Urban Freight Deliveries Research Articles

Quantification of truck accessibility in urban last-mile deliveries using GPS probe data

This study presents the Urban Freight Mobility Energy Productivity (UF-MEP), a novel metric that evaluates the performance of last-mile urban freight delivery systems. Incorporating factors like delivery frequency, operational costs, and energy intensity, UF-MEP uses a data-centric approach for estimations. We introduce a pipeline that derives truck activity from GPS data, enabling the calculation of accessible delivery opportunities and UF-MEP. The work emphasizes the importance of comprehensive data collection in less populated areas and the critical role of GPS and link speed data in isochrone generation. Applied to Philadelphia’s large-scale network, our methodology demonstrates decreasing UF-MEP with rising residential and industrial delivery frequencies, while an increase is seen with commercial deliveries. Operational costs and energy intensity negatively impact UF-MEP. Our study suggests the highest potential for UF-MEP improvement lies in enhancing energy efficiency for medium and heavy-duty vehicles.

Shopping trips and deliveries then and now: How the behavioral shift differs by income level and urban size

Online shopping has significantly increased for the past decade. As e-commerce grows and evolves since its first appearance, consumers’ shopping behaviors also change, leading to the co-evolution of people’s shopping trips and urban freight deliveries. This co-evolution has important impacts on traffic, vehicle-miles-traveled (VMT) and parking demand in the short term, and is expected to influence residential choice and land-use patterns in the long term. However, the existing studies have not examined how the relationship among shopping channels changed over time. To address this knowledge gap, this research investigated how the complex relationship between delivery and in-store shopping trips changed from when e-commerce was relatively small to a stabilized stage, i.e., from 2009 to 2017, using the National Household Travel Survey (NHTS) data. The results from path analyses showed that the relationships among delivery, non-motorized shopping trip, and motorized shopping trip significantly shifted during an eight-year period. Not only the relationship among shopping channels but also the impacts of socio-demographic factors on shopping had changed over time. Furthermore, the behavioral shift varied by income level and urban size. This research is the first attempt investigating a heterogeneous historical trend from the transportation perspective. An understanding of how the relationship has been shifted and affected by income and urban environments will provide valuable insights into proactively planning and establishing freight demand management as well as addressing the inequality issue. The lessons learned will also be of assistance to better understand how consumers will respond to the emerging freight delivery service forms (e.g., prepared meals and grocery delivery) and their impacts on both passenger and freight transportation systems.

The Impacts of the Pandemic on Urban Freight Deliveries: a Case Study in a Brazilian Carrier

The Impacts of the Pandemic on Urban Freight Deliveries: a Case Study in a Brazilian Carrier

Seattle microhub delivery pilot: Evaluating emission impacts and stakeholder engagement

Urban freight deliveries using microhubs and e-cargo cycles have been gaining attention in cities suffering from congestion and emissions. E-cargo cycle deliveries and microhubs used as transshipment points in urban cores can replace trucks to make cities more livable. This study describes and empirically evaluates an e-cargo tricycle pilot conducted with multi-sector stakeholders in Seattle to report the potential benefits and pitfalls of such practices. The pilot held stakeholder workshop sessions to collect inputs of interest and expectations from the project. Mobile devices used by drivers on e-cargo tricycle and cargo van routes collected delivery data to use for empirical assessment. Total vehicle miles traveled and tailpipe carbon emissions served as performance metrics when comparing e-cargo tricycle and cargo van deliveries. The results showed the net-benefit of the microhub and e-cargo tricycle routes depend on the upstream operations when replenishing packages.The participatory approach to pilot design also provided insights into the factors of a successful pilot, with implications for scaling future e-cargo cycle delivery systems in North American cities. Namely, microhubs’ ability to host alternative revenue sources and value-added services is a boon for long-term financial competitiveness. However, lack of digital/physical infrastructure and work training/regulations specific to e-cargo cycle delivery operations present a barrier.

