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.

The outward migration of urban freight infrastructure, known as logistics sprawl, has triggered severe spatial conflicts and NIMBY (Not in My Back Yard) resistance in host communities. While previous literature predominantly examines the operational optimization of these facilities, empirical investigations into the public’s social acceptance remain limited and often assume population homogeneity. This study investigates the multi-level determinants of social acceptance toward urban logistics facilities utilizing a survey of 1,507 residents in Gyeonggi Province, South Korea. Employing a multi-stage analytical framework, the research integrates a Binary Logistic Regression (BLR), a Multi-Level Latent Class Model (MLLCM), and a Cumulative Link Mixed Model (CLMM). The BLR results reveal a profound perceptual duality, where localized physical externalities and property value anxieties significantly deter acceptance, whereas anticipated economic benefits strongly drive public support. Moving beyond average estimates, the MLLCM uncovers six distinct latent attitudinal classes, demonstrating that socio-demographic factors, such as age and the presence of preschool children, act as powerful polarizing catalysts. Notably, the integration of regional spatial metrics exposes the phenomenon of "logistics sprawl fatigue". High physical concentrations of warehouses may contribute to shifting communities toward chronic apathy and significantly erode overarching residential satisfaction. These findings underscore the need for tailored community engagement strategies and regional-level zoning thresholds to foster socially sustainable urban freight environments.

Effective modal shift policies require accurate valuation of the external costs associated with freight transport. However, South Korea’s logistics policy currently relies on valuation parameters that have not been systematically updated since 2013. This study recalibrates the unit benefits of shifting freight from roads to rails using a hybrid estimation framework that combines bottom-up emission estimation with a top-down allocation of aggregate external costs, grounded in national statistics harmonized to a 2022 base year (with some inputs drawn from the most recent earlier survey years and price-adjusted to 2022) and link-level national traffic data (KOTI View-T 3.0). We quantify the reductions in average external costs per ton/kilometer across five impact domains: air pollution, climate change, noise, traffic accidents, and congestion. The analysis yields a total unit benefit of KRW 151.81 per ton/kilometer, a figure approximately 3.7 times the inflation-adjusted benchmark currently in use. A decomposition of this difference shows that updated emission inventories, accident statistics, and valuation parameters alone raise the unit benefit to KRW 69.6 KRW per ton/kilometer (approximately 1.7 times the benchmark), while the revision of the road freight traffic share, from the 3.5% assumed in earlier travel demand models to the 21.2% observed at the link level, accounts for the remainder. Scenario analyses covering the principal allocation and valuation assumptions place the total unit benefit between KRW 38.6 and 217.7 per ton/kilometer: the benefit exceeds the benchmark in every scenario except the single most conservative variant, which combines all unfavorable assumptions simultaneously—including the legacy freight share—and falls approximately 5% below it. These findings indicate that the parameters currently used in policy appraisal may materially underestimate the social benefits of rail freight under the assumptions adopted here. We recommend that policymakers utilize these updated unit values to recalibrate freight subsidies and investment appraisals to align with the 2050 Carbon-Neutral Strategy.