THE POTENTIAL OF IMPLEMENTING URBAN

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THE POTENTIAL OF IMPLEMENTING URBAN FREIGHT STRATEGIES IN THE ACCOMMODATION AND FOOD SERVICES SECTOR Iván Sánchez-Díaz, Ph.D. Senior Lecturer, Department of Technology Management and Economics Chalmers University of Technology, Vera Sandbergs Allé 8, Room 3376, Göteborg, 41296, Sweden, phone number: +46(0)31 772 5154, email: [email protected]

ABSTRACT This paper studies freight demand patterns of establishments in the accommodation and food services sector, and the potential of implementing urban freight strategies in this sector to improve local and city traffic conditions. This research uses data from the City of Stockholm and assesses the potential implementation of two strategies (i.e., consolidation and off-peak hour deliveries) proposed in The Stockholm Freight Plan to foster safe, clean and efficient freight deliveries. The results show that accommodation establishments have different freight generation patterns and opinions about the strategies proposed than other food services establishments. The results also show that the strategies proposed have good acceptance among 20% of the receivers surveyed, which would facilitate the implementation of the strategies and lead to significant benefits both at the local and city levels. KEYWORDS: urban freight strategies, receivers, accommodation and food services, off-hour deliveries, consolidation. 1. INTRODUCTION The increasing urban population and the congestion that this growth entails are consistently ranking very high among the issues concerning major cities around the world. Congestion has numerous negative effects including hampering the accessibility for people and goods, producing environmental and health issues, diminishing livability, and distressing the competitiveness of local economies. Schrank, Eisele et al. (1) estimates that, in 2014, congestion lead to USD$160 billion in extra cost only from increased travel times and fuel consumption in the US. It is noteworthy that although freight only represents 7% of the traffic, it accounts for 17% of this extra cost (1). Congestion has major impacts on local commercial establishments that, in addition to assuming higher logistics costs due to increased transportation and inventory costs (caused by lower reliability of deliveries), must cope with the impacts on attractiveness for their customers. The latter is a particularly important issue for the Accommodation and Food Services (AFS) sector for which the price of the service sold barely depends on the cost of the goods provided (e.g., food cost represent as low as 30% of the meal price), as other intangible elements, such as the establishment’s environment, are the main source of added-value to their service (2). Hence, the interest of this sector in initiatives that decrease congestion and make their neighborhoods more attractive. The AFS sector is defined by a common business purpose focused on service, it covers establishments with a wide range of services offered, i.e., basic/luxury accommodation, catering, café and bakery services, fast food or fancy dinner. Verlinden, Van de Voorde et al. (3) propose four different market typologies for this sector to characterize the flows and explain linkages, i.e., (i) small

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local specialists and cash-and-carry, (ii) big local and goods specialists, (iii) Ho.Re.Ca. supermarkets with deliveries, and (iv) franchises. AFS establishments can also be classified as independent or part of a chain, this distinction is relevant because it may influence the level of integration with suppliers and carriers. Chain establishments share a brand and standardized business practices with other establishments that are part of the same chain. In some cases, chain establishments are part of a franchise scheme where an existing business model is adopted and the supplies are bought from the franchisor or designated suppliers. Cherrett, Allen et al. (4) explain that independent retailers generate larger traffic impacts because of their decentralized distribution system, while larger chains given their centralized system generate less trips of a larger size. AFS businesses do not only suffer the impacts of traffic, they are also a major contributor to it. AFS establishments are often located in central areas of cities (5) and generate a large amount of freight trips. Schoemaker, Allen et al. (6) estimate that this sector is responsible for about 31% of the freight moved in urban areas, and Holguín-Veras, Sánchez-Díaz et al. (7) estimate that this sector is responsible for 14-17% of the total freight traffic in the main metropolitan areas of Minneapolis, Pennsylvania, New Jersey and Florida, USA. These numbers reveal the important contribution of this sector to urban freight traffic and the need to study in detail the freight demand of AFS establishments. This knowledge can be instrumental for planning and implementing efficient strategies that reduce the impacts of deliveries and alleviate traffic conditions, thus benefiting commercial activity and at the same time enhancing the city’s livability. Developing and implementing urban freight plans is becoming an important priority for city authorities in recent years. The City of Stockholm has developed The Stockholm Freight Plan 20142017 to foster safe, clean and efficient freight deliveries (8). The Plan seeks to enable more reliable delivery times, to facilitate access for freight vehicles, to promote clean vehicles, and to advance freight delivery partnerships. To achieve those goals, the City proposes nine strategies including developing a freight consolidation project, studying the feasibility of off-peak hours deliveries (OPHD), and increasing the number of loading zones for large freight vehicles, among others. The Plan also highlights the importance of collecting freight data to analyze the freight system and to support the implementation of the strategies proposed. The consolidation strategy proposed seeks to promote consolidation centers, i.e., “staffed freight reception areas where distributors can unload freight which is then consolidated and dispatched with other freight destined for the same part of the city.” While OPHD seeks to shift “freight deliveries to times when the city’s road network is less congested. This does not necessarily mean night delivery, but can also be late-evening or early morning delivery.” The increase in the number of loading zones can be achieved via local traffic regulation, adopted by the municipality or the county administrative board, to modify waiting and parking restrictions (8). Similar strategies have been successfully implemented in the AFS sector in various countries (9-13). Key findings from these experiences include the necessity to gain receivers’ support for a successful implementation, as well as finding cost-effective schemes to implement OPHD and consolidation. This paper focuses on assessing receivers’ support. The purpose of this paper is twofold, it seeks (i) to analyze the freight demand of AFS receivers; and (ii) to investigate their opinion about freight deliveries in general (e.g., satisfaction, problems) and about two strategies aimed at alleviating the impacts of deliveries (i.e., consolidation and OPHD). To this effect, the paper is organized in 4 sections in addition to this introduction. Section 2 describes the method adopted. Section 3 and 4 present the results and practical implications. Section 5 discusses the main conclusions from this research.

