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    <title>Research in Progress (RIP)</title>
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    <copyright>Copyright © 2026. National Academy of Sciences. All rights reserved.</copyright>
    <docs>http://blogs.law.harvard.edu/tech/rss</docs>
    <managingEditor>tris-trb@nas.edu (Bill McLeod)</managingEditor>
    <webMaster>tris-trb@nas.edu (Bill McLeod)</webMaster>
    <image>
      <title>Research in Progress (RIP)</title>
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      <link>https://rip.trb.org/</link>
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    <item>
      <title>The Reverse Side of Online Shopping: Examining Sociodemographic and Built-Environment Determinants of Delivery Returns</title>
      <link>https://rip.trb.org/View/2553166</link>
      <description><![CDATA[The rise of online shopping has led to a significant increase in the return rate for items purchased online (or "delivery returns"). The process of returning items, once a rare occurrence in the traditional retail setting, has become a commonplace aspect of the e-commerce experience. Online purchase return rates (30%) significantly exceed those of physical stores (8.89%). Overall, these high return rates, have substantial financial, logistical, and transportation-related repercussions. From a transportation perspective, the large volume of returns necessitates additional truck trips, leading to increased freight vehicle miles traveled. This trend also results in more truck traffic at residential locations or return points. Despite the acknowledged impacts, this topic remains under-researched, with existing studies focusing on product characteristics, or retailer policies while overlooking consumer-level perspectives. This study aims to bridge this gap by examining how sociodemographic and built-environment factors influence the frequency and channel choice (physical store, mail carrier, Amazon drop-off, home pickup) for returning online purchases. Utilizing the National Household Travel Survey (NHTS) 2022 dataset, the research team analyzes responses on delivery return frequency across four channels. The team employs a multivariate probit ordered-response model to jointly analyze the full product returning behavior spectrum. This approach recognizes that behaviors are multifaceted, involving both the decision to return an item and the choice of a channel, and accounts for the interconnectedness of decision-making processes. The findings provide a foundation for developing targeted strategies to reduce return rates, streamline reverse logistics, manage travel demand, enhance customer satisfaction, and contribute to a more sustainable e-commerce future. ]]></description>
      <pubDate>Thu, 15 May 2025 14:53:38 GMT</pubDate>
      <guid>https://rip.trb.org/View/2553166</guid>
    </item>
    <item>
      <title>Modeling the Sustainability of Small Unmanned Aerial Vehicles Technologies</title>
      <link>https://rip.trb.org/View/2408379</link>
      <description><![CDATA[The integration of more sustainable vehicles in goods distribution and service delivery depends on a number of factors related to vehicle costs, technology, infrastructure, energy sources, and financial incentives (Feng and Figliozzi, 2012). Energy cost fluctuations and the rapid evolution of vehicle types and engine technologies creates a highly uncertain environment. Problems and issues faced by policymakers, transportation planners, and private companies are rapidly evolving over time. In addition, new vehicle technologies such as unmanned aerial vehicles (UAVs) or drones have the potential to disrupt last mile deliveries and supply chains.

The comparison of conventional diesel vehicles and cleaner vehicles is not straightforward because there are substantial trade-offs regarding vehicles costs, payload, range, and supporting infrastructure. The comparison is even more complex regarding UAVs since this is a new vehicle type and technology. There is scant or no research that addresses the fundamental environmental, logistics and cost trade-offs associated with the widespread adoption of UAVs for last mile deliveries.

