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To support a modal shift toward sustainable freight solutions, such as inland waterway transport (IWT), researchers and practitioners require long-term historical data on IWT freight flows. However, such comprehensive time series have been unavailable until now. This study addresses this gap by presenting a harmonized dataset encompassing 50 years (1970-2023) of IWT freight data across Europe, with a focus on the Rhine-Alpine Corridor. The dataset includes transport volumes (in tonnes) and transport performance (in ton-kilometers), classified according to NST-R, NST2007, and CCR nomenclatures. To ensure data continuity and completeness, processing techniques-including imputation and optical character recognition-were applied. The dataset offers valuable insights for researchers, policymakers, and transport planners aiming to comprehend and enhance the role of IWT in Europe's freight transport landscape.

Rail freight volume trend prediction faces challenges due to data fuzziness, complexity and nonlinearity, and traditional deterministic prediction methods frequently fall short of practical application needs, particularly in addressing uncertainty. To overcome these limitations, we proposed a freight volume trend prediction model that integrated Fuzzy Information Granulation (FIG) with evolutionary optimization. The three-phase methodology establishes: (1)A FIG method was utilized to transform raw time-series into tri-granular representations (Low, R, Up) through fuzzy c-means clustering with temporal constraints, extracting feature information from the raw time-series data and encapsulating it into information granules (2) For complex predictions with small samples, we applied a Support Vector Machine (SVM) for granular modeling, combined with an Improved Particle Swarm Optimization (IPSO) algorithm featuring dynamic inertia weights and mutation operators to prevent premature convergence during training. (3) A hybrid FIG-IPSO-SVM architecture implementing granular-level regression with uncertainty quantification. Validation using 9-year operational records (2013-2022) from the Lanzhou Freight Center (n = 114 monthly observations) in China reveals statistically significant enhancements: compared to the FIG-GS (grid search)-SVM and FIG-PSO (Particle Swarm Optimization)-SVM algorithms, the proposed IPSO-SVM algorithm achieved the smallest prediction error for each granulated set (Low, R, Up) and the smallest mean maxima of absolute percentage error ([Formula: see text]) for the prediction interval of freight volume, at 5.03%. Moreover,it yielded the tightest prediction interval, characterized by a relative width (Rw) of just 8.53% and a corresponding interval width (W) of only 516,209 tons, surpassing all benchmark models.These findings validate that the FIG-IPSO-SVM framework substantially improves interval prediction precision and trend detection reliability, providing actionable intelligence for railway infrastructure planning and operational optimization.

The development of digital twins for railway freight vehicles represents a key step toward more efficient, data-driven maintenance and safety assessment. This study focuses on the creation of a digital twin of the T3000 articulated freight wagon, one of the most widespread intermodal transport solutions in Europe. Despite its relevance, the dynamic behavior of this vehicle type has been scarcely investigated so far in scientific literature. A dedicated onboard measurement layout was defined to enable comprehensive monitoring of vehicle dynamics and the interactions between adjacent wagons within the train. The experimental setup integrates inertial sensors and a 3D vision system, allowing for detailed analysis of both rigid-body and vibrational responses under real operating conditions. A high-fidelity multibody model of the articulated wagon was developed and tuned using the acquired data, achieving optimal agreement with experimental measurements in both straight and curved track segments. The resulting model constitutes a reliable and scalable digital twin of the T3000 wagon, suitable for predictive simulations and virtual testing. Future developments will focus on a deeper investigation of the buffer interaction through an additional experimental campaign, further extending the digital twin's capability to represent the full dynamic behavior of articulated freight trains.

The Los Angeles (LA) metropolitan region remains in nonattainment for ozone despite decades of reductions of ozone precursors, nitrogen oxides (NO x ) and volatile organic compounds (VOCs). NO x emissions from freight vehicles (ships, heavy duty trucks, trains, and airplanes) are expected to exceed emissions from passenger vehicles in southern California by 2030. Here, we use random forest machine learning to estimate the impact of freight activity on hourly NO x concentrations and determine summertime ozone production regimes across the LA basin. We find that freight activity contributes over half of weekday NO x impacts relative to non-truck traffic. During peak ozone hours, coastal areas, south LA, areas downwind (east) of downtown LA, and downtown San Bernardino are VOC-limited. Our results suggest that as of 2021, the Los Angeles urban core and nearby downwind areas have not transitioned to a NO x -limited regime on most days during the May to September ozone season. This study shows the applicability of machine learning to estimate concentration impacts from specific sources in the face of uncertain emission inventories and to analyze current ozone production regimes in areas with hourly ground observations.