Modeling and Forecasting Freight Delivery for Blocked and Unblocked Streets through Machine Learning Approaches and Analysed by Microsimulation Model

Deliveries of freight on signalized city roads often lead to lane obstructions, contributing to urban traffic congestion. This issue has garnered increasing attention as traffic engineers and city planners seek sustainable solutions to meet growing demand while working within the constraints of limited road capacity. The primary objective of this study is to assess a model designed to quantify the impact of freight deliveries on both the capacity and delay experienced on signalized city roads in Ahmedabad. The model is structured similarly to the approach outlined in the Highway Capacity Manual (HCM2010). The aim of this research is to provide insights into the utilization of these analytical tools in the context of urban freight delivery policies. This research explores the estimation of delay and vehicle capacity, accounting for various factors such as delivery locations, durations, and their distinct effects on different lanes. To predict vehicle capacity and estimate delays, this study employed machine learning techniques, specifically Support Vector Machines and Artificial Neural Networks. The results demonstrate a high level of agreement between our predictions and actual observations.

Simulation Model for Operational Planning of City Cargo Transportation by Trams in Conditions of Stochastic Demand

A city’s transport and distribution system requires the effective organization of urban freight deliveries that take into consideration the economic, ecological, and social impact. Implementation of the concept of green logistics necessitates the use of environmentally friendly and energy-efficient modes of transport, which consider the existing infrastructure and the possibility of its development. The aim of this work was to facilitate the transfer to rail transport, using the existing tramway infrastructure, those shipments which previously entered the city center by road. The paper proposes a simulation model for small consignments delivered by freight trams in the city of Poznań, Poland. Operational planning is carried out in conditions of uncertainty and risk. The uncertainty is due to the stochastic nature of the demand for products. The risk is characterized by the probability that, due to technological and technical limitations, a given customer will not be fully served from the distribution center. The authors provide a decision support tool for evaluating the possible locations of tram depots and for route planning. The assignment of the routes, with the criterion of minimizing the costs for the carrier, and with a limitation on tram operating time, is based on the use of genetic algorithms, which makes it possible to obtain a solution that is close to optimal, within a reasonable time period. As a result of a series of simulation experiments and statistical data processing, the distribution laws and expected average values of the technological parameters were determined for the functioning of the city transport and distribution system under conditions of uncertainty and risk.

Assessing the Viability of Using Autorickshaws for Urban Freight Delivery in India

The new WRI India research paper ‘Assessing Viability of Using Autorickshaws for Urban Freight Delivery’ explores the feasibility of a dual-utility strategy for autorickshaws and examines its potential impacts on driver incomes, safety, and the environment. It finds that using autorickshaws to transport freight as well as people could enable drivers to make more productive use of existing vehicles. It could also raise driver incomes by around 15 percent, reduce logistics costs for small and medium enterprises, make vehicle use more efficient, and help mitigate greenhouse gas emissions.

Urban logistics transportation system combining passenger and freight flows: a heuristic approach

This research addresses the dynamic operational planning of an urban logistics system where an existing bus network is integrated with the urban freight delivery process, and with a last mile delivery service, to send freight to city centres. The operational planning involves the assignment of each freight request to a bus hub, where requests are dropped by clients, to a bus service starting at the assigned hub, and to a bus stop to be offloaded by the last mile operator (LMO). A heuristic algorithm focusing on the last mile delivery phase is proposed aiming, first, to minimize the number of bus offloads (i.e., the number of times the LMO needs to go to bus stops) and then, to minimize the average last mile delivery time of the LMO. The computational tests over a real bus network dataset in the city of Porto, Portugal, defined in SOLFI project, show the merits of the proposed algorithm.

