Search Results (740)

Last-mile delivery routing requires simultaneous control of distance, travel time, service deadlines, vehicle capacity, workload balance, operating cost, emissions, and uncertainty. This study provides a controlled low-budget comparison of ten classical metaheuristics for a LaDe-calibrated stochastic CVRPTW. The GA, DE, PSO, ACO, ABC, SA, GWO, WOA, TLBO, and JAYA used the same random-key representation, capacity-aware split decoder, repair rules, CRN scenarios, and exactly 50 objective evaluations per run. The experiment comprised 1065 fixed design conditions, 30 seeded runs per algorithm-condition cell, and 319,500 metaheuristic runs, with NNS, Clarke–Wright Savings, and OR-Tools Guided Local Search as benchmarks. PSO achieved the lowest grand-mean weighted logistics cost, whereas the WOA achieved the lowest median and best Friedman mean rank. The grand-mean difference between the WOA and PSO was only 125.2 objective units, and the context winner changed across the three cities and five customer-size levels (PSO: 4, WOA: 2, and JAYA: 2). Accordingly, corrected p-values are treated as conditional design diagnostics, while practical interpretation prioritises paired magnitude, stratified consistency, feasibility, and benchmark proximity. A design-stratified sensitivity analysis showed that the preferred method changed across cities and customer sizes. Crucially, every metaheuristic and benchmark recorded 0% full feasibility under the common evaluator; capacity- and lateness-violation profiles are therefore elevated to primary outcomes, and no algorithm is claimed to be operationally superior. The 50-evaluation protocol is interpreted as an early-budget screening regime rather than evidence of asymptotic convergence. The results show that algorithm choice is conditional on the tested data, weights, problem sizes, uncertainty settings, and evaluation budget and that feasibility-preserving decoding is more important for deployment than small differences in penalised objective value. Full article

The expansion of e-commerce and last-mile food delivery has created an increasingly relevant but understudied context for food safety surveillance. However, the occurrence of enteric viruses in ready-to-eat (RTE) foods within this distribution context remains largely unexplored. This study aimed to investigate the presence of enteric viruses in multi-component RTE salad bowls obtained through last-mile delivery. The study was designed to characterize the virological status of products received through this distribution pathway, without attributing any detected contamination specifically to the delivery process itself. A total of 30 salad samples were collected in two independent batches over a two-month interval via a third-party e-commerce platform and delivered directly to the laboratory. Virus concentration was performed using a method based on ISO 15216-1:2017, followed by RNA extraction and reverse transcription quantitative polymerase chain reaction (RT-qPCR) detection of norovirus (NoV) genogroups I (GI) and II (GII), hepatitis A virus (HAV), hepatitis E virus (HEV), human astrovirus (HAstV), and rotavirus (RV). HEV-target RT-qPCR signals were observed in 5 of 30 samples (16.7%); however, all positive signals corresponded to levels below the estimated limit of quantification (LOQ). No RT-qPCR amplification was observed for the other targeted viruses. This study provides initial occurrence data for enteric viruses in multi-component RTE meals obtained through last-mile delivery and supports the inclusion of this food category and emerging distribution pathways in foodborne virus surveillance. Full article

Estimated time of arrival (ETA) models for last-mile delivery are commonly evaluated within the same data source, leaving their reliability under cross-dataset distribution shift insufficiently characterized. This study evaluates internal generalization, independent external transfer, limited target-domain supervision, support-set sensitivity, and uncertainty calibration using five LaDe-D cities as the source domain and an independent planned-versus-actual route dataset from two countries as the target domain. CatBoost, LightGBM, XGBoost, and an uncertainty-aware neural reference model (DG-UCETA) were evaluated under a harmonized 21-feature Common-Core representation. External zero-shot MAE reached 150.76–185.47 min, despite substantially lower internal errors. A predefined 1% target-support condition reduced MAE to 57.60–64.87 min, although five independent support-set selections revealed substantial instability in this ultra-low-data regime; performance became markedly more stable at 5–10% support. A matched target-only control further showed that source pretraining improved MAE in only 4 of 16 model-support combinations, indicating that much of the observed recovery resulted from exposure to labeled target-domain data rather than from source initialization itself. Grouped feature ablation also localized the Common-Extended negative-transfer effect primarily to speed-derived variables, with speed-history features increasing external MAE by 28.02 min relative to Common-Core. Conformal recalibration improved coverage but did not correct systematic point-prediction bias. These findings indicate that representative target-domain supervision, feature compatibility, and adaptation stability are more critical to reliable cross-domain ETA deployment than architectural complexity alone. Full article

