2021

Cheng Cheng, Singapore-MIT Alliance for Research and Technology
Takanori Sakai, Tokyo University of Marine Science and Technology (TUMST)
André Alho, Singapore-MIT Alliance for Research and Technology
Lynette Cheah, Singapore University of Technology and Design
Moshe Ben-Akiva, Massachusetts Institute of Technology (MIT)

The rapid growth in online shopping and associated parcel deliveries prompts for understanding the factors that contribute to online shopping activity.  In this paper, we evaluate the influence of the locational and household characteristics on parcel delivery demand. While past research has largely focused on the impacts of the adoption of online shopping using individual/household survey data, we make use of data from an e-commerce carrier. A linear regression model was estimated considering factors such as degree of urbanization, transit and shopping accessibility, and household attributes. The results both confirm and contradict prior research findings, highlighting the potential for a non-negligible influence of the local context on demand for parcel deliveries.

Carla Tejada (ctejada001@citymail.cuny.edu), City College of New York
Alison Conway, City College of New York

This paper aims to identify what variables affect online shopping frequency for three different categories: groceries, prepared food, and other packages. For the study, we used data from the Citywide Mobility Survey and other publicly available sources implemented by the New York City Department of Transportation in 2017. Among the studied variables, we consider demographic variables, built-environment variables, and the relationship with existing shopping trips. Using binomial and multinomial logit models, we analyzed each of the selected variables' effects on the online frequency decision choice. Among the main results observed in this study is that people who purchase online for one product will most likely also purchase online from the other two types of products. Furthermore, we can see that demographic and built environment effects on online shopping behaviors vary for different product types. We believe that these results are significant and add to the existing research by considering the relationship between online shopping, built-environment and land use, and existing trips. Understanding these relationships is essential for city planners aiming to establish city logistics practices that meet the specific needs of individual neighborhoods.

Miguel Figliozzi (figliozzi@pdx.edu), Portland State University
Avinash Unnikrishnan, Portland State University

E-commerce volumes and house deliveries have experienced steady growth in the last two decades. Lately, COVID-19 lockdowns have made house delivery an essential service and a lifeline for many households that, for travel restrictions or health concerns, are not able to utilize traditional shopping methods.  This research studies the impact of six product categories (groceries, meals, electronics, household and office goods, recreational items, and fashion, beauty and personal care products) on household deliveries during the lockdown period in the greater Portland metropolitan region. This research focuses on answering two novel research questions: (i) What factors propel the delivery rates of each product type during the lockdown? and (ii) How do these products contribute to overall household delivery rates and expenditures? To answer these questions, ordered choice models are estimated utilizing data collected from an online survey that is representative of the population in the greater Portland metropolitan region. Household delivery rates before and after the pandemic are estimated and compared with previously known figures. The results clearly indicate that each product has a unique profile regarding the sociodemographic, e-commerce, and delivery variables that impact its level of consumption and its contribution to the overall household delivery rate.

Ioannis Karakikes, University of Thessaly
Eftihia Nathanail, University of Thessaly

Crowdsourced deliveries or crowdshipping is identified in recent literature as an emerging urban freight transport solution, aiming at reducing delivery costs, congestion and environmental impacts. By leveraging the pervasive use of the mobile technology, crowdshipping is the next stop of sharing economy in the transport domain, as parcels are delivered by private car drivers or – in our case – public transport users, rather than corporations. The objective of this research is to evaluate the impacts of crowdshipping through a number of alternative scenarios that consider various level of demand and adoption by public transport users who act as crowdshippers, based on a real case study example in the city of Volos, Greece. This is achieved through the establishment of a tailored evaluation framework and a city scale urban freight traffic microsimulation model. Results show that crowdshipping has the potential to mitigate last mile delivery impacts and effectively contribute in improving system’s performance.