Sustainable Transportation Networks Incorporating Green Modes for Urban Freight Delivery

The sustainability of transportation networks is threatened by growing amounts of emissions generated by vehicles. Urban freight delivery is one of the major sources of these emissions. Cargo cycles are attracting increasing attention for urban delivery owing to their numerous benefits, such as being less pollutant, requiring less space on roads, and high maneuverability. Electric vans have also been proposed as another green mode of freight delivery with a larger delivery capacity and range relative to cargo cycles. In this study, urban freight delivery is targeted through a heterogeneous fleet vehicle routing problem. The proposed framework is used to capture the optimal configuration of a mixed freight delivery fleet offering cargo cycles (i.e., pedal-assisted bikes and trikes as well as electric bikes and trikes) alongside the often-used diesel or gasoline vans or small trucks and electric vans. The formulated problem aims to minimize the delivery costs for companies, including maintenance, purchase, fuel, and labor costs and noise and emission taxes. The methodological contribution of this study is to develop a new metaheuristic algorithm, based on variable neighborhood search and simulated annealing, that finds a better solution with up to 7% lower objective function value and 50% to 80% lower solution times relative to the existing approaches in the literature. This study also demonstrates that bikes and trikes are selected at short delivery distances in the Chicago downtown network. Electric vans are more suitable for large distances, while cargo vans are selected for medium distances. Imposing an emission tax would change the model from cargo vans to electric vans. The results also show that increasing tax levels does not necessarily reduce societal costs because the delivery company tends to maintain its profit margin by increasing delivery costs for customers.

A combined GA-TS algorithm for two-echelon dynamic vehicle routing with proactive satellite stations

The make-to-stock strategy can improve the operational efficiency of urban freight delivery, especially in a dynamic transportation system with idle storage resources. This study proposes a two-echelon dynamic vehicle routing problem with proactive satellite stations (2E-DVRP-PSSs), which converts customers with available idle storage to satellite stations and optimizes the operating cost and make-to-stock cost. In 2E-DVRP-PSSs, heavy trucks depart from the depot, serve original static customers and proactive satellite stations (PSSs), and then return to the depot in the first-echelon network. In the second-echelon network, light vehicles depart from the PSSs to serve the dynamic customer demands. To compute the delivery routes efficiently, a hybrid algorithm integrating the cutting plane and improved genetic algorithm-tabu search (GA-TS) algorithm is proposed and implemented. An exact method using Gurobi solver and an improved GA-TS algorithm with multiply optimization strategies are also incorporated. Furthermore, the effectiveness of the 2E-DVRP-PSS formulation and the applicability of the hybrid algorithm for various instances are experimentally evaluated. An empirical case study of a two-echelon dynamic network in Dalian, China indicates that the proposed make-to-stock strategy with the PSS network can reduce costs and improve the efficiency of operations in urban transportation networks.

Análisis del uso de blockchain para la gestión de zonas de cargas para la distribución urbana

The lack of efficiency in the use of the on-street infrastructure dedicated to loading and unloading freight in the city, with their shortage and inefficient placement, generates congestion and delays during urban freight deliveries. An exploratory study is presented on the feasibility of incorporating technology into the management of dedicated loading/unloading areas for urban logistics in downtown Montevideo (Uruguay). After data of the neighborhood was collected, and the main stakeholder interest gathered, the paper analyzes a feasible technological solution to the problem, considering the restrictions posed by the stakeholders for sharing information. Due to the increasing influence of blockchain and its implementation for specific logistic problems, such as product traceability, the introduction of blockchain as part of the solution is considered and discussed in the system architecture

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.

Unloading Bays as Charging Stations for EFV-Based Urban Freight Delivery System—Example of Szczecin

The problem of urban logistics operations in the context of their impact on the environment has become the key challenge. Due to that, there has been a growing interest in increasing the use of alternative fuels, including electro-mobility. However, an important barrier to the utilisation of electric freight vehicles (EFVs) is their travel range and battery capacity. The paper is focused on the idea of EFV utilisation improvement by implementation of charging stations in unloading bays. First, the Authors analysed the efficiency of chosen vehicles during daily work. Next, the potential improvement of their travel range was analysed, considering the short-time charging processes carried out during delivery operations, using the charging systems provided in unloading bays. Moreover, the concept of wireless chargers utilisation was proposed as a challenge for future work. According to the analysis, utilisation of unloading bays equipped with short-time battery chargers could improve significantly the travel range of EFVs. As a result, it could improve the efficiency of electric vehicles in last mile deliveries in city areas.

