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Leveraging Socially Networked Mobile ICT Platforms for the LastMile Delivery Problem Kyo Suh,† Timothy Smith,*,‡,§ and Michelle Linhoff‡ †

Department of Rural Systems Engineering, Seoul National University, Seoul 151-921, South Korea Institute on the Environment, University of Minnesota, St. Paul, Minnesota 55108, United States § Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, Minnesota 55108, United States ‡

S Supporting Information *

ABSTRACT: Increasing numbers of people are managing their social networks on mobile information and communication technology (ICT) platforms. This study materializes these social relationships by leveraging spatial and networked information for sharing excess capacity to reduce the environmental impacts associated with “last-mile” package delivery systems from online purchases, particularly in low population density settings. Alternative package pickup location systems (PLS), such as a kiosk on a public transit platform or in a grocery store, have been suggested as effective strategies for reducing package travel miles and greenhouse gas emissions, compared to current door-to-door delivery models (CDS). However, our results suggest that a pickup location delivery system operating in a suburban setting may actually increase travel miles and emissions. Only once a social network is employed to assist in package pickup (SPLS) are significant reductions in the last-mile delivery distance and carbon emissions observed across both urban and suburban settings. Implications for logistics management’s decades-long focus on improving efficiencies of dedicated distribution systems through specialization, as well as for public policy targeting carbon emissions of the transport sector are discussed.

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and interact with people − enhancing function and generating substantial added value. For example, radio frequency identification (RFID) tags and readers are being extensively used to identify and trace products, people and vehicles to improve efficiencies of inventory control and management in supply chain and logistics systems.13−15 Specifically, these technologies are being deployed to monitor real-time road contexts (i.e., weather, traffic jams, construction) to inform optimal delivery routing and scheduling,16−18 identify and track sensitive or high-security products in delivery,19 and improve just-in-time and just-in-sequence coordination between supply chain actors.20,21 By connecting RFID tracking of “things” to mobile-access social networks, opportunities emerge for applications well beyond simply connecting people in a virtual playground or improving centralized inventory and logistics systems. We argue in this paper that it becomes possible to put to work once disparate, underutilized and mobile assets associated with individual network participants (e.g., an empty car trunk, a set of jumper cables, the two people needed to create a “high occupancy vehicle”) in potentially new, highly efficient, and environmentally beneficial ways.

INTRODUCTION We leave a tremendous amount of digital information behind as we go about our daily activities. Recent developments in or ability to link spatial and relational data to this information provide new insights into patterns of human life, such as vehicle flows within cities, individual mobility and proximity of social relations1−5 Mobile phones, in particular, allow for large scale tracing of people’s movements and physical proximities over time,6 allowing the possibility of inferring cognitive relationships and social ties from observed behavior.7,8 In application, popular social network sites (SNS), like Foursquare, Facebook Places and Google Latitude, increasingly extend this real-time and spatially specific information across rapidly growing and highly connected networks of users. It is believed that more than 235 million mobile users in the U.S. and Europe consumed mobile media in 2011, up nearly 30% over 2010.9 Globally, through the rapid adoption of smartphone technologies and the rise of geo-social networking, mobile social media users are expected to reach 1.3 billion by 2016.10 Beyond the ubiquitous nature of information technology in our personal and collective lives, concepts emerging in the computer science and operations research communities, such as the “internet of things,” extend the Internet by embedding these technologies within physical items.11,12 “Smart” objects, in theory and practice, are increasingly able to perceive context around them, communicate with each other, access the cloud © 2012 American Chemical Society

