase Generation Inhabitants, Visitors Quantity purchase model Attracted quantities Quantity O-D flows for transport services TRIP ORDER and VEHICLE TYPE Produced on-line Purchases Warehouses and Distribution Activities ACQUISITION SHIPMENT SIZE DELIVERY LOCATION ACQUISITION AND COURIER SERVICE Quantity O-D matrices Delivery O-D matrices Delivery Tours Freight Vehicle O-D matrices model data 20

21 Urban freight modelling framework Produced shopping trips i, in store i o. o. i, in store acqo D s = ACQ s s D i o.[s] is the (weekly) average number of trips undertaken by end consumers belonging to category i for purchasing goods of type s with origin in zone o, obtained also (see below) by a trip generation model. ACQ i,in store o [s] is the average number of (weekly) in-store purchases of goods type s made by end consumer belonging to category i and living in zone o; acq i,store o [s] is the average number of purchases of goods type s made by end consumer belonging to category i for each shopping trip. 21

22 Urban freight trips structure truck flows (on-line delivering) warehouse/courier location end consumer location passenger flows (store shopping) truck flows (shop restocking) passenger flows (store shopping) retailer location goods flow shopping trip flow 22

23 Urban freight modelling framework 23

24 Demand models for shopping trips 1/2 s p k / so p d / kso p m / dkso D skm =D i i i i i od o. D i od [skm] is the average number of trips with origin in zone o undertaken by end consumers of category i for purchasing freight of type s in retail outlet k located in zone d by using transport mode m; D i o. [s] is the mean number of relevant trips undertaken by end-consumers belonging to category i for shopping freight of type s with origin in zone o obtained by a trip generation model; p i [k/so] is the probability that users, undertaking a trip from o, travel for purchasing at shop type k, obtained by a shop type choice model; p i [d/kso] is the probability that users, undertaking a trip from o, travel to destination zone d for purchasing at shop type k, obtained by a location shop model; p i [m/dkso] is the probability that users, traveling between o and d for purchasing in shop type k, use transport mode m obtained by a modal choice or split model. 24

25 Demand models for shopping trips i i i / Q sk = Q sk D skm p dim mks dim. d. d od i i o, m, dim 2/2 Q i.d [sk] is the goods quantity bought/sold in retail outlet k in zone d given by the demand of end consumers belonging to category i living/working in a zone within the study area; dim is the dimension of purchases, expressed in kg; p i [dim/mks] is the probability that a trip concludes with a purchase of dimension dim conditional upon undertaking a trip to retail outlet k for a purchase of goods type s using transport mode m; obtained by a quantity choice model. 25

26 Demand models for shopping trips Trip generation model ns i [o] the number of end-consumers in zone o belonging to category i; ms i [o] is the mean number of trips undertaken by the individual in category i, departing from zone o for shopping. The average index ms i [o] can be estimated by two main categories of models: behavioral (or more properly, random utility models) and descriptive models: behavioral models with x number of trips and p i [x/o] probability of undertaking x trips; descriptive models with means of values of socio-economic variables such as income, number of cars owned, and b j parameters to calibrate. i i Do. ns o ms o i i / i ms o x p x o i ms o x j b X j i jo 26

27 Demand models for shopping trips Trip generation model Model D s ns o ms o ns o b X o. j jo j Do[s] weekly average number of relevant trips undertaken by end-consumers for purchasing goods of type s with origin in zone o ns[o] are the number of inhabitants older than 20 years resident in the traffic zone o; ms[o] is the average number of weekly trips undertaken by inhabitants older than 20 years; it is expressed as a linear function in the coefficients b j of attributes X jo. Freight type Number of household components Age between years Age between years Over 65 Housewife (H)/ Student (S) Female (F)/ Male (M) Type of variable 0/1 0/1 0/1 0/1 0/1 Foodstuffs (-2.82) 0.24 (4.25) 0.61 (10.27) 0.72 (7.62) 0.19 H (2.82) 0.20 F (5.22) Hygiene and household products (-1.63) 0.14 (3.13) 0.35 (7.51) 0.22 (2.98) 0.03 H (0.60) 0.07 F (2.51) Other 0.39 (8.34) 0.53 (13.96) 0.37 (3.54) 0.15 S (2.91) 0.08 M (1.79) ( - ) t-st value 27

