Lately, research has shown the potential of automation in freight transport by reducing energy consumption, emissions, noise, and operating costs—often focused exclusively on either freight (e.g., robots and drones) or passenger (e.g., automobiles and buses) transport. Hence, this research investigates the potential of technological advancements in self-driving and connected vehicles for better coordination between freight and passenger vehicles. To achieve the objective of integrating robotic last-mile deliveries with autonomous minibuses, Chalmers University in Sweden has been chosen as a case study. Pilot tests were conducted to measure the travel, handling, boarding and alighting times, and energy consumption for robots and the minibus. Freight and passenger demand were obtained from the historical daily demand data. Several scenarios for routing and bus stop locations were examined for the energy efficiency of freight deliveries via electric pickup trucks. The minibus required around 34 daily trips, operating two to three buses during peak times. The results demonstrated that robots exhibited superior energy efficiency at lower demand levels, while integrating with minibuses led to a further reduction in energy use by 20-50%. Two-way minibus movement maximised the overlapping of routes between the robot and the minibus, reducing passenger travel time; however, with an increase in the bus kilometres travelled. To reap maximum benefits of integration, it is vital to prepare an optimal plan considering the demand, vehicle size, routing, and bus stops to overlap with freight destinations.

Freight Trip Generation (FTG) models link establishment characteristics to delivery activity and are widely used in urban freight planning. While their reliability depends on establishment surveys, the implications of sampling design remain poorly understood. This paper examines how alternative sampling strategies affect FTG model performance and cost, using population data to sample from establishments in Stockholm. The authors compare simple random, stratified, and hybrid sampling across proportional, Neyman, and optimal allocation rules, as well as three levels of sector aggregation. Results show that simple random sampling overrepresents large, low-impact sectors and underrepresents freight-intensive ones. Stratified approaches improve accuracy, but only when allocation accounts for sectoral variance and survey costs. Aggregation reduces required sample sizes, with moderate aggregation offering a practical compromise and high aggregation suited for strategic planning.

Freight transportation is essential for urban life and economic growth, yet cities continue to face persistent challenges in managing its impacts. Estimating Freight Trip Generation (FTG)—the number of freight trips attracted or produced by establishments—provides a foundation for urban freight transportation planning, helping cities estimate freight-related travel demand and evaluate targeted initiatives. This paper presents the first international comparison of FTG patterns across metropolitan areas worldwide, addressing the lack of comparative FTG research and examining the transferability of FTG models across contexts. The study enables benchmarking of freight activity and identification of structural drivers such as urban form and economic composition. Establishment-level analyses show that establishment size and industry sector are consistent drivers of FTG, while aggregate results reveal that freight activity concentrates in urban cores and is dominated by the Accommodation and Food Services, Retail Trade, Wholesale Trade, and Manufacturing sectors. In addition, the paper introduces a conceptual framework highlighting the multifaceted drivers of FTG and provides practical recommendations for harmonized data collection practices, which are essential for advancing FTG research and strengthening the empirical basis for urban freight transportation planning.

Urban planners seek to resolve tensions in public space by promoting street designs that prioritize active mobility. However, there is a tendency in overlooking freight operations in the allocation of street space, exacerbating conflicts in the access to space which threaten the safety, environmental, social and economic values of streets. While studies in European and US cities (Global North) have examined these issues, little attention has been given to the Global South. This research aims at examining conflicts in the access to street space, particularly between pedestrians and freight, to identify conditions that shape or restrict both walking as a mode of transport and efficient freight deliveries. Several methods supported data collection in five streets in Nairobi (Kenya), including secondary data analysis, focus groups, direct observation, surveys, and workshops. Findings reveal mismatches between policies, practices, power, and business models, driven not only by factors reported in Global North contexts but also by local dynamics, including politization of space occupation, informal last-mile structures, and illegal encroachment of public space. Additionally, the research provides insights to the design of practice-informed rules for access management, emphasizing the need to align access rules with everyday street practices and social dynamics. Outcomes from this research expand the understanding of how urban conditions influence freight–pedestrian interactions and the viability of walking as a mode of transport. Comprehending these dynamics becomes imperative for creating liveable cities.

