Search Results (194)

Last-mile delivery is constrained by manual cargo handling, which consumes a large share of the operating window and limits the number of delivery rounds per shift. This study evaluates the operational and economic effects of the Automated Horizontal Loading and Unloading System (AHLUS), a retrofittable in-vehicle technology that converts the cargo bed into an active handling platform using a belt conveyor and a movable bulkhead. Using daily records from two AHLUS-equipped one-ton trucks operated in a South Korean fresh-food network over four months, an interrupted time-series regression with Newey-West standard errors estimated the effect of the deployment process—encompassing driver adaptation and the dispatch reallocation enabled by the system’s enhanced handling capacity—while controlling for the pre-intervention trend. After stabilization, daily throughput was approximately 19.8 percent higher than the learning-phase baseline (approximately 14.8 percent relative to the model-implied counterfactual trend), a gain that was statistically significant, held for both drivers, and corresponded to an increase from two to three delivery rounds per shift. A transparent total cost of ownership model incorporating payload-loss, power, maintenance, and downtime yielded a base-case break-even point of 4.1 months, and a Monte Carlo simulation indicated a positive net benefit across all sampled parameter combinations under the assumed input distributions, with a median break-even point of 4.6 months. As an exploratory single-company field validation without an untreated control series, the study estimates the effect of the deployment as implemented in practice rather than the isolated effect of the hardware. The findings provide field-based evidence that in-vehicle handling automation can deliver measurable throughput and economic benefits in last-mile operations, pending confirmation in larger multi-company and multi-region deployments. Full article

With the continued electrification and digitalization of urban logistics, electric freight routing increasingly requires the coordinated consideration of customer time windows, vehicle capacity, limited battery range, and en-route charging. This study formulates an electric vehicle routing problem with time windows (EVRPTW) for smart-city electric freight and develops a multi-strategy improved ant colony optimization algorithm (IACO). The proposed model integrates customer service, route continuity, time windows, vehicle capacity, battery-energy propagation, and en-route charging. IACO combines a route–charging-state representation with feasibility-guided sweep-insertion initialization, max–min pheromone control, multi-representative guidance, reachable charging-station insertion, greedy feasibility repair, and 2-opt local search, forming a multi-stage search process that integrates global exploration, feasibility restoration, and local intensification. Computational experiments on an R-C benchmark scenario with 51 customers and 9 charging stations compare IACO with ACO, GA, TS, LNS, SA, PSO, and WOA over 100 independent runs under a common 300-iteration limit. Under the current experimental protocol, IACO records a representative generalized cost of 570.88, with reductions of 7.75–44.65% relative to the seven comparison methods, while its median CPU time is 28.42 s. These results demonstrate a clear solution-quality–computation trade-off and indicate the potential of IACO for plan-level electric freight routing and en-route charging coordination. Full article

Air pollution in urban and coastal regions is increasingly shaped by interactions among port operations, freight logistics, urban mobility systems, spatial development patterns, and governance arrangements. Although emission reduction technologies and regulatory measures have advanced significantly, their implementation frequently remains distributed across sector-specific institutional and operational domains. This paper introduces the concept of Air-Aware Port–City–Logistics Systems, proposing an integrated governance framework that treats air quality as a system-level outcome of linked maritime, logistics, urban transport, spatial, technological, and adaptive processes. Drawing on a structured interdisciplinary synthesis of literature in transport planning, maritime economics, logistics, environmental governance, air-quality management, and resilience, the study identifies key gaps in cross-sectoral coordination and in the governance of interdependencies influencing air-quality outcomes. The framework maps relationships among emission sources, transport and logistics flows, spatial configurations, population exposure, institutional coordination, technological and data systems, and adaptive capacity, thereby identifying potential intervention points across the port–city–logistics system. Its distinctive contribution lies not merely in combining these dimensions, but in organising them through a governance-oriented architecture in which monitoring, operational decisions, institutional responses, and adaptive learning are treated as interconnected processes. The framework also incorporates resilience and risk perspectives, emphasising adaptive governance approaches capable of responding to disruptions, changing demand patterns, climatic pressures, and other external environmental influences. By advancing a systems-based perspective, the paper contributes to air quality management and policy development through the integration of port–city interfaces, freight distribution networks, mobility planning, exposure considerations, and institutional decision-making within a unified analytical framework. As a conceptual framework, AAPCLS provides a transferable basis for future empirical application, contextual adaptation, and policy-oriented analysis rather than a validated operational model. Full article