Avinash Unnikrishnan (uavinash@pdx.edu), Portland State University
Miguel Figliozzi, Portland State University

The COVID-19 pandemic and government-mandated lockdowns have significantly affected all facets of our life, including our shopping behavior. While e-commerce’s popularity has been gradually increasing over the past two decades, there has been an explosion in demand for e-commerce and home delivery services in the past few months. This study analyzes the impact of COVID-19 on the number of home deliveries.  We conducted an online survey targeting people living in the Portland-Vancouver-Hillsboro Metropolitan area. Data regarding household and demographic characteristics, e-commerce and home delivery service and product preferences, and the number of home deliveries made before COVID-19, during COVID-19 lockdown and expect to make in the future post-COVID-19 were collected. Ordered choice models are developed to understand the factors that affect the number of online deliveries made. Results indicate that the number of home deliveries increased during COVID-19 compared to before COVID-19. In the post-COVID-19 world, this number is expected to reduce but remain higher than before COVID-19.   Higher-income populations are more likely to use home deliveries before and during COVID-19 lockdown. Tech-savvy respondents with delivery subscriptions are more likely to order more home deliveries before, during, and after COVID-19. Households with members with disabilities and larger household sizes are more likely to make a higher number of home deliveries in the post-COVID-19 time frame.

Santhanakrishnan Narayanan (santhanakrishnan.narayanan@tum.de), Technische Universitat Munchen
Johannes Gruber, German Aerospace Center (DLR), Institute of Transport Research

There is a growing interest among industry and policymakers for the application of cargo cycles in commercial transport. Although the potential of this type of vehicle has been shown in the pertinent literature, the factors affecting the purchase decision of cargo cycles by commercial users are yet to be explored. Hence, this research aims at identifying the relevant factors, using data from Europe’s largest cargo cycle testing project ‘Ich entlaste Städte’. Two different binary logit models are estimated, one for the intention to purchase cargo cycles (stated at the end of a 3-month vehicle trial), and another for the actual purchase decision made (queried three months or later, after the trial). A comparison of both the models shows that the actual purchase decision is significantly influenced by hard facts like the deteriorating conditions (e.g., vehicle access restrictions) for conventional vehicles, while the purchase intention is not. Factors that influence the actual purchase decision include catchment area of cargo cycle trips, daily usage during the trial phase, trial phase season, type of cargo cycle tested, mode substituted by cargo cycles during the trial phase and business sector. Further, three other factors, which are latent variables constructed through exploratory factor analysis, are found to have significant influence: perception of operational and non-operational benefits, and importance of deterioration of conditions for conventional vehicles. Insights from the research include the effectiveness of the trial schemes, substitution potential for car trips, favorable business sectors and requirement of campaigns to promote soft benefits.

Christian Rudolph, DLR - German Aerospace Center
Alexis Nsamzinshuti, Universite Libre de Bruxelles
Samuel Bonsu, Deutsches Zentrum fur Luft- und Raumfahrt DLR Standort Berlin
Alassane Ndiaye, Universite Libre de Bruxelles
Nicolas Rigo, DART Consulting sprl

The use of cargo cycles for last mile parcel distribution requires urban micro depots. We develop an approach to localize suitable locations for micro depots with the consideration of three criteria, i.e. demand, land use and type of road. The analysis considers metric levels (demand), linguistic levels (land use) and cardinal levels (Type of Road). Land use is distinguished by commercial, residential, mixed of commercial and residential, and other types of land use. The type of road distinguishes Bicycle Road, Pedestrian zone, Oneway road and traffic-calmed roads. The approach is a hybrid multi criteria analysis combining AHP and PROMEETE methods. We apply the approach to the city center of Stuttgart, Germany using real demand data provided by a large logistics service provider. We compared different scenarios weighting the criteria differently with DART software. The different weight allocation results in different numbers of required micro depots and slightly different locations. This research was able to develop, implement and apply successfully the proposed approach but in a next step, urban freight stakeholders such as logistics companies and cities must be involved at all level of this approach thought the validation of the selected criteria and depicting their requirements regarding the weight allocation of each criteria.