The main goal of this project is to develop robust tools and models to analyze UAVs environmental, logistical, and cost tradeoffs. More specifically, the research project will: (1) review and analyze key UAV characteristics, limitations, and capabilities, (2) model and compare UAV and conventional ground vehicle lifecycle emissions and sustainability aspects, (3) model and compare UAV operational and long-term costs, and (4) analyze the potential impacts of widespread UAV adoption on freight planning models and tools.]]></description>
      <pubDate>Wed, 31 Jul 2024 18:11:58 GMT</pubDate>
      <guid>https://rip.trb.org/View/2408379</guid>
    </item>
    <item>
      <title>Road charging for online retailers</title>
      <link>https://rip.trb.org/View/2250736</link>
      <description><![CDATA[This research project will investigate a socially responsible model for road charging for Online Merchandise Delivery Vehicles (OMDVs). This concept is planned to be achieved through a ‘cost and reward’ traffic control priority system. The methodology will first develop the ‘right formula’ to charge extra fees for OMDVs, which will be implemented as a traffic control priority at signalized intersections and other relevant parts of the urban intersection. Then, such policy-based priority decision support system will be tested under several policy and traffic demand and multimodal use scenarios. It is expected that the results will be able to show a right trade off between priority given to OMDVs and the extracted fees which can be utilized to improve road infrastructure. The results of this research are expected to be disseminated at various professional and scientific events both nationally and internationally. Also, the technology transfer will be sought with the most relevant industry partners while educational benefits will be achieved through training of PhD scholars.  ]]></description>
      <pubDate>Thu, 21 Sep 2023 14:53:45 GMT</pubDate>
      <guid>https://rip.trb.org/View/2250736</guid>
    </item>
    <item>
      <title>Y4R1 -  Evaluating the Adoption and Impact of Autonomous Delivery Modern Technologies</title>
      <link>https://rip.trb.org/View/1874081</link>
      <description><![CDATA[The tremendous potential of technology-driven innovations to address the inefficiencies in last mile deliveries has prompted e-commerce companies, retail chains, logistic providers and
technology start-ups to invest in sidewalk autonomous delivery robots (SADRs) and road
autonomous delivery vehicles (RADRs). The growing appeal for utilizing SADR and RADR
technologies arises from the increased demand for same-day deliveries in business to consumer
(B2C) e-commerce and the associated challenges for logistics providers (Cárdenas et al., 2017;
Jennings and Figliozzi, 2019; Wang et al., 2016). Apart from improving the delivery efficiency,
autonomous vehicles have the potential for initiating a more sustainable, and customer focused
delivery practice with limited externalities on road congestion, noise and CO₂  emissions (Hardi
and Wagner, 2019; Stolaroff et al., 2018). Due to the rapid advancements sensing technology and
artificial intelligence algorithms, large-scale deployments of autonomous delivery vehicles are on
the verge of becoming a reality in some delivery scenarios with known and repeatable routes.
SADRs and RADRs developed by Amazon, FedEx, Starship, and Nuro are already deployed and
being tested in multiple U.S. cities.
The ongoing COVID-19 pandemic and the need for contactless deliveries that avoid the risk
of person-to-person infection has made it clear that autonomous robot deliveries have many
advantages. Consumers, businesses, and governments have switched from cautious beta testers
into eager early adopters. Despite this unprecedented requirement necessitated by the pandemic,
SADRs and RADRs need to be deployed by logistics service providers and Government agencies
in a way that is generally accepted by the public. In fact, if not widely accepted by the public, the
development and introduction of autonomous delivery vehicles can be a substantial waste of
resources for logistics service providers and vehicle developers alike. Therefore, it is imperative
to conduct micro-level behavioral research on user acceptance early in the deployment roadmap
of delivery robots to be able to design, develop and promote them as an accepted alternative to its
conventional delivery practices (i.e., van-based human delivery). One of the contributions of the
proposed project is to address this urgent research gap by investigating the psychological factors
that determine public acceptance of ADRs (Autonomous Delivery Robots) from an end-consumer
perspective.