The rapid growth of e-commerce has intensified last-mile delivery activities in urban areas, creating challenges for city logistics systems related to operational efficiency, congestion, and environmental impacts. In this context, understanding the factors that influence customer satisfaction with logistics operators is increasingly important, as mismatches between customer expectations and delivery service characteristics may lead to operational inefficiencies such as failed delivery attempts and repeated delivery rounds. This study proposes a machine learning framework for predicting customer satisfaction with postal and logistics operators in urban delivery systems using survey data on customer characteristics, preferences, and service perceptions. Several machine learning algorithms were developed and evaluated to identify the key determinants of customer satisfaction and assess their predictive performance. Beyond predictive accuracy, the study interprets customer satisfaction as an indicator of the alignment between customer expectations and delivery service configurations. Improved alignment may support service configurations that reduce delivery mismatches and repeated delivery attempts, which are recognized as a significant source of additional transport activity in urban freight systems. By identifying customer segments whose expectations are not adequately addressed by existing delivery services, the proposed framework can support more informed service design and operational decision-making. From a city logistics perspective, the potential reduction in failed deliveries and repeated delivery rounds may contribute to lower vehicle kilometers travelled, congestion, energy consumption, and emissions associated with urban freight transport, although these operational and environmental indicators were not directly measured in this study. The proposed approach therefore provides a data-driven decision-support tool that can help operators improve service quality and serve as a basis for future integration with operational and environmental indicators in sustainable last-mile delivery planning. Full article

With growing emphasis on green and low-carbon development and rising urban delivery demand, electric vehicles (EVs) have been increasingly adopted in logistics distribution systems. However, their limited driving range, relatively long charging durations, and the limited capacity of charging stations pose substantial challenges to real-world electric delivery operations. When multiple vehicles arrive at a station with a limited number of chargers, queueing delays may disrupt subsequent customer service and increase total operating costs. To address this issue, this study investigates an electric vehicle routing problem with capacitated charging stations and queueing delays. A mixed-integer linear programming model is formulated, and an enhanced adaptive large neighborhood search (ALNS) algorithm is developed to efficiently solve medium- and large-scale instances. In the proposed model, each vehicle visit to a charging station is represented as a charging event, while finite station capacity is enforced through charging-event assignment and temporal non-overlap constraints. Computational results show that the enhanced ALNS matches the proven optimal solution for the 10-customer instance. For the 15- and 20-customer instances, the best objective values obtained by the enhanced ALNS were 0.39% and 4.84% lower than the corresponding time-limited Gurobi incumbents, respectively. For the 30-, 50-, and 100-customer instances, the enhanced ALNS consistently generates feasible solutions within the prescribed computational budget, whereas Gurobi does not obtain a feasible incumbent within substantially longer time limits. Compared with the baseline ALNS, the enhanced version generally achieves lower mean objective values and more favorable convergence behavior. Sensitivity analyses further show that increasing the number of chargers and improving the charging rate can reduce queueing delays and total charging duration. The proposed approach provides practical decision support for reliable and sustainable urban electric freight operations. Full article