Increased traffic congestion, limited delivery windows, vehicles' diverse fleets, and dynamic environments are all contributing factors to the inefficiency of urban freight systems. Due to the inability of traditional routing techniques to adjust in real-time to partially visible multi-constrained conditions, delivery delays, fuel consumption, and operating costs all increased. This research proposes a constraint-aware projected policy learning-reinforcement learning (CAPPL-RL) framework to optimize urban freight delivery routes while enforcing real-world constraints, including traffic congestion, delivery time windows, and vehicle capacity. The routing problem is formulated as a partially observable Markov decision process, with autonomous vehicles as agents navigating stochastic urban traffic networks. Using the UFVOD dataset, CAPPL-RL integrates Q-learning with ε-greedy exploration and projection-based constrained policy optimization (PCPO) to enable adaptive, constraint-aware routing. Simulation results demonstrate that CAPPL-RL outperforms PCPO-RL, reducing average delivery time from 65.3 to 52.1 min (20.2%), fuel consumption from 0.12 to 0.093 L/km (22.5%), and constraint violations in time windows from 12 to 3 (75%) while achieving 100% compliance with vehicle capacity limits. These results validate CAPPL-RL as a robust, scalable, and adaptive framework for dynamic urban freight logistics.

Truck-hailing is an emerging business model that could potentially transform the road freight sector. As a new form of horizontal collaborative transport, it offers great potential to improve operational efficiency-an important yet underrepresented demand-side strategy in current transport decarbonization research. Drawing on a proprietary national truck-hailing dataset (N = 51,021) and an enhanced vehicle fleet model that incorporates operational factors, here we assess the potential carbon emission impacts of truck-hailing and operational efficiency improvements in China under multiple scenarios. Under medium projections, enhancements in load factor and reductions in empty running could cut China's road freight emissions by 886-2203 Mt (9%-24%) in the near term (2020-2035) and 2504-3327 Mt (23%-31%) in the long term (2035-2060), compared to a business-as-usual pathway. However, capacity constraints of zero-emission vehicles may reduce these benefits, resulting in roughly 3% higher emissions in the near term and nearly 10% in the long term.

In August 2023, the launch of Shanghai Containerized Freight Index (SCFI) futures provides a suitable tool for risk management in the container shipping market, as well as new options for risk management of other financial assets. However, limited research exists on the influencing factors behind container freight rate fluctuations. This paper explores the nonlinear dynamic interdependence between the SCFI and 12 factors from the stock, commodity, carbon, and other markets using a data decomposition-reconstruction-based time-varying copula method, which can assist the stakeholders in hedging risk at different timescales. The findings reveal that most factors show no or limited upper tail dependence with SCFI in the short term. Medium- and long-term dependence is significantly stronger, indicating structural connections over longer horizons. Moreover, the dependence intensifies during extreme risk events. Generally, downside tail risks exert a greater influence on SCFI in the medium to long term, while upside tail risks are found to affect SCFI at any time horizon. This paper focuses on the tail risk interdependence analysis between SCFI and other assets, because the launch of SCFI futures makes the stakeholders to use this future to build risk management portfolios with other assets inevitably. The result provides useful implications to stakeholders with varying financial or investment attributes associated with shipping industry, aiding them in clarifying the different tail risk associations between SCFI futures and other assets at different timescales.

Ballastless track continuous welded rails (CWR) are used on a multi-span 100 m simply supported steel truss bridge of a mixed passenger and freight railway, to study the mechanical characteristics and influence factors of CWR on the 100 m simply supported steel truss bridge, a track-bridge-piers spatial finite element model was established based on the track-bridge interaction (TBI) principle. The influence of design parameters such as the number of bridge spans, the longitudinal stiffness of piers of the simply supported steel truss bridge, the arrangement of bridge bearings, the type of rail, and the track longitudinal resistance type on the mechanical characteristics of CWR on the simply supported steel truss bridge were systematically examined. The research results indicate that for multi-span simply supported steel truss bridges, the model can be simplified by considering 8 spans when the total number of spans exceeds 8. Likewise, when the number of adjacent concrete box girder spans exceeds 5, 5 spans can be adopted. The longitudinal stiffness of piers of the simply supported steel truss bridge has a significant influence on the rail braking force and the rail broken gap. Increasing the longitudinal stiffness of the piers from 399kN/cm to 5000kN/cm has a much greater effect on the forces and deformations of CWR on the bridge than increasing it further from 5000kN/cm to 10000kN/cm. The bridge expansion length is primarily governed by the arrangement of fixed bearings on the adjacent concrete box girder. Therefore, to reduce the forces in the CWR on the bridge, the simply supported steel truss bridges and the adjacent concrete box girders should adopt a consistent bearing arrangement. In addition, rails with a larger cross-sectional area can reduce the additional rail stress. The used of small resistance fastener systems for ballastless track decreases the rail expansion force and rail braking force by 56.72% and 18.05%, respectively, but it increases the rail broken gap by 37.49%. The research results can provide references for the design of CWR on the simply supported steel truss bridge.

The present study evaluates the effect of real-world operational factors and driving behaviors that significantly contribute to CO2 emissions and total energy consumption of the port-based heavy-duty vehicles (HDVs). Interpretable machine learning techniques are applied within an eXplainable Artificial Intelligence (XAI) framework to assess the impact of input variables on prediction accuracy. The inherent simplifications in these approaches often limit their ability to capture the complex, nonlinear characteristics of vehicular emission determinants, particularly under dynamic, micro-operational conditions associated with real-world settings. XGBoost showed higher predictive accuracy over conventional regression and other ensemble methods, with up to 46% improvement in R 2 and over 80% reduction in estimation errors. To address the black-box nature associated with the model, this study adopts XAI techniques, with SHapley Additive exPlanations (SHAP) employed to quantify feature contributions and enhance the interpretability. The results show that real-world CO2 emission levels remain substantially high under dynamic operational conditions, emphasizing the need for improved transit and freight management strategies to mitigate vehicular emissions. This further reinforces the importance of regulatory frameworks that incorporate CO2 emission and fuel-efficiency standards alongside conventional pollutant limits. Such progressive targets are intended to curb the climate impact, stimulate technological innovation, and support long-term low-carbon transition goals.