To evaluate delivery robot adoption rates and tradeoffs it is necessary to model how vehicles are
likely to evolve. Technology is evolving rapidly and new players like Tesla are entering the heavy
and light truck electric vehicle (EV) market pushing up freight EV efficiency and capabilities
(Ulrich, 2020). Delivery cost is an important variable that is changing as technologies and vehicles
are evolving. In addition, cities are reevaluating how to assign and prioritize roadway and curb
infrastructure during and after COVID-19 (Davies, 2020). Autonomous delivery vehicles can also
interact with smart curb and parking management technology in ways that may increase delivery
efficiency and resource utilization by reducing double parking and congestion (Jennings and
Figliozzi, 2019). ADRs are also likely to affect not just the last mile but also the last echelons of
supply chains.]]></description>
      <pubDate>Wed, 25 Aug 2021 10:58:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/1874081</guid>
    </item>
    <item>
      <title>Y3R8 - Incorporating Freight in Regional Land Use
Planning Models</title>
      <link>https://rip.trb.org/View/1868908</link>
      <description><![CDATA[The tremendous potential of technology-driven innovations to address the inefficiencies in lastmile deliveries has prompted e-commerce companies, retail chains, logistic providers and
technology start-ups to invest in sidewalk autonomous delivery robots (SADRs) and road
autonomous delivery vehicles (RADRs). The growing appeal for utilizing SADR and RADR
technologies arises from the increased demand for same-day deliveries in business to consumer
(B2C) e-commerce and the associated challenges for logistics providers (Cárdenas et al., 2017;
Jennings and Figliozzi, 2019; Wang et al., 2016). Apart from improving the delivery efficiency,
autonomous vehicles have the potential for initiating a more sustainable, and customer focused
delivery practice with limited externalities on road congestion, noise and CO₂  emissions (Hardi
and Wagner, 2019; Stolaroff et al., 2018). Due to the rapid advancements sensing technology and
artificial intelligence algorithms, large-scale deployments of autonomous delivery vehicles are on
the verge of becoming a reality in some delivery scenarios with known and repeatable routes.
SADRs and RADRs developed by Amazon, FedEx, Starship, and Nuro are already deployed and
being tested in multiple U.S. cities.
The ongoing COVID-19 pandemic and the need for contactless deliveries that avoid the risk
of person-to-person infection has made it clear that autonomous robot deliveries have many
advantages. Consumers, businesses, and governments have switched from cautious beta testers
into eager early adopters. Despite this unprecedented requirement necessitated by the pandemic,
SADRs and RADRs need to be deployed by logistics service providers and Government agencies
in a way that is generally accepted by the public. In fact, if not widely accepted by the public, the
development and introduction of autonomous delivery vehicles can be a substantial waste of
resources for logistics service providers and vehicle developers alike. Therefore, it is imperative
to conduct micro-level behavioral research on user acceptance early in the deployment roadmap
of delivery robots to be able to design, develop and promote them as an accepted alternative to its
conventional delivery practices (i.e., van-based human delivery). One of the contributions of the
proposed project is to address this urgent research gap by investigating the psychological factors
that determine public acceptance of ADRs (Autonomous Delivery Robots) from an end-consumer
perspective.
