Search Results (171)

The Greater Mekong Subregion (GMS) has emerged as a strategic logistics hub due to rapid economic integration and the expansion of cross-border road transportation. However, international road logistics networks in the region must address several competing objectives, including minimizing transportation costs, reducing delivery times, and balancing transport distances among trading partners. To overcome these challenges, this study proposes an innovative hybrid computational framework that integrates the classical Transportation Problem with the Pythagorean methodology (TPPM). The proposed approach consolidates multiple transportation objectives into a unified performance metric based on the Pythagorean concept, thereby enabling simultaneous optimization under practical constraints. In addition, geographic inputs derived from Google Maps and Google Earth via web platforms, which are reliable open-source GIS tools, are incorporated into the transportation model to improve spatial accuracy. A simulated dataset comprising 35 suppliers and 42 customers, representing major logistics nodes in the GMS, is developed to evaluate the proposed method. The computational results indicate that the TPPM approach outperforms the conventional single-objective Classical Transportation Problem (CTP) by producing higher solution quality and more balanced performance. Overall, the findings demonstrate that the proposed hybrid method is a robust decision-support tool for sustainably enhancing the resilience of international logistics planning in emerging economic regions. Full article

Vehicular Ad Hoc Networks (VANETs) are essential components of Intelligent Transportation Systems (ITS), allowing communication exchanges between vehicles and road infrastructure elements. These networks face challenges from vehicular mobility, including frequent topology changes, link instability, and variable wireless channel quality. This paper presents an extensive evaluation of Software-Defined Networking (SDN) integrated with two traffic engineering technologies, Multi-Protocol Label Switching (MPLS) and Segment Routing (SR), applied to the AODV and OLSR routing protocols. Nine incremental configurations are evaluated for each protocol, ranging from the default protocol through MPLS-enhanced forwarding, SDN-based centralized optimization, combined SDN-MPLS and SDN-SR integration, to advanced configurations using distance-based IS-IS weighted topology metrics with both Fixed and Adaptive metric computation approaches. Two distinct SDN topology construction methods are compared: a Protocol-based approach that uses routing table entries with equal hop-count metrics, and a distance-based approach using IS-IS weighted metrics. The simulation uses a realistic urban topology with 50 vehicles and 5 RSUs, evaluated across several traffic patterns, representing different application types. Results demonstrate that SR with distance-based IS-IS metrics achieves the highest Packet Delivery Ratio (PDR) and lowest delay by leveraging RSU infrastructure as reliable forwarding relays. Moreover, the proposed SDN-SR framework reduces routing overhead and control-plane signaling, improving network resource utilization and thereby indicating its potential to enhance the energy efficiency of vehicular communication infrastructures. Full article

Sustainable highway maintenance is increasingly important as road agencies face ageing assets, fiscal constraints and growing requirements for service reliability and resilience. Although digital technologies are widely used in inspection, prediction and construction control, less is known about how they support governance across the full maintenance process. This study examines a Chinese scientific highway maintenance pilot through a document-based qualitative case study. Drawing on project documents and public reports, it develops a framework linking three mechanisms: datafied demand assessment, model-based decision-making and adaptive scheduling. The findings show how digital inspection, remote sensing, multi-source data models and smart construction-control platforms turn dispersed deterioration information into traceable condition evidence, translate this evidence into life-cycle maintenance priorities and connect planning with implementation feedback. Project-reported indicators also suggest gains in inspection efficiency and monitoring timeliness, although the evidence does not permit a comprehensive assessment of sustainability outcomes. Together, the mechanisms strengthen the capacity to move from reactive repair towards evidence-based life-cycle maintenance, resource-conscious prioritisation and adaptive service delivery. The study contributes to sustainable transport infrastructure research by explaining the governance processes through which digital technologies can support highway maintenance and the conditions shaping their transferability. Full article