Worksite health promotion programs (WHPPs) are able to promote a healthier lifestyle for blue-collar workers in freight transport, yet their participation is generally low. This study aims to identify different health lifestyle classes among blue-collar workers in freight transport and investigate the relationship between class membership and WHPP participation. Data from 16,897 employees were obtained from an online health questionnaire as part of a sector induced WHPP (89.1% male, 71.0% blue-collar worker, Mage = 49.3 years (SD = 12.7)). A latent class analysis was conducted to identify health lifestyle classes. Classes for blue-collar and white-collar workers were compared. Characteristics of the blue-collar workers’ classes were examined together with the likelihood of WHPP participation. For blue-collar workers, a 5-class solution provided the best fit. These classes were labeled: (1) “unhealthy diet” (14%), (2) “health promoting” (29%), (3) “lack of moderate physical activity” (31%), (4) “low physical activity” (15%), and (5) “sleep deprived” (11%). For white-collar workers, a 4-class solution provided the best fit, with three comparable classes and one “health compromising” class. Blue-collar workers of the “unhealthy diet” and “sleep deprived” class reported the lowest perceived health, and showed highest WHPP participation levels. “Low physical activity” class members reported unhealthy behaviors, yet showed lowest participation levels. These findings indicate that different lifestyle classes exist among blue-collar workers within the freight transport industry which can be linked to WHPP participation. Consequently, WHPPs and implementation strategies can be adjusted to serve existing classes among blue-collar workers within the industry, in order to enhance participation.

The global freight forwarding industry confronts complex and cascading risks. Conventional analytical methods are often inadequate for managing these risks. This paper proposes a protocol to construct and utilize a causal Artificial Intelligence (AI) framework for systemic risk management. The protocol comprises three primary stages. The first stage involves the construction of a hierarchical Bayesian Network (BN). This BN serves as a causal knowledge graph. Its construction synthesizes domain expertise to map the relationships between fundamental risk drivers and core Key Performance Indicators (KPIs): Cost, Time, and Reliability. The second stage is the parameterization of the BN through the definition of conditional probabilities. This step fuses expert judgment with insights from industry reports. The Noisy-MAX model is employed to manage uncertainty in data-scarce environments. In the final stage, the parameterized model is used to perform predictive simulations and diagnostic analyses. Forward inference generates a baseline risk profile. Concurrently, sensitivity analysis identifies high-leverage intervention points. The application of this protocol yields several critical insights: (i) market supply and demand are identified as the central node of systemic risk. (ii) The model generates distinct risk fingerprints for each KPI. Cost exhibits the highest vulnerability to commercial factors. Time is most sensitive to logistical disruptions. Reliability is uniquely exposed to cybersecurity threats. This work contributes an interpretable AI tool. The tool functions as a what-if engine. It enables freight forwarders to shift from reactive crisis management to proactive, precision-guided risk mitigation.

Subject to the risk of disruptions in high-speed rail (HSR) logistics networks caused by natural disasters or equipment failures, this study proposes an emergency scheduling optimization framework based on truck transshipment. By establishing a Mixed-Integer Linear Programming (MILP) model that integrates vehicle deployment point selection, route planning, and timeliness constraints, it achieves, for the first time, multi-level collaborative decision-making covering "vehicle deployment point selection - truck scheduling - goods transshipment" following an HSR logistics disruption. An Adaptive Large Neighborhood Search (ALNS) algorithm is designed, incorporating a dynamic strategy combining destroy operators (random/worst/Shaw/depot consolidation removal) and repair operators (greedy/regret-2/regret-3 insertion) to generate high-quality scheduling schemes. Using both the Zhengzhou-Qingdao Express Rail Line disruption case and multi-scale random instances, the model's effectiveness is validated: ALNS achieves solution quality comparable to CPLEX with a maximum gap of only 0.037% while substantially reducing computation time, and significantly outperforms GA in both solution quality and efficiency.

Electrifying long-haul heavy-duty vehicles (HDVs) entails high private costs but offers substantial reductions in external costs by substituting diesel combustion with electricity generation. We combine technoeconomic analysis and life-cycle assessment of lithium-ion battery electric (BE) and diesel HDVs to estimate total private costs and monetized climate and health damages in the United States. In 2025, BE-HDVs are estimated to have 46% higher private costs ($0.71 mile⁻¹) than diesel trucks, decreasing to 33% ($0.52 mile⁻¹) by 2035. However, their external costs are 64-69% lower in 2025 and 70-80% lower in 2035. Overall, BE-HDVs yield positive net societal benefits by 2035, contingent on policies that accelerate their adoption.