To evaluate delivery robot adoption rates and tradeoffs it is necessary to model how vehicles are
likely to evolve. Technology is evolving rapidly and new players like Tesla are entering the heavy
and light truck electric vehicle (EV) market pushing up freight EV efficiency and capabilities
(Ulrich, 2020). Delivery cost is an important variable that is changing as technologies and vehicles
are evolving. In addition, cities are reevaluating how to assign and prioritize roadway and curb
infrastructure during and after COVID-19 (Davies, 2020). Autonomous delivery vehicles can also
interact with smart curb and parking management technology in ways that may increase delivery
efficiency and resource utilization by reducing double parking and congestion (Jennings and
Figliozzi, 2019). ADRs are also likely to affect not just the last mile but also the last echelons of
supply chains.]]></description>
      <pubDate>Wed, 28 Jul 2021 12:23:37 GMT</pubDate>
      <guid>https://rip.trb.org/View/1868908</guid>
    </item>
    <item>
      <title>Y3R3- Integrate Autonomous Delivery Vehicle into Sustainable Urban Logistics Planning and Optimization: Economic and Environmental Evaluation</title>
      <link>https://rip.trb.org/View/1868885</link>
      <description><![CDATA[The ongoing urbanization presents a challenge both to enterprises who want to adapt to the change
and deliver goods more efficiently, also the policymakers who can positively impact urban logistics in terms of
trade-off decisions on economic and environmental impact. As sustainable urban distribution system becomes
one of the most recent trends, hubs are augmented with smaller logistic centers in the city. In urban logistics
system, autonomous delivery vehicles (ADV) as a new element of ground-based delivery services are a more
practical solution in cities. The study team discusses the potential ADV combined with delivery van regarding the urban
sustainable freight delivery network. A multi-modal and multi-objective optimization model is proposed to
simultaneously minimize the aggregate operating cost and reduce carbon dioxide emission. Also, delivery hubs
from alternative hubs are chosen for ADV positioning. An improved Non-dominated Sorting Genetic Algorithm was used to solve the problem. The study team applies it to a case study comparing the Economic and Environmental
performance in both unimodal delivery method and combined delivery using Van-Autonomous delivery vehicle
(V-ADV). Finally, sensitivity analysis is used to assess the robustness of the results of the model in the presence
of uncertainty. This research provides decision support to government authorities, logistics service providers,
and other relevant decision makers.
Motivated both by the adaption of new delivering technologies (ADV) in practical
transportation industry and the theoretical gap existing in the present literature, a network planning problem for
the cooperated van and ADV is studied. The primary objective of this study is to develop an urban logistics
optimization model in terms of minimizing the logistics operating cost and carbon emission using V-ADV
from the point of authorities. Simultaneously, the best locations from a set of existing stations are selected for
ADV positioning and optimal flow assignment.]]></description>
      <pubDate>Tue, 27 Jul 2021 17:29:03 GMT</pubDate>
      <guid>https://rip.trb.org/View/1868885</guid>
    </item>
    <item>
      <title>Portable and Integrated Multi-Sensor System for Data- Driven Performance Evaluation of Urban Transportation Networks</title>
      <link>https://rip.trb.org/View/1425030</link>
      <description><![CDATA[In urban areas, obstructions of traffic such as double parking, commercial vehicle deliveries, pedestrian jaywalking, taxi pick-ups and drop-offs, are potential impediments to road capacity and vehicular speed, and causes traffic delay as well as safety risks. In 2014, double parking violation had 502,082 ticketed cases in New York City according to New York City Department of Finance (NYCDOF) records. New York City and many urban areas need to keep track of the impacts of these violations on their transportation system as well as on the residents of the city. Hence there is a strong need to develop robust methodologies to monitor these activities and analyze their potential impacts that can be in the form of longer travel times, crashes, emissions, or noise pollution. Due to the advances in sensors and computing, obtaining information related to the parking, individual vehicle or pedestrian trajectories has become remarkably feasible. Video cameras coupled with computer vision algorithms can provide very accurately, detect, track and classify vehicles. Similarly relatively inexpensive mobile sensors for measuring noise and emission levels have also become commercially available. However, integrated inexpensive multi-sensor solutions that combine these sensing solutions are not yet commercially available.]]></description>
      <pubDate>Mon, 03 Oct 2016 16:46:51 GMT</pubDate>
      <guid>https://rip.trb.org/View/1425030</guid>
    </item>
    <item>
      <title>Feasibility of Waterborne Distribution at Hunts Point Terminal Market</title>
      <link>https://rip.trb.org/View/1395735</link>
      <description><![CDATA[Hunts Point Food Distribution Center, also referred to as Hunts Point Terminal Market (HPTM), is the center or hub for food distribution for the New York Metropolitan area. Hunts Point Peninsula, located in the South Bronx, is also home to over 40,000 residents. HPTM is a cooperative owned by stockholders. The cooperative has a long-term lease with the City of New York for its cooperation and tenants.