The emergence of unmanned aerial vehicles (UAVs) introduces new opportunities for the design of intelligent and flexible transportation systems beyond traditional road-based logistics. This study investigates the integration of UAVs as an alternative transportation mode within industrial environments, focusing on the rapid delivery of critical spare parts in large-scale production facilities. A two-stage optimization framework is developed, combining demand pre-processing with a routing algorithm that determines fleet utilization and delivery schedules under operational constraints. The proposed framework utilizes a data pre-processing stage, which converts enterprise resource planning order records into delivery-ready item data, with a mixed-integer linear programming (MILP) routing model that assigns eligible spare parts to UAV trips and determines the use of a fixed fleet under payload, dimensional, service-time, and battery-related constraints. The approach is evaluated using real annual order data from a metal-industry plant, combined with simulated intra-day arrival profiles due to the absence of exact order-placement timestamps in the ERP records. The results indicate that UAV-based transportation can serve a substantial share of internal demand while achieving shorter delivery-response times for the modeled UAV layer under the simulated dispatch instances and significantly lower direct energy-related transportation costs compared with the existing pickup-based process. The results highlight the role of UAVs as a complementary transportation layer in controlled industrial networks, supporting the transition toward more responsive and intelligent future transportation systems. Full article

This paper examines the evolution of urban transport policy in Saudi Arabia from a car-dependent paradigm toward sustainability-oriented planning and early implementation between 2000 and 2025. Using a longitudinal qualitative analysis of national strategies, municipal plans, and giga-project documents, this study traces shifts in policy discourse, governance arrangements, and delivery evidence across three phases: an expansionist phase (2000–2015), a vision transition phase (2016–2020), and a sustainability implementation phase (2021–2025). These phases were selected to capture the transition from pre-Vision 2030 automobile-oriented planning to the early implementation of sustainability-oriented transportation reforms. The findings reveal a clear transition from road-expansion-oriented planning—characterized by highway development, fuel subsidies, and limited public transport—toward system performance, decarbonization, and multimodal integration. Recent years have seen the rollout of metro and bus networks, expansion of rail systems, early electrification of vehicles and public transport, and fuel price rationalization. However, persistent behavioral lock-in, low-density urban forms, climatic constraints, and complex multi-level governance arrangements continue to limit modal shift and equitable mobility outcomes. The findings suggest that infrastructure investment alone cannot achieve substantial modal shift without integrated land-use planning, feeder systems, and demand-management measures. By linking policy ambition to implementation pathways over time, this study provides transferable insights for sustainable mobility transitions in oil-dependent and arid urban contexts. 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

Passengers choose a mode of public transport from the available options based on characteristics that are important to them. The importance of these characteristics has received little research attention and varies. This study presents 10 characteristics of passenger transport, the significance of which was examined using four MCDM (multi-criteria decision-making) methods. The questionnaire was conducted and 27 specialists (experts) in road, rail and air transport rated the importance of various characteristics (criteria) using rankings, percentage weights and intensity of importance values derived from pairwise criterion comparisons using the Analytic Hierarchy Process (AHP). The results of the study show that the opinions of the expert panel, expressed as ratings, were consistent, as the Kendall’s coefficient of concordance (0.64) was 9.2 times greater than its minimum threshold value of 0.07. The ranks of the criteria were used to calculate their relative weights using the ARTIW-L (average rank transformation into weight–linear) and ARTIW-N (non-linear) methods. The relative weights were calculated from the criteria percentage weights using the DPW (direct percentage weight) method. The consistency ratios of all 27 matrices calculated using the AHP method were less than 0.1. This demonstrates their consistency. The average calculated for each criterion using the four MCDM methods is the final measure of the significance of the passenger transport characteristic. For experts, the most important factors were safety (0.2234), travel costs (0.1488), travel time (0.1465), and comfort (0.1181). Factors of moderate importance included door-to-door delivery (0.0847), environmental friendliness (0.0685), and vehicle capacity (0.0597). The following factors were deemed the least important: service quality (0.0571), the impact of weather conditions (0.0532) and the risk of contracting COVID-19 (0.0400). The most important criterion was 5.6 times more significant than the least important criterion. This data will be used to carry out a thorough evaluation of the different transport options and select the most suitable one for intercity passenger transport. Full article

