Search Results (110)

Accessibility in bus-based public transport is essential for inclusive and sustainable urban mobility, yet bus stops and stations are often overlooked compared with vehicle modernization and network planning. This study presents a bibliometric and thematic review of research (2020–2026) on accessibility in bus stop and station infrastructure across European cities. Literature indexed in the Web of Science Core Collection was analyzed using thematic synthesis supported by bibliometric network analysis. A total of 685 publications were examined to identify research trends, barriers, regional differences, assessment methods, and planning implications. The findings show that accessibility is a multidimensional concept shaped by physical design, information systems, user experience, first- and last-mile connectivity, and governance capacity. Persistent barriers include poor boarding interfaces, inadequate maintenance, weak wayfinding, safety concerns, and uneven implementation of accessibility standards. The reviewed studies suggest that evidence of stronger integration and higher user satisfaction is more frequently reported in Northern and some Western European contexts, whereas rural and smaller cities often face lower service accessibility. Emerging topics include real-time passenger information, crowding management, electric buses, and smart mobility technologies. The review concludes that bus stops and stations should be treated as strategic mobility assets. Integrated planning, participatory design, and territorially differentiated policy support are essential to advance inclusive bus-based public transport across Europe. Full article

Multi-UAV cooperative delivery is a key technology for intelligent low-altitude logistics, with applications in mountainous-area transport, urban last-mile delivery, and emergency resupply. In complex three-dimensional (3D) low-altitude environments, obstacle-constrained airspace, fleet heterogeneity, payload limits, and time windows make the realistic representation of flight costs difficult and substantially restrict the feasible region of cooperative planning. To address these challenges, this paper proposes TeCoR-UAV, a two-stage topology extraction and cooperative route planning framework. The proposed method first precomputes executable flight trajectories in obstacle-constrained airspace and constructs a topological graph that captures realistic flight costs. A bi-objective optimization model is then formulated to minimize operational cost and maximize service quality. Furthermore, a hierarchical genetic solver is designed to improve solution quality and feasibility jointly through global task allocation and single-UAV execution sequence optimization. Experimental results show that the proposed method can better reflect realistic flight costs in complex environments. Compared with existing benchmark methods, TeCoR-UAV achieves better bi-objective trade-offs in most medium- and large-scale scenarios, as well as in topologically constrained scenarios, and improves service quality by an average of 18.5 percentage points, indicating its scenario adaptability and potential for practical application. Full article

Medical and pharmaceutical last-mile deliveries are simultaneously expected to be fast, reliable and temperature-safe for patients and to become measurably greener, yet these objectives often pull transport operations in opposite directions. Third-party logistics (3PL) providers are therefore increasingly required not only to report transport CO₂ emissions, but also to learn from them; however, it remains unclear whether the routine operational data they collect are sufficiently informative to enable such emission-based learning in this regulated and service-critical setting. This study examines the predictive limits of machine learning models in estimating CO₂ emissions in medical and pharmaceutical last-mile deliveries performed by a 3PL operator. Using operational data from six customers, we compare global and customer-specific models for the following two dependent variables: total CO₂ emissions per transport operation and CO₂ emissions per pallet. Linear and non-linear models, including linear regression, ElasticNet, Random Forest, HistGradientBoosting and XGBoost, are evaluated using chronological train-test splitting and cross-validation. The results show that global models fail to outperform a naïve benchmark, with negative R² values for both emission measures. Customer-level models reveal substantial heterogeneity as follows: for selected customers, especially those with more regular operational patterns, moderate predictive performance is achieved, while for others, emissions remain largely unpredictable using the available variables. The findings suggest that routine shipment-level data are insufficient for robust emission prediction in 3PL last-mile operations. Emission-based learning requires richer contextual, vehicle, route, traffic and telematics data, as well as customer-sensitive modelling approaches. The study contributes by identifying the data and modelling limits of sustainability intelligence in medical and pharmaceutical last-mile logistics. Full article