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2. METHOD 2.1.Data Collection Framework This paper studies the AFS sector using data collected in the City of Stockholm during the fall of 2016. The geographical area of the study was selected based on the target of the strategies on consolidation and OPHD formulated in the Stockholm Freight Plan. It covers the central zone of the City that suffers from high congestion and where a congestion fee is currently being charged. The sample size was designed taking into account past experience of mean and variance in the AFS sector as proposed by Ortúzar and Willumsen (14), and respecting a budget constraint to allow data collection for all commercial sectors of interest. An aspirational sample size was designed using the mean and variance of weekly deliveries from a past study (15) and assuming a response rate of about 80%, as follows: 2

100  S  (1) n    240 80  0.051  Where n : The sample size for the AFS sector. S and µ: The sample variance and mean for weekly deliveries in (15), i.e., S=9.9 and µ=14.0. 0.051 : Assuming a desired 95% confidence level and a Normal distribution for the variable of interest. The data was segmented in two strata using commercial sectors as designated by the Swedish National Industry (SNI) code 55 which includes accommodation services such as hotels, youth hostels and bed and breakfast; and 56 which includes restaurants, cafés, pubs, bakeries, hotels and catering services (16). A random-sampling was used to select the establishments for the survey. No prior information was available about the variance in the accommodation sector. Therefore, the criterion for the partition of the sample between strata was to ensure a minimum of observations for each sub-sector (i.e., 40 observations). The data collection combined internet surveys, computer-assisted telephone interviews and inperson interviews to ensure a high response rate and that the respondent was the right person to respond to questions. In the case of accommodation, the study zone has 190 hotels, from which 60 (or 31.6% out of the universe) were sampled and 46 (or 76.7% out of the sample) provided complete data. For the food sector, the zone is home of 1,896 establishments from which 180 (or 9.5% out of the universe) were sampled and, after a first call, 28 establishments were excluded from the sample either because the business had closed or because the establishment was not in Stockholm, leading to an effective sample of 152 establishments. From this sample, only 43 (or 28.2% out of the sample) provided complete data. The main reasons for not participating in the study were confidentiality and lack of time. Although the sample size obtained (i.e., 89 observations) is less than the sample size expected based on equation (1), it is still higher than the number of observations required for a 90% confidence level (i.e., 67 observations), and thus considered valid for this study. The questionnaire was developed together with representatives from the public sector and was pilot tested with a few establishments before full deployment. It included a set of structured questions and an open question at the end where respondents could give their opinion about current deliveries and the strategies. More specifically, the first set of questions inquired about the type of commercial activity, the type of business (i.e., independent or part of a chain), the size of the establishment in number of employees and area, the number of weekly deliveries and shipments, estimates of the

