Search Results (283)

Cycling represents a significant share of urban transportation, especially in terms of last-mile delivery. It has clear benefits for delivery times, as well as for environmental issues related to freight distribution. Furthermore, the increasing accessibility of low-cost environmental sensors (LCSs) provides an opportunity for urban monitoring in any situation. Moving in this direction, this research aims to investigate the use of LCSs to monitor particulate PM2.5 and PM10 levels and map them over delivery ride paths. The calibration process took 49 days of measurements into account, spanning different seasonal conditions (from May 2024 to November 2024). The employment of multiple linear regression and robust regression revealed a strong correlation between pollutant levels from two sources and other factors such as temperature and humidity. Subsequently, a one-month trial was carried out in the city of Faenza (Italy). In this study, a commercially available LCS was mounted on a cargo bike for measurement during delivery processes. This approach was adopted to reveal biker exposure to air pollutants. In this way, the operator’s route would be designed to select the best route in terms of delivery timing and polluting factors in the future. Furthermore, the integration of environmental monitoring to map urban environments has the potential to enhance the accuracy of local pollution mapping, thereby supporting municipal efforts to inform citizens and develop targeted air quality strategies. Full article

Background: Rapid urbanization has intensified the challenges of freight transport, particularly in last-mile (LM) delivery, leading to rising costs and environmental externalities. Autonomous vehicles (AVs) have emerged as a promising innovation to address these issues. While much of the existing literature emphasizes business and operational perspectives, this study focuses on the acceptance of AVs from the standpoint of e-consumers—individuals who make purchases via digital platforms—in an emerging market context. Methods: Grounded in an extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), which is specifically suited to consumer-focused technology adoption research, this study incorporates five constructs tailored to AV adoption. Structural Equation Modeling (SEM) was applied to survey data collected from 304 e-consumers in Northeast Brazil. Results: The findings reveal that performance expectancy, hedonic motivation, and environmental awareness exert significant positive effects on acceptance and intention to use AVs for LM delivery. Social influence shows a weaker, yet still positive, impact. Importantly, price sensitivity exhibits a minimal effect, suggesting that while consumers are generally cost-conscious, perceived value may outweigh price concerns in early adoption stages. Conclusions: These results offer valuable insights for policymakers and logistics providers aiming to implement consumer-oriented, cost-effective AV solutions in LM delivery, particularly in emerging economies. The findings emphasize the need for strategies that highlight the practical, emotional, and environmental benefits of AVs to foster market acceptance. Full article

Autonomous vehicles (AVs) have emerged as a promising sustainable urban mobility solution, expected to lead to enhanced road safety, smoother traffic flows, less traffic congestion, improved accessibility, better energy utilization and environmental performance, as well as more efficient passenger and freight transportation, in terms of time and cost, due to better fleet management and platooning. However, challenges also arise, mostly related to data privacy, security and cyber-security, high acquisition and infrastructure costs, accident liability, even possible increased traffic congestion and air pollution due to induced travel demand. This paper presents the results of a survey conducted among 654 residents who experienced an autonomous bus (AB) service in the city of Trikala, Greece, in order to assess their willingness to use (WTU) and willingness to pay (WTP) for ABs, through testing a range of factors based on a literature review. Results useful to policy-makers were extracted, such as that the intention to use ABs was mostly shaped by psychological factors (e.g., users’ perceptions of usefulness and safety, and trust in the service provider), while WTU seemed to be positively affected by previous experience in using ABs. In contrast, sociodemographic factors were found to have very little effect on the intention to use ABs, while apart from personal utility, users’ perceptions of how autonomous driving will improve the overall life standards in the study area also mattered. Full article

While the explosive growth in e-commerce stresses urban logistics systems, city planners lack of fine-grained data in order to anticipate and manage the resulting freight flows. Using a three-stage analytical approach combining descriptive zonal statistics, hotspot analysis and different regression modeling from univariate to geographically weighted regression, this study integrates one year of parcel deliveries from a leading courier with open spatial layers of land-use zoning, census population, mobile-signal activity and household income to model last-mile demand across different land use types. A baseline linear regression shows that residential population alone accounts for roughly 30% of the variance in annual parcel volumes (2.5–3.0 deliveries per resident) while adding daytime workforce and income increases the prediction accuracy to 39%. In a similar approach where coefficients vary geographically with Geographically Weighted Regression to capture the local heterogeneity achieves a significant raise of the overall R² to 0.54 and surpassing 0.70 in residential and institutional districts. Hot-spot analysis reveals a highly fragmented pattern where fewer than 5% of blocks generate more than 8.5% of all deliveries with no apparent correlation to the broaden land-use classes. Commercial and administrative areas exhibit the greatest intensity (1149 deliveries per ha) yet remain the hardest to explain (global R² = 0.21) underscoring the importance of additional variables such as retail mix, street-network design and tourism flows. Through this approach, the calibrated models can be used to predict city-wide last-mile demand using only public inputs and offers a transferable, privacy-preserving template for evidence-based freight planning. By pinpointing the location and the land uses where demand concentrates, it supports targeted interventions such as micro-depots, locker allocation and dynamic curb-space management towards more sustainable and resilient urban-logistics networks. Full article