E-commerce has significantly altered urban logistics, driving an unprecedented surge in last-mile deliveries while amplifying labor, regulatory, and environmental challenges. In many cities, particularly in developing economies, these challenges are compounded by institutional weaknesses and the widespread emergence of informality in logistics operations. This study examines how informality has evolved into a defining characteristic of last-mile e-commerce, shaping precarious labor conditions, increasing road safety risks, and exposing regulatory gaps. Employing an inductive qualitative approach, this research follows an explanatory and instrumental case study design based on semi-structured interviews with five key stakeholder groups: delivery drivers, e-commerce companies, public sector representatives, logistics and road safety experts, and society representatives. Findings indicate that outsourcing last-mile logistics has allowed companies to minimize costs while transferring operational risks to informal workers, reinforcing a system where economic efficiency takes precedence over labor protections and sustainability. Additionally, the lack of regulatory oversight has led to the institutionalization of informality, perpetuating a cycle in which negative externalities—such as congestion, road crashes, and environmental degradation—are borne by society rather than private actors. These findings underscore the need to reassess governance strategies in last-mile logistics, ensuring operational flexibility does not come at the expense of social and economic sustainability, and informing more inclusive and adaptive transport policy responses.

Urban last-mile logistics is increasingly challenged by the competing demands for curbside access. As e-commerce expands and urban populations grow, the demand for curbside space intensifies, leading to conflicts among freight, pedestrians, cyclists, and other street users. These conflicts result in collision risks, traffic obstructions, overuse of space, and pavement damage, all of which undermine urban safety, efficiency, and livability. This study explores the potential of computer vision to address these issues by identifying and assessing curbside conflicts. This paper presents an analysis of video data utilizing algorithms such as YOLO (You Only Look Once) and tools like OpenCV (Open-Source Computer Vision Library), to detect interactions between freight activities and curbside users. By integrating computer vision driven data analytics, the paper contributes with tools for conflict detection in the access to curbside space, providing the ground for strategies that mitigate the negative effects of freight operations on public spaces. This research underscores the critical role of technology in navigating the complexities of urban logistics and advocates for continued exploration in this field to develop sophisticated tools to improve curbside access management and foster solutions for future urban environments.

Urban freight activity generated by the food industry has received surprisingly little research attention despite the unprecedented changes in food ordering behavior because of the advancement of technology. Not only did more consumers take advantage of the options offered online, but it also resulted in the diversification of the food industry into three establishment types: (i) in-person dining restaurants, (ii) hybrid restaurants, and (iii) dark kitchens. While the traditional in-person dining restaurants continue to be major freight trip generators, the emergence of tight-knit networks of small, delivery-only establishments called ‘dark kitchens’ have magnified the overall freight impacts from the food industry. This study attempts to contribute to this emerging research gap in urban freight research by conducting a freight survey in Delhi (India) targeting the above-mentioned food industry establishments to compare and contrast the evolving freight traffic impact generated by hybrid restaurants/dark kitchens relative to the traditional in-person dining restaurants. The freight generation (FTA/FTP) was quantified in this study using a host of models, such as the multivariate ordered probit model, zero-inflated negative binomial model, and hierarchical tree-based regression. Novel explanatory variables such as night-time light and cost of dining are explored for developing actionable, sustainable city logistics policies aimed at regulating delivery at pick-up traffic related to emerging urban freight generators.

