Leveraging the passenger and freight spatiotemporal flow to optimize metro passenger-freight mixed transportation: a new mode for urban logistics systems

AI technologies are increasingly applied to optimize operations in both port and urban logistics systems, yet integration across the full maritime city chain remains limited. The objective of this study is to assess, using a simulation-based experiment, the impact of an AI-orchestrated control policy on the performance of port–city logistics relative to a baseline scheduler. The study proposes an AI-orchestrated approach that connects autonomous ships, smart ports, central warehouses, and multimodal urban networks via a shared cloud control layer. This approach is designed to enable real-time, cross-domain coordination using federated sensing and adaptive control policies. To evaluate its impact, a simulation-based experiment was conducted comparing a traditional scheduler with an AI-orchestrated policy across 20 paired runs under identical conditions. The orchestrator dynamically coordinated container dispatching, vehicle assignment, and gate operations based on capacity-aware logic. Results show that the AI policy substantially reduced the total completion time, lowered truck idle time and estimated emissions, and improved system throughput and predictability without modifying physical resources. These findings support the expectation that integrated, data-driven decision-making can significantly enhance logistics performance and sustainability in port–city contexts. The study provides a replicable pathway from conceptual architecture to quantifiable evidence and lays the groundwork for future extensions involving learning controllers, richer environmental modeling, and real-world deployment in digitally connected logistics corridors.

Agri-food supply chains are important for national food security and economic stability but remain highly vulnerable to multiple natural and socioeconomic shocks. This study examines how such compound shocks affected transport and trade of the US agri-food supply chain from 2018 to 2022. Using the Freight Analysis Framework database, we quantify temporal and spatial changes in domestic, import, and export agri-food flows, and apply a weighted node-degree network analysis to assess regional resilience during this time period, which included the US-China trade war, COVID-19 pandemic, and severe flood and drought events. Our results identify the locations and commodities that were most impacted and resilient to shocks. For example, Midwestern agricultural hubs were severely affected during floods, while urban logistics centers exhibited prolonged recovery following the pandemic. Our analysis highlights regional differences in network adaptability and identifies key commodities driving these dynamics. These findings provide insights for strengthening transport infrastructure, diversifying supply routes, and improving systemic resilience of national food supply chains under future shocks.

Urban logistics has become increasingly complex due to rapid urbanization, spatial constraints, and growing environmental concerns. Within this context, diffuse logistics emerges as a critical challenge, characterized by fragmented demand, dispersed origins and destinations, and irregular operational patterns. One of the most representative examples of diffuse logistics in urban areas is the removal of construction debris and bulky waste, which differs significantly from standardized freight distribution and scheduled municipal waste collection. This article examines the main challenges associated with diffuse logistics in urban environments, with particular emphasis on debris removal. Issues related to traffic congestion, spatial and temporal unpredictability, environmental regulation, technological limitations, and fragmented governance are discussed based on existing academic literature. The analysis highlights that although smart technologies and sustainability-oriented policies offer potential improvements, effective solutions require integrated planning, reliable data, and coordinated stakeholder engagement. The study concludes that debris removal should be understood as an integral component of urban logistics systems and a key factor in achieving sustainable urban development.

Air route network planning for urban logistics with vertihub: An airspace model

The simultaneous pursuit of operational efficiency and strict data sovereignty presents a critical dilemma for modern urban logistics, particularly within the constrained infrastructure of developing economies. While Federated Learning (FL) offers a decentralized pathway to mitigate privacy risks, its direct application in Vehicle-to-Everything (V2X) networks is frequently compromised by heterogeneous data quality and prohibitive communication overhead. To resolve these limitations, this study proposes a Privacy-Preserving V2X Federated Learning (PPVFL) framework specifically engineered for sustainable last-mile delivery. Unlike conventional approaches that treat data quality and privacy as separate domains, the proposed method enforces a strict local outlier filtering protocol to sanitize traffic beacons at the source, integrated synergistically with sparse ternary compression and differential privacy. This unified architecture not only safeguards sensitive driver trajectories against model inversion attacks but also drastically reduces the bandwidth consumption required for global model aggregation. Empirical validation using real-world logistics data from Vietnam demonstrates that delegating quality control to the network edge enables the framework to outperform centralized and basic federated baselines in both prediction accuracy and energy efficiency. These findings articulate a scalable solution for green logistics that reconciles the trade-off between robust traffic modeling and compliance with stringent data protection standards.