Urban last-mile delivery is a rapidly growing source of city-centre emissions, and decarbonizing it without eroding service quality has become imperative for climate goals. Operators are turning to multimodal systems that integrate road vehicles, off-peak metro freight, and electric drones—yet the optimal delivery channel varies dynamically with location and time. Current RL-based schedulers handle constraints via manually tuned penalty weights, lacking formal safety guarantees, and the feasibility of online carbon-cap enforcement under partial observability remains an open question. To address this, we model the problem as a Constrained Markov Decision Process (CMDP) and propose a demonstration-regularized Lagrangian deep RL algorithm. Our approach learns an online policy that is model-free at deployment—it controls emissions in expectation against a hard carbon budget, makes per-order decisions using only state observations, and operates without an emission model at test time (the demonstrator used at training time does access the emissions model, so “model-free” refers strictly to the deployment phase). Experiments on synthetic benchmarks and a Nanjing-inspired scenario—grounded in real metro topology and population-weighted demand—show that our policy achieves emissions within 1.3% of the offline optimum. It robustly tracks a ±17% carbon-budget band across a threefold daily volume range and a threefold city-scale range, with zero per-instance tuning. By contrast, a standard PPO with fixed penalty weights consistently degrades to single-mode selection. Our findings suggest that hard carbon budgets can be controlled in expectation online at modest cost—a step toward operator-facing low-carbon logistics whose average emissions honour a binding carbon budget, though external validation on operational data and a risk-sensitive formulation that upgrades this average control into per-day compliance are still required before deployment. Full article

This study investigates the key drivers and barriers influencing the adoption of electric vehicles (EVs) in urban freight logistics, using Lithuania as a case study. An integrated methodological framework combining Delphi, Fuzzy logic, DEMATEL, and System Dynamics is applied to identify critical factors and analyse their interdependencies. Four main drivers are identified: infrastructure, acquisition costs, technological development, and policy measures. Expert evaluations are transformed into fuzzy values to quantify factor importance, which are then incorporated into a dynamic simulation model to assess EV adoption and CO₂ emission trends. In addition to baseline scenarios, extreme scenario analysis is conducted to evaluate system sensitivity to economic, technological, and policy changes. The results reveal strong nonlinear relationships between factors and highlight the importance of their balanced development. The findings suggest that rapid EV adoption in urban logistics requires a coordinated approach integrating infrastructure expansion, financial incentives, technological progress, and policy support. The study provides practical insights for policymakers and logistics companies aiming to accelerate sustainable urban transport transitions. Full article

Urban logistics supports retail, public services, healthcare, construction, and household consumption, but delivery decisions often fail to account for the social costs imposed on congested roads, curbside space, air quality, noise exposure, safety, and public-space use. This study addresses the decision-maker mismatch that arises when the vehicle operator physically generates an externality while receivers, shippers, platforms, building managers, or consumers control delivery timing, shipment fragmentation, service level, fleet choice, and receiving conditions. It develops a Pigouvian policy framework that integrates dynamic road-user charging, curbside pricing, emission-based instruments, off-hour delivery incentives, clean-vehicle support, consolidation incentives, and revenue recycling. The study combines a structured narrative synthesis, decision-maker mapping, an illustrative Korean urban logistics scenario, cost–benefit comparison, and deterministic sensitivity screening. Under the stated scenario assumptions, a carrier-only peak charge reduces monetized daily external costs by only 1.3%, whereas a combined road-and-curbside package reduces them by 12.5%, and an integrated Pigouvian package reduces them by 23.6%. Sensitivity results preserve this ranking. The paper contributes a transferable policy design architecture for internalizing urban logistics externalities while maintaining freight functionality and stakeholder acceptability. Full article

Urban freight externalities are increasingly addressed through regulation policies targeting both vehicle access and loading zones management. While urban vehicle access regulations and loading and unloading zone management are widely applied, existing research has largely regarded them as separate policy domains, overlooking their potential interdependence within urban freight governance. This study develops an exploratory comparative typology of UVARs and loading zone management across selected global cities. A hierarchical clustering method was applied to a harmonized set of indicators to identify distinct urban freight governance typologies. The UVAR clustering analysis was conducted on 39 cities with freight-related UVARs, while the loading zone clustering analysis was conducted on 39 cities with formal loading management zones. The cross-analysis suggests some co-occurrence patterns between UVARs and loading zone typologies. But the chi-square test does not provide statistical evidence of dependence. Therefore, this study can be interpreted as an exploratory mapping of regulatory configurations. The findings provide a comparative basis for future research linking urban freight regulatory typologies with environmental, operational, economic, and social performance indicators. Full article

