1 Introduction
2 Literature review
2.1 Survey structure and content definition
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The issue of concern;
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The planning and policy framework in which the issue arises;
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Established practice in data collection;
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The availability of previously collected data.
2.2 Sampling
2.3 Guidelines to survey implementation
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Self-completion (sample of 19): average response rate of 25 %, standard deviation of 16.5 %.
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Interview (phone-based or face-to-face, sample of 11): average response rate of 59 %, standard deviation of 22.3 %.
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All (sample of 35): average response rate of 38 %, standard deviation of 24.7 %.
2.4 Extrapolation of freight delivery details
3 Research methodology
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The issue: the survey was developed to allow the extrapolation of daily deliveries per time period, for each establishment, reflecting freight parking demand. Establishments were defined as retail establishments, a physically delimited private operation where commodities are sold to the public in relatively small quantities for use or consumption rather than for resale.
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Planning and policy framework: there was no government-related planning or policy framework backing this survey. Albeit its aims and framework were purely research related, freight parking demand is a pressing planning issue and EBFS allow understanding urban freight movements in a way that can facilitate the definition of policies or plan to better accommodate parking demand.
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Established practice in data collection: as far as the authors can attest, this is the first survey of this kind (and scope) made in Portugal. In Portugal, there is no established practice in urban freight data collection.
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Availability of previously collected data: there is no previously collected data minimally aligned with what was collected by the survey. Out of a comprehensive list of general data gaps in Browne and Allen [23], the following were the target of the proposed survey:
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Data about light goods vehicle activity;
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Data about urban freight and logistics infrastructure;
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Data about loading and unloading operations and infrastructure for goods vehicles;
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Geographical data about goods vehicle trips in the urban areas.
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Objectives of the data collection process: the main objective of this survey was to allow estimating the total number and patterns of deliveries for retail establishments present in the study area. Such data will be used to build models of freight vehicles parking demand. In addition, the survey should allow exploring the relevance of several freight trip generation predictor variables worth for retail system characterization. For that the EBFS serves the purpose of collecting information about the freight vehicles visiting an establishment (e.g., to unload goods), the characteristics of the establishment and of the goods ordering process.
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Definition of urban freight transport: only within an urban/metropolitan context, in accordance with the definition presented in Allen et al. [5].
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Description of movements to be captured: the focus of this data collection process will be the vehicle activity to and at urban retail establishments, excluding vehicle activity departing from establishments. It is defined as any activity that involves loading/unloading operations to retail establishments.
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Category of establishments: City Council establishment industry classifications - categories - were adopted (10 categories). The share of each category was calculated for a selected group of zones that overlapped the case-study area. This was considered a reasonable assumption as the total number of establishments in the zones (Census-based) was similar to the total number of establishments in the non-official database.
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Land-use clusters: six clusters of zones, arising from previous analyses (Fig. 2), were selected due to hypothesized significant differences in land-use (e.g., residents density, shops density). The shape of the zones is defined by Lisbon’s parking authority managerial discretion. As clusters were selected using land-use variables, geographical continuity is not mandatory and neither is homogeneity of cluster size (number of member zones). Land-use variables such as numbers of buildings, dwellings and residents were obtained from the last Census [28]. All variables represent the average for each cluster of zones. Average speed loss was calculated subtracting the average speed on links from the average maximum speed from 8 AM to 12 PM using aggregate estimates for all types of vehicles. Retail establishments locations were obtained from [26] and the retail heterogeneity indicator calculated as detailed in [15].