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amount of cargo attracted, the number of carriers, the type of vehicles used for deliveries, the proportion of freight trips that included a service (i.e., defined as trips that include an activity or service performed by the carrier including cleaning, vending and coffee machine refill, paper refill, etc.), the type of receiving facility (e.g., main entrance, loading dock), and the time of the day when deliveries are received. The second set of questions inquired about their level of satisfaction with deliveries, the importance they give to personal contact with carriers, their interest in consolidating deliveries and receiving OPHD, as well as their ability and preference to receive deliveries during different hours of the day. 2.2.Data Analysis and Model Development The variables were classified by nature (i.e., continuous, discrete and categorical) and by attribute (i.e., freight quantity, establishment attributes, delivery characteristics and attitudinal variables). Nonparametric tests were then performed to assess statistically significant relationships, chi2 tests were used for assessing continuous vs. discrete/categorical variables, and Cramer’s V tests were used to assess relationships between discrete/categorical variables. The t-stat, the Cramer’s V values and probability values are reported to support the descriptive analysis. The random sampling implemented enabled the use of regression models for inferential statistical analyses (i.e., the findings can be generalized to the overall AFS sector). Inferential analyses are used in this paper to relate establishments’ attributes to freight trip generation (FTG) patterns and to the opinion of the person in charge of deliveries at the establishment towards the strategies proposed. The terms used to explain FTG patterns are defined as follows. FTG can be divided into Freight Trip Attraction (FTA) and Freight Trip Production (FTP). FTA is defined as the number of deliveries attracted by an establishment to fulfill the freight needs that allows its commercial activity; while FTP is defined as the number of shipments produced by an establishment (15, 17). The summation of FTA and FTP, as well as the freight trips that involve both a delivery and a shipment pick-up, is defined as FTG. As explained by Holguín-Veras, Jaller et al. (17), the estimation of FTG models requires an understanding of establishments’ logistics costs explaining ordering patterns. In particular, the economic order quantity (EOQ) can provide a good basis to explain the optimal order quantity (or shipment size) and frequency as a function of demand, setup cost, and inventory cost. The EOQ model shows that an increase in demand can be satisfied via an increase in the shipment size without necessarily increasing frequency, thus optimal delivery frequency (or FTA) is a nonlinear function of demand (which depends on business size) (17, 18). In essence, the EOQ model shows that FTA, FTP and FTG are better modeled without assuming direct proportionality between freight trips and business size. The regression models proposed in this paper follow the method explained in (15) to estimate FTA, FTP and FTG as a function of establishments’ attributes. The main methodological novelty in this paper is the focus on an explanatory purpose (in line with this paper purpose) instead of on forecasting, and the focus on AFS establishments, which allows the assessment of unexplored variables. The estimation of nonlinear FTP and FTG models is also a contribution of this paper not present in others. Following (15), both linear and nonlinear functional forms (lin-log and log-log) are assessed and the best specification is selected based on the lowest value of the Akaike’s Information Criterion (AIC) (19). Area and employment are not used in the same model to prevent imperfect multicollinearity that could harm the precision of the parameters estimates. The parameters are estimated using sandwich estimators that are robust to specification errors and outliers (20). To ensure strong statistical

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relationships, only variables that are statistically significant at the 5% level are kept in the models. The overall form of the model can be expressed as: Yn  f ( , λδ n , βX n , θδ n X n )

(2)

Where, Yn: FTA, FTP or FTP which is a continuous dependent variable for establishment n α : The intercept λ : A vector of estimable parameters for the binary variables δn : A vector of binary variables denoting an establishment attribute or the commercial sector of establishment n; for the latter case each binary variable denotes a sector and takes the value of 1 if establishment n belongs to the sector, 0 if not. β : A vector of estimable parameters Xn: A vector of continuous variables or attributes proper to establishment n θ : A vector of estimable parameters For the attitudinal analysis, in addition to the description analysis, a set of discrete choice models are estimated to analyze the opinion of respondents towards the strategies proposed in the Stockholm Freight Plan. The opinion of respondents was captured using a scale from one to six, where one denotes a very negative opinion about the action and six a very positive opinion. However, due to the limited number of observations, the scale was recoded to have only 3 levels, negative, neutral and positive. Given the ordinal nature of the data, an ordered logit model is proposed to model the respondents’ opinion following (21, 22). In this type of models, the dependent variable is estimated using a utility function and a set of thresholds to replicate the choice probabilities captured in the calibration data. The model uses the postulates from random utility theory, in which the respondents select a rate based on the level of utility that the action proposed represents for them. The utility function depends on the respondent’s establishment attributes and the action proposed (14, 23). The utility function can be expressed with the following equation: U n    λδ n  βX n  θδ n X n

(3)

Where, Un: The utility function for the respondent in establishment n The other terms are as defined for equation (1). The set of thresholds, µ(1)