The electrical road system (ERS) truck formation operation is the most common working condition of this new road freight transportation system. Because two neighbouring trucks with unfavourable distance can interrupt the current collection process, the favourable distance must be obtained to prevent it. In this work, the influence of truck formation operation on pantograph-catenary interaction dynamics in ERS is first investigated to determine this favourable distance. Based on the structure of the ERS test line, an ERS pantograph-catenary model with double pantograph is formulated, and the measurement data from the ERS test line are used to validate the present model and provide truck vibration inputs. Through the present model, the influence of double pantograph distance on its dynamic behavior is investigated. It is found that the pantograph distance without truck vibration has little influence on pantograph-catenary interaction, but the truck vibration can heavily influence the same within a certain distance. This influence is dominated by the second frequency peak of the truck vibration, and its change in the panhead can be used to monitor the distance. Based on this, a method is finally given to determine the closest favorable distance between neighboring trucks in ERS.

Input–output tables provide a useful tool for analyzing economic and environmental impacts. The main purpose of this paper is to evaluate the accuracy of different regionalization methods in estimating input–output tables based on location quotients. Specifically, the paper aims to identify the most accurate methods and to propose a practical procedure to improve their estimation. In doing so, this study compares the accuracy of various methods, using the 2015 Korean multi-regional input–output table as a benchmark. In the analyzed location quotient methods, smoothing adjustments to the estimated quotients depend on the choice of smoothing parameter values. An additional contribution of this study is the proposal of a simple and efficient procedure for estimating these parameters based on commonly available information on road freight transport and goods imports from the rest of the world. The results show that this procedure improves the accuracy of these estimation methods.

Purpose This explorative study investigates the role of policy in freight transport systems and its implications for post-disaster freight movement. Using a qualitative scenario-based approach in the context of Aotearoa New Zealand, it examines how policy-related underlying causes affect freight performance after a major natural disaster. Design/methodology/approach Data were collected through semi-structured interviews with 30 industry and policy experts, followed by a thematic analysis to identify the key constraints in the post-disaster transport system under study. Building on the Theory of Constraints' theoretical perspective that most constraints within a system originate from policy-related root causes, a Current Reality Tree was built to capture and visualise these constraints, their cause-and-effect relationships, and their root causes. Findings The analysis identifies a complex web of interconnected constraints and causal relationships, revealing 18 underlying root causes that drive cascading and escalating effects across the freight system. These root causes trace back to six policy areas: government funding, infrastructure governance, emissions policy, land use and development, labour policy, and information governance. Originality/value This research shows that the performance of post-disaster transport systems is strongly influenced by government policy. Addressing key policy areas can enhance freight resilience, support continuity in the movement of goods, and ensure availability for production, trade, and consumption after a natural disaster.

Institutional drivers and sustainability priorities in blockchain adoption for an emerging economy

A game-theoretic approach for pricing policy under urban rail mixed passenger–freight transport

How passenger satisfaction affects the sustainable development of integrated passenger and freight transport based on public traffic: An evolutionary game approach

Towards sustainable and equitable accessibility for rural communities: An optimization model for an integrated passenger and freight transportation system with parcel lockers

The rapid growth of road freight has led to an increasing number of trucks, where their long braking distance, large mass, and limited maneuverability make them particularly vulnerable to safety risks, especially at high-risk nodes such as tunnels, bridges, and step grades. These risks are generated at the microscopic level but accumulate and propagate along the entire trip, creating the need for a modeling framework capable of capturing both local safety mechanisms and system-level traffic evolution. To address this need, this study proposes a multiscale hybrid traffic flow model that integrates an improved Cellular Automata (CA) model for detailed characterization of accident-prone segments and a Cell Transmission Model (CTM) for long-distance freight transport. Two transition areas are designed to ensure consistent information exchange between the discrete CA and continuous CTM domains. Using naturalistic driving data for calibration and validation, the hybrid model accurately reproduces truck-car interaction patterns and system-level traffic dynamics. The results show that vehicle heterogeneity and different vehicle combinations lead to distinct car-following behaviors and safety outcomes, as reflected in surrogate safety measures such as Time Headway and Time-to-Collision. Although trucks tend to adopt more cautious strategies, truck–truck interactions still exhibit the highest risk levels due to reduced maneuverability and accumulated disturbance. These findings provide actionable insights for freight operations and road safety management, supporting the development of targeted regulations and truck-specific safety strategies at high-risk locations.