Reactive UAV Fleet’s Mission Planning in Highly Dynamic and Unpredictable Environments

Unmanned aerial vehicles (UAVs) create an interesting alternative for establishing more sustainable urban freight deliveries. The substitution of traditional trucks in the last-mile distribution by a UAV fleet can contribute to urban sustainability by reducing air pollution and increasing urban freight efficiency. This paper presents a novel approach to the joint proactive and reactive planning of deliveries by a UAV fleet. We develop a receding horizon-based approach to reactive, online planning for the UAV fleet’s mission. We considered the delivery of goods to spatially dispersed customers over an assumed time horizon. Forecasted weather changes affect the energy consumption of UAVs and limit their range. Therefore, consideration should be given to plans for follow-up tasks, previously unmet needs, and predictions of disturbances over a moving time horizon. We propose a set of reaction rules that can be encountered during delivery in a highly dynamic and unpredictable environment. We implement a constraint programming paradigm, which is well suited to cope with the nonlinearity of the system’s characteristics. The proposed approach to online reactive UAV routing is evaluated in several instances. The computational experiments have shown that the developed model is capable of providing feasible plans for a UAV fleet’s mission that are robust to changes in weather and customer’s orders.

Flexible Assignment of Loading Bays for Efficient Vehicle Routing in Urban Last Mile Delivery

Urban freight deliveries are often subject to many access restrictions which creates the need to establish a system of loading bays and to split the last mile delivery into driving and walking parts. A new model based on hard and soft clustering approach is developed to solve the loading bay assignment problem for efficient vehicle routing and walking in last mile delivery. The flexibility of the model is provided by the soft clustering approach based on different membership degrees of customers to loading bays. Especially for instances with large numbers of loading bays, soft clustering seems to give better results, it leads to higher flexibility of city logistics systems, minimal driving distances, and adequately short walking paths, which contribute to the goal of reaching sustainable urban freight deliveries.

Modeling and Prediction of Freight Delivery for Blocked and Unblocked Street Using Machine Learning Techniques

Freight deliveries on signalized city road are recognized as lane obstructions throughout delivery. Traffic jamming associated with urban freight deliveries has gained increasing attention recently. As traffic engineers and planners are tasked with findingsolutions to accomplishcomprehensive demand more sustainably with restrictedroad capacity. The goal of this research is to evaluatethe model for quantifying the capacity and delay effect of a freight delivery on a signalized city road in Ahmedabad. The all or nothing model similar to the procedure used in the highway capacity manual (HCM2010). The persistence is to provide an understandingof the use of these tools for analysis of urban freight delivery policy. The present study covers delay and vehicle capacity estimation that can account for the changeable locality of deliveries, duration, and different impact on different lane groups. To predict the vehicle capacity and delay estimation, machine learning models, Support vector machine and Artificial neural network was utilized. Result shows excellent agreement between experimental and predicted observations.

Retailers and carriers’ viewpoint on Sorocaba’s city logistics: a spatial analysis

Abstract The spatial differentiation of the main constraints and challenges on urban freight transportation can support more assertive public policies and measures, subsidizing city logistics solutions. This paper presents the use of spatial analysis to characterize retailers and carriers’ perceptions on the most important constraints and challenges faced during the urban freight delivery in the central area of Sorocaba/SP. To identify possible clustered patterns concerning problems and solutions from the stakeholders’ viewpoint, the data were analyzed through two spatial techniques: Average Nearest Neighbors and Getis Gi Ord* Hot Spots analysis. These two methodological approaches provide a spatial concentration analysis. As a result, we determined the spatial patterns of stakeholders’ perception for the two groups of variables assessed: (i) the Northwestern portion of Sorocaba’s central area is not prepared to receive the demands of vehicles, and the stakeholders present restrictions related to off-peak deliveries; (ii) the Northeastern region has the greatest negative perception regarding the assessed attributes, and shows little evidence that the loading and unloading space is not appropriate.

