Modeling Urban Freight Generation: A Study of Commercial Establishments’ Freight Needs

Increasing urbanization, and the environmental and liveability impacts associated with urban activity, have directed attention to the need for sustainable cities. Achieving sustainable urban development requires including freight systems in strategic urban development plans. In this context, joint efforts involving academia and public- and private sector to collect the right data and develop suitable models, can contribute toward a better understanding of establishments’ freight needs, the quantification of freight’s traffic impacts and the development of appropriate methods to support decision making and strategic plans. This paper studies urban commercial establishments’ freight needs and impacts on traffic using data collected from establishments in the City of Gothenburg (Sweden). The data cover different zones of the city and include commercial sectors found typically in urban cores (e.g., retailers, food services, health care, public sector offices and education). The paper introduces a set of statistical models—developed based on regression analyses and discrete choice models—to estimate the number of freight trips produced and attracted per week, and the attraction of weight and volumes of freight. In addition to shed light on the factors determining establishments’ freight- and freight trips generation, the models are designed with the purpose of assisting planning and policy design efforts, thus the explanatory variables are selected based on suitability and availability. The results show that retailers of perishable goods have the highest freight trip generation per establishment, followed by public sector offices and education establishments, retailers of non-perishable goods and restaurants. The results also reveal a heterogeneity between sectors, and a differential business size effect across commercial sectors

Assessing the magnitude of freight traffic generated by office deliveries

This paper studies the impact of offices on urban freight traffic. Research on freight activity generated by offices is very limited because they are not seen as important contributors to urban freight traffic, and because the amount of deliveries per office is very small compared to the number of deliveries per establishment in freight-intensive sectors (e.g., retail, wholesale, manufacturing). However, the number of offices in cities is so large that altogether they represent a significant share of urban deliveries and generate a nonnegligible share of urban freight traffic. Hence, the relevance of quantifying their freight trip generation. This paper uses the City of Stockholm as a case study. The author collected data from offices and other establishments, estimated regression models and applied them to the city. The results show that offices represent 36% of establishments in Stockholm, 62% of employees and are responsible for 15% of freight trips generated in the city

Managing household freight: The impact of online shopping on residential freight trips

Freight transport management and planning traditionally relies on freight transport models. However, e -commerce has changed the way freight is transported and requires a paradigm shift in such models. In contrast to conventional purchases in physical outlets, there is a spatial and temporal disconnection between the purchase and the reception of goods bought online. While traditionally the shopper brings home the purchases, the courier, express and parcel (CEP) sector must bridge this leg for the online retail channel by delivering the parcel to the household. These new type of freight trips have been ignored in the literature on freight modeling. Given the increasing number of urban freight trips destined for households, this omission implies significant errors when demonstrating transport impacts, identifying potential innovations, or assessing policy initiatives with these models. Therefore, we develop a framework that demonstrates how households\u27 online consumption translates into freight trips. Three key factors in this framework seem to determine the magnitude of freight traffic originated by household\u27s online shopping: (i) consumer shopping behavior, (ii) the supplier network and distribution system designed by the online store, and (iii) the fragmentation of the CEP market and the density of the delivery network. The identification of these three key factors provides a framework for policy action to mitigate the impact of household freight

Property owners as possible game changers for sustainable urban freight

Commercial activity and the underlying freight movements supporting them are essential to urban life. Unfortunately, urban freight has negative effects on sustainability and requires innovative approaches to enhance the positive aspects and minimize the negative ones. There is a common understanding among practitioners, with support from the literature, that the negative impacts of urban freight can be alleviated with better knowledge about the system and by identifying and engaging influential stakeholders. This paper focuses on property owners and their role in encouraging initiatives that lead to more sustainable urban freight delivery practices. Property owners engage in sustainability issues in real estate because they are interested in developing their properties to increase their attractiveness, competitiveness, and future market value. A shopping mall is an example of a property that generates a lot of freight traffic due to intense commercial activity. This paper studies the delivery patterns to a centrally located shopping mall in the city of Gothenburg and identifies how property owners could influence the delivery patterns of their tenants. A range of initiatives is proposed based on the literature and considering the delivery patterns observed. The results show that property owners can include freight deliveries in their sustainability strategy, focusing on fostering collaboration and communication between the tenants and promoting supply chain strategies that consider the type of activities that take place and the scope to reduce the number of truck movements and increase the efficient and sustainable goods deliveries

Assessing the inequalities in access to online delivery services and the way COVID-19 pandemic affects marginalization

This paper discusses the importance of incorporating online home delivery services (OHDS) into the concept of accessibility and marginalization. The authors propose a method to quantify access to OHDS and assess levels of\ua0inequalities\ua0in access to OHDS using data from OHDS providers in the pharmaceutical and food sectors, as well as from transport operators delivering parcels. The V\ue4stra G\uf6taland Region in the West coast of Sweden is used as a case study. The results show significant inequalities in access to OHDS. Moreover, there are segments of population under a compound marginalization during the COVID-19 pandemic due to (i) limited accessibility to OHDS services, (ii) high incidence of COVID-19 cases in their area that makes physical visits to a store a risk activity, and (iii) high vulnerability (e.g., high share of individuals older than 65). These results reveal a need for the public sector to prioritize innovations in services that target specific clusters of the population that are vulnerable and marginalized, but also shows the imminent risk for some of these segments during the pandemic

A time-dependent freight tour synthesis model

This paper introduces a model of urban freight demand that seeks to estimate tour flows from secondary data sources e.g., traffic counts, to bypass the need for expensive surveys. The model discussed in this paper, referred as Freight Tour Synthesis (FTS), enhances current techniques by incorporating the time-dependent tour-based behavior of freight vehicles, and the decision maker’s (e.g., metropolitan planning agency planner) preferences for different sources of information. The model, based on entropy maximization theory, estimates the most likely set of tour flows, given a set of trip generation estimates, a set of traffic counts per time interval, and total freight transportation cost in the network. The type of inputs used allows the assessment of changes in infrastructure, policy and land use. The ability of the model to replicate actual values is assessed using the Denver Region (CO) as a case study

Enabling Factors and Durations Data Analytics for Dynamic Freight Parking Limits

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

Seeking equity and justice in urban freight: where to look?

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

Exploring the Application of Urban Form Profiles in Freight Trip Generation

This research aims to incorporate urban form characteristics in freight trip generation (FTG) models, and thus enable more interaction between practitioners through integration of their instruments and policies. From this perspective, the research addresses two questions:To what extent does the urban form profile of a location influence the freight trip generation patterns of local establishments?How much do the urban form profiles relate to the location preference of different types of establishment

Dynamic Stochastic Electric Vehicle Routing with Safe Reinforcement Learning

Dynamic routing of electric commercial vehicles can be a challenging problem since besides the uncertainty of energy consumption there are also random customer requests. This paper introduces the Dynamic Stochastic Electric Vehicle Routing Problem (DS-EVRP). A Safe Reinforcement Learning method is proposed for solving the problem. The objective is to minimize expected energy consumption in a safe way, which means also minimizing the risk of battery depletion while en route by planning charging whenever necessary. The key idea is to learn offline about the stochastic customer requests and energy consumption using Monte Carlo simulations, to be able to plan the route predictively and safely online. The method is evaluated using simulations based on energy consumption data from a realistic traffic model for the city of Luxembourg and a high-fidelity vehicle model. The results indicate that it is possible to save energy at the same time maintaining reliability by planning the routes and charging in an anticipative way. The proposed method has the potential to improve transport operations with electric commercial vehicles capitalizing on their environmental benefit