Integrating wind field analysis in UAV path planning: Enhancing safety and energy efficiency for urban logistics

The increasing prevalence of electric vehicles (EVs) in urban logistics presents challenges such as route planning, energy constraints, and demand management. EVs’ limited range, charging requirements, and sensitivity to traffic conditions necessitate advanced optimization strategies. Fleet management systems are thus evolving into intelligent, modular platforms that not only plan delivery tasks but also interact with real-time data and respond to dynamic disruptions. Among these, traffic congestion remains a critical factor that can severely affect route reliability and lead to time window violations. In this study, a modular fleet management system architecture is proposed, capable of real-time monitoring, dynamic rerouting, and traffic-aware decision-making. The system introduces a standardized data structure called the Routing Markup Language (RML), which formalizes the communication between components and supports various route outputs including simulation and vehicle-level execution. Adaptive Large Neighborhood Search (ALNS) is applied for route planning using real-world order data from a water distribution company operating in the Büyükdere district of Eskişehir. The system also features a dynamic reassignment mechanism that responds to vehicle failure scenarios, ensuring continued operation with minimal disruption. Traffic scenarios are evaluated through the Simulation of Urban Mobility (SUMO) environment to assess route robustness under varying conditions. The proposed approach integrates routing optimization, dynamic disruption handling, and simulation-supported fleet monitoring into a cohesive system, offering a responsive and data-driven solution for sustainable urban logistics.

ABSTRACT The following case study presents a method for estimating space to dedicate to logistics activities in planned mixed-use urban projects in the Paris region. As the place for logistics in cities is currently being questioned, the standardization of these facilities with respect to size, location, and form has proven difficult. Incorporating logistics space into urban projects can be a way of enabling developers to achieve their objectives for the promotion of logistics real estate while also satisfying the wants of public actors who have objectives linked to decreased externalities and a better-integrated logistics system. The method in this case study combines three categories of data to arrive at initial guidelines in terms of space to be dedicated to logistics in three mixed-use urban projects. Calling upon existing operational urban planning tools and data from a semi-private logistics real estate developer, the method involves three steps to arrive at the initial dimensions of a hypothetical logistics infrastructure in an urban project. As the projected dimensions are based strictly on the tool and project data and cannot consider the relevant surrounding activities and operator strategies, the paper then positions the assessment within a larger iterative design approach for an urban logistics infrastructure.

The ongoing full-scale military conflict in Ukraine has necessitated a fundamental paradigm shift in urban logistics management, particularly in frontline cities such as Kharkiv, where the operational focus has transitioned from commercial efficiency to strategic resilience. While traditional supply chain strategies prioritize cost minimization, the current environment demands the reliable delivery of critical goods, including food, medicine, and fuel, to support both the civilian population and defense capabilities. This study addresses the limitations of existing vehicle routing frameworks, which typically treat disruptions as isolated, independent events and fail to adequately account for the systemic and correlated nature of wartime threats. To resolve this, the authors propose a novel mathematical model defined as the Vehicle Routing Problem for a Heterogeneous Fleet with Stochastic Demand and Military Risks. The research employs a two-stage stochastic programming approach to manage the combined uncertainty of commercial demand and security threats. In the first stage, the model determines an optimal set of a priori routes representing the baseline plan, while the second stage evaluates the expected recourse costs associated with corrective actions required upon the realization of stochastic events. A significant contribution of this work is the formal integration of multi-component military risk indicators into the optimization process. Specifically, the occurrence of air raid alerts is modeled as a Poisson process, allowing for the quantitative estimation of expected downtime based on the total exposure time of vehicles in the network. Furthermore, the risk of physical infrastructure damage from kinetic attacks is incorporated as the probability of arc failure, necessitating the calculation of rerouting costs to the shortest available detours. By parameterizing the model with empirical statistical data regarding the intensity and duration of air alarms in the Kharkiv region, the study provides a quantitative framework for decision-making in crisis logistics. The resulting optimization minimizes the aggregate of deterministic planning costs and the mathematical expectation of risk-related expenditures, thereby enabling logistics operators to generate robust transportation plans that ensure supply chain continuity amidst extreme uncertainty and constant military threats.