Agnivesh Pani, University of Memphis
Sabya Mishra (smishra3@memphis.edu), University of Memphis
Mihalis Golias, University of Memphis
Miguel Figliozzi, Portland State University

The introduction of autonomous delivery robot (ADR) technology for last-mile freight deliveries is a valuable step towards low-carbon logistics. However, there is very limited research on consumer preferences for such advanced delivery technologies. The deployment and penetration of ADR technology in the emerging marketplace requiring contactless deliveries due to COVID-19 pandemic, and planning for possible market adoption scenarios, calls for the collection and analysis of consumer preference data related to ADRs. This study addresses ADR public acceptance research need, offering a detailed analysis of consumer preferences, trust, attitudes, and willingness to pay (WTP) using a sample of 483 consumers in Portland. The results indicate that there is considerable heterogeneity in consumer preferences for ADR technology, although, on a cursory level, 61.3% of consumers are willing to pay extra for adoption of ADRs. Latent class analysis based on attitudinal responses revealed six consumer segments: Direct Shoppers, E-Shopping Lovers, COVID Converts, Omnichannel Consumers, E-Shopping Skeptics, and Indifferent Consumers. Among the segments, mean WTP values range from $1.33 to $2.92. In analyzing WTP determinants, latent class Tobit regression results further reaffirm the presence of consumer heterogeneity and accordingly suggest that treating the population as a single homogeneous group, as is common in existing literature on consumers’ technology acceptance, may lead to biased parameter estimates and flawed decision-making. The study findings will offer actionable insights for deploying and pricing ADRs in such a way that is accepted by different consumer segments, and in turn, fostering mass adoption of low-carbon deliveries in the last-mile.

Giacomo Dalla Chiara (giacomod@uw.edu), University of Washington
Klaas Fiete Krutein, University of Washington
Andisheh Ranjbari, University of Washington
Anne Goodchild, University of Washington

As e-commerce and urban deliveries spike, cities grapple with managing urban freight more actively. In order to be able to effectively manage urban deliveries, city planners and policy makers need to better understand driver behaviors and challenges they experience in performing deliveries. In this study, we have collected data on commercial vehicle (CV) driver behaviors by performing ridealongs with various logistics carriers. Ridealongs were performed in Seattle, WA, covering a range of vehicle (car, van and truck), goods (parcels, mail, beverage, and printed materials), and customer types (residential, office, large and small retail). Observers collected qualitative observations and quantitative data on trip and dwell times, while also tracking vehicles through GPS devices. The results showed that on average urban CVs spend 80% of their daily operating time parked. It was also found that, unlike the common belief, drivers (especially those operating heavier vehicles) park in authorized parking locations, with only less than 5% of the stops taking place in the travel lane. Dwell times associated with authorized parking locations were significantly longer than those of other parking locations, and mail and heavy goods deliveries generally showed longer dwell times. We also identified three main criteria for choosing a parking location by CV drivers: avoiding unsafe maneuvers, minimizing conflicts with other users of the road, and coopetition with other commercial drivers. The results provide estimates on trip times, dwell times, and parking choice types, as well as insights on why those decisions are made and the factors affecting driver choices.

Nala Shin, Inha University
Yong Jin Kim, Inha University
Hyunwoo Lim, Inha University

This paper investigates the application of linear regression models in analyzing parcel demand at the nationwide level within South Korea. Using empirical delivery volume data, this paper explores the spatial dimensions of parcel demand as well as its socioeconomic features. We employ regression analysis to account for spatial autocorrelation latent in georeferenced parcel delivery volume data. We first conduct conventional ordinary least square regression, and then conduct spatial lag and error models to evaluate spatial regression variables. Furthermore, we separate analysis zones into two parts by each zone’s urbanization level to examine the distinction between two regions. We conclude that the spatial regression approaches show better fits for parcel demand at the national level, and parcel demand is positively associated with family types, gender ratio, young population, economic status, and degree of commercialization. Another key finding is that parcel delivery demand models for urban and non-urban area show different, and sometimes thoroughly contrasting, implications depending on their urbanization level. This result provides insights for future research on georeferenced variables and spatial dimensions in logistics.