Received: Revised: Accepted: Published: 9481

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are known to deliver more rapidly and at lessor cost than home delivery. And, in the U.S., Redbox kiosks can be found in over 29 000 locations, for what the company promotes as convenient, affordable movie rentals, and self-service Amazon Delivery Lockers have recently been introduced at grocery and convenience stores in key markets. In the academic literature, Kim et al. (2008) showed that a package delivery system like these, using pickup point locations, can reduce CO2 emissions by 81% compared to a home delivery e-commerce system.51 This study assumes that 100% of online book customers will go to a designated pickup location, not only for book pickup, but for other purposes as well. Xu et al. (2009) applied agent-based modeling (ABM) to “self-pickup options” in the retail book market to examine GHG emissions reductions.52 Results from these simulations suggest that future gasoline consumption and CO2 emissions per book can be reduced by 12.4−14.8%, depending on market share, customer convenience, and price assumptions. While the results of these papers are compelling and pioneering, their scope is limited by a number of potentially restrictive assumptions. Understanding the last-mile issue is in large part to consider the association between the existing environment (where people live or work, and the accessibility of services), travel behavior (customers’ self-selected travel paths), and the degree to which information might influence this relationship.53 Therefore, we contribute to the existing literature by examining alternative last-mile distribution systems in both high population density (urban) and lower density (suburban) settings. In addition, we build on previous research addressing pickup location delivery systems by relaxing “trip chain” assumptions and adding the possibility of a socially networked delivery agent. Rather than assuming a package recipient’s daily travel activity will bring them to a pickup location without incremental cost, we develop a “detouring” approach to the trip chain. We also develop a scheme that allows a networked “friend” with a shorter detour to serve as a pickup agent, under a number of constraints. In this paper, we first explore an alternative to the current delivery system where packages are dropped for target customer pickup at a central location consistent with a symmetric location of typical delivery route hub. Next, we overlay a socially networked, nondedicated “last-mile” system, where target recipients can “opt-in” for packages to be delivered to their final destinations by a network of transport actors who potentially intercept packages at the pickup location as part of their daily travel routine. Specifically, we develop a simulation model that accounts for package travel miles and carbon emissions across these three scenarios: a current door-to-door delivery system (CDS), a designated package pickup location system (PLS), and a socially networked PLS (SPLS). We build upon and contribute to the existing literature in three primary ways. First, we expand upon the pickup location distribution system (PLS) research by explicitly accounting for variations in population density across urban and suburban settings and by exploring the influence of trip-chain and trip-planning assumptions associated with customers’ travel behavior. Second, we introduce a new derivation on a package pickup location distribution system where multiple locationally aware actors become available to act as agents on behalf of an online customer for package pickup (SPLS), based on their spatial proximity, strength of social relations between actors and their willingness to share resources toward package delivery. Operationally, we envision an SPLS where a smartphone

We focus our analysis on the potential for leveraging social media, mobile ICT, and the Internet of things to improve a specific physical logistics problem, transport efficiencies and carbon emissions associated with the local delivery of items purchased online. Online shopping has grown dramatically in recent years, and this growth has been accompanied by dramatic growth in the transportation and distribution of residential packages. According to Forrester Research, U.S. online retail sales grew 12.6% in 2010, reaching $176.2 billion, and is expected to reach $278.9 billion in 2015.22 As a result, it has been reported that up to 72 percent of all parcels delivered in 2010 in the United State originate on the Internet,23 causing distribution and logistics companies to adapt operations to meet this demand. UPS, for example, employs 95 244 delivery vehicles, which travel nearly 2.5 billion miles per year.24 This shift has exasperated the so-called “last-mile” of local delivery to individual customers in both economic and environmental terms. Many home deliveries have been shown to be relatively inefficient due to small (often single item) orders, purchased from separate web-based companies and delivered to highly geographically dispersed locations by less fuel efficient delivery vans.25−29 Impacts from this system are particularly heightened in the U.S., where lower density suburban populations grew by 12.3% over the past decade, 40% faster than the general U.S. population.30 Population density has long been causally linked to per capita passenger vehicle travel, gasoline consumption and carbon emissions.31−33 For example, per capita gasoline consumption in the U.S. (1618 L/year) is four times greater than that of the much more densely populated UK (396 L/year),34 contributing to an additional 3 t of CO2 emissions per person, per year.35 The advent of online retail has sharpened operational challenges related to local delivery. Research addressing the operational challenges underlying consumer direct delivery has largely focused on the vehicle routing problem and the application of the branch-and-bound procedure for the traveling salesman problem.36 More recently, and specific to the online ordered home delivery setting, the literature has focused on issues such as the distribution structure and item assortment,37 the relationship between length of service cycles, customer demand, company profits,38 the delivery time window,39,40 the number and location of relay and sorting locations,41 the timing and frequency of route planning,42 and the influence of population density on delivery costs.43−46 Even with these operational challenges, from an environmental perspective, reasonable consensus exists with regard to online retail delivery’s favorable environmental performance compared to traditional retail. Though the last-mile of local delivery is often reported as the largest contributor to fossil fuel consumption, CO2 and local air emissions, comprising 30− 55% of the total impact of the retail system,29,47 the benefits of coordinated drops by delivery companies tend to outweigh the highly inefficient vehicle miles associated with personal shopping trips to and from retail locations.29,48−50 Online retailing can play a role in reducing the high impact of personal vehicle miles, however, significant potential also exists for improvement in the environmental performance of the home delivered last-mile. One such strategy for reducing the impact of last-mile local delivery has been to explore the effectiveness of accessible “pickup” locations, strategically placed in high population and heavily trafficked areas to reduce package travel miles and carbon emissions. In places like Seoul, South Korea, package delivery to kiosks in mass transit stations 9482