This paper investigates the strategic zone design problem for a bus-based passenger–parcel sharing delivery system. In the envisioned system, parcels are first transported along an existing bus line and transshipped at bus stops, after which dedicated vehicles perform last-mile deliveries from bus stops to customers. By leveraging the underutilized capacity of existing bus services to support parcel distribution, the system contributes to a more resource-efficient and less truck-dependent urban logistics structure, thereby supporting sustainability in urban transportation. The problem is to partition the corridor-level service area into multiple contiguous service zones along the corridor under an m-nearest feasibility requirement. A nonlinear integer programming model is developed that jointly captures parcel and passenger perspectives. On the parcel side, the objective combines zone compactness, parcel-demand balance, and parcel-delivery-distance balance; on the passenger side, it minimizes passenger impact. In this way, the model balances logistics efficiency with social-equity and service-quality considerations and operationalizes sustainability through measurable planning indicators embedded in the objective function. A tailored adaptive large neighborhood search algorithm is proposed to exploit the specific problem structure and solve the model. Case studies based on a real-world bus line in Yancheng, China, illustrate the effectiveness of the proposed method and yield managerial insights into the choice of the number of zones, the influence of passenger-flow patterns, and the role of objective-function weights in shaping trade-offs between parcel delivery efficiency and equity, as well as passenger service quality. Full article

With the rapid development of e-commerce and the increasing complexity of urban logistics, traditional delivery methods face significant challenges due to regional traffic restrictions and congestion. This paper presents a two-stage optimization approach for urban delivery routing, integrating the Split Delivery Vehicle Routing Problem (SDVRP) and truck-drone collaboration to address these challenges. In the first stage, a transportation route optimization model based on SDVRP is proposed, which accounts for regional constraints and vehicle capacity limitations. The model allows for demand splitting, reducing the number of vehicles required and minimizing transportation costs. In the second stage, a truck-drone collaborative delivery model is introduced to handle the “last mile” distribution, where drones complement trucks by delivering to areas with restricted vehicle access. The optimization model aims to minimize overall delivery costs while ensuring timely service. An enhanced genetic algorithm is further developed to solve this complex, multi-constrained model. Experimental results show that the proposed collaborative strategy reduces delivery costs by over 10% compared to truck-only delivery, and the improved algorithm achieves a 4.77% average cost reduction over traditional approaches. This study provides valuable insights for optimizing urban logistics systems under regional constraints, offering both theoretical and practical contributions to smart logistics development. Full article

The dynamic development of the food delivery sector and the resulting increase in last-mile distribution operations generate the need to simultaneously improve the efficiency of delivery processes and reduce the environmental impacts of urban logistics. In this context, shortening delivery time contributes not only to higher service quality and competitiveness but also to lower energy consumption and carbon dioxide emissions, which are key elements of sustainable urban mobility and logistics. Therefore, the aim of this study is to develop a delivery time optimization algorithm for the food delivery sector using selected machine learning methods, supporting the implementation of sustainable development principles in the operations of transport enterprises. This study presents an integrated approach to modelling delivery time in food distribution as a tool for building the competitive advantage of logistics enterprises under the conditions of implementing sustainable development principles. The study combines a literature review on sustainable last-mile logistics and data-driven optimization with an empirical analysis using traditional methods such as multiple regression and selected machine learning methods: decision trees, the Gradient Boosting Machine (GBM) method, and the XGBoost algorithm. The operational data include parameters related to delivery execution, such as supplier characteristics, vehicle type, order execution date, weather conditions and traffic situation. The developed mathematical models enable high-accuracy prediction of delivery time and the identification of the most important factors affecting both timeliness and potential energy consumption in the delivery process. The comparative assessment of the applied methods makes it possible to indicate the algorithms that provide the best forecast quality and practical usefulness in logistics decision-making. The proposed delivery time optimization algorithm supports data-driven decision-making that leads to shorter delivery times and lower energy intensity and thus to a reduction in the carbon footprint of last-mile operations, simultaneously strengthening the competitiveness and environmental responsibility of logistics enterprises. The results contribute to the development of sustainable urban logistics by linking predictive modelling with the economic, environmental and operational dimensions of efficiency in last-mile transport processes. Overall, this study offers an original, high-quality contribution to sustainable last-mile food delivery by integrating large-scale operational data with advanced machine learning models to deliver practically relevant, highly accurate delivery time predictions for logistics enterprises. Full article