The article explores the management mechanisms of sustainable development in the hospitality industry through the lens of the Airport City concept, which integrates transportation, logistics, and service infrastructures into a unified economic ecosystem. The study highlights how aviation hubs function not only as transport nodes but also as powerful catalysts for the transformation of hospitality and tourism sectors, creating new models of regional competitiveness and spatial development. The research subject encompasses the interrelation between airport-centered logistics systems and the management of the HoReCa sector under conditions of growing global mobility. The methodological foundation of the research is based on a system approach and principles of strategic and sustainable management. Comparative and structural analyses were applied to examine the dynamics of hospitality development in European airport hubs such as Vienna, Frankfurt, Warsaw, and Budapest. The study also used statistical data from the Vienna Airport Group, ACI Europe, and national tourism authorities to evaluate investment trends, hotel performance indicators, and the efficiency of integrated logistics systems. The research combines theoretical synthesis with case-based analysis, focusing on the Airport City Vienna as a benchmark of effective integration between transport and hospitality infrastructures. The purpose of the study is to identify the managerial patterns and economic factors that determine the sustainability of hospitality development in regions with active airport-based economies. The research aims to reveal how the synergy between air transport, urban logistics, and service industries influences hotel performance, investment attractiveness, and the diversification of hospitality services. The findings indicate that airport cities represent a new model of territorial management where hospitality enterprises become integral elements of the transport and logistics chain. The analysis of Airport City Vienna demonstrates that the expansion of passenger flows and transport accessibility directly correlates with the growth of hotel occupancy, average daily rates, and investment volumes. Effective management of sustainable development in this context relies on integrated strategies combining public–private partnerships, digital transformation, ESG principles, and innovations in smart logistics. In conclusion, the study substantiates that the Airport City model can serve as a framework for managing the sustainable evolution of the hospitality industry. This framework enhances operational efficiency, supports green transition objectives, and strengthens the economic and social resilience of regions integrated into global air transport networks.

Urban solid waste management is transitioning, with waste sorting and transportation network optimization being central to enhancing system resilience and efficiency. This study aims to develop a multilevel, multi-objective optimization paradigm to address the complexity of urban waste transportation systems. The research begins with a basic Capacitated Vehicle Routing Problem (CVRP) model designed to minimize the total transportation distance as a benchmark. Based on this, we constructed a comprehensive cost optimization model that incorporates multiple waste categories, heterogeneous vehicles, and spatiotemporal constraints. Ultimately, the framework was elevated to the strategic level by integrating the transfer station facility location and low-carbon route planning, thus forming an integrated “strategic location–tactical allocation–execution route” decision-making model. T used an improved genetic algorithm (IGA) based on an elite retention strategy to solve the model. Empirical analysis shows that the basic model establishes a baseline of 1,049.29 km in total mileage; the multi-constraint collaborative model achieves a multi-objective balance at a daily cost of 4,511.33 yuan; while the integrated model outputs the optimal spatial layout of transfer stations and quantifies that an asymmetric road network leads to increases in transportation cost and carbon emissions by 0.62% and 1.12%, respectively. The hierarchical optimization framework not only provides an end-to-end decision support tool for urban solid waste management but also offers methodological references for solving complex urban logistics network problems.