Seth Contreras, Fehr & Peers
Shangyou Zeng, Fehr & Peers
Fatemeh Ranaiefar, Fehr & Peers

Fulfillment centers have redefined the intermodal freight landscape of the 21st century, but little is known about their travel efficiency with respect to vehicle miles traveled (VMT) per thousand square feet. This study explores this gap in knowledge by evaluating time series trip activity of eight fulfillment centers using location-based services (LBS) data. The results show that fulfillment centers located in urbanized versus rural areas do not necessarily result in more VMT efficient operations. Perhaps even more surprising is the role that the size of the center plays, which is not as substantial as previously thought. Online shopping, or E-commerce, in the United States has grown substantially in recent years. For the first time ever, it surpassed the 10% mark of the total share of US retail sales in 2019. This is also evident in the growing number of fulfillment centers from retailers such as Amazon and Walmart. The implications of these findings can help guide municipalities and agencies as they consider policies, such as infill, and impact analysis criteria for these emerging land use types. For example, an amendment to California’s Senate Bill 743 mandate, which replaces level of service (LOS) with VMT as the required California Environmental Quality Act (CEQA) metric for determining a development’s potential transportation impact. Lastly, the authors evaluated the effects of the COVID-19 pandemic on the changes in travel activity of the analyzed centers; which could provide useful insights for the spectrum of stakeholders that are adjusting to the new normal.

Travis Glick, Portland State University
Miguel Figliozzi, Portland State University
Avinash Unnikrishnan, Portland State University

Drones are increasingly being utilized to deliver medical supplies, and the COVID-19 pandemic has accelerated this trend. Drones arrive quickly by taking more direct paths and avoiding ground-based obstructions. However, drones are not completely reliable and may also experience failures and delays. For consumer products, delivery delays are an inconvenience, but for some medical supplies, delays may be fatal. This research focuses on the drone reliability of one special type of supply and event: automatic defibrillators for out-of-hospital cardiac arrests (OHCA). A modeling framework is developed to analyze drone delivery reliability with stochastic demands and meteorological conditions. Using probability distributions based on real data from Portland, OR, this research quantifies the failure rates as a function of drone range and meteorological conditions that include temperature, precipitation, and wind. Tradeoffs among drone reliability, fleet size, population size, and meteorological conditions are analyzed.

Ioanna Kourounioti, Delft University of Technology
Ioannis Tsouros, University of the Aegean
Panagiotis Georgakis, University of Wolverhampton
Angelica Salas, University of Wolverhampton
Michiel de Bok, Technische Universiteit Delft
Athena Tsirimpa, University of the Aegean
Ioanna Pagoni, University of the Aegean
Sebastiaan Thoen, Significance
Larissa Eggers, Significance
Amalia Polydoropoulou, University of the Aegean
Lóránt Tavasszy, Technische Universiteit Delft

he emergence of internet and smartphones had played an important role in the increase of on-demand economy. Crowdshipping (CS)  is an emerging trend that is expected to reduce the externalities caused by Urban Freight Transport (UFT).  However, modelling the CS services, predicting their market share and their effect in the network is not a trivial task. CS matches the demand created by freight transport companies with the available capacity offered by passengers. Currently a gap exists in the literature on models that integrate the decisions related to the supply and the choices that identify the demand and matches them in the real-time. This paper presents a theoretical methodological framework that proposes an innovative collection of preference data in order to develop choice models that identify the need willingness of commuters to crowdship. In parallel it calculates the demand and proposes the development of a real-time matching simulator for the assignment of packets to crowdshippers and then to the network.

