Search Results (351)

This study develops a compact elasticity-based framework for assessing how autonomous truck adoption influences corridor-level performance, freight demand, modal competition, and CO₂ emissions in multimodal freight Intelligent Transportation Systems. The model specifies the constant elastic (log-linear) responses of traffic performance and generalized costs to adoption, enabling the closed-form characterization of system-level rebound and road–rail reallocation effects. The analytical results show that an internal adoption threshold $P^{*}$ emerges, defined by $d\widehat{E}/dP=0$, which separates a beneficial regime (efficiency gains dominate) from an adverse regime (rebound and modal shift dominate). Comparative statics indicate that $P^{*}$ decreases with stronger ITS capability A and increases with rebound intensity R and the road–rail carbon intensity contrast K. Numerical experiments across representative corridor contexts illustrate induced demand