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

Truck–multi-drone collaborative delivery can reduce last-mile costs, but fleet sizing and routing are often optimized separately, making it difficult to match resources with demand under a delivery-period constraint. This study addresses the scenario of collaborative delivery involving multiple trucks and multiple drones by constructing a two-stage optimization framework that integrates fleet sizing and route planning. In the first stage, queueing models and continuous approximation are employed to determine the initial configuration of trucks and drones based on demand intensity and delivery cycle constraints. The second stage introduces continuous drone delivery and cross-vehicle retrieval to enhance the flexibility of truck–drone collaboration; while optimizing collaborative routes, the framework adjusts the allocation of trucks and drones—adding or reducing resources based on route feasibility and equipment utilization—thereby achieving the joint optimization of transport capacity and collaborative routes with the objective of minimizing total system costs. A node–resource–flow-separated three-chain encoding and an adaptive large neighborhood search–simulated annealing algorithm are designed to solve the model. Multi-scale numerical experiments show that, compared with four simplified fleet-sizing strategies, the proposed framework achieves average cost savings of 15.4–15.5% for medium- and large-scale instances. The results reveal an economic saturation point of the delivery period that shifts with node scale and a non-monotonic relationship between fleet size and coordination efficiency. The framework supports demand-driven fleet configuration and provides operational guidance for cost-effective truck–drone last-mile delivery. Full article

Urban micromobility systems have become increasingly important for short-distance and last-mile travel, particularly with the growing use of electric bicycles in dense urban environments. However, e-bike performance is not determined only by distance or travel time; road slope, segment direction, rolling resistance, aerodynamic drag and regenerative braking potential also affect energy demand. This paper develops a GIS-based, segment-level framework for mapping slope-dependent energy consumption and regenerative energy recovery in an urban e-bike network. Road segments were enriched with length, elevation-difference and slope information, and energy indicators were calculated using a simplified physics-based model. Energy consumed, energy regained, and net energy values were visualized as GIS thematic maps. The results show that energy performance is spatially heterogeneous and strongly sensitive to slope direction. The proposed workflow provides a practical spatial decision-support layer for energy-aware micromobility planning, route evaluation and sustainable urban mobility assessment. Full article

Cities are increasingly addressing mobility challenges by restricting road traffic, particularly traditional road vehicles that generate greenhouse gas emissions. As an alternative, unmanned aircraft systems (UASs) are emerging as a promising solution for future mobility, offering fast, quiet, cost-effective and environmentally friendly operations. For last-mile delivery, small drones operating at low altitudes are considered especially promising and are already being deployed in some urban areas. According to the EU Drone Strategy 2.0, drone services could generate a market of 14.5 billion and create 145,000 jobs in Europe by 2030. As this sector is still in its early stages, there is a significant uncertainty about the optimal organisation of urban air traffic. In this paper we present a realistic prognosis of how unmanned traffic over a city will utilise the urban very-low-level airspace and assess it using two concepts of operations, with non-structured or structured airspace, both aiming to facilitate the coexistence of competitors sharing the same urban low level airspace. The two concepts are evaluated with delivery operations at scale for a large and densely populated European city. Results for a normalised scenario of 3500 daily operations show that structured airspace produces more conflicts than non-structured airspace (e.g., 316 vs. 54, respectively), and that only for the non-structured airspace can all conflicts be solved with a simple strategic altitude reassignment (vs. 10% of unresolved conflicts for structured airspace). The artificial organisation of slim urban airspace may limit the scalability of business delivery while not reducing the conflict rate. Full article

Unmanned aerial vehicles (UAVs) are increasingly considered as enabling technologies for last-mile delivery, emergency medical response, and smart-city applications. However, drone-based logistics, emergency communication, sensing activities, and future aerial mobility are often addressed as separate research domains. This article proposes a four-dimensional planning framework for drone-enabled mobility, integrating goods, information, sensing, and human mobility within a unified conceptual structure. The framework is developed through a literature-informed conceptual analysis and previous applied research experiences related to drone-assisted logistics and emergency communication. Goods mobility includes parcel delivery, medical logistics, emergency supply transport, and hybrid operational models involving trucks, public transport, depots, and micro-hubs. Information mobility refers to the use of drones as mobile communication tools for emergency warnings, citizen interaction, drone-to-infrastructure communication, and infomobility services. Sensing mobility concerns traffic monitoring, environmental observation, disaster mapping, crowd monitoring, and infrastructure inspection. Human mobility is considered as an emerging extension related to urban air mobility (UAM), electric vertical take-off and landing (eVTOL) systems, and low-altitude aerial corridors. Cross-cutting issues such as energy autonomy, solar-assisted drones, multimodal integration, safety, communication, regulation, sustainability, and public acceptance are discussed. The proposed framework provides a structured basis for assessing drones as components of sustainable, resilient, and multimodal mobility systems. Full article