Katherine Keeling, Portland State University
Jaclyn Schaefer, Portland State University
Miguel Figliozzi, Portland State University

Transit goals have typically focused on commuter trips but facilitating urban last-mile freight logistics is a potential strategy to mitigate the demands of parcel distribution on the transportation network. Presently, most parcel lockers operate out of private businesses, but consumer surveys have found that transit users may be interested in locker facilities at transit connections. The implementation of an unmanned, secure, common carrier parcel locker system could have benefits for non-transit users as well. Consolidation of deliveries would benefit courier companies by allowing operations at increasingly competitive rates, and retailers would benefit from these low shipping rates. This evaluation includes a case study of the light rail stations, transit centers, and transit malls in the greater Portland, OR metro. Their potential of hosting transit sites is reviewed based on ridership (the number of ons/offs at transit facilities), selecting a balance of central city and suburban locations, the size of populations in influence areas (whether transit users of not), and a framework for prioritizing locations based on best-practice equity metrics. A common carrier locker system at a transit site may help improve last-mile logistics and can provide transit users convenient access to parcel retrieval, complementing the dynamic goals of transit-oriented development.

Daniel Olejarz (dan.olejarz@gmail.com), University of Toronto
Matthew Roorda, University of Toronto

Last mile parcel delivery is a particularly costly element of the freight supply chain. This challenge is exacerbated for courier companies by the rise of e-commerce and the business-to-consumer model. Autonomous ground vehicles (AGVs) are an emerging technology that have the potential to reduce the cost of last mile delivery. This paper quantifies the cost of a last mile parcel delivery system supplemented with AGVs and compares it to the cost of a manual operating system. A literature review which focuses on automated parcel delivery and split delivery vehicle routing problems (SDVRPs) is first presented. This literature review highlights the applicability of SDVRPs to routing AGVs but recognizes the inconveniences for the customer and the courier of splitting deliveries between vehicles. A synchronized split delivery vehicle routing problem is formulated. This model ensures deliveries split between multiple vehicles arrive at their destinations concurrently. A heuristic solution to this model is then applied to sample data provided by one of Canada’s largest courier companies. Scenarios with different vehicle capacities are considered when analyzing cost implications of using AGVs. These results are compared with a baseline value for manual deliveries. The results show cost savings scale with the rate of customers that can be served by the AGV. Cost savings are observed for all capacity scenarios, with higher capacity vehicles achieving higher savings. These results are contextualized for courier companies looking to implement automated delivery systems.

Benito Perez, District Department of Transportation
David Lipscomb, District Department of Transportation
Laura MacNeil, District Department of Transportation
Robert Ferrin, City of Columbus
Amanda Ford, City of Columbus
Jonathan Heider, City of Columbus
hillip Hanson, City of Columbus

Curbside management is evolving, rapidly changing in the past decade with changes in how people are moving and increased demands on goods movement in our urban fabric. To date, there has been the concept of curbside demand management, which manifested itself as value pricing / demand-based curb pricing. With the rise of new mobility and increased e-commerce demands, curb usage has been turned on its head. Once it was about encouraging turnover at the curb on vehicle storage/parking related activities. Now the municipal challenge is how to regulate and manage dynamic curbside exchanges/transactions that are moving people and goods. How do municipalities regulate curb access for ridesourcing, On-Demand Delivery Services (ODDS), commercial freight, and other uses just pursuing access for a ‘micro-period’ of less than 15 minutes in most cases? This paper, covering case studies from Washington, DC and Columbus, OH, will discuss (1 what ‘dynamic’ curbside management is; (2 operational, planning, and policy considerations for dynamic curbside management; and (3 lessons learned and next steps in refining the state of the practice regarding dynamic curbside management. Keywords: Ridesourcing, On-Demand Delivery Services, Curbside Management, Pick-Up/Drop-Off, Passenger Loading, Freight Loading, Dynamic Curb Access