Urban freight distribution is essential for supplying commercial activities, but it also increases pressure on curb space, vehicular circulation, pedestrian movement, and public space management, especially in medium-sized cities where dedicated loading and unloading infrastructure is often limited. Although recent literature emphasizes the need for data-driven urban logistics planning, empirical evidence from intermediate Latin American cities remains scarce. This study develops and applies a spatial-operational framework to characterize urban freight distribution, identify patterns of conflict and informality, estimate loading and unloading bay requirements, and prioritize intervention areas in a medium-sized city. A quantitative, observational, exploratory–descriptive, and correlational design was applied, based on 642 georeferenced loading and unloading operations recorded through a digital field survey. The analysis integrated data cleaning, descriptive and inferential statistics, logistic models, an operational sustainability risk/pressure index, DBSCAN spatial clustering, logistics pressure and sustainable transport priority indices, and a capacity model based on average daily operations. The results revealed spatial concentration of logistics activity, a predominance of light trucks, frequent use of paid parking areas and roadways, and a high presence of operational conflicts. The study provides a replicable and planning-oriented framework for prioritizing curbside management interventions for sustainable urban freight distribution in medium-sized Latin American cities. Full article

The rapid electrification of urban freight transport requires new optimization approaches that jointly consider logistics operations and energy system constraints. The problem is formulated as a mixed-integer linear programming (MILP) model that captures the interdependencies between vehicle operations, battery constraints, charging infrastructure availability and the temporal variability of photovoltaic energy. A multi-objective structure is adopted to minimize total energy costs and CO₂ emissions while maximizing the utilization of locally generated renewable energy. The model is evaluated using scenario-based simulations under three solar integration levels (0%, 30% and 60%). The results demonstrate that integrating solar energy into routing and charging decisions significantly reduces grid dependency, lowers emissions and improves overall system efficiency. Three types of charging stations are considered in the study (S1, S2, and S3), differing in photovoltaic (PV) energy penetration levels, ranging from conventional grid-based charging (S1) to high renewable integration stations (S3). The quantitative analysis reveals a clear resource and emission structure across the simulated scenarios. Incorporating charging stops grid-wide increases the total distance from theoretical routes to real tracks with stops to overcome the 120 kW battery limit. However, the integration of solar energy significantly alters the system’s environmental costs: total CO₂ emissions drop non-linearly by 33.4%, decreasing from 364.64 kg in the ‘Low Sun’ scenario to 243 kg in the ‘High Sun’ scenario. Furthermore, the localized impact shows that utilizing pure grid energy (S1) results in 405 kg of CO₂, while maximizing solar integration up to 60% (S3) reduces emissions to 162 kg. The sensitivity analysis showed how varying the share of solar energy at the two main stations (S2 and S3) affects the total CO₂ emissions, while maintaining the same routes. Three scenarios were examined: low (10% and 30%), base (30% and 60%) and high (50% and 90%) solar energy shares. As the share of solar energy in the system increases, a clear effect of emission reduction and energy cost optimization is observed. Full article

The electrification of urban freight is a central pathway for advancing China’s dual-carbon agenda, yet the spatial and temporal mismatch between charging supply and logistics demand remains a major bottleneck. Using Shanghai as a case study, this paper develops an integrated framework of hotspot identification, mechanism interpretation, and planning response for electric logistics vehicle (ELV) charging demand. Based on the operating records of more than 1200 pure electric logistics vehicles in Shanghai from 1 March to 30 November 2023, 85,367 valid charging events were extracted. ST-DBSCAN is used to detect charging demand hotspots, and a negative binomial model is employed to examine their determinants. The results show that charging demand is highly differentiated in space and time, following a pattern of daytime concentration in core logistics areas and nighttime dispersion toward peripheral parking and recharging spaces. Initial state of charge, daily mileage, logistics point of interest (POI) density, and road network density are all significantly associated with hotspot intensity, while the effects of time vary across daytime and nighttime charging contexts. The predominance of slow charging, together with a pronounced midday charging peak (12:00–17:00), points to a potential fast-charging pressure of fast-charging capacity in major logistics nodes. Based on these findings, the paper proposes targeted recommendations for hub-oriented fast-charging deployment, fleet–charging coordination, and data-driven governance. The study provides empirical evidence for improving the spatial planning and refined governance of urban freight energy infrastructure. Full article