Streets are contested urban public spaces due to their limited availability. While they serve various functions, the needs of certain uses—such as freight—often have been overlooked in space allocation policies affecting urban livability. Recently, freight curbside management has emerged to address these conflicts, allowing service and delivery vehicles to better use street space, contributing to cities’ sustainability targets. Although pilots testing freight curbside interventions are a first step for policymakers to evaluate the effectiveness of these interventions, the transition from pilot to established practices remains underexplored. Therefore, the aim of this research is to understand the success and failure factors that influence the institutionalization process of interventions tested in freight curbside pilots. To achieve this, this paper analyses cases from various cities worldwide that have implemented such pilots, using the lens of institutional theory. Case selection criteria were based on the maturity level of freight curbside pilots. Specifically, the paper focused on those cases that had already implemented pilots, undergone monitoring, evaluation, and possible continuation processes. Data collection and analysis revealed coercive, normative, and mimetic forces driving change towards institutionalized practices. The data analysis identified 23 themes across four content domains, i.e., organizational, economic, technological, and regulatory. Successful institutionalization process relies on strategically selecting high demand loading zones and demonstrating public benefits. Enhancing user experience is also crucial. However, some interventions fail to become institutionalized due to regulatory constraints, business model issues, and land use regulations. This highlights the need for flexible, context-specific approaches. The analysis of institutional pressures revealed that coercive pressures influence transitions from themes related to the legal mandate of public agencies, pilot scope definition, and user experience, while normative pressures shape transition regarding public benefit, business models, stakeholder involvement, and data management themes. Mimetic forces guided early-stage pilots through lessons learned from cities with prior experience in curbside pilots. The findings provide recommendations and guidelines for the development of future pilots, useful for planners aiming at generating long-term curbside policies that solve freight-related street space conflicts.

The rapid rise of app-based food delivery platforms has redefined how restaurants shape urban space. However, little is known about how these evolving restaurant types cluster and interact with urban land use. Using spatial analysis involving Ripley's-K and Moran's-I and predictive models involving decision trees, random forest, and multinomial logit models, this study attempts to explain the location choices of restaurants based on their relative distance to the city centre, rent, population density, and night-time light (NTL) intensity. Analysis results reveal that dark kitchens exhibit the tightest clustering, often in low-rent, high-density zones, while in-person dining is concentrated in high-rent, high-NTL areas. Among the models tested, random forest outperformed decision trees and multinomial logit models in predicting restaurant types, with night-time light emerging as the strongest spatial predictor. The clustering patterns observed in emerging urban restaurant types differ significantly from traditional brick-and-mortar establishments; study findings highlight the urgent need for adaptive freight planning and zoning policies to address the growing logistical footprint of digitally mediated food establishments. While based in Indian cities, the framework and insights of this study are transferable to other global contexts where on-demand food delivery and mixed-use zoning intersect in urban areas.

Urban freight systems embed and reflect spatial inequities in cities and imbalanced power structures within transport decision-making. These concerns are principal domains of "transportation justice" (TJ) and "mobility justice" (MJ) scholarship that have emerged in the past decade. However, little research exists situating urban freight within these prevailing frameworks, which leaves urban freight research on socio-environmental equity and justice ill-defined, especially compared to passenger or personal mobility discussions. Through the lens that derives from TJ and MJ's critical dialogue, this study synthesises urban freight literature's engagement with equity and justice. Namely, the review evaluates: How do researchers identify equitable distributions of urban freight's costs and benefits? At what scale do researchers evaluate urban freight inequities? And who does research consider entitled to urban freight equity and how are they involved in urban freight governance? The findings help inform researchers who seek to reimagine urban freight management strategies within broader equity and justice discourse.

Freight curbside management has become a contentious issue as various stakeholders claim access to public urban space. Although prior research has offered solutions to mitigate freight-related conflicts in the use of space, a deeper understanding of the extent to which those interventions contribute to cities’ sustainable development goals is needed. This paper presents the results of a meta-analysis that examines the effects of four freight curbside interventions: curbside space allocation for freight, data sharing, parking duration limits, and enforcement. The paper pinpoints benefits and drawbacks of those interventions on last-mile deliveries, the urban environment, and the use of public transport infrastructure. The findings suggest positive impacts and underscore the necessity of incorporating people-centred approaches in the design, implementation, and evaluation of policies concerning public space. Nevertheless, trade-offs when implementing those interventions have been identified. The paper concludes by outlining directions for future research and suggesting implications for urban freight policies.