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and 0.260 ± 0.05 kg CO2 equiv/km for passenger cars 22.1− 23.9 mpg of PLS and SPLS.35,58,59 Various sources provide different mileages from 8 to 26 for delivery truck or van.29,35,60,61 For the PLS and SPLS options, delivery distances and carbon emissions are calculated based on the incremental travel distance determined by the difference between personal daily trip distances with package pickup and without package pickup. Individual daily trip distances (dtd) are calculated by randomly assigning x and y coordinates of scheduled locations for all network actors’ daily travel activities. We calculated the daily trip distances of all the actors in the system based on five different locations to which each individual travels over the course of a day. In addition, a package pickup location is added as an additional daily location when considering online shopping item pickup (see Figure 1). We calculate a detouring

would notify network participants when within a specified distance from a RFID enabled package at a pickup location. The agent, then, has the option to pick up the package and to deliver it at the next occasion where the agent and recipient are within walkable distance of each other, generally, within 24 hours of pickup. Finally, we explore broader system-wide dynamics associated with the increased penetration of a social network-enabled pickup delivery system, shedding light on efficiency reductions associated with delivering fewer remaining packages using traditional home delivery (CDS), as well as reductions associated with the difficulty of finding effective and willing agents as more packages seek their engagement.

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METHODS The modeling objective for this research is to compare calculated last-mile delivery distances and GHG emissions across three different systems: CDS, PLS, and SPLS (see Supporting Information, SI1). It is assumed that upstream outbound logistics and transportation processes are the same regardless of which local delivery option is selected; thus, our approach only focuses on delivery of goods from a central warehouse to its final use location (see Supporting Information, SI 2). Our first step in specifying the model simulation is to select the geographic system settings. Individual simulation models are created to examine last-mile distribution options across broad urban and suburban divisions. Housing unit densities of 1160 housing units/km2 and 246 housing units/ km2 (see Supporting Information, SI 3) are applied for urban and suburban simulations, respectively, to specify individual daily residence locations.54 In addition, we consider online order rates to meet a typical door-to-door delivery system’s package drops per round. Edwards (2010) previously reported per round delivery drops for online shopping orders at 120 drops per delivery round. Likewise, Mintel market reports (June 2010) reported that 44% of households place online orders in any given week (see Supporting Information, SI 4). Therefore, the urban residential area analyzed in this study is set to 1.13 km by 1.46 km (1.65 km2) and the suburban residential area is set to 3.22 km by 2.41 km (7.76 km2). We overlay a geographic area, which we classify as an “activity zone,” whereby daily residents typically travel to obtain daily services (groceries, fueling stations, cleaners, etc.). This area is specified as 2.25 times larger than the residential area considering the distance to retailer and supermarket (0.8 km) for 71 and 38% of households in metropolitan counties and 62 and 27% of households in nonmetropolitan counties.55 Finally, we overlay a second “employment zone,” which is four times the area of the residential area. Residents travel to this area as part of a daily commute to their place of employment or for major services, such as healthcare or entertainment attractions (see Supporting Information, SI 5). Our second objective is to determine the delivery path and distance for each delivery option. For the CDS option, we calculate the shortest delivery route for 120 packages to household units within the residential study area using a genetic algorithm (GA). GA is a search heuristic that mimics the process of natural evolution and is routinely used to generate useful solutions to optimization and search problems associated with the traveling salesman problem (TSP). Specifically, we use the MATLAB TSP function 56 to find the shortest CDS route and calculate delivery distances and GHG emissions considering 30% road winding 57 and emission factors of 0.327 kg CO2 equiv/km for a CDS delivery service truck (17.4−17.6 mpg)

Figure 1. Individual daily activity and region boundaries. Five trips to account for commuting to work, child/adult care, shopping, entertainment, etc. 65 span residential, activity and employment regions. On the basis of housing unit densities (1160 units/km2, urban; 246 units/km2, suburban), online ordering behavior (44% of households ordering online per week) and the typical size of a conventional delivery route (120), residential regions are determined (1.13 km × 1.46 km, urban; 2.41 km × 3.22 km, suburban). Activity and employment regions are assumed to be 2.25 and 4.0 times the size of residential areas, respectively. A single pickup location (P) is placed at the boundary of the residential region, to reflect a symmetric warehousing hub for packages delivered through the conventional CDP system, for comparison. Colored circles represent individuals’ personal daily locations, beginning and ending at a home location within the residential region (location 1). PLS and SPLS pickup detour distance is determined by the daily trip distance of individuals as they go about their normal daily activity. One hundred percent of the minimum daily detour distance is attributed to package delivery.