Transportation emissions raise critical environmental justice concerns, yet most studies overlook the distinct inequity patterns between passenger and freight systems. This study aims to compare the spatial disparities and driving mechanisms of exposure injustice from passenger and freight emissions at the U.S. county level. Using 2020 county-level cross-sectional data, we construct an environmental injustice index (EII) and apply spatial autocorrelation analysis, a two-stage multi-task TabNet model, and SHAP interpretation to identify spatial divergence, key determinants, and heterogeneous effects of urban compactness. Results show that passenger EII features continuous regional clustering, while freight EII concentrates along corridors and nodes with limited spatial overlap. Passenger injustice is driven by population density, auto dependence, and public transit, whereas freight injustice is dominated by truck intensity, freight network location, and logistics employment. Urban compactness has dual impacts on passenger injustice but consistently exacerbates freight injustice. These findings highlight the necessity of differentiated governance and provide empirical support for equitable low-carbon transport policies. Full article

Sustainable urban logistics requires infrastructure that can support routine low-carbon freight delivery while maintaining emergency supply capacity under disruptions. However, existing underground logistics system studies mainly focus on routine freight efficiency and network feasibility, whereas emergency logistics research is largely based on surface transport systems. Limited attention has been paid to the integrated design and operational validation of dual-use underground logistics networks under uncertain routine and emergency demand. To address this gap, this study proposes a dual-use underground logistics system (DULS) framework that combines robust layout optimization with dynamic simulation. A multi-echelon network consisting of supply centers, primary nodes, secondary nodes, and demand points is constructed. Candidate primary nodes are screened using an entropy-weighted TOPSIS method, and a Wasserstein-based distributionally robust optimization model is formulated to jointly determine node location, resource allocation, and freight paths under demand uncertainty. A hybrid heuristic is developed to solve the model, and an AnyLogic-based discrete-event simulation model is used to evaluate operational performance under different demand-generation patterns and train operation strategies. In the Nanjing case, the optimized DULS includes 19 primary nodes and 72 secondary nodes, achieves an emergency-demand fulfillment rate of 84.84%, and keeps the average end-to-end emergency supply time within 4 h. Cross-station operation performs better than the all-stop mode in both transport time and deprivation cost. An ex-post operational emission comparison further indicates that the DULS can reduce road-based freight emissions by 60.20% under routine operations. The proposed framework provides methodological support for planning sustainable dual-use underground logistics infrastructure serving both routine freight delivery and emergency supply. Full article

Large language models (LLMs) have shown promise in intelligent transportation systems, but their direct use in constrained route planning remains unreliable because such tasks require exact numerical consistency and strict compliance with operational constraints. This challenge is particularly important in urban freight and logistics, where routing errors can reduce efficiency and undermine sustainability. To address this issue, this study proposes a supervised fine-tuning (SFT) framework that specializes a general-purpose LLM as an orchestration agent for route planning. Instead of generating routes directly, the model translates natural-language requests into structured function calls that invoke deterministic optimization solvers for the Traveling Salesperson Problem (TSP), Capacitated Vehicle Routing Problem (CVRP), and Vehicle Routing Problem with Time Windows (VRPTW). Experiments on a controlled synthetic benchmark with thousands of routing instances show that direct generation is ineffective for constrained routing, while tool augmentation substantially improves reliability. More importantly, SFT further strengthens function-calling performance, especially on the most challenging VRPTW task, where the overall success rate of the 8B model increases from 0.408 in the zero-shot setting to 0.792 after fine-tuning. The fine-tuned 8B model also outperforms a much larger zero-shot 235B model while requiring far fewer computational resources. These findings indicate that reliable LLM-based transportation decision support is better achieved by combining compact language models with deterministic optimization tools rather than relying on larger models for direct route generation, offering a lightweight and more sustainable path for real-world logistics deployment. Full article