The rise of e-commerce over the last decade has increased the pressure on urban logistics and highlighted important sustainability challenges. The COVID-19 pandemic exacerbated this trend and underscored the need to address social sustainability challenges with e-commerce. In particular, the pandemic drew attention to the uneven access to home deliveries and the importance of having a logistic system that is aligned with the principles underlying society and sustainable development goals. The purpose of this paper is to identify urban logistics services that emerged in response to access limitations linked to the COVID-19 pandemic, and to analyse how these services can enhance inclusivity and fair access to goods. We adopt an exploratory and qualitative research design based on a deductive content analysis approach. This method aims to systematize, objectively analyse, and draw assumptions from secondary evidence and semi-structured interviews with key actors. We identified several urban logistics innovations that spanned organizational, transportation modal shift, informational and technological approaches to face existing restrictions. The findings indicate a deficiency in inclusivity and equitable access to logistics innovations, prompting ad-hoc organization by citizens and private initiatives in response to the extraordinary circumstances of the pandemic. We introduced the concept of logistics divide to analyse the inequalities on who benefits from logistics innovations. This divide is a consequence of the uneven ability of different consumer segments to get access to those services either due to digital mastery, geographical barriers, legal barriers, or to economic reasons. The findings also showed that urban logistics actors have started to innovate to increase access to goods after the pandemic disclosed this logistics divide. In essence, this paper shows the importance of integrating transport and logistics research with transformative service research to decrease the logistics divide and achieve more equitable urban logistics.

Urban public space is often provided for freight delivery operations in the form of on-street (un)loading zones (LZ). Since public space is scarce and demanded by several users, city authorities have the challenge of managing LZ by gaining knowledge about freight curbside needs and utilization. Although technological solutions and enforcement practices have become popular among policymakers to capture curbside dynamics, there is still an open and promising research field for designing analytical frameworks that shape LZ decision-making processes. This fact has motivated the authors to define the concept of Smart Loading Zones (SLZ) as the involvement of technology and data analytics in the planning and management of LZ in a responsive and user-oriented way. Besides proposing a conceptual approach for the study of SLZ, this paper implements data analytics tools for enhancing decisions on LZ network design, using the City of Vic (Spain) as a case study. The machine learning techniques k-means++, DBSCAN, and integer linear programming prescribed the LZ number, location and service assignment based on establishments' coordinates, walking distances and freight demand. Results from the case study showed how an optimized number, location, and size of LZ improved occupation levels, i.e., from 18 % to 80 %, while freeing up curbside space for other users. Service coverage was also improved by allocating LZ to establishments within walking distances no greater than 75 m. Further development of methods and tools for SLZ at tactical and operational decisions are recommended for future studies.

Academic papers are the cornerstone of knowledge dissemination and crucial for researchers’ career development. This is particularly true for rapidly evolving research domains such as transportation, as evidenced by the surge of journals and papers in the past decade. While abundant literature offers guidance on successful publication strategies, insights into the reasons for rejection are rare. This study fills in this gap by examining why papers are rejected in the area of transportation. We present concrete evidence based on data from over 5,000 rejected transport papers. Quantitative analyses are conducted to reveal the impacts of similarity rate, duplication submission rate, and topic on desk rejections. Additionally, we shed light on the distinct focus reviewers have when serving different journals. We hope the results could equip transport researchers with a deeper comprehension of publication criteria and a better awareness of common but avoidable mistakes.