distance by subtracting the individual daily trip distance including the given pickup location (dpd) from the original dtd without pickup. Pickup detouring can occur at any time in an individual’s daily trip to minimize the trip distance. m

ddpt = dpdt − dtd t = min ∑ di − i=1

n

∑ di i=1

As such, ddpt is the detouring distance of actor t for pickup, where dpdt is the total daily trip distance for actor t with pickup, dtdt is the daily trip distance for actor t without pickup. Detouring distance is minimized across detour opportunities (slots within an actors daily travel regime), where d is distance 9483

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Table 1. Local Delivery Distances (km) and GHG Emissions (CO2e kg)a socially networked PLS (SPLS) local delivery distance and emissions urban

suburban

network leverage (km) (kg CO2e) network leverage (km) (kg CO2e)

current delivery system (CDS)

pickup location system (PLS)

s = 0.1, w ≥ 0.9, c ≤ 500 m

s = 0.2, w ≥ 0.9, c ≤ 500 m

s = 0.2, w ≥ 0.75, c ≤ 500 m

s = 0.3, w ≥ 0.75, c ≤ 500 m

s = 0.3, w ≥ 0.65, c ≤ 500 m

NL = 0

NL = 0

NL = 0.02

NL = 0.03

NL = 0.05

NL = 0.08

NL = 0.11

26.28 8.59 NL = 0

25.43 (3.23) 6.73 (21.6) NL = 0

9.49 (63.89) 2.49 (71.01) NL = 0.01

4.18 (84.09) 1.09 (87.31) NL = 0.02

1.86 (92.9) 0.48 (94.4) NL = 0.03

0.90 (96.58) 0.23 (97.32) NL = 0.05

0.54 (97.95) 0.14 (98.37) NL = 0.06

60.84 19.88

77.37 (−27.2) 20.05 (−0.8)

42.00 (30.97) 10.86 (45.37)

24.86 (59.14) 6.44 (67.61)

13.47 (77.9) 3.50 (82.4)

7.23 (88.12) 1.89 (90.49)

4.66 (92.34) 1.22 (93.86)

a

Results are presented in total km and kg CO2e to deliver a typical CDS delivery route of 120 packages to unique residences. Values in parentheses () show the percentage reduction of distance and emissions, compared to CDS. Values for PLS reflect travel distances and emissions from personal vehicles detouring from the travel path of their normal daily activity to pickup packages at a single pickup location (at a symmetric location to a distribution hub of the CDS). Values for SPLS are reported at network leverage rates between 0.01 and 0.11 as determined by a social network relation matrix (s = 0.1, s = 0.2, s = 0.3), a willingness matrix (w ≥ 0.65, w ≥ 0.75, w ≥ 0.9) and a spatial closeness matrix (c ≤ 500 m). These values indicate that SPLS last mile distribution could reduce total delivery distance by 31−98% and emissions from 45% to 97%, compared to CDS.

of slot i, n is the number of slots between daily locations, and m is the number of slots between daily locations including a pickup location. In this way, a pickup slot can be selected based on minimizing detouring distance for pickup). Slot distance is specified as di =

within a walking distance and willingness to share excess capacity (time and resources). In the absence of attitudinal or perceptual data from participants, we used a Monte Carlo simulation method to estimate uncertain potential participation. Social network relations, spatial closeness and willingness to participate in excess capacity sharing matrices are therefore developed to explain the probability of agency pickup. A social network relation matrix (S) represents the relationship between actor i and actor j. We consider sij = 1, if i and j have any social connection, and regard sij = 0, if i and j have no social relation. Packages are assumed to change hands without additional miles for pickup agents. We identify spatial closeness of original package end recipients and potential pickup agents, considering a walking distance (500 m, 0.31 miles) to hand over packages. A spatial closeness matrix (C) therefore represents that people can (c = 1) or cannot (c = 0) meet each other in a walking distance through their daily trip locations. A willingness matrix (W) approximates social ties and willingness toward sharing excess capacity. In this case, actor i is thought to be willing to pick up actor j’s package when wij is greater than the given threshold for willingness (0.75), though this does not guaranty that he or she will do so. This system proposes that actors will participate in sharing their excess capacity when the network leverage score, which is subject to social relations (s), willingness score (w), and spatial closeness (c), is 1. Matrices are first simulated such that the average likelihood for potential social relations within a network (S) is less than 20% and the threshold for the willingness to share (W) is 0.75 when daily routes place agent and package recipient with within 500 m (C). Therefore, the average network leverage scores, which identifies the likelihood of finding social participants, are approximately 5% in urban and 3% in suburban. Matrices as specified as follows:

(xie − xis)2 + (yie − yis )2

where, xei is the end point latitude of slot i, xsi is the start point latitude of slot i, yei is the end point longitude of slot i, and ysi is the start point longitude of slot i. Figure 1 illustrates these relationships visually. Only one pickup location is assumed, and is placed at the boundary of the residential and activity zone to allow for a reasonably fair comparison with the CDS, which is assumed to have one distribution hub located at the symmetric point of the pickup location. Individual network actors travel along a dedicated daily route, which are randomly assigned to simulate mobility between each geographic zone, though not necessarily representing the optimal path for their daily activities due to the time sensitivities of certain tasks. Under the PLS package delivery system, the package recipients pick up their own packages where the detour distance is minimized. Considering the individual’s daily path denoted in black within Figure 1, optimal pickup is between time/location points 4 and 5, whereas optimal pickup for the individual indicated in white is between time/location points 3 and 4. Under the SPLS package delivery system, the network actor selected for others’ package pickup depends on the calculated network leverage score and the network actors’ detour distance. Generally, social network members will not participate or choose the burden of pickup if they have to go to a pickup point solely for the purpose of making a pickup for another person. Pickup trips for social network actors can be regarded as a chain event and is a determining variable. We assumed a 100% trip chain to additional mileage for pickup in both PLS and SPLS − in other words, the entire detour distance for pickup is attributed to the package. By contrast, previous research has applied a 0% trip chain effect for pickup.30 To leverage a social network for last-mile package delivery, we focus on the particular case where agent-based modeling depends explicitly on participation in delivery load sharing activities.62 Network leverage scores are calculated based on the relation of given social network actors, likelihood to meet

⎧ n = 1 if sij = 1, cij = 1, wij ≥ 0.75 ⎪ ij ⎨ ⎪ ⎩ nij = 0 else when sij ∈ S , cij ∈ C , wij ∈ W

where nij is the network leverage score of network actor j for package end recipients i and, s, c, and w denote social relations, spatial closeness, and willingness to participate, respectively. sij is randomly generated with uniform distribution, and wij is also randomly given by normal distribution with average willingness 9484

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Figure 2. Individual daily activity trip and detouring distances. The average daily trip distance of suburban residents in our simulation is 15.3 km, compared to 7.0 km of urban residents. Average detouring distances are 0.64 km and 0.21 km in suburban and urban, respectively. Thus, detouring distances represent 3−4% of individuals’ total daily trip distance in the PLS simulation. Only 23 of the actors in the suburban simulation (19%) required a detouring distance for pickup of more than 1 km, 17 of 120 actors in the urban model (14%) have a detouring distance greater than 0.5 km. Particularly, among actors with relatively large pickup detour distances, the SPLS attempts to find, from many potential agents with much shorter pickup detour distances, an agent able and willing (based on relation, willingness and closeness) to act on behalf of the package recipient.

emissions of the three different systems (CDS, PLS, SPLS) under urban and suburban housing density scenarios. The current door-to-door optimized delivery route is estimated to require a total last-mile travel distance of 26.28 km to deliver all 120 packages in the urban model and 60.84 km in the suburban model, resulting in GHG emissions of 8.59 kg CO2e and 19.88 kg CO2e, respectively. This translates to 0.219 km per package drop in the urban setting and 0.507 km per package drop in the suburban setting (0.072 kg CO2e and 0.166 kg CO2e per drop). The pickup location system (PLS) generates slightly fewer total local kilometers to deliver all packages (25.43 km) in the urban scenario (a 0.85 km or 3% reduction from CDS). Because smaller and lighter personal vehicles divert from their normal daily travel paths to make these pickups, the emissions factors of these vehicles are slightly smaller, resulting in a 22% GHG emissions reduction. It is important to note that while we assume travel distances and deviations from an individual’s normal daily travel path are made using a vehicle, the per package distances are quite small in the urban setting (0.212 km on average, approximately 2 city blocks). Therefore, it is highly possible that some of these deliveries could be made using alternative modes such as walking or biking, reducing emissions further. As distances increase between locations along individuals’ normal daily travel paths, our less densely populated suburban model results begin to reflect previous research findings

(μ) generated with uniform distribution and standard deviation (σ). Additional simulations were conducted at a variety of S and W thresholds to explore sensitivities to these assumptions. It is important to note that determination of final pickup may not satisfy economic considerations of delivery companies, participating last mile network participants or end recipients. Package pickup is simply provided by either an available network actor or the end package recipient depending on the shortest detouring distance. Therefore, only delivery distance and subsequent GHG emissions are considered in this study.