Urban passenger hubs, such as airports and railway stations, generate concentrated last-mile demand from arriving passengers to spatially dispersed urban destinations. Fluctuating passenger arrivals and changing vehicle availability can create a mismatch between accepted demand and available service capacity. This paper aims to coordinate zone-level pricing and vehicle dispatching, so that fare-responsive accepted demand can be better aligned with available vehicle resources, while balancing operator financial performance and service reliability. Under the zonal pricing scheme, the transit operator determines a quoted zone-level fare for each service zone at every decision epoch. Newly arriving service requests accept the service when the quoted fare does not exceed their maximum acceptable per-passenger fare, after which the fare is committed. A rolling-horizon optimization model jointly determines zone-level fares and dispatching plans as request states and vehicle states evolve over time. The fare discretization property reduces the continuous pricing decision to a finite candidate zone-level fare selection problem, and a customized Rolling-Horizon Adaptive Large Neighborhood Search (RH-ALNS) algorithm is developed to solve the resulting problem efficiently. Case studies based on Nanjingnan Railway Station in Nanjing, China, demonstrate the operational value of coordinating pricing and dispatching decisions. In the baseline case, the proposed method achieves a passenger service rate of 76.75%, an accepted-passenger fulfillment rate of 96.07%, and an operating surplus of 1.145 CNY per passenger-kilometer. Holding the RH-ALNS dispatching method fixed, dynamic zonal pricing increases the objective value by 4.39%, the operating surplus per passenger-kilometer by 5.46%, and accepted-passenger fulfillment by 3.63 percentage points relative to fixed zonal fares. The findings indicate that coordinating dynamic zonal pricing with vehicle dispatching can better align accepted demand with available vehicle resources and provide practical guidance for designing reliable, resource-efficient, and financially balanced hub-based demand-responsive last-mile transit services. Full article

As China’s dominant urban housing form, gated residential communities deploy layered spatial access controls governing who may enter and move through neighbourhood space. While neighbourhood social sustainability has attracted substantial scholarly attention, how micro-spatial governance arrangements affect the inclusiveness of residential built environments toward essential service workers remains underexplored. Drawing on Lefebvre’s theory of the production of space, this study uses food delivery riders—who navigate gated community access controls dozens of times daily—as an analytical lens to evaluate how neighbourhood spatial governance shapes social inclusiveness. Vignette-based survey data from 445 riders across 157 cities in 27 Chinese provinces were analysed using structural equation modeling. Results show that cumulative anxiety from procedural delays, detours, and elevator waiting is the dominant pathway through which spatial governance undermines riders’ perceived spatial inclusion—operationalised through occupational dignity indicators—with elevator-based spatial stratification identified as a concrete, low-cost intervention target within the spatial-channeling mechanism. Technology-mediated access partially mitigates face-to-face exclusion but leaves underlying spatial inequalities intact. Demographic invariance across all tested variables confirms that diminished inclusion arises from the governance regime itself. The findings position neighbourhood spatial governance as a measurable dimension of urban social sustainability—one concretely testable through the experience of essential service workers—and identify low-cost built-environment interventions for inclusive residential design. While the study focuses on riders as a single user group, the analytical framework is transferable to evaluating how residential built environments accommodate diverse non-resident populations. Full article

Personal mobility (PM) devices have expanded rapidly as an urban transport mode supporting short-distance travel and first- and last-mile access to public transport, while conflicts involving PM users, pedestrians, and other road users have emerged as an important traffic-safety concern. This study reconstructed police-reported PM crash records from Seoul for 2017–2024 into a crash-level dataset of 2398 crashes and examined changes in crash composition and monthly crash-count trajectories around the strengthening of PM safety regulations on 13 May 2021. Grouped-binomial models using monthly outcome and non-outcome counts were treated as the primary analysis. The descriptive PM–pedestrian share increased from 42.9% before the regulation to 49.7% after the regulation. However, the grouped-binomial models did not identify statistically significant PM–pedestrian or severe-or-fatal level or slope changes. The only statistically significant post-regulation composition term in the full-period model was a declining late-night slope; this term was not significant under the conservative month-level HC3 covariance check in the January 2019 sensitivity window. Segmented Poisson models were retained as secondary, descriptive, and exploratory analyses. Observed post-regulation counts were lower than the trajectory obtained by extrapolating the pre-regulation trend; however, the counterfactual became implausibly large within two to three years, so the model could not distinguish a regulatory discontinuity from the natural deceleration or saturation of PM diffusion and other concurrent temporal changes. Among crashes with known helmet-use status, no statistically detectable pre/post change was observed, and license type was not recorded before the regulation. The evidence therefore does not establish either a beneficial or adverse causal effect of the regulation. PM safety policy should combine user-oriented enforcement with multi-indicator monitoring, pedestrian-conflict management, continuous and clearly separated PM travel space, and targeted monitoring of late-night single-vehicle crashes. Full article