Vehicular ad hoc networks (VANETs) operating in dense urban environments are characterized by highly dynamic topology, fluctuating traffic conditions, and stringent latency requirements, which significantly complicate reliable data routing and packet forwarding. To address these challenges, this paper proposes an Intelligent Fuzzy Protocol (IFP) for adaptive vehicle-to-vehicle data routing under uncertain and rapidly changing traffic scenarios. The proposed protocol integrates fuzzy logic decision making with the real-time vehicular context, including vehicle velocity, traffic congestion level, distance to road junctions, and data urgency, to dynamically select appropriate forwarding actions. IFP employs a structured fuzzy inference engine comprising fuzzification, rule evaluation, inference aggregation, and centroid-based defuzzification to determine routing and forwarding decisions in a decentralized manner. To further enhance performance robustness, the fuzzy membership parameters and rule weights are optimized using metaheuristic techniques, namely, genetic algorithms (GAs) and particle swarm optimization (PSO). Extensive simulations are conducted using NS-3 coupled with SUMO under realistic urban mobility scenarios and varying network densities. The simulation results demonstrate that IFP significantly outperforms conventional routing approaches in terms of end-to-end delay, packet delivery ratio, and routing overhead. In particular, the optimized IFP variants achieve notable reductions in latency and improvements in delivery reliability under high-congestion conditions, while maintaining low computational and communication overhead. These findings confirm that IFP offers an interpretable, scalable, and energy-aware routing solution suitable for large-scale intelligent transportation systems and next-generation vehicular networks. 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

This work examines the results of research into internal combustion engine malfunction using the example of a vehicle control group of a specific transport company during the warranty and post-warranty periods. Studies have established that the most frequently encountered distribution laws for random variables during vehicle operation are normal, Weibull, log-normal, and exponential, and research has determined the patterns of units’ malfunctions and the internal combustion engine parts. The reliable operation of vehicles is achieved by maintaining a stock of spare parts, the size and range of which play an important part in the ensuing costs. It is important to forecast the need for spare parts to improve the efficiency of vehicle operation. A common drawback of forecasting methods, from the point of view of material resource management is the limited consideration of important factors such as the spare parts’ delivery time from the moment of ordering, the time frame for performing the repair work, and the spare parts’ cost. We determined that 65.7% of spare parts are delivered within one day, and 15.7% are delivered within 2 weeks. Further, it takes up to 3 h for the replacement of 82.45% of the spare parts. To determine the need for spare parts, it is important to consider the actual operational reliability and the listed factors to enable optimizing the repair fund of the motor transport enterprise and increase the efficiency of use of rolling stock. Full article

The Internet of Vehicles (IoV) can transmit the status information of vehicles and roads through single-hop or multi-hop broadcast communication, which is a key technology for building intelligent transportation systems and enhancing road safety. However, in dense traffic environments, broadcasting Emergency messages via vehicles can easily trigger massive forwarding redundancy, leading to channel resource selection conflicts between vehicles and affecting the reliability of inter-vehicle communication. This paper analyzes the forwarding near the single-hop transmission radius boundary of the sending node in a probability-based inter-vehicle multi-hop forwarding scheme, pointing out the existence of the boundary forwarding redundancy problem. To address this problem, this paper proposes two probability-based schemes with boundary optimization: (1) By optimizing the forwarding probability distribution outside the transmission radius boundary of the sending node, the forwarding nodes outside the boundary can be effectively utilized while effectively reducing the forwarding redundancy they bring. (2) Additional forwarding backoff timers are allocated to nodes outside the transmission radius boundary of the sending node based on the distance to further reduce the forwarding redundancy outside the boundary. Experimental results show that, compared with the reference schemes without boundary forwarding probability optimization, the proposed schemes significantly reduce forwarding redundancy of Emergency messages while maintaining good single-hop and multi-hop transmission performance. When the reference transmission radius is 300 m and the vehicle density is 0.18 veh/m, compared with the probability-based forwarding scheme without boundary optimization, the proposed schemes (1) and (2) improve the single-hop packet delivery ratio by an average of about 5.41% and 11.83% and reduce the multi-hop forwarding ratio by about 18.07% and 36.07%, respectively. Full article

Access to healthcare is a crucial factor in achieving health equity. In rural South Africa, geographical isolation and inadequate transportation systems continue to hinder access to healthcare services, despite the existence of progressive national health policies. This study examines the impact of transportation on healthcare access in Mt Elias, uMshwathi Municipality, KwaZulu-Natal. Using a qualitative, exploratory design, semi-structured interviews were conducted with 52 community members. The data were analyzed thematically using ATLAS.ti. The findings reveal several structural barriers, including high transportation costs, long distances to clinics, unreliable ambulance services, poorly maintained roads, and unsafe travel conditions. In Mt. Elias, even when mobile clinics are available, their infrequent visits and fixed locations prevent access for individuals who are sick or those with a disability or living in widely scattered homesteads. Access to healthcare in rural communities is fundamentally shaped by transportation. To achieve universal health coverage, it is essential to improve rural road infrastructure, expand mobile clinic services, subsidize transportation costs, and integrate transportation planning into healthcare policies. Addressing these transportation barriers is not only a matter of service delivery but also of equity, justice, and human rights. Full article