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RESULTS Although this study assumes a very favorable case for a traditional CDS (assuming the shortest possible route and a one-time successful delivery), simulated results estimate significant travel distance, and CO2 reductions associated with the SPLS option in both urban and suburban areas. In contrast, the PLS proves to be highly sensitive to differences in population density across urban and suburban models. We estimate modest reductions associated with a PLS in an urban setting at 100% market share. However, in less dense, larger geographic areas of the suburban model, PLS adds to the overall travel distance and generates more CO2 than the comparative CDS option. Total Delivery Miles and Emissions. Table 1 provides simulation results for the total delivery distances and GHG 9485

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Figure 3. Delivery distance reductions (km) by CDS market share transition and network leverage of SPLS in urban and suburban areas. As CDS looses market share of deliveries in a particular locality, the efficiency of route optimization is likely to decrease on any given day. Charts a and b present system-wide delivery distance reductions assuming CDS efficiency reductions, while charts c and d assume that the CDS maintains constant delivery efficiencies (e.g., through acquisition or consolidation). Sensitivities resulting from network leverage rates (PLS, 0%; SPLS 1−5%) are also reported. When CDS efficiency reductions are included, higher levels of penetration and network leverage are required for the SPLS to display significant improvements (e.g., 12 km in suburban simulation, 20% reduction compared to CDS, at 50% CDS market share and 5% network leverage). If the CDS is able to maintain current levels of delivery efficiencies, system-wide improvements from SPLS are immediate and large. Overall, very small degrees of network leverage (1−2%) can neutralize the largely negative system-wide impacts of PLS.

leveraged by 1−11% across urban and suburban settings, with corresponding GHG emissions reductions ranging from 45% in a suburban environment where social relations are low (s = 0.1) and high willingness thresholds are required for engagement (w ≥ 0.9) to 98% in urban environment where nearly all distribution emissions are eliminated through a highly related (s = 0.3) and willing (w ≥ 0.65) network. Our results indicate that a very small (1−5%) network leverage rate can dramatically increase efficiency. While PLS without the opportunity for social network support is shown to be less efficient in suburban settings, a SPLS system with less than 5% chance of finding an agent for pickup can create large reductions in delivery distance and emissions. Also the average number of packages delivered per agent participating in the SPLS is about 2.5 in urban and 2.6 in suburban. Though somewhat surprising, SPLS efficiencies can best be understood by examining the detour distances of package recipients under the PLS delivery scheme. Figure 2 shows the daily trip distance and detouring pickup distance in urban and suburban areas for all 120 actors in the system. Average detouring distances (standard deviation) are 0.21 km (0.26) and 0.64 km (0.82) in urban and suburban, respectively, resulting in total detouring distances that are roughly 3−4% of average total daily trip distances. Only 23 of 120 (19%) of the actors in our suburban

comparing e-commerce home delivery and traditional retail pickup. In the suburban model, we estimate total local delivery distances for PLS at 77.37 km, a 27% increase over the CDS. GHG emissions of the suburban PLS delivery system are similar to CDS (total emissions of 20.05 kg CO2e compared to 19.88 kg CO2e under CDS). Large reductions in both local delivery distances and GHG emissions are observed for the socially networked pickup location system (SPLS) within urban and suburban models. Compared to CDS, SPLS reduces local delivery distance by 93% (24.4 km, 0.203 km/package) in an urban setting and 78% (47.4 km, 0.395 km/package) in a suburban setting, based on 120 packages in the route and our initial network leverage conditions (s = 0.2, w ≥ 0.75, c ≤ 500 m). With regard to GHG emissions, the carbon intensity of a package’s local distribution is reduced by 94% in urban, and 82% in suburban, settings. Although the same conditions for social relations (S), spatial closeness (C), and willingness (W) for participation are applied to urban and suburban areas the network leverage scores are much different for urban and suburban areas because of the spatial closeness (97% of urban agents are spatially relevant for a package hand-off, whereas 55% of suburban agents are viable). Table 1 also provides simulation results for (s = 0.1; s = 0.3; w ≥ 0.65; w ≥ 0.9). Under these conditions, the network is 9486