Abstract Diesel-powered delivery vans still dominate the parcel delivery sector. However, due to their negative environmental impact, delivery service providers are urged to switch to more innovative and eco-friendly delivery vehicles like cargo bikes, delivery robots, or drones. Unfortunately, relying on only one type of delivery system is not always feasible, either because the customer’s site lacks the requirements or because the parcel size is too large. A possible solution is to combine multiple such delivery systems. In this context, this paper focuses on a problem in city logistics based on the two-echelon vehicle routing problem. To accommodate a diverse range of delivery vehicles, it involves deploying various vehicle types in the second echelon, direct delivery by first-echelon vehicles, transfer of second-echelon vehicles and parcels at satellite locations, and preference constraints for customers and vehicle types. To solve the problem, we propose an Adaptive Large Neighborhood Search (ALNS) heuristic that features problem-specific local search and destroy operators. Experiments are conducted for an urban delivery area in Hamburg, Germany. They show that the ALNS can handle various fleet compositions and instance sizes. Our findings indicate that the combination of delivery systems can lead to significant cost savings compared to traditional van delivery and that customer preferences heavily influence the optimal fleet composition.

A promising approach to advancing sustainable urban mobility is the increased use of light electric vehicles, such as e-cycles and their cargo-carrying variants: e-cargo cycles. These micromobility vehicles fall between e-cycles and conventional vehicles in terms of transport capacity, range, and cost. A key advantage of e-cargo cycles over their non-electrified counterparts is the electric powertrain, which enables them to carry heavier payloads, travel longer distances, and reduce driver fatigue. Since the primary use of e-cargo cycles is urban parchment deliveries, trip efficiency plays a critical role in their effectiveness within urban logistics. This efficiency is influenced by factors such as travel distance, traffic density, and the weight and volume of the delivery payload. While higher delivery capacity generally enhances efficiency, studies have shown that as the drop size increases, the efficiency of e-cargo cycle delivery trips tends to decline. A practical way to address this limitation is the use of cargo attachments, such as trailers. These micromobility solutions are already widely implemented globally and significantly enhance transport capacity. This paper reports the process of designing and testing the control algorithm of an electrical system for an experimental attachment demonstrator that can be used to convert most cycle vehicles into cargo variants. The system integrates two 250 W BLDC hub motors, two 576 Wh lithium-ion batteries, dual load-cell sensing in the coupling element, and an STM32-based controller to provide independent propulsion and synchronization with the leading cycle. The force-based control strategy enables automatic adaptation to varying payloads typically encountered in urban logistics, which is supported by the variable storage volume capable of transporting payloads of up to 200 kg.

The accelerated growth of e-commerce and ongoing urban expansion have intensified the challenges associated with last-mile delivery, making it a critical issue in sustainable urban logistics. Therefore, our paper presents a scoping review to systematically delineate the current state of research on smart and sustainable last-mile delivery systems. We explore both innovative technologies—such as artificial intelligence, autonomous vehicles, the Internet of Things, and digital twins—and human-centered dimensions, including urban design, policy development, and collaborative stakeholder engagement. Using the PRISMA-ScR-based methodology, 140 peer-reviewed articles (2015–2025) have been analyzed to highlight key trends, gaps, and prospective directions. The study underlines how the technologies of Industry 4.0 have improved visibility and operational efficiency, but holistic thinking that incorporates environmental, human, and policy factors remains undeveloped. Based on these findings, this article provides a conceptual framework for smart and sustainable last-mile delivery, focusing on the intersection of digital and simulation tools and human-centric governance to achieve optimized efficiency, environmental performance, and equity. This framework helps both academics and decision-makers to advance data-driven, resilient, and integrative city logistic ecosystems.