Container surface damage detection is critical for ensuring the structural integrity and operational safety of intermodal freight transport. However, visual pseudo-textures arising from rust stains, specular reflections, and paint weathering cause frequent false positives, while the scarcity of puncture-type defects (Hole class) leads to missed detections. Existing YOLO-family detectors address neither the frequency-domain characteristics of such pseudo-textures nor the physical priors inherent in genuine structural damage. In this paper, we propose PPFS-YOLO, a physics-prior frequency-spatial fusion framework built upon YOLOv12s. Two lightweight modules are introduced: (1) Frequency-Spatial Fusion (FSF), which applies a learnable spectral mask in the Fourier domain and performs gated fusion with spatial features to suppress pseudo-texture responses; and (2) Edge-Guided Auxiliary Supervision Module (FIM), which encodes Sobel-derived edge priors as a differentiable constraint () to regularize feature learning toward physically plausible damage boundaries. Three pairs of FSF–FIM are inserted into the YOLOv12s neck and head at P3, P4, and P4-head scales. Experiments on a container damage dataset containing 7013 images and three classes (Dent, Hole, Rusty) demonstrate that PPFS-YOLO achieves 64.86% mAP@50, a +12.35 percentage-point improvement over the YOLO12s baseline (SGD, unified optimizer), with only +0.79 M additional parameters (+8.6%) and a modest latency overhead of 2.9 ms (17.2 ms vs. 14.3 ms at on an NVIDIA RTX 3090 GPU (NVIDIA Corporation, Santa Clara, CA, USA)). Ablation analysis reveals that is the critical catalyst: without it, the combined FSF+FIM modules yield only +0.83 pp, whereas the full model achieves +12.10 pp—underscoring the synergy between frequency-domain fusion and physics-prior regularization.

Abstract Purpose
Despite growing interest in alternative heavy-duty truck (HDT) powertrains, limited research has evaluated their techno-economic competitiveness across major freight markets while explicitly considering logistics and transportation cost structures. This study examines whether diesel‚ BEVs (Battery Electric Vehicles), and FCEVs (Fuel Cell Electric Vehicles) are market-competitive for freight trucks in the US‚ EU‚ and China by assessing the competitiveness of these powertrains‚ taking into account logistics and transport costs such as vehicle purchase price‚ maintenance and insurance costs‚ payload‚ infrastructure investment and operation costs and policy-related cost exemptions․
Methodology
To ease the relative competitiveness assessment of alternative HDT powertrains‚ a three-stage multi criteria decision making method approach is proposed․ In the first stage of the framework‚ the key techno-economic evaluation criteria are identified and their relative weights are computed using the Criterion Impact Loss (CILOS) method․ Second‚ the diesel‚ BEV and FCEV alternatives are ranked by applying the weighted criteria using two ranking techniques: namely‚ the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) and the Combined Compromise Solution (COCOSO) approaches․ Finally‚ a robustness analysis is performed using the Evaluation based on Distance from Average Solution (EDAS) approach․
Findings
The results indicate that diesel trucks are the most cost-competitive in the United States and the EU‚ due to lower cost volatility and established logistics infrastructure․ BEVs are also cost-competitive in certain regional and methodological settings‚ particularly in the EU․ In China, the relative competitiveness of powertrain technologies varies across methods, with no single technology consistently dominating. FCEVs generally rank as the least competitive option across regions.
Implications
Infrastructure readiness, energy pricing, regulatory incentives, and logistics cost structures strongly influence HDT powertrain competitiveness. Incorporating logistics cost considerations into techno-economic assessments provides more realistic insights for policymakers and industry stakeholders when designing freight transport decarbonization strategies and investment priorities.