Integrating mobility data sources to define and quantify a vehicle-level congestion indicator: an application for the city of Turin

PurposeTraffic congestion is a large-scale problem in urban areas all over the world that can lead to substantial costs for travellers and business operations. This paper focus on how to measure the way in which congestion selectively affects different traffic streams, with special emphasis on light duty vehicles travelling around a city.MethodsThe idea is to integrate a dataset collecting Global Positioning System (GPS) vehicle traces with road side data sources related to traffic conditions in a road network, which on the other hand usually lack focus on specific traffic streams. The core of the data integration method is the creation of a specific indicator focusing on the time lost in congestion. This is a Key Performance Indicator (KPI) of an urban network that is of paramount importance as a decision support tool for policy makers, also because it has an impact on other key issues such as air pollution, noise emissions, energy efficiency and health problems. Then, a method is proposed to quantify the congestion KPI in a highly disaggregated fashion (each single vehicle travelling on each single link or street segment).ResultsThis KPI can be used to inform a wide range of policy actions within the transport sector, both from the viewpoint of a city and from that of an individual actor of the transport system, such as the operator of a fleet of vehicles for urban freight deliveries. Some preliminary examples of how the aggregation of the KPI at different scales can provide insights into the transport system are presented.

Multi-objective optimization of a two-echelon vehicle routing problem with vehicle synchronization and ‘grey zone’ customers arising in urban logistics

We present a multi-ob’jective two-echelon vehicle routing problem with vehicle synchronization and ‘grey zone’ customers arising in the context of urban freight deliveries. Inner-city center deliveries are performed by small vehicles due to access restrictions, while deliveries outside this area are carried out by conventional vehicles for economic reasons. Goods are transferred from the first to the second echelon by synchronized meetings between vehicles of the respective echelons. We investigate the assignment of customers to vehicles, i.e., to the first or second echelon, within a so-called ‘grey zone’ on the border of the inner city and the area around it. While doing this, the economic objective as well as negative external effects of transport, such as emissions and disturbance (negative impact on citizens due to noise and congestion), are taken into account to include objectives of companies as well as of citizens and municipal authorities. Our metaheuristic – a large neighborhood search embedded in a heuristic rectangle/cuboid splitting – addresses this problem efficiently. We investigate the impact of the free assignment of part of the customers (‘grey zone’) to echelons and of three different city layouts on the solution. Computational results show that the impact of a ‘grey zone’ and thus the assignment of these customers to echelons depend significantly on the layout of a city. Potentially pareto-optimal solutions for two and three objectives are illustrated to efficiently support decision makers in sustainable city logistics planning processes.

Application of a variable neighborhood search algorithm to a fleet size and mix vehicle routing problem with electric modular vehicles

The emergence of new promising technologies used in electric transportation offers opportunities to achieve sustainable urban freight deliveries. In particular fleets of electric vehicles are seen as an efficient ecologic and economic transportation alternative for operating last-mile goods distribution in cities. This paper deals with the use of electric modules which can be added or removed from a freight vehicle to help gain capacity or respect delays when delivering customers. Since the electric vehicles have a limited battery capacity, they have to stop and recharge them at dedicated stations during the delivery tours. However, with the modular feature, this problem of disturbing the whole distribution process for recharging the batteries can be reduced and sometimes overlooked. This paper objective is to design and find the optimal routing strategy for a fleet of electric modular vehicles operating the last-mile delivery, with the minimal cost including the acquisition, the travel and the recharging ones. The research problem arising from the structure of the fleet can be associated with both a truck and trailer problem and a fleet size and mix vehicle routing problem with time windows using electric vehicles. This problem is presented in the first part of the article with the proposed solving procedure. More precisely, for big instances of the problem which is NP-Hard, a Variable Neighborhood Search (VNS) is designed and adapted. Experimental studies carried out on some benchmark instances show that the VNS performs impressively well in comparison with the best solving approaches of the literature.