Sampling zones | A | B | C | D | E | F | Sum | % of total |
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Category 1 – Specialized foodstuffs | 5 | 0 | 9 | 10 | 7 | 0 | 31 | 5 % |
Category 2 – Non-specialized foodstuffs | 4 | 0 | 7 | 8 | 6 | 0 | 25 | 4 % |
Category 3 – Personal usage articles | 16 | 2 | 27 | 33 | 24 | 2 | 104 | 17 % |
Category 4 – Culture and leisure | 9 | 1 | 15 | 19 | 14 | 1 | 59 | 10 % |
Category 5 – Various | 12 | 1 | 22 | 23 | 18 | 1 | 77 | 13 % |
Category 6 – Home appliances | 8 | 1 | 14 | 16 | 12 | 1 | 52 | 9 % |
Category 7 – Non-specialized | 2 | 0 | 3 | 4 | 3 | 0 | 11 | 2 % |
Category 8 – Health and hygiene | 4 | 0 | 8 | 8 | 6 | 0 | 26 | 4 % |
Category 9 – Repairs | 4 | 0 | 7 | 8 | 6 | 0 | 25 | 4 % |
Category 10 – Food and drinks | 30 | 3 | 50 | 62 | 45 | 3 | 193 | 32 % |
% of total | 16 % | 1 % | 27 % | 32 % | 23 % | 1 % | - | - |
Variable | Cluster | |||||
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A | B | C | D | E | F | |
Buildings (Km2) | 871 | 264 | 1262 | 2074 | 1674 | 4759 |
Dwellings (Km2) | 7132 | 2170 | 8102 | 12,289 | 7948 | 22,565 |
Dwellings / Building | 8 | 8 | 6 | 6 | 5 | 5 |
Residents (Km2) | 11,328 | 3592 | 11,984 | 18,606 | 9659 | 30,857 |
Average Speed Loss (Km/h)a
| 17 | 16 | 22 | 21 | 19 | 25 |
Retail Establishments (Km2) | 370 | 62 | 728 | 643 | 1198 | 616 |
Retail Heterogeneityb
| 0.80 | 0.61 | 0.78 | 0.81 | 0.79 | 0.67 |
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Characteristics of the establishment (e.g., location, category, area, employees, fleet) and of the contact person (e.g., name, job, e-mail);
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General characteristics of loading/unloading operations (e.g., urgency of deliveries, ordering process, supply chain details);
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Specific characteristics of loading/unloading operations (e.g., deliveries per daily time period, seasonal changes);
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Characteristics of the core loaded/unloaded goods (e.g., volume, weight, perishability, fragility, requires refrigeration);
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Perception/opinion about the loading/unloading operations.
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Establishment and warehouse area;
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Types of deliveries (answer in % of e.g.,: core goods, services, mail and express deliveries);
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% of deliveries by element in the supply chain (e.g.,: own-account, 3rd Party Logistics – 3PL);
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Total deliveries per daily time period (within a time frame of 2 h intervals);
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Type of vehicle delivering (number of weekly deliveries and most frequent duration of the delivery);
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Parking location (frequency and distance according to type, e.g.,: loading/unloading bay, lane);
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Amount of commodities received weekly (type and volume).
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Total (weekly) deliveries;
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Establishment size (represented by establishment sales area, warehouse area, establishment “shop front” width, total number of employees, total number of suppliers);
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Duration of deliveries (the declared most frequent delivery duration for the most frequent vehicle type/size visiting the establishment);
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Commodities’ characteristics (weight, volume, perishability, fragility, special requirements such as refrigeration);
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Quantity of commodities (it was assumed that, independently of commodity volume, every item would be considered as a single item. i.e., bag, box or crate, regardless of size, would count as one item);
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Supply chain characteristics:
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Urgency of deliveries;
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Interval between orders;
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Order arrival time;
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Delivery routine (i.e., day and time of deliveries as well as choice of supplier which could be Defined, Non-defined, Mixed);