The article examines the current challenges and prospects for the development of the railway freight transportation system of Ukraine in the context of European integration and full-scale war. The impact of military operations on the functioning of the railway infrastructure, changes in the geography of freight flows, the state of logistics routes and the increase in the load on border crossings with the countries of the European Union are analyzed. Particular attention is paid to the problems of technical incompatibility of tracks, wear and tear of rolling stock, limited capacity of transport nodes and insufficient development of multimodal logistics. The requirements of the European Union for the development of railway transport are considered, in particular the implementation of technical specifications for interoperability, integration into the Trans-European Transport Network, the development of digital technologies for transportation management and environmental modernization of the transport system. Key areas of industry transformation have been identified, including infrastructure modernization, border logistics development, digitalization of freight flow management, rolling stock renewal, and reform of the railway sector management system in accordance with the principles of the European Union. To assess the efficiency of rail freight transportation, an integrated transport efficiency coefficient has been proposed, which takes into account the volume of transportation, average speed, and operating costs. A comparative analysis of the performance indicators of the Ukrainian operator with European companies has been conducted, which made it possible to assess the competitiveness of the Ukrainian transport system and determine the potential for its integration into the single European transport space. The results of the study indicate that the modernization of railway infrastructure, the introduction of digital technologies, the development of multimodal transportation, and the harmonization of technical standards with EU requirements are key prerequisites for increasing the efficiency of freight transportation and strengthening Ukraine's role as an important transit hub between Europe and Asia.

The truck ban policy on freeways in central business districts (CBD) is extensively used in China nowadays to improve traffic safety and reduce traffic congestion. However, this policy will drastically impact freight transportation, especially when the freeway truck volume is high. To mitigate the negative effects and encourage truck drivers to use alternative freeways, this study proposes a freeway tolling problem for different types of trucks to reduce travel costs following the truck ban policy in CBD. It is formulated as a bi-level optimization problem. The upper-level problem optimizes the freeway toll rates for different types of trucks to reduce the total travel cost (TTC) of the network. The lower-level problem is a multiclass traffic assignment model to characterize the equilibrium flow mixed with passenger vehicles and different types of trucks following the toll strategy. The bi-level problem is solved using a line search algorithm developed based on a feasible direction method. Application of the proposed method in Ningbo, China, finds that the proposed solution algorithm can efficiently solve the bi-level problem and converges only after 11 iterations. Compared to the initial state where the truck ban is not implemented, the optimal tolling strategy can effectively reduce the TTC of the network by 8.5%, with an increase of only 1.15% for all trucks. This indicates that the proposed method can effectively nudge truck drivers to use alternative routes with a minor rise in travel costs. Therefore, it can help traffic managers design better strategies to avoid the resistance of truck users following the truck ban policy in CBD.

The article presents a scientific and methodological approach to selecting a rational transport and technological scheme for international road freight transportation, taking into account stochastic factors, military risks, and the scale of enterprise activity. The proposed model is based on the principles of multicriteria optimization and considers the structure of logistics costs, the intensity of freight flows, and the number of shippers and consignees. A comparative analysis of four alternative delivery schemes is carried out: direct delivery, delivery using a logistics center in the country of dispatch, delivery with a logistics center in the country of destination, and a combined scheme with two centers. For each scheme, the components of logistics costs are identified, including costs for order processing, shipment formation, transportation, loading and unloading operations, customs clearance, downtime, and addition-al expenses related to military risks. To verify the model, a series of experiments was conducted using a full-factorial experimental methodology, varying key factors such as the intensity of order arrivals, the average order volume, and the number of shippers and consignees. The obtained results showed that for small enterprises, the most efficient scheme is consolidation in the country of dispatch, while for medium and large enterprises, the optimal solution is the scheme with a logistics center in the country of destination. The combined scheme with two centers is advisable only under conditions of complex network supply structures and high flexibility requirements.The proposed model ensures minimization of logistics costs, improvement of efficiency in organizing international road freight transportation, and can serve as a practical tool for managerial decision-making in the field of transport logistics.