Rural Sub-Saharan Africa (SSA) faces limited transport options, with many dispersed settlements dependent on poorly maintained roads. Light delivery vehicles (LDVs) can improve mobility, but conventional internal combustion engine vehicles are costly to operate and contribute to emissions. Electric vehicle (EV) conversions offer a practical alternative by extending vehicle life and reducing energy, maintenance, and environmental costs. This study presents a simulation-based framework to guide LDV conversion design for rural SSA. The framework includes component sizing, subsystem modeling, and full-vehicle benchmarking under representative conditions. Scenario-based simulations include trips ranging from shorter local access routes to longer remote trips on both paved and dirt roads, allowing the conversion’s performance to be quantified under representative conditions. A sensitivity analysis indicates that road grade, aerodynamic drag, and rolling resistance are the primary factors driving energy use variation. Using the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) drive cycle, the conversion energy consumption (∼217 Wh/km) comparable to that of commercial electric vans, though the range is reduced relative to its battery capacity. The framework establishes a benchmark for EV conversion performance in SSA and supports broader adoption of sustainable rural mobility solutions. Full article

Background: Kazakhstan’s transport system plays a key role in Eurasian logistics due to its position along the Middle Corridor. However, multimodal freight transport remains under-optimized due to infrastructure bottlenecks, uneven cargo flows, and limited digital tools for forecasting and planning. Methods: This study presents the development of an agent-based simulation model for analyzing multimodal transportation in Kazakhstan. The model integrates railway, road, and maritime components, simulating cargo flows across export, import, and transit scenarios. Key agents include orders, transport vehicles, logistics hubs, and border checkpoints. The model is implemented in AnyLogic 8.9 and calibrated using a mix of official statistics, industry data, and field estimates. Results: The simulation replicates key logistics processes, identifies congestion points, and evaluates delivery performance under different scenarios. Experiments demonstrate how bottlenecks at terminals and border crossings affect delivery times, vehicle utilization, and hub load. The model allows testing infrastructure development options and scheduling policies. Conclusions: The approach enables a dynamic assessment of logistics efficiency under uncertainty and can support decision-making in transport planning. The novelty lies in the integrated simulation of multimodal freight flows with infrastructure constraints. The model serves as a foundation for digital twin applications and scenario-based planning. Full article

The increasing frequency and intensity of extreme disasters, exacerbated by climate change, pose significant challenges to sustainable development by disrupting critical infrastructure and hampering relief efforts. Enhancing disaster resilience—a core objective of sustainable development—requires integrated approaches that simultaneously address infrastructure restoration and efficient resource allocation. This study proposes a sustainable optimization framework for post-disaster response, integrating road rehabilitation decisions with emergency logistics planning within a three-tier supply chain network. We develop a mathematical model that synergistically optimizes repair crew scheduling, depot location, and vehicle routing, with the objective of maximizing a comprehensive satisfaction index that balances timely delivery (time satisfaction) and fulfillment of material needs (demand satisfaction). This integrated approach directly contributes to sustainable disaster management by ensuring more reliable and equitable access to vital resources in affected communities. A tailored variable neighborhood search algorithm is designed to solve the model efficiently, as demonstrated through large-scale numerical experiments. Our findings highlight several policy-relevant insights for sustainable emergency planning: adequate budgeting is crucial for uninterrupted relief operations; strategic investments in rapid road repair capabilities or vehicle fleets significantly enhance system efficiency; and prioritizing time satisfaction (rapid response) yields greater overall benefits than merely increasing delivered quantities. Furthermore, restoring critical road infrastructure is shown to mitigate transportation uncertainties, thereby strengthening the resilience of the entire relief system. This work provides a quantifiable methodology and practical decision support tools for building more sustainable and resilient communities in the face of disasters. Full article