The rapid growth of e-commerce has intensified last-mile delivery challenges in Thailand, where rising parcel volumes contribute to failed deliveries and increased carbon emissions. Smart parcel lockers offered a promising solution, though their adoption depends on consumer acceptance. However, existing research has not sufficiently examined how trialability, performance expectancy, and perceived risk operate alongside core TAM beliefs within an integrated framework, particularly in emerging markets. This study extends the Technology Acceptance Model (TAM) with constructs from Diffusion of Innovation (DOI) theory and the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the determinants of smart parcel locker adoption, incorporating trialability, performance expectancy, and perceived risk. A quantitative survey was conducted with 397 Thai consumers with prior online shopping and parcel delivery experience. Data were analyzed using covariance-based structural equation modeling (CB-SEM). The model explained 79.3%, 94.3%, and 82.7% of the variance in perceived ease of use, attitude, and intention. Trialability is the strongest predictor, working through perceived ease of use, while attitude and performance expectancy together drove intention. Contrary to traditional TAM, perceived usefulness did not significantly affect attitude, and perceived risk had no significant effect. These findings suggest that, for simple self-service delivery technologies, first-hand experience is more influential than emphasizing usefulness or safety concerns. This contributes to technology acceptance theory and offers practical guidance to increase smart parcel locker usage. Full article

With the rapid growth of global e-commerce and the shift towards online consumption among university staff and students, the volume of campus courier services has experienced explosive growth, and the quality of last-mile services has become a key factor influencing the satisfaction of staff and students with campus life. Therefore, this paper constructs a dedicated evaluation system for campus courier service quality comprising six dimensions and 23 indicators, based on the Service Quality (SERVQUAL) and Logistics Service Quality (LSQ) models and incorporating data mining from online reviews. Empirical research was conducted using Structural Equation Modelling (SEM) and the Fuzzy Comprehensive Evaluation (FCE) method. The results indicate that the overall quality of campus courier services in China currently stands at a moderately satisfactory level. Based on the evaluation results and an analysis of weaknesses, targeted optimization and improvement strategies are proposed, providing both theoretical and practical support for enhancing the quality of campus courier services. Full article

The Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP) are two classical combinatorial optimization problems. In recent years, their drone-assisted variants, the Traveling Salesman Problem with Drones (TSP-D) and the Vehicle Routing Problem with Drones (VRP-D) have attracted growing attention. Generally, these problems are solved using exact algorithms or metaheuristic algorithms. However, as the problem complexity increases and the scale of instances grows, these approaches often become less efficient. In this paper, we propose a reinforcement learning method with a shared attention encoder and a hierarchical dual-decoder architecture, where truck–drone coordination is achieved by first decoding the truck’s next node and then conditionally decoding the drone action. To further explore the solution space of large-scale instances, the proposed method adopts a multi-rollout learning strategy. We conducted experiments on large-scale TSP-D and VRP-D instances, and the results show that this model outperforms traditional metaheuristic algorithms in terms of both solution quality and computational efficiency. Full article

High-density central business districts (CBDs) often appear highly walkable because they combine transit accessibility, functional density and active frontages. Yet their pedestrian environments may lose usability under routine disturbances such as crowding, e-bike encroachment, blind-path obstruction and effective-width erosion. This study reframes walkability as baseline pedestrian capacity and everyday resilience as the ability of that capacity to remain safe, continuous, inclusive and comfortable under ordinary urban stressors. Taking Beijing Guomao as a case, we integrated a field street audit, sidewalk-width measurement, route timing, traffic-interference observation, POI-based vitality assessment and 145 valid questionnaires. The objective indicator table produced a capacity-oriented score of 0.868 on a 0–1 scale, while the perception index was 3.17/5. The gap was explained by weak disturbance-sensitive indicators: non-motorized vehicle intrusion (0.46), blind-path continuity (0.60), motor-vehicle encroachment (0.67) and acoustic comfort (0.72). Field measurements showed mean physical and effective sidewalk widths of 3.62 m and 2.36 m, respectively. The mean section-level width loss was 30.9%, while the maximum loss of 88.2% occurred at one severely obstructed section, where the effective width was 0.50 m. The findings suggest that CBD pedestrian improvement should shift from facility provision to operational resilience through width recovery, micromobility governance, barrier-free continuity and last-mile route integration. Full article