The article examines the work of the transport document processing centre in interaction with designated stations. An analysis was conducted of the monthly number of transport documents processed in the Kharkiv region and the indicators of processed documents per day per employee. In addition, the volumes of transport documents processed by each commercial agent per month were examined. The analysis revealed significant unevenness in railway transport operations, leading to overloading of employees at the transport document processing centre during certain periods of operation. The average processing time for transport documents exceeds the norm, which leads to unproductive downtime for wagons waiting in line for transport documents to be processed, as well as a deterioration in service conditions for shippers and consignees. This unevenness leads to many risks in the process of documenting transportation and financial losses for participants in the transportation process. The magnitude of the overall risk depends on the probability of an event occurring and the magnitude of potential losses.An optimised mathematical model of document flow organisation technology for railway freight transport has been developed, taking into account the risk and the time required for document processing, while minimising operating costs. The model takes into account the costs of processing transport documents, the costs of paying commercial agents, energy costs and possible financial losses to the railway in the event of certain risks. The presented technology for interaction between station employees and freight stations takes into account certain system limitations. The proposed model allows determining the required number of commercial agents and avoiding unproductive downtime of wagons in the queue while waiting for the processing of transport documents. The results of the calculations showed that the minimum operational and time costs for processing transport documents are achieved by increasing the number of employees per person.

This article develops a comprehensive concept for a rail container delivery system designed to effectively compete with road transport over short and medium distances. The core innovation lies in the synergistic combination of Precision Scheduled Railroading (PSR) principles, regular express logistics, and digital twin technologies to create a highly efficient transportation network featuring a scientifically substantiated topology. The proposed methodology addresses the critical need for rail systems to overcome inherent infrastructural rigidities and adapt to dynamic market demands by leveraging advanced optimization and simulation techniques. To model the network, a mathematical framework grounded in graph theory was formulated. This framework enables multi-criteria optimization that simultaneously considers five key performance indicators: balancing train load factors, minimizing the total number of trains, reducing delivery time, optimizing the number of hub stations, and ensuring the uniformity of eigenvector centrality across stations. The inclusion of eigenvector centrality as a distinct optimization criterion represents a novel contribution, as it allows for the quantification and enhancement of each station's strategic importance within the overall network, promoting a more resilient and evenly utilized infrastructure. To solve this complex optimization challenge, the NSGA-II (Non-dominated Sorting Genetic Algorithm II) was adapted to the specific context of transportation networks. The adaptation incorporates unique mechanisms such as the collapsing of transit stations to simplify network complexity and the enforcement of constraints on the graph's algebraic connectivity to maintain structural integrity and robustness. Experimental validation of the approach confirmed its effectiveness, yielding network configurations characterized by high structural stability, a uniform distribution of segment lengths, and acceptable values for network diameter and average inter-station distance. These resulting topologies provide a balanced foundation that is both operationally efficient and physically realizable. Furthermore, the study proposes a three-tier architecture for a transportation system digital twin, wherein the optimized network topology serves as the structural core. This core acts as the foundational layer for subsequent simulation modeling of freight flows, enabling real-time operational analysis, predictive insights, and robust decision support for managing disruptions and optimizing resource allocation. The integration of optimization results directly into the digital twin concept bridges the gap between strategic network design and tactical operational management. The scientific novelty of this research is twofold: it pioneers the use of eigenvector centrality as a direct criterion for transport network topology optimization, and it establishes a clear pathway for integrating these optimization outcomes into a functional digital twin architecture. The practical value lies in providing a viable blueprint for creating competitive rail container systems capable of delivering fast, economically efficient, and reliable service on short and medium hauls, thereby enhancing the modal share of railways in the freight transportation market.