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Delivery entity (own-account; 3PL, producer, wholesaler, mixed, other);
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Delivery planner (own-account; 3PL, producer, wholesaler, other);
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Stock management (visual, computer-based, external, other);
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Distribution chain (decentralized, centralized, hybrid, other);
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Type of delivery (between chain establishments, between unrelated establishments, single trip, mixed);
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Origin of the delivery (company warehouse, wholesaler warehouse, producer, other).
4 Results and discussion
4.1 Overall perspective of retail establishments’ characteristics
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Up to 5: ~32 %
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Between 5 and 10: ~35 %
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From 10 to 25: ~24 %
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Over 25: ~10 %
4.2 Deliveries per time period and category of establishment
4.3 Correlation analysis
Variable 1 | Variable 2 | Correlation (V1 and V2) |
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Total number of Weekly Deliveries | Category 1 – Specialized foodstuffs | 0.113** |
Category 2 – Non-specialized foodstuffs | 0.097* | |
Category 3 – Personal usage articles | −0.432** | |
Category 4 – Culture and leisure | −0.197** | |
Category 5 – Various | −0.005 | |
Category 6 – Home appliances | −0.138** | |
Category 7 – Non-specialized | −0.071 | |
Category 8 – Health and hygiene | 0.145** | |
Category 9 – Repairs | −0.025 | |
Category 10 – Food and drinks | 0.436** | |
“Sales” area | 0.109** | |
Warehouse area | 0.163** | |
Not having a warehouse | −0.245** | |
Frontage width | 0.116** | |
Employees | 0.449** | |
Total number of Weekly Deliveries | Time gap between orders | −0.671** |
Daily ordering | 0.559** | |
Number of Suppliers | 0.404** | |
Commodity Weight | Commodity Volume | 0.649** |
Quantity of Commodities | Total no. of Weekly deliveries | 0.699** |
Employees | 0.511** | |
Urgency | −0.419** | |
Time gap between orders | −0.577** | |
One day between orders | 0.489** | |
Origin is “wholesaler”/ is “producer” | 0.483**/−0.404** | |
Delivery duration | 0.276** | |
Employees | Sales area | 0.422** |
Variable 1 | Variable 2 | Correlation (V1 and V2) |
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Category 3 – Personal usage articles | Time gap between orders | 0.417** |
Category 10 – Food and drinks | Order based on visual stock inspection | 0.500** |
Order based on computer stock monitoring | −0.466** | |
Average time gap between order and delivery | −0.438** | |
Number of small trucksa delivering | 0.579** | |
Average time delivering | 0.552** | |
Perishable items are majority | 0.537** | |
(Establishment) Orders daily | Origin is “company warehouse” | 0.426** |
(Establishment) Several orders p/ week | Hybrid Supply Chainb
| 0.576** |
(Establishment) Orders once per week | Origin is “company warehouse” | −0.408** |
Establishment-based Fleet | Own vehicles deliver | 0.534** |
Establishment defines deliveries | 0.419** | |
Urgency | Deliveries by 3PL | 0.454** |
Origin is “wholesaler” / is “producer” | −0.511** / 0.531** | |
Average delivery time by light goods vehicle | −0.448** | |
3PL delivers | 3PL / establishment defines delivery time/day | 0.583** / −0.485** |
Origin “producer” | 0.488** | |
Wholesaler delivers | Wholesaler defines delivery time/day | 0.797** |
Establishment fleet delivers | Establishment/ 3PL defines delivery time/day | 0.652** / −0.541** |
Decentralized supply chainc
| −0.405** | |
Deliveries between unrelated establishments | −0.463** | |
Only deliveries to own establishment | 0.563** | |
Origin “company warehouse” | 0.401** | |
Wholesaler delivers | Origin “wholesaler” | 0.421** |
3PL defines deliveries | Origin “producer” | 0.412** |
Establishment defines deliveries | Only deliveries to own establishment | 0.424** |
Time gap between orders | Time gap between order and delivery | 0.546** |
Delivering vehicles | −0.620** | |
Total delivery time | −0.506** | |
Total delivery time (time gap = 1 day) | 0.495** | |
Order based on visual stock inspection | Perishable items are majority | 0.409** |
Time gap between deliveries | Delivering vehicles | −0.436** |
Decentralized Supply Chain | Deliveries between unrelated establishments | 0.507** |
Only deliveries to own establishment | −0.414** | |