The COVID-19 pandemic has highlighted critical vulnerabilities in urban logistics systems, particularly in last-mile delivery. To enhance logistics resilience and efficiency, the Korean government has initiated an innovative project that repurposes idle spaces in subway vehicle bases within the Seoul Metropolitan Area into logistics centers. This study proposes a comprehensive multi-criteria evaluation framework combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to assess the suitability of ten candidate sites. The evaluation criteria span four dimensions, facility, geographical, environmental, and social factors, derived from the literature and expert consultations. AHP results indicate that geographical factors, especially proximity to urban centers and major logistics facilities, hold the highest weight. Based on the integrated analysis using TOPSIS, the most suitable locations identified are Sinnae, Godeok, and Cheonwang. The findings suggest the strategic importance of aligning infrastructure development with spatial accessibility and stakeholder cooperation. Policy implications include the need for targeted investment, public–private collaboration, and sustainable logistics planning. Future research is encouraged to incorporate dynamic data and consider social equity and environmental impact for long-term urban logistics planning. Full article

The rapid development of the logistics industry has underscored the urgent need for efficient and sustainable urban freight systems. As a core component of freight systems, heavy-duty trucks (HDT) have been researched regarding surface-level descriptive statistics of their heterogeneities, such as trip volume, frequency, etc., but there is a lack of in-depth analyses of the spatial interaction between freight travel and freight functional clustering, which restricts a systematic understanding of freight systems. Against this backdrop, this study develops a data-driven framework to analyze HDT behavioral heterogeneity and its spatial interactions with a freight functional zone in Shanghai. Leveraging the high-frequency trajectory data of nearly 160,000 HDTs across seven types, we construct a set of regional indicators and employ hierarchical clustering, dividing the city into six freight functional zones. Combined with the HDTs’ application scenarios, functional characteristics, and trip distributions, we further analyze the spatial interaction between the HDTs and clustered zones. The results show that HDT travel patterns are not merely responses to freight demand but complex reflections of urban industrial structures, infrastructure networks, and policy environments. By embedding vehicle behaviors within their spatial and functional contexts, this study reveals a layered freight system in which each HDT type plays a distinct role in supporting economic activities. This research provides a new perspective for deeply understanding the formation mechanisms of HDT trip distributions and offers critical evidence for promoting targeted freight management strategies. Full article

The integration of passenger and freight transport by urban–rural buses is an effective approach to address two critical issues: the inefficiency of parcel delivery services and the financial struggles of public transport operators. This paper studies the county–township–village station location-routing problem for the integration of passenger and freight transport by urban–rural buses, aiming to develop an efficient transport network by establishing rational stations and designing optimal operation routes. A three-level county–township–village station network is proposed for the integration of passenger and freight transport, and a mixed-integer linear programming model is developed, including the constraints of location, allocation, capacity, and routing. A comprehensive series of numerical experiments is conducted on a randomly generated dataset to evaluate the feasibility and advantages of the proposed model. Lastly, key managerial insights are discussed. Full article

Background: This paper proposes a conceptual framework for a multifunctional mobility hub (MMH) that co-locates shared e-mobility services, urban logistics, and charging/storage infrastructure within a single site. Aimed at high-density European cities, the MMH model addresses current gaps in both research and practice, where multimodal mobility services, logistics, and energy are rarely planned in an integrated manner. Methods: A mixed-methods approach was applied, including a systematic literature review (PRISMA), expert interviews, case studies, and a stakeholder workshop, to identify synergies across fleet types and operational domains. Results: The analysis reveals key design principles for MMHs, such as interoperable charging, the functional separation of passenger and freight flows, and modular, scalable infrastructure adapted to urban constraints. Conclusions: The MMH serves as a preliminary concept for planning next-generation mobility stations. It offers qualitative insights for urban planners, operators, and policymakers into how multifunctional hubs may support lower emissions, more efficient operations, and shared infrastructure use. Full article