The urban remoteness of warehouses and distribution centres, known as logistics sprawl, has been observed for several decades. According to some, this increase in distances between logistics facilities and hypercentres contributes to the environmental worsening of transport operations, especially in densely populated places such as the Paris metropolitan area. Therefore, the question of logistics tightening—the opposite phenomenon—arises in the context of reducing pollutant emissions in the territories concerned. The objective of this work is to clarify the “hidden” mechanisms of freight transport services. It evaluates, through a simulation, the carbon footprint and operational efficiency of logistics tightening in the city of Paris. The input data we use comes from a large courier service company that can be regarded as an interesting case study when it comes to the Paris region. In our scenario, the ecological consistency of the journeys and the logistical requirements of the transport chain may be contested. Indeed, the inner resettlement of hubs for greener deliveries suggests the actual scheme of the company gets closer to optimum and ironically illustrates the relevance of the current locations. Logistics tightening mainly focuses on the last mile, but such a problem is complex, as each link of the chain has its own peculiarities, meaning the sustainability of one can undermine that of another. Full article

In the context of rapid urban tourism expansion and the growing emphasis on equitable and sustainable transport development, understanding how transport systems support different types of attractions has become increasingly important. This study investigates how attraction hierarchy and functional type interact with public transport accessibility to shape urban tourism patterns and equity. Whereas prior work emphasizes objective metrics, the alignment between perceived accessibility and actual transport conditions remains understudied. Using Wuhan’s A-rated and popular unrated attractions as a case, we have developed an innovative “ objective–perceived coupling framework that integrates GIS network analysis, travel cost matrix, non-parametric testing, and online comment text mining methods to examine how scenic spot levels (A-level and unrated popular scenic spots) and functional types interact with the public transportation system from both objective and perceptual dimensions. Results show: (1) A-rated attractions cluster in suburbs with low accessibility, while unrated sites concentrate centrally with high rail-bus connectivity, revealing a “high-grade–low-accessibility” mismatch. (2) Accessibility varies by type: natural sites are lowest, cultural/leisure venues intermediate, and comprehensive sites highest due to multimodal hub proximity. (3) Sentiment and topic analyses based on transport-related review content suggest that some A-rated attractions receive less favorable evaluations of access conditions (e.g., transfers, waiting, last-mile walking, wayfinding, and parking), whereas many popular unrated sites are evaluated more positively in these transport-specific aspects. (4) Quadrant analysis shows many highly rated attractions fall into a “low objective–low perceived” disadvantage, while most unrated ones exhibit strong objective–perceived coupling. These findings underscore structural imbalances among administrative grading, attraction function, and transit provision, offering evidence for optimizing public transport service to tourist attractions. They help optimize the spatial structure of urban tourism, improve resource allocation efficiency, guide differentiated scenic spot development strategies, and promote sustainable and experience-oriented urban tourism governance. Full article

Ensuring product integrity across Malaysia’s East Malaysian states (Sabah and Sarawak) requires a cold chain that is resilient to tropical heat, long multimodal routes, intermittent power, and dispersed rural populations. This paper proposes a sustainability-first architecture for vaccine and blood component logistics that combines World Health Organization and the United Nations International Children’s Emergency Fund Effective Vaccine Management (EVM 2.0) criteria with energy-aware transport planning, solar-hybrid edge refrigeration, phase-change materials, and digital temperature monitoring compliant with ISO 23412 for temperature-controlled delivery services. In this study, a mixed-methods methodology was employed, including (1) route and mode optimization under temperature risk and carbon intensity constraints; (2) equipment right-sizing using duty-cycle energy models and IEC 60068 environmental tests as design baselines; (3) governance with real-time earned value management (EVM) and key performance indicators (KPIs); and (4) scenario analysis for riverine, road, air, and drone last-mile segments relevant to remote East Malaysian communities. Results from realistic logistic scenarios indicate a 45–65% reduction in dose-weighted temperature-excursion minutes, 28–41% reduction in CO₂e per successful dose delivered, and 35–52% reduction in product loss compared with status quo planning. For blood components, solar-hybrid storage and mixed-mode routing reduced breach risk by 37% while maintaining red cells (2–6 °C), platelets (20–24 °C, continuous agitation surrogate), and fresh frozen plasma (≤−18 °C) requirements aligned with WHO guidance and Malaysia’s national transfusion policies. We provide a reference architecture, implementation bill of materials, and an EVM-aligned KPI dashboard to guide scale-up. Full article