28 Demand models for shopping trips Trip generation model Model D s ns o ms o ns o b X o. j jo Elders travel j Do[s] weekly average number of relevant trips undertaken by end-consumers more than for purchasing goods of type s with origin in zone o ns[o] are the number of inhabitants older than 20 years resident in the traffic zone o; ms[o] is the average number of weekly trips undertaken by inhabitants older than 20 years; it is expressed as a linear function in the coefficients b j of attributes X jo. Freight type Number of household components Age between years Age between years Over 65 Housewife (H)/ Student (S) Female (F)/ Male (M) Type of variable 0/1 0/1 0/1 0/1 0/1 Foodstuffs (-2.82) 0.24 (4.25) 0.61 (10.27) 0.72 (7.62) 0.19 H (2.82) 0.20 F (5.22) Hygiene and household products (-1.63) 0.14 (3.13) 0.35 (7.51) 0.22 (2.98) 0.03 H (0.60) 0.07 F (2.51) Other 0.39 (8.34) 0.53 (13.96) 0.37 (3.54) 0.15 S (2.91) 0.08 M (1.79) ( - ) t-st value youngers 28

29 Demand models for shopping trips The shop type choice logit model / exp exp Parameter Unit Alternative Value t-st value p-value Older than 65 years 0/1 Small outlet Number of household member 0/1 Small outlet Housewife 0/1 Small outlet Hygiene and household products 0/1 Small outlet Older than 65 years 0/1 Medium outlet Number of household member 0/1 Medium outlet Hygiene and household products 0/1 Medium outlet Foodstuffs 0/1 Medium outlet Alternative Specific Attribute 0/1 Medium outlet Age between years 0/1 Large outlet Other products 0/1 Large outlet Housewife 0/1 Large outlet Alternative Specific Attribute 0/1 Large outlet r p k so V V k k ' k Small retail outlet, e.g. small specialized and nearby shops Medium retail outlet, e.g. supermarket Large retail outlet, e.g. hyper-market 29

30 Demand models for shopping trips The shop type choice logit model probability of making purchases in small retail outlets increases for / exp exp Parameter Unit Alternative Value t-st value p-value Older older than 65 peoples years and 0/1 Small outlet Number of housewives. household member 0/1 Small outlet Housewife 0/1 Small outlet Hygiene and household products 0/1 Small outlet Older than 65 years 0/1 Medium outlet Number of household member 0/1 Medium outlet Hygiene As and household the number productsof 0/1 Medium outlet Foodstuffs 0/1 Medium outlet household members Alternative Specific Attribute 0/1 Medium outlet Age between increases, so yearsdoes the 0/1 Large outlet Other probability products of buying in 0/1 Large outlet Housewife larger retail outlets 0/1 Large outlet Alternative Specific Attribute 0/1 Large outlet r k p k so V V k ' k Small retail outlet, e.g. small specialized and nearby shops Medium retail outlet, e.g. supermarket Large retail outlet, e.g. hyper-market 30

31 Demand models for shopping trips Location shop logit model exp exp p d / ko V V V d is the systemic utility and it is generally a function of attributes of a possible zone d pair (e.g. number of shop of type k) and level-of-service attributes (e.g. travel time and costs). Parameter Unit Commodity types Value t-st value Retail employees 10 3 all Travel distance km all ASA 0/1 Hygiene and household products ASA 0/1 Other r Linear combination of the number of employees in retail employment related to freight type s in zone d, the distance between zone o and d (calculated on the road network according to the path of minimum generalized travel cost) and a dummy variable (ASA) equal to 1 for close trips (travel length less than 3 km). d d ' d ' 31

32 Demand models for shopping trips Modal choice logit model exp exp m p m / kdo V V m' m ' V m is a function of attributes of a possible transport mode in relation to zone od pair (e.g. travel time and costs, number of wholesalers at zone o) and socioeconomic attributes of the end-consumer (e.g. gender, income, car availability). 32

33 Modal choice model Revealed shares The main mode used is car: its share increases when shop size and trip length rise. Travel distance Less than 1 km Between 1 and 2 km More than 2 km Small retail outlet On foot 37.7% 7.1% 6.6% Private car 59.6% 79.6% 62.3% Transit 2.7% 13.3% 31.1% Total 100.0% 100.0% 100.0% Medium-size retail outlet On foot 32.5% 12.1% 12.5% Private car 66.5% 72.7% 68.8% Transit 1.0% 12.1% 18.8% Total 100.0% 100.0% 100.0% Large retail outlet On foot 8.8% 2.5% 0.0% Private car 87.3% 92.4% 92.9% Transit 3.9% 5.1% 7.1% Total 100.0% 100.0% 100.0% 33

34 Demand models for shopping Quantity choice logit model exp exp p dim / mks V V dim dim' dim' where V b EC b PC b JO dim i i j j k k i j k Used Attributes EC i PC j JO k attributes of end-consumer (e.g. age) attributes of purchase (e.g. freight type) attributes of journey (e.g. passive accessibility of purchase zone) 34

35 Example of application to a medium size urban area 35

36 Application to a medium size urban area Study area Padua Traffic zones 25 Number of shops 6,761 Number of warehouses 4,750 Number of shop employees Number of warehouse employees 23,144 [Istat, 2001] 17,016 [Istat, 2001] 36

37 Application to a medium size urban area Demographic trends In order to emphasise the effects of changes in age, in the following we assume that the total population remains constant while age distribution changes 37