Abstract The study analyzes the levels of pollutant emissions from road transport in order to assess its environmental safety during freight transportation from border railway transfer stations to the cities of Dnipropetrovsk region. The main focus is placed on comparing the specific emissions of carbon monoxide (CO), nitrogen oxides (NO x ), and hydrocarbons (CH) generated during the operation of freight vehicles. Average specific emissions depending on vehicle load modes were determined and assessed against existing environmental standards. Emissions of key pollutants were calculated both per hour and per kilometer of transport, considering driving speeds and their effect on total environmental load. The results enable comparative analysis of the environmental efficiency of road transport, forming a basis for improving logistics schemes for petroleum products in line with modern environmental requirements and the specific conditions of martial law, where minimizing the accumulation of petroleum products in frontline regions is a crucial task.

• Some road freight transportation decisions remain uninvestigated. • Paucity of application to real-world road freight transportation decisions. • Operationalisation of a framework to investigate data-driven decisions. • A possible theoretical lens is proposed to further expand the research on the topic. Road freight transportation represents the dominant mode for inland freight transportation and is a critical component of logistics systems. Decisions in this domain are inherently complex due to the scale of operations, the numerous actors involved, and the dynamic operating environment. Machine learning (ML) represents a valid tool to address this complexity. However, literature on the topic is largely technical and focused on specific applications, lacking broader managerial insights that researchers and practitioners can leverage when applying ML to road freight transportation management (RFTM) decisions. To address this gap, this paper presents a systematic literature review investigating the characteristics of decision making when ML is applied to RFTM, encompassing both the managerial and technical aspects. The findings indicate that, while numerous studies focus on vehicle routing problems, other decisions within RFTM remain largely underexplored. Furthermore, only a limited number of studies develop and validate machine learning (ML) solutions using real-world data, thereby constraining the assessment of their practical applicability and impact. In addition, drawing from the analysis of the review results, this study proposes a conceptual framing of ML applications in RFTM, depicting ML as a potential mitigator of the limitations associated with bounded rationality in RFTM decision-making. This paper lays the foundation for future research on the application of ML for RFTM. It also proposes a replicable approach for analysing data-driven decisions in other contexts. Moreover, it offers practical guidance to decision makers by highlighting the elements that ML can act upon, thus supporting its adoption in practice.

An e-delivery system based on pick-up points: demand forecast, system design and scenario assessment

This study examines supervision strategies for freight transport at the Balonggandu and Losarang Motor Vehicle Weighing Implementation Units (UPPKB) to address the Over Dimension Over Loading (ODOL) phenomenon, which threatens national road safety and infrastructure. The primary problem analysed is the gap between the high volume of freight vehicles and the limited capacity of manual supervision, leading to logistics inefficiencies. The study employed a mixed-method descriptive approach with data collection through field observations, questionnaires administered to 100 drivers, and in-depth interviews with key informants from the Ministry of Transportation. Data were analysed using SWOT matrices and AHP weighting. The novelty of this research lies in formulating priority strategies that integrate technical operational constraints analysis with inter-agency digital enforcement design for two specific UPPKB sites using a hybrid SWOT and AHP approach. Empirical findings indicate that external opportunity factors, specifically technological support, hold the highest weight (0.490). The strategy of inter-agency integration based on Information Technology (IT) was selected as the top priority (0.113) to overcome the limitations of manual supervision, representing a priority ranking for implementation focus rather than an absolute measure of effect size. The study concludes that a transformation towards integrated digital supervision is a strategic priority, with policy implications requiring the implementation of automated sanctions and the expansion of legal liability to cargo owners to achieve national logistics efficiency.

Construction waste transportation (CWT) is a tightly regulated urban activity central to urban development, traffic operations, and environmental governance. As an underexamined yet policy-sensitive segment of intra-city freight transportation, CWT exhibits complex operational logics that warrant systematic analysis for effective supervision and management. This study adopts a dynamic complex network perspective, leveraging large-scale GPS data from over 8,000 construction waste transport trucks in Chengdu, China. Dynamic community detection is applied to characterise the temporal evolution of network structures, revealing a clear two-phase daily operational pattern and community structures dominated by different transportation enterprises. In addition, a motif-based node importance identification method tailored to CWT networks is developed to uncover critical motifs and structurally important nodes. These insights provide managerial guidance for regional governance of construction waste transport and targeted supervision of key nodes within urban transportation networks.