Origin “Company Warehouse” | −0.507** | |
Centralized Supply Chaind
| Deliveries between unrelated establishments | −0.484** |
Only deliveries to own establishment | 0.405** | |
Origin “Company Warehouse” | 0.544** | |
Origin “producer” | Refrigerated items are majority | 0.408** |
Deliveries with “small truck” | Perishable items are majority | 0.480** |
Refrigerated items are majority | Perishable items are majority | 0.606** |
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Increased urgency in receiving deliveries is correlated with a smaller amount of commodities received weekly (commodities were labeled as a unit despite size or weight). Also, increased urgency has the highest average of deliveries (12/week versus 8/week for other answers), but overall, receiving more items is associated with an increased number of deliveries.
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Furthermore, a higher total number of items received per week is barely related to a higher average delivery duration,
4.4 Total (weekly) deliveries and Establishment “size” relationships
Cat. | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Avg. | All | |
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Sample | Obs. | 27 | 23 | 103 | 55 | 76 | 48 | 11 | 23 | 24 | 168 | – | 558 |
Dif. Original | −4 | −2 | −1 | −4 | −1 | −4 | 0 | −3 | −1 | −25 | – | −45 | |
Deliveries | Avg. | 9.78 | 9.83 | 2.53 | 3.89 | 7.84 | 4.60 | 4.64 | 13.17 | 6.67 | 11.39 | – | 7.54 |
S.D. | 5.28 | 6.95 | 2.68 | 4.31 | 6.06 | 4.62 | 4.54 | 8.09 | 5.16 | 6.69 | – | 6.61 | |
C.V. % (var) | 54 | 71 | 106 | 111 | 77 | 100 | 98 | 61 | 77 | 59 | 81 | 88 | |
Employees | Avg. | 2.81 | 2.43 | 2.21 | 3.09 | 2.37 | 2.81 | 2.09 | 4.48 | 3.58 | 4.01 | – | 3.10 |
S.D. | 1.44 | 1.04 | 1.20 | 1.75 | 1.13 | 1.65 | 1.04 | 2.48 | 2.17 | 2.22 | – | 1.91 | |
C. V. % (var) | 51 | 43 | 54 | 56 | 48 | 59 | 50 | 55 | 60 | 55 | 53 | 62 | |
r2
| 0.01 | 0.27 | 0.11 | 0.62 | 0.00 | 0.13 | 0.08 | 0.31 | 0.37 | 0.25 | 0.22 | 0.22 | |
Coef. | 0.35 | 3.46* | 0.76** | 0.06 | −0.26 | 0.99* | −1.25 | 1.81** | 1.45** | 1.50** | – | 1.61** | |
RMSE | 5.36 | 6.10 | 2.54 | 4.21 | 6.09 | 4.36 | 4.59 | 6.88 | 4.18 | 5.81 | 5.01 | 5.86 | |
C.V. % (model) | 55 | 62 | 100 | 108 | 78 | 95 | 99 | 52 | 63 | 51 | 76 | 78 | |
SSE | 718 | 781 | 650 | 939 | 2744 | 876 | 190 | 995 | 384 | 5611 | – | 19,079 | |
Sales Area1 (m2) | Avg. | 48.70 | 61.52 | 62.77 | 96.64 | 63.62 | 125.52 | 45.00 | 91.74 | 121.67 | 63.69 | – | 74.54 |
S.D. | 35.88 | 45.71 | 69.31 | 78.10 | 90.14 | 91.02 | 25.59 | 92.33 | 173.57 | 47.23 | – | 78.63 | |
C. V. % (var) | 74 | 74 | 110 | 81 | 142 | 73 | 57 | 101 | 143 | 74 | 93 | 105 | |
r2
| 0.09 | 0.16 | 0.18 | 0.04 | 0.05 | 0.01 | 0.01 | 0.01 | 0.24 | 0.03 | 0.08 | 0.00 | |
Coef. | −0.05 | 0.06 | 0.02** | 0.01 | −0.02* | 0.00 | −0.02 | 0.01 | 0.01* | 0.03* | – | 0.00 | |
RMSE | 5.12 | 6.52 | 2.44 | 4.26 | 5.94 | 4.64 | 4.77 | 8.26 | 4.59 | 6.59 | 5.31 | 6.61 | |
C.V. % (model) | 52 | 66 | 96 | 109 | 76 | 101 | 103 | 63 | 69 | 58 | 79 | 88 | |
SSE | 656 | 891 | 603 | 962 | 2608 | 992 | 205 | 1434 | 464 | 7216 | – | 24,272 | |
Suppliers | Avg. | 8.89 | 10.22 | 10.27 | 11.33 | 9.96 | 16.35 | 8.64 | 14.91 | 7.42 | 10.27 | – | 10.82 |
S.D. | 9.54 | 8.19 | 9.96 | 11.15 | 9.49 | 14.37 | 4.32 | 9.78 | 5.98 | 6.42 | – | 9.44 | |
C. V. (var) | 107 | 80 | 97 | 98 | 95 | 88 | 50 | 66 | 81 | 63 | 82 | 87 | |
r2
| 0.00 | 0.36 | 0.06 | 0.39 | 0.00 | 0.30 | 0.00 | 0.00 | 0.50 | 0.34 | 0.20 | 0.07 | |
Coef. | 0.02 | 0.51** | 0.06* | 0.24** | 0.01 | 0.18** | 0.04 | −0.03 | 0.61** | 0.61** | – | 0.18** | |
RMSE | 5.38 | 5.69 | 2.62 | 3.39 | 6.10 | 3.91 | 4.79 | 8.28 | 3.74 | 5.43 | 4.93 | 6.38 | |
C.V. (model) | 55 | 58 | 103 | 87 | 78 | 85 | 103 | 63 | 56 | 48 | 74 | 85 | |
SSE | 723 | 679 | 691 | 609 | 2749 | 703 | 206 | 1440 | 308 | 4899 | – | 22,663 | |
Items | Avg. | 54.11 | 71.87 | 11.09 | 16.31 | 30.21 | 20.02 | 13.64 | 61.87 | 33.67 | 47.43 | – | 33.62 |
S.D. | 68.11 | 91.93 | 18.98 | 24.62 | 36.16 | 25.98 | 12.55 | 58.69 | 38.45 | 57.79 | – | 49.29 | |
C. V. (var) | 126 | 128 | 171 | 151 | 120 | 130 | 92 | 95 | 114 | 122 | 125 | 147 | |
r2
| 0.23 | 0.78 | 0.02 | 0.16 | 0.09 | 0.23 | 0.00 | 0.56 | 0.17 | 0.32 | 0.26 | 0.35 | |
Coef. | 0.04* | 0.07** | 0.02 | 0.07** | 0.05** | 0.09** | 0.02 | 0.10** | 0.06* | 0.07** | – | 0.08** | |
RMSE | 4.71 | 3.32 | 2.67 | 3.99 | 5.83 | 4.09 | 4.78 | 5.52 | 4.79 | 5.51 | 4.52 | 5.33 | |
C.V. (model) | 48 | 34 | 105 | 103 | 74 | 89 | 103 | 42 | 72 | 48 | 72 | 71 | |
SSE | 556 | 232 | 721 | 843 | 2515 | 768 | 206 | 639 | 505 | 5040 | – | 15,808 |