Background: As e-commerce expands and delivery services diversifies, understanding the factors that shape consumer preferences becomes critical to designing efficient and sustainable urban logistics. This study examines how perceived walkability influences consumers’ preferences for shopping channels (in-store or online) and delivery methods (home delivery versus pickup points). Method: The analysis is based on structural equation modeling and utilizes survey data collected from 444 residents of Belo Horizonte, Brazil. Results: The findings emphasize the importance of walkability in supporting weekday store visits, encouraging pickup for online purchases and fostering complementarity between different modes of purchase and delivery services. Perceived walkability positively affects the preference to buy in physical stores and increases the likelihood of using pickup points. Educated men, particularly those living in walkable areas, are the most likely to adopt pickup services. In contrast, affluent individuals and women are less likely to forgo home delivery in favor of pickup points. Conclusions: The results highlight the role of perceived walkability in encouraging in-person pickup as a sustainable alternative to home delivery, providing practical guidance for retailers, urban planners, and logistics firms seeking to align consumer convenience with sustainable delivery strategies. Full article

Increasing road congestion is the main constraint that may influence the economic development of cities and urban freight transport efficiency because it generates additional costs related to delay, influences social life, increases environmental emissions, and decreases service quality. This may result from several factors, including an increase in logistics activities in the urban core. Therefore, this paper aims to define the relationship between the logistics sprawl phenomenon and congestion level. In this sense, we explored the literature to summarize the phenomenon of logistics sprawl in different cities and defined the dependent and independent variables. Congestion level was defined as the dependent variable, while the increasing distance resulting from logistics sprawl, along with city and operational flow characteristics, was treated as independent variables. We compared the performance of several models, including decision tree, support vector machine, gradient boosting, k-nearest neighbor, logistic regression and random forest. Among all the models tested, we found that the random forest algorithm delivered the best performance in terms of prediction. We combined both logistic regression—for its interpretability—and random forest—for its predictive strength—to define, explain, and interpret the relationship between the studied variables. Subsequently, we collected data from the literature and various databases, including transit city sources. The resulting dataset, composed of secondary and open-source data, was then enhanced through standard augmentation techniques—SMOTE, mixup, Gaussian noise, and linear interpolation—to improve class balance and data quality and ensure the robustness of the analysis. Then, we developed a Python code and executed it in Colab. As a result, we deduced an equation that describes the relationship between the congestion level and the defined independent variables. Full article

The impact of urbanization on the agricultural economy has long attracted scholarly attention. Taking China as a case, this study investigates the relationship between urbanization and agricultural development under the dual progress of urbanization and the rural revitalization strategy. Based on panel data from 31 mainland provinces, this paper measures agricultural economic efficiency using the global slack-based measure (SBM) model and employs quantile regression to systematically analyze the influence of various urbanization factors across different levels of agricultural efficiency. A Tobit regression model is further adopted for robustness checks. The results show that representative urbanization factors, such as the proportion of urban population and the prevalence of higher education, exert significant negative impacts on agricultural efficiency, particularly in regions with higher efficiency levels. Freight volume has a significantly negative effect in regions with medium and low efficiency, while freight turnover negatively impacts medium- to high-efficiency areas. In contrast, improvements in healthcare services and digital infrastructure are found to consistently enhance agricultural efficiency. Although the corporatization of agriculture is often regarded as a key outcome of urbanization, its efficiency-improving effect is not statistically significant in most models and is mainly concentrated in high-efficiency regions. Overall, the improvement in China’s agricultural economic efficiency relies more on direct support from the rural revitalization strategy, while rapid urbanization has failed to bring substantial benefits and has even led to structural negative effects. These adverse outcomes may stem from the rapid occupation of suburban farmland, increased logistics costs due to the relocation of agricultural activities, and the ineffective absorption of surplus rural labor. This study highlights the need for future urbanization policies in China to pay greater attention to the coordinated development of the agricultural economy. The methods and findings of this research also provide reference value for other developing regions facing similar urbanization-agriculture dynamics. Full article