38 Application to a medium size urban area E-shopping share Based on the revealed shares and the current trends revealed in some worldwide countries, the e-shopping share has been considered, pointing out that the inclination to make e-shopping changes with age and type of freight. Age Foodstuffs Hygiene and household products Other Total Younger than 19 years 0.0% 0.0% 28.6% 26.7% Age between years 0.0% 14.5% 27.9% 21.3% Age between years 0.4% 4.1% 12.7% 6.3% Older than 65 years 0.0% 0.0% 0.0% 0.0% Total 0.3% 6.6% 20.3% 11.9% Youngers purchase more than elders, especially other products 38

39 Application to a medium size urban area Scenario trip comparison Age Foodstuffs Hygiene and Other household products products Total % -42.0% -46.5% -38.6% % 41.1% 23.0% 42.3% 65 and more 42.7% 26.0% 13.0% 28.3% Total 10.7% -4.0% -14.1% -1.5% Age small retail medium retail large retail Total Foodstuffs 7.9% 26.8% -0.3% 10.7% Hygiene and household products -2.0% 8.6% -22.5% -4.0% Other products 7.5% -20.7% -33.6% -14.1% Total 6.3% 2.5% -15.7% -1.5% trips for foodstuffs purchases increase trips to small and medium retail outlets increase Quantity [tons/day] City Area % city center % first ring % second ring % Total 1,120 1, % City centre: city zone where the density of end consumers and small retailers is usually higher; The prof. first ing. ring: Agostino areas with medium Nuzzolo end-consumer - Corso di density Logistica and the Territoriale presence of warehouses; The second ring: areas where end-consumer density is low and large shopping malls and freight distribution facilities are located, 39

40 Application to a medium size urban area Scenario trip comparison Number of trips and vehicle-km changes small retail medium retail large retail Total Trips On foot 5.9% 2.5% -14.4% 3.6% Private car 6.1% 2.5% 15.7% -3.0% Transit 7.0% 2.6% -15.7% 2.7% Total 6.3% 2.5% -15.7% -1.5% Passenger / Vehicle-km On foot 8.8% 1.1% -0.3% 6.0% Private car 7.1% 10.9% 16.4% 13.2% Truck (for restocking) -3.1% Total (equivalent veh-km) 13.1% 40

41 Application to a medium size urban area Scenario comparisontrips on foot and transit increase due to the increase in trips undertaken by the elderly Number of trips and vehicle-km changes small retail medium retail large retail Total Trips On foot 5.9% 2.5% -14.4% 3.6% Private A car steady decrease6.1% in 2.5%.15.7% -3.0% Transit commercial vehicle-km, 7.0% while 2.6% -15.7% 2.7% Total total equivalent veh-kms 6.3% 2.5% -15.7% -1.5% increase due to increase of Passenger / Vehicle-km On car-kms foot 8.8% 1.1% -0.3% 6.0% Private car 7.1% 10.9% 16.4% 13.2% Truck (for restocking) -3.1% Total (equivalent veh-km) 13.1% 41

42 Application to a medium size urban area Scenario comparison With and without e-shopping changes 30% 25% 20% 15% 10% Shopping Trips no e-shopping changes e-shopping changes 5% 0% -5% -10% -15% -20% Small retail outlet Medium retail outlet Large retail outlet The increase of e-shopping can produce ameliorative effects in terms of total shopping trips (-1.5% respect to the status quo) and contain the increase of total distance travelled by vehicle. Total 30% 25% 20% 15% 10% 5% 0% -5% -10% -15% -20% Vehicle-km Car-km Truck-km Equivalent veh-km no e-shopping changes e-shopping changes 42

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44 Purchase choice tree CANCELLARE 1 purchase n purchases... number of purchases (both on-line and in-store) no purchase h n number of on-line purchases 44

45 Demand models for purchases???? [1/ CANCELLARE, / p i x k p i k p i x k i = consumer class k = set of alternatives: purchase or no-purchase x = purchase way: in-store or on-line or in-store ed on-line p i [x,k] = probability of purchasing by way x for the user class i; p i [k] = probability of purchasing or not purchasing (nest k); p i [x/k] = probability of purchasing by way x having decided to do purchases 45

46 Demand models for purchases????? Average number of weekly purchases N [3/ N in store N in store n acqx p x purchase i i x i i i, in store i CANCELLARE, i i i, on line i, N on line N on line n acq p on line purchase where: N[ ] is the number of weekly purchases (in-store or on-line) is the number of weekly purchase (in-store or on-line) made by user class i. N i [ ] is the number of users of class i. n i i acq j is the average number of purchases made in-store or on-line. i

47 Demand models for purchases Example of model [4/ CANCELLARE purchases more than 1 purchase in store and at least 1 purchase on-line (alt 3) 1 purchase in store (alt 2) no purchase (alt 1) 47