Urban Goods Flow Modelling: Purchasing and Shopping Mobility Demand Models

Transcription

1 2018 Winter School of University of Padova on Methods and models in freight transportation and logistics Padova (Italy), 1 st to 9 th of March 2018 Urban Goods Flow Modelling: Purchasing and Shopping Mobility Demand Models Agostino Nuzzolo - Antonio Comi Department of Enterprise Engineering University of Rome Tor Vergata 1

2 Contents Shopping mobility demand modelling Trip generation Choice of shop type and shop location Mode choice Application example 2

3 Urban goods mobility: Shopping trips 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 3

4 Purchase modelling 4

5 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 5

6 Purchase modelling 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 6

7 Purchase modelling Purchase generation model (week) 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 channel h (i.e. in store or on line); 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 channel h by end consumer belonging to the category i; 7

8 Number of purchases Example of Average weekly number of purchases less than 19 years old between years old between years old more than 65 years in-store on-line total all employed housewife student other in-store on-line total

9 Shopping trip demand models 9

10 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 10

11 Shopping trips demand modelling 11

12 Demand models of shopping trips 1/2 D skm = D s p k / so p d / kso p m / dkso 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 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 (share) 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 (share) 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 (share) 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. 12

13 Shopping trip models Trip generation model 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 ACQ i, in store i o. o. i, in store acqo D s = s s 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, obtained with a purchase generation model; 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. 13

14 Shopping trip models Trip generation model Example of Average number of purchases made per trip purchases per trips less than 19 years old between years old between years old more than 65 years all purchases per trips employed housewife student other

15 Demand models for shopping trips Trip generation model for short-term forecasting ns i [o] the number of end-consumers in zone o belonging to category i; ms i [o] is the average 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: utility (or more properly, random utility models) and regressive models: utility models i i Do. ns o ms o i i / i ms o x p x o with x number of trips and p i [x/o] probability of undertaking x trips; x regressive models i i ms o j X jo j with means of values of socio-economic variables such as income, number of cars owned, and b j parameters to calibrate. 15

16 Demand models for shopping trips Example of regression trip generation model D s ns o ms o ns o 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 16

17 Demand models for shopping trips Example of regressive trip generation model Model D s ns o ms o ns o 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 17

18 Demand models for shopping trips Shop type choice model Example of shop classification: Small retail outlet, e.g. small specialized and nearby shops Medium retail outlet, e.g. supermarket Large retail outlet, e.g. hyper-market 18

19 Demand models for shopping trips Example of 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 19

20 Demand models for shopping trips Example of 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 household members 0/1 Medium outlet Alternative increases, Specific Attribute so does the 0/1 Medium outlet Age probability between of years buying in 0/1 Large outlet Other products larger retail outlets 0/1 Large outlet Housewife 0/1 Large outlet Alternative Specific Attribute 0/1 Large outlet r k p k so V V k ' k 20

21 Demand models for shopping trips Shop zone choice model: logit model exp exp V d is the systemic utility and it is generally a function of attributes of a possible zone d (e.g. number of shop of type k) and level-of-service attributes (e.g. travel time and costs). d p d / ko V V d ' d ' 21

22 Demand models for shopping trips Example of shop zone choice logit model exp exp p d / ko V V V d is 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 ' 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

23 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). 23

24 Modal choice model Example of 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% 24

25 Demand models for shopping trips Purchase quantity model i i i / Q sk = Q sk D skm p dim mks dim. d. d od i i o, m, dim Q i.d [sk] is the goods quantity bought/sold in retail outlets 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 a quantity class of purchases, expressed in kg; p i [dim/mks] is the probability that a trip concludes with a purchase of class 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 Quantity choice logit model exp exp p dim / mks V V dim dim' dim' where V EC PC 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) 26

27 Example of application to a medium size urban area (Padua) Forecasting the effects of demographic changes to urban goods distribution 27

28 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] 28

29 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 29

30 Application to a medium size urban area E-shopping share in 2025 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 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 Total 0.3% 6.6% 20.3% 11.9% Youngers purchase more than elders, especially other products 30

31 Application to a medium size urban area 2025 scenario trip variation 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% Goods type 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 first ring: areas with medium end-consumer density and the presence of warehouses; The second ring: areas where end-consumer density is low and large shopping malls and freight distribution facilities are located, 31

32 Application to a medium size urban area 2025 scenario trip variation 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% 32

33 Application to a medium size urban area 2025 scenario variations trips 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 decrease 6.1% 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% 33

34 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 34

35 Appendix Random Utility Purchasing Model 35

36 Purchase generation: alternative approach 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 by end consumer belonging to the category i; p i,s,h [y] is the probability to made y purchases of goods type s by end consumer belonging to category i using the purchase mode h; it is obtained by a purchase choice model. 36

37 Example of purchase characteristics Rome 2015 weekly purchases* 69% made at least one purchase 80% only in store 20% both in store and on line in store and online; 14% Purchases on line one purchase (89%) two purchases (10%) three purchases (1%) no purchases; 31% only in store; 55% Two purchases 10% Three purchase One purchases 89% 37

38 Purchase choice modelling 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 38

39 Purchase choice modelling 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 39

40 Purchase choice modelling [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; Socio-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. 40

41 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 x,z, model parameter T x,z, attributes of alternative x

42 Purchase choice model [2/3] 42

43 Purchase choice model [3/3] 43