Contemporary urbanisation in hot-arid cities presents coupled challenges related to sustainability, spatial efficiency, and climate resilience. This study applies Research by Design as a preliminary methodological approach to adapt the superblock typology for Riyadh’s Al-Raed neighbourhood, integrating GIS-based territorial diagnosis with Grasshopper parametric iterations. Sixteen geospatial layers, including land use, density, road hierarchy, transit access, service distribution, green cover, and climatic exposure, inform attractor-based scenario generation and a structured comparative evaluation framework assessing regulatory compliance, human scale, connectivity, and environmental and economic feasibility. The resulting loop-and-courtyard configuration reorganises local streets to strengthen first- and last-mile access, shaded pedestrian continuity, and microclimatic comfort, while supporting Saudi Vision 2030 programs, such as the Quality of Life Program, National Transport and Logistics Strategy, Riyadh Public Transport Program, and Saudi Green Initiative. Quantitative spatial indicators are interpreted alongside design-based morphological reasoning to inform spatial decisions, acknowledging climatic and cultural constraints. This study contributes a reproducible, policy-relevant digital workflow for neighborhood-scale urban transformation in Riyadh and comparable hot-arid contexts. As a preliminary Research by Design phase, it structures iterative scenarios and a structured comparative evaluation framework, providing a foundation for subsequent quantitative and empirical validation. Full article

Background: The rapid growth of e-commerce has intensified the need for last-mile delivery systems that can navigate urban congestion while minimizing environmental impact. Hybrid truck–drone networks offer a promising solution by combining heavy-duty ground transport with aerial flexibility; however, their deployment faces significant challenges in jointly managing operational risks, energy limits, and regulatory compliance. Methods: This study proposes a hybrid matheuristic framework to solve this multi-objective problem, simultaneously minimizing transportation cost, service time, energy consumption, and operational risk. A two-phase approach combines a metaheuristic for initial truck routing with a Mixed-Integer Linear Programming (MILP) formulation for optimal drone assignment and scheduling. This decomposition strikes a balance between exact optimization and computational scalability. Results: Experiments across various instance sizes (up to 100 customers) and fleet configurations demonstrate that integrating MILP enhances solution diversity and convergence compared to standalone strategies. Sensitivity analyses reveal significant impacts of drone speed and endurance on system efficiency. Conclusions: The proposed framework provides a practical decision-support tool for balancing complex trade-offs in time-sensitive, risk-constrained delivery environments, thereby contributing to more informed urban logistics planning. Full article

Smart-city last-mile rail access, referred to in this entry simply as last-mile access, captures how travelers connect to and from rail stations during the first or last leg of a journey. It encompasses both the design of multimodal connections and the experience of accessibility that results from them. On the supply side, last-mile access involves the coordination of walking, cycling, micromobility, and feeder transit with rail services, supported by digital systems that unify planning, ticketing, and payment. On the demand side, it reflects how efficiently and equitably travelers can reach stations within these coordinated networks. Together, these physical and institutional dimensions extend the functional reach of rail, reduce transfer barriers, and reinforce its role as the backbone of sustainable urban mobility. As cities strive to reduce car dependency while promoting inclusivity and accessibility, last-mile access has become a key indicator of how infrastructure, technology, and governance intersect to deliver more equitable transportation systems. Full article