PurposeDespite large bodies of research related to the impacts of e-commerce on last-mile logistics and sustainability, there has been limited effort to evaluate urban freight using an equity lens. Therefore, this study proposes a modeling framework that enables researchers and planners to estimate the baseline equity performance of a major e-commerce platform and evaluate equity impacts of possible urban freight management strategies. The study also analyzes the sensitivity of various operational decisions to mitigate bias in the analysis.Design/methodology/approachThe model adapts empirical methodologies from activity-based modeling, transport equity evaluation, and residential freight trip generation (RFTG) to estimate person- and household-level delivery demand and cargo van traffic exposure in 41 U.S. Metropolitan Statistical Areas (MSAs).FindingsEvaluating 12 measurements across varying population segments and spatial units, the study finds robust evidence for racial and socio-economic inequities in last-mile delivery for low-income and, especially, populations of color (POC). By the most conservative measurement, POC are exposed to roughly 35% more cargo van traffic than white populations on average, despite ordering less than half as many packages. The study explores the model’s utility by evaluating a simple scenario that finds marginal equity gains for urban freight management strategies that prioritize line-haul efficiency improvements over those improving intra-neighborhood circulations.Originality/valuePresents a first effort in building a modeling framework for more equitable decision-making in last-mile delivery operations and broader city planning.

Scarce urban space needs to be wisely managed to avoid cities’ unsustainability. Overlooking freight activities in urban mobility policies leads to conflicting scenarios in public space use with negative consequences in congestion, pollution, crashes, and productivity losses. This paper aims at identifying the unsustainable effects of freight parking practices and the solutions reported in the literature to overcome them. A systematic literature review was conducted to collect quantitative evidence of curbside management impacts on sustainability. Key performance indicators from the 11th Sustainable Development Goal (SDG11) were linked to the 55 selected studies. Findings suggested positive impacts from four practices of freight curbside management and the need to include people-centred approaches in the design, implementation, and evaluation of public space policies.

Trade imbalances and global disturbances generate mismatches in the supply and demand of empty containers (ECs) that elevate the need for empty container repositioning (ECR). This research investigated dry ports as a potential means to minimize EC movements, and thus reduce costs and emissions. We assessed the environmental and economic effects of two ECR strategies via dry ports-street turns and extended free temporary storage-considering different scenarios of collaboration between shipping lines with different levels of container substitution. A multiparadigm simulation combined agent-based and discrete-event modelling to represent flows and estimate kilometers travelled, CO2 emissions, and costs resulting from combinations of ECR strategies and scenarios. Full ownership container substitution combined with extended free temporary storage at the dry port (FTDP) most improved ECR metrics, despite implementation challenges. Our results may be instrumental in increasing shipping lines' collaboration while reducing environmental impacts in up to 32 % of the inland ECR emissions.

Freight parking operations occur amid conflicting conditions of public space scarcity, competition with other users, and the inefficient management of loading zones (LZ) at cities’ curbside. The dynamic nature of freight operations, and the static LZ provision and regulation, accentuate these conflicting conditions at specific peak times. This generates supply–demand mismatches of parking infrastructure. These mismatches have motivated the development of Smart LZ that bring together technology, parking infrastructure, and data analytics to allocate space and define dynamic duration limits based on users’ needs. Although the dynamic duration limits unlock the possibility of a responsive LZ management, there is a narrow understanding of factors and analytical tools that support their definition. Therefore, the aim of this paper is twofold. Firstly, to identify factors for enabling dynamic parking durations policies. Secondly, to assess data analytics tools that estimate freight parking durations and LZ occupation levels based on operational and locational features. Semi-structured interviews and focus group analyses showed that public space use assessment, parking demand estimation, enforcement capabilities, and data sharing strategies are the most relevant factors when defining dynamic parking limits. This paper used quantitative models to assess different analytical tools that study LZ occupation and parking durations using tracked freight parking data from the City of Vic (Spain). CatBoost outperformed other machine learning (ML) algorithms and queuing models in estimating LZ occupation and parking durations. This paper contributes to the freight parking field by understanding how data analytics support dynamic parking limits definition, enabling responsive curbside management.