The “carbon neutral power generation” policy of the European Union requires the phasing out of fossil fuel power plants. These plants still play a crucial role in the energy mix in many countries; therefore, efforts are put forward to lower their CO₂ emissions. The available solution for an existing coal plant is the implementation of biomass co-firing, which allows it to reduce twice its carbon footprint in order to achieve the level of natural gas plants, which are preferable on the way to zero-emission power generation. However the side effect is a significant increase in the bulk fuel volumes that are acquired, handled, and finally supplied to the power plant units. A necessary extension of the complex logistic system for unloading, quality tagging, storing, and transporting biomass may increase the plant’s noise emissions beyond the allowed thresholds. For a comprehensive assessment of the concept of expanding the power plant’s biofuel supply system (BSS), a discrete simulation model was built to dimension system elements and verify the overall correctness of the proposed solutions. Then, a dedicated noise emission model was built for the purposes of mandatory environmental impact assessment procedures for the planned expansion of the BSS. The noise model showed the possibility of exceeding the permissible noise levels at night in selected locations. The new simulations of the BSS model were used to analyze various scenarios of biomass supply with regard to alternative switching off the selected branches of the whole BSS. The length of the queue of unloaded freight trains delivering an average quality biomass after a period of 2 weeks is used as a key performance parameter of the BSS. A queue shorter than 1 freight train is accepted. Assuming the rising share of RESS in the Polish energy mix, the thermal plant’s 2-week average power output shall not exceed 70% of its maximum capacity. The results of the simulations indicate that under these constraints, the biofuel supplies can be sufficient regardless of the nighttime stops, if 50% of the supplied biomass volumes are delivered by trucks. If the trucks’ share drops to 25%, the plant’s 2-week average power output is limited to 45% of its maximum power. The use of digital spatial simulation models for a complex, cyclical-continuous transport system to control its operation is an effective method of addressing environmental conflicts at the design stage of the extension of industrial installations in urbanized areas. Full article

Electrifying urban last-mile logistics is an important step towards reducing carbon emissions which requires replacing conventional vehicles with low-carbon alternatives that offer comparable operational and cost characteristics. This study presents a methodology for evaluating the feasibility of electrifying an urban delivery fleet, using data from a major beverage company in Seville as a case study. Applying a fleet and route optimization algorithm for various vehicle combinations, we demonstrate that emerging electric vehicle options, combined with a redesigned fleet mix and an optimized routing, can already enable cost-efficient electrification of distribution activities in the city centre. Furthermore, our analysis suggests that full electrification of the company’s local distribution network may be possible by 2030, depending on the availability of larger electric trucks. Our results show that currently available electric vehicles can fully substitute conventional options in the case study context, with higher capital costs offset by lower energy costs in most cases. The electrification of urban logistics can yield significant environmental benefits, particularly if powered by a clean energy mix. Full article

The fast growth of global e-commerce has made cargo transportation and package delivery more important in cities. However, the limited resources for urban road traffic have made urban logistics distribution less efficient. The global movement toward green sustainability, energy conservation, and emission reduction has heightened awareness of the necessity to enhance urban mobility and transportation. This work further investigates the optimization of distribution hub locations based on subway systems, informed by research on urban distribution modes and the current state of underground logistics. This work presents two unique models: a metro-integrated evaluation model and a distribution hub location model, aimed at identifying the ideal subway logistics station and establishing the distribution center with minimal total logistics costs. A heuristic method, the jellyfish search algorithm (JS) in particular, is carefully explained in order to find a good answer for the model. From an empirical perspective, the district of Chaoyang in Beijing, China, was taken as a case to simulate the progress of identifying an ideal metro station as a city distribution hub, aimed at minimizing total logistical costs. The results indicate that the subway system can be used for city deliveries, and the proposed model and method are very useful for improving the location of delivery hubs in the city. Consequently, when subway facilities allow, we should fully utilize the extensive capacity of the subway transit system to enhance the efficient, environmentally friendly, and sustainable advancement of urban logistics. Full article

With the rapid acceleration of global urbanization and the advent of smart city initiatives, large metropolises confront the dual challenges of surging logistics demand and constrained surface transportation resources. Traditional surface logistics networks struggle to support sustainable urban development in high-density areas due to traffic congestion, high carbon emissions, and inefficient last-mile delivery. This paper addresses the layout optimization of a hub-and-spoke underground space logistics system (ULS) network for smart cities under stochastic scenarios by proposing an immune-inspired multi-objective particle swarm optimization (IS-MPSO) algorithm. By integrating a stochastic robust Capacity–Location–Allocation–Routing (CLAR) model, the approach concurrently minimizes construction costs, maximizes operational efficiency, and enhances underground corridor load rates while embedding probability density functions to capture multidimensional uncertainty parameters. Case studies in Beijing’s Fifth Ring area demonstrate that the IS-MPSO algorithm reduces the total objective function value from 9.8 million to 3.4 million within 500 iterations, achieving stable convergence in an average of 280 iterations. The optimized ULS network adopts a “ring–synapse” topology, elevating the underground corridor load rate to 59% and achieving a road freight alleviation rate (RFAR) of 98.1%, thereby shortening the last-mile delivery distance to 1.1 km. This research offers a decision-making paradigm that balances economic efficiency and robustness for the planning of underground logistics space in smart cities, contributing to the sustainable urban development of high-density regions and validating the algorithm’s effectiveness in large-scale combinatorial optimization problems. Full article