This paper is dedicated to analysing sustainability and digitalisation in the transport systems of the European Union (EU) and Ukraine, with a particular focus on three representative subsectors: freight rail, urban public transport and last-mile postal logistics. It explores how technological innovation, operational efficiency and environmental responsibility interact within these sectors under distinct institutional and economic conditions: mature, market-based systems in the EU and resilience-driven systems in wartime Ukraine. This study applies a comparative, descriptive–analytical methodology using secondary data drawn from corporate sustainability reports, official statistics and sectoral databases for 2022. Quantitative KPls were complemented with a qualitative assessment of digitalisation maturity to ensure cross-country comparability. Through a comparative analysis of KPIs, such as freight volumes, emissions intensity, revenue efficiency and digital maturity, this study identifies structural and policy gaps that hinder progress toward sustainable mobility. This study develops a multi-dimensional framework combining operational, financial, environmental and digital indicators. In this paper, digital integration refers to the degree to which transport operators embed digital tools such as tracking, data management and automation into their core processes, while environmental efficiency denotes the ability to deliver transport services with minimal resource consumption and carbon emissions per operational unit. Institutional resilience is understood here as the capacity of transport organisations and governing institutions to maintain functionality, adapt and recover under crisis or systemic stress, which is particularly relevant for Ukraine’s wartime context. The findings demonstrate that while EU operators lead in transparency, digital integration and environmental performance, Ukrainian actors exhibit rapid adaptive innovation and significant potential for technological leapfrogging during reconstruction. This paper concludes that the EU must overcome regulatory inertia and infrastructure fatigue, while Ukraine should institutionalise resilience and transparency. Full article

This paper investigates the spatiotemporal patterns and accessibility implications of shared e-scooter use in Tel Aviv, drawing on a complete year (2024) of trip-level data from all licensed providers. Shared micromobility services are often promoted as tools for reducing car dependency and improving urban accessibility, yet their actual usage patterns and equity outcomes remain underexplored, especially outside North America and Western Europe. This study aims to address this gap by integrating over 9 million reconstructed scooter trips with public transport accessibility data, local weather records, and institutional calendar effects. Multivariate regression was applied to quantify temporal and environmental determinants of demand, seasonal-trend decomposition to reveal cyclical usage patterns, and spatial analysis to assess whether scooters extend or reinforce existing mobility hierarchies. Findings indicate that scooter use in Tel Aviv is highly structured, peaking during afternoon hours, dropping during holidays and rain, and reflecting the weekly rhythms of the workweek in Tel Aviv. However, spatial patterns show a strong concentration of usage within already well-connected central areas, with limited activity in low-accessibility zones. These results suggest that shared e-scooters are not currently fulfilling their potential as first- or last-mile connectors; instead, they primarily serve as short-range, intra-core alternatives to walking. Full article

Background: Urban freight transport is essential for the functioning of cities. The COVID-19 pandemic accelerated the growth of e-commerce, creating new challenges for courier services. While consumer satisfaction has been extensively studied, little attention has been paid to courier drivers’ own perceptions and preferences. This study aims to fill that gap. Methods: A questionnaire survey was conducted among 139 drivers employed in eight courier companies in Serbia. Data were analyzed using parametric statistical methods (Independent Samples T-Test, Paired-Samples T-Test, and One-way ANOVA), with additional post hoc tests to explore group differences. Results: Statistically significant differences were observed across demographic, operational, and safety-related factors (gender, age, residence, occupation, license ownership, delivery area, and type of goods). A strong preference emerged for passenger vehicles as the safest mode of delivery, highlighting a misalignment between current operational practices and drivers’ safety perceptions. Conclusions: The findings emphasize the importance of tailoring delivery strategies to demographic and operational contexts. Practical recommendations include improving transport safety, optimizing delivery zones, and addressing driver satisfaction as a determinant of service quality. The study contributes new insights into last-mile delivery by focusing on the perspectives of courier drivers rather than consumers. Full article