Digital transformation has become a decisive factor in reshaping courier logistics, particularly in emerging economies such as Kazakhstan. This article investigates how the integration of advanced digital technologies – including Internet of Things (IoT) solutions, artificial intelligence (AI) for routing, big data analytics, and platformbased service models – affect the efficiency and sustainability of last-mile deliveries in Kazakhstan’s major urban centers, specifically Almaty and Astana. Drawing on the Logistics Performance Index [1], OECD reports [2], and peerreviewed Kazakhstani studies, the paper applies a mixed-method approach combining systematic literature review, comparative benchmarking, and descriptive correlation analysis. The findings confirm a positive association between the adoption of digital tools and improvements in LPI sub-indicators, notably tracking and tracing and logistics competence. However, the study highlights several systemic barriers to scaling digital solutions: insufficient ICT infrastructure, fragmentation of data standards across platforms, and the absence of strong incentives for small and medium-sized enterprises (SMEs). From a managerial perspective, the paper proposes the implementation of urban micro-hubs, open API standards for data exchange, and targeted support for SMEs and green fleets (EV and NGV). The research contributes to academic debates on sustainable urban logistics and provides practical guidance for policymakers and courier operators in Kazakhstan. Limitations include the lack of micro-level operational datasets, which constrain causal inference. Future research should apply panel econometrics and quasi-experimental designs to evaluate the measurable impacts of digital transformation initiatives on delivery times, costs, and carbon footprints.

Innovating the last mile: Consumer acceptance and economic drivers of drone deliveries in urban logistics

Abstract Urban courier services face growing pressure to improve efficiency while meeting sustainability goals. This study aimed to optimize seller package pick-up operations at PT. XYZ Logistic by applying Ant Colony Optimization (ACO), Nearest Neighbor (NN), and Business Process Improvement (BPI). Using data from 10 seller locations and two vehicle types, existing routes and processes were analyzed for inefficiencies. ACO and NN were implemented to generate optimized routes, and business processes were evaluated to identify non-value-added activities. The ACO algorithm reduced the box car’s distance by 8.24%, motorbike A’s by 10.6%, and motorbike B’s by 9%, outperforming NN. Business process mapping revealed that 12% of pick-up time was wasted on unnecessary activities. These improvements translated to reduced travel times, fuel consumption, and emissions, supporting sustainable urban mobility and climate action goals. The findings demonstrate the effectiveness of integrating advanced algorithms and process optimization in enhancing urban logistics operations while contributing to sustainability targets.

Optimization of urban logistics with multi-modal systems: A comprehensive study of the airship-vehicle routing problem

Wu Zhong (吴仲) played a pivotal yet underexplored role in the restoration and governance of the Tonghui Canal during the Ming dynasty. This study critically examines his multifaceted contributions as a hydraulic engineer, policymaker, and historiographer. It explores how he led the technical and administrative efforts in restoring the Tonghui Canal and evaluates his intellectual legacy, especially through his authorship of the Gazetteer of the Tonghui Canal (通惠河志), which offers valuable insights into the canal’s history and governance. Addressing the scholarly gap concerning Wu Zhong’s historical significance, this research employs library research and qualitative textual analysis to reconstruct the historical, technical, and institutional dimensions of his work. The findings demonstrate that Wu Zhong’s engineering initiatives significantly enhanced transportation efficiency, reinforced political stability, and stimulated economic growth in the Beijing region. His policy advocacy, conveyed through strategic memorials and institutional reforms, contributed to long-term improvements in water conservancy and urban logistics. Furthermore, his compilation of the Gazetteer of the Tonghui Canal provided an enduring epistemic foundation for subsequent hydraulic governance and historiography. By reassessing Wu Zhong’s contributions, this study offers a more comprehensive understanding of the intersection between technology, statecraft, and knowledge production in the Ming dynasty. It concludes that Wu Zhong’s leadership and intellectual legacy generated lasting societal and scholarly impacts, positioning him as a central figure in the development of Ming dynasty infrastructure and governance.