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in the area) to maintain efficiencies. Figure 3c and 3d presents simulated system-wide delivery distance reductions under this assumption. The suburban SPLS, with 20% market penetration (24 of 120 packages) and 5% network leverage, results in a nearly 25% reduction in total system delivery distance. Similarly, reductions are observed in the urban local delivery simulation. Under the same market penetration and network leverage rates, urban SPLS can reduce delivery distances by approximately 30%. While this assumption may be unrealistic as market share losses become large, a range of possible efficiencies stemming from SPLS can be established as being on the order of 14−28% delivery distance reductions in a suburban setting (at 30% SPLS market penetration and 5% network leverage effects). Results discussed above assume 5% network leverage, however, the negative impacts to local delivery distances of PLS can be mitigated by even lessor levels of social network participation (see Figure 3). With only 1−2% network leverage effects, total local delivery distance is improved over CDS, even under conditions of very small SPLS market penetration, across both urban and suburban models. Furthermore, the marginal reductions in delivery miles are greatest for the first 1% to 3% of network leverage, with marginal reductions decreasing significantly thereafter. Similar to our previous discussion on network leverage, the networked nature of many daily paths results in few people facing long detouring distances to package pickup, and many having very short detouring distances. Consequently, after simulating over 100 times, people with long detour distances maintain a high probability of finding someone with a much shorter detouring distance right away (even at only 1−3% network leverage), saving significant kilometers of travel. So, even at just 4−5% network leverage rates, most of the long detour distance people have already found an agent much closer to the pickup than themselves. The probability of marginal improvement between the first 1−3 available agents and the next 4−6 available agents is very small. Total pickup distance from the suburban SPLS model with 1%, 3%, and 5% network leverage effects is 40.24, 12.93, and 5.65 km, respectively. Therefore, at only 3% network leverage, 87% of the delivery distance reductions of our baseline SPLS model (the 5% network leverage model) are accounted for.

simulation required a detouring distance for pickup of more than 1 km, the other 81% come within a 1 km detouring distance through their normal course of activity in a day. The urban setting is even more pronounced, as only 17 of 120 (less than 14%) have a detouring distance of more than 0.5 km. Therefore, significant efficiencies are likely among the relatively few “long detour” actors under SPLS, each of which attempts to reach into a relatively large pool of “shorter detour” actors, potentially able and willing to serve as an agent. If even a small handful of the largest detours can be taken on by another actor, significant efficiencies are gained because the likelihood of this agent having a much shorter detour distance is high (see Supporting Information, SI 7). A couple of caveats worth mentioning. First, the average daily trip distance of suburban residents calculated in this research is 15.3 km and 7 km for urban residents, somewhat shorter that typically reported through national surveys (26.9 miles per day in U.S. urban and suburban combined settings).63 While our approach allows for noncommuters to participate in the network, which may account for some of the difference, more active daily travel across larger geographic areas or longer travel through large uninterrupted corridors would certainly impact detour distances. Second, the average number of packages delivered per agent participating in the SPLS is about 2.5 in urban and 2.6 in suburban, but in some simulation runs, some agents (given their favorable proximity, popularity and willingness to help their friends) can be called on 6 or 7 times. Although this 120 agent scenario was assembled for its comparative traits to CDS and hundreds of potential agents could reside within a nearly 8 km2 neighborhood, greater understanding of geographic “hot spots” around key transport or pickup infrastructure and the saturation point of “super actors” willing and able to engage are important areas for future research. Sensitivity Analysis: Market Share transitions and Network Leverage. Figure 3 illustrates results for total local delivery miles as market share of the CDS declines to make way for pickup location systems (PLS and SPLS), across different network leverage rates. It is important to recall that the local delivery distances of SPLS are the same as PLS when the network leverage rate is zero. Therefore, results incorporate network participation of 0% (PLS), and a range of SPLS network leverage from 1 to 5%. We find that even in the urban setting, where PLS was found to be a marginal improvement to CDS, system-wide improvements are only realized in the last increment of market penetration (Figure 3a). In both the urban and suburban models, a PLS system provides no local delivery distance reductions until capturing 90% of the delivery market (Figure 3a and 3b). Modest gains are observed only when the last packages in an urban setting migrate to the PLS. With regard to SPLS, local delivery distance reductions improve as market penetration increases, however, the largest improvements also come once this new delivery system completely captures the CDS market. The previous analyses assume that the structure and operations of existing CDS delivery providers do not adjust to the shifting market dynamics and therefore become increasingly less efficient as packages migrate to a PLS/SPLS. In the following analysis, we fix the delivery efficiency of the CDS at 0.4 mile/drop on average in suburban settings, and as 0.23 miles/drop in urban settings.29 Under this assumption, delivery companies are able to respond strategically to market changes and package reductions in the given route (e.g., collaboration with, or acquisition of, other delivery companies

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DISCUSSION This study shows that social network potential can be materialized using mobile ICT platforms providing real-time communication as an effective delivery option for the last-mile issue. Currently, the technology platform needed to integrate this system exists, but has yet to be applied to this problem. Further limitations are that, in the United States, we lack coordinated central pickup locations for items purchased online and left for pickup, with the exception of brick and mortar online orders made available for in-store pickup. However, as societies adapt and shift relative to real-time mobile information technologies, this efficient and eco-friendly socially networked system may become increasingly viable. A number of potential methodological limitations and barriers to adoption exist, and provide important avenues to future research. A number of simplifying assumptions were made in this study to facilitate modeling efforts and focus the research: (1) Only 120 potential actors concentrated in the residential region are examined in each simulation. Future studies should include individual networks associated with each package recipient, informed by proximity indicators of key daily 9487