The HPTM distribution center is a vital part of the food supply chain in the New York metropolitan area. Firms’ agents arrive at the food distribution center to select and pick up food. Some food is ordered in advance; therefore, it is only picked up. The food is hauled away by truck, van or other commercial vehicles many of which are refrigerated. Two types of truck trips are made to HPTM:

     (1) The inbound truck trip is for delivering food products to the distribution center (56% west of the Hudson
      River and 44% east of the Hudson River).
 
     (2) The outbound truck trip is for hauling food products from the distribution center to grocery stores,
      restaurants, and other food venues (17% west of the Hudson River and 79% east of the Hudson River).
The demand for the distribution center’s services generates 15,000 truck trips per day, including some from large distances away. Traffic flow is constant, occurring 24 hours a day, especially during the very early morning hours. There are times during the daily operating cycle when congestion is formed on the roads leading to and from the distribution center and congestion is formed in the terminal itself. Congestion implies air pollution, noise pollution, additional road wear-and-tear, an increase of public spending on health and infrastructure maintenance and other negative externalities.

The large HPTM distribution center does not have simple and quick access. The geographical layout of the New York Metropolitan area of highways, bridges and tunnels along rivers and creeks complicates the surface transportation delivery. Delivery takes more miles to drive and more time to deliver. As a result, at times of peak demand, vehicles spend excessive time waiting and idling and on the road wasting fuel and emitting CO₂ and other gases.]]></description>
      <pubDate>Wed, 20 Jan 2016 17:39:21 GMT</pubDate>
      <guid>https://rip.trb.org/View/1395735</guid>
    </item>
    <item>
      <title>Impacts of Freight Parking Policies in Urban Areas: The Case of New York City</title>
      <link>https://rip.trb.org/View/1362120</link>
      <description><![CDATA[Freight flows are a physical expression of the economy, so fostering efficiency in the movement of freight from producers to consumers will spur growth for the economy and employment. However, the transportation of freight generates a large amount of traffic, with resultant congestion, pollution, noise, infrastructure damage, and threats to the quality of life. The main goal of transportation policy should therefore be to maximize the net social benefits of freight activity by ensuring efficient freight flows, while mitigating the negative impacts associated with freight activity. Achieving a proper balance is always a challenge, as it is in the case of urban parking and curb allocation. In most city centers and business districts (CBDs), parking is very limited, translating into trucks double parking, idling, circling blocks, or extending into sidewalks and roadways while using undersized loading areas. This is not only an enforcement issue; frequently, the number of available parking spaces is not enough to satisfy the needs of delivery trucks. In Manhattan for instance, there are ten ZIP codes where the linear capacity of the streets is insufficient to accommodate the trucks needed to make deliveries (Jaller et al., 2013). As a result, carriers are in most cases, forced to park illegally and pay large amounts of money in parking fines ($500 to $1000 per truck per month) (Holguín-Veras et al., 2008). Freight vehicles need to park close to their customers, as the cost of walking from a parking facility to their destinations is very high; parking further away reduces the size of the loads drivers can carry, all of which increases delivery and parking times. As demand for parking increases in CBDs, there is an increasing need for analysis tools to support decisions that will strike a viable balance between supply and demand. Policy makers need these tools to identify and analyze the impacts of potential policies. Traffic simulation tools have been developed to support efforts devoted to modeling passenger car traffic, but most of these pay almost no attention to the effects of the parking allocations, travel times and congestion generated by delivery trucks. There is a lack of data, and knowledge about how the whole system works, and how best to influence it to achieve policy goals. The development of tools aimed at a fuller understanding of parking conditions in urban areas will help to fill this void, and allow public agencies to manage and regulate parking--for passenger and freight traffic--more effectively.]]></description>
      <pubDate>Wed, 22 Jul 2015 01:01:39 GMT</pubDate>
      <guid>https://rip.trb.org/View/1362120</guid>
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