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research, but are avenues for increased value creation. In general, significant additional research is needed to better understand the complexities associated with the behavioral dimensions of peer-to-peer marketplaces and the emergence of the “sharing economy”. While significant uncertainty remains regarding online shoppers’ willingness to adopt this type of delivery system and the system’s ability to leverage networked agents, this study provides evidence toward the potential for significant environmental and economic improvements as seen through large travel distance reductions in the last mile of distribution. By leveraging socially networked mobile resources, we have illustrated that with even small levels of market penetration and network leverage significant efficiencies can be gained. Specifically, the undesirable impacts of local delivery transport, fossil fuel consumption, toxic and particulate emissions, greenhouse gas emissions, congestion, traffic accidents, infrastructure wear and tear, etc., may be radically reduced by imagining new opportunities to decentralize physical distribution systems through socially networked mobile ICT platforms.

activities (work, school, organizational affiliation, etc.). (2) Only one pickup location was included in each simulation. As future research incorporates longer and more active daily activity paths, approximating more realistic U.S. daily travel routines, future research should explore efficiency implications of the number and placement of pickup locations. (3) Straightline distances, with winding (circuitry) factors, were used to estimate travel distances between daily locations. Actual spatial mapping data should be integrated into future research to more realistically assess physical proximity and geographic barriers associated with actors and packages. (4) Vehicle mileage and emissions assumptions are based on static and potentially dated technologies. Future research should explore the impacts PLS and SPLS distribution efficiencies in the context of improving transportation technologies, such as electric-fuel hybrid or electric vehicle fleets. In many ways, PLS or SPLS shifts the delivery burden of local distribution from the seller to the buyer (and his/her network). Issues of economic allocation and environmental/ social attribution across these new supply chains, therefore, represent significant opportunities for future analysis. Specifically, incentive mechanisms for increased penetration and participation, including mileage rewards, loyalty points, or direct payment may be reasonable avenues for improved efficiencies. Brand identity could also increase as companies provide eco-friendly delivery services. Order fulfillment burdens could be shared across firms upstream and downstream, therefore generating lower transaction costs, bundled complementary logistic services, and expanded services as the consumer chooses the method for the last delivered mile.64 Privacy and security issues are also not included in this study. Third-party purveyors could be engaged to manage personal data, with built-in security and safeguards (real-time locations, daily schedules, predicted actor movement, available capacity of agents, etc.), however the legal implications of privacy are critical areas requiring additional inquiry. Methods for identifying and approving agents, as well as the access to information regarding historical or projected personal behavior made available to network actors, is an open question. In addition, issues of delivery timing and drop-off infrastructure are key barriers requiring additional inquiry into economic and environmental impacts of a networked distribution system. While PLS and SPLS could effectively eliminate packages left unattended outside of residences, our analysis does not account for any delays in delivery within the network or account for economic costs or environmental impacts associated with dropboxes, kiosks, or retail-based services. For example, pickup locations could be partitioned mailboxes or vending machinelike kiosks, with electronic or biometric verification. Pickup locations might also take the form of new services performed by existing staffed retailers (grocery stores, drycleaners, bookstores, etc.), where the network might be leveraged for repetitive buying activity and increase “store traffic” for participating locations. Despite these uncertainties, there is potential for significant benefits from greater connectivity in both economic and social dimensions. Given the physical proximity of socially networked participants, a system like this might increase neighborhood acquaintance or at least an increased knowledge of neighbors’ whereabouts, which could improve personal and home security (i.e., a virtual community watch), or improve community service integration (e.g., healthcare monitoring, ride sharing, dog walking, etc). All of which are outside the scope of this

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ASSOCIATED CONTENT

S Supporting Information *

System flow for simulations, engaged assumptions and parameters, housing unit density and residential areas, residential, activity, and working space, virtually created density by individual daily trip in urban and suburban areas, daily trip and detouring pickup distances, trip planning effects, and additional references. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Phone: +1 612 624 6755. E-mail: [email protected]. Author Contributions

All authors made intellectual contributions to this manuscript. Suh developed and programed the model and contributed to writing. Smith designed the project, contributed to analysis, and was the primary writer. Linhoff contributed to writing and project management. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS We thank consortium partners of NiSE at IonE, particularly Robin Chase, Leo Raudys, and Saif Benjaafar, and Accenture Technology Labs for input and feedback. We also thank the Institute on the Environment and the Center for Transportation Studies at UMN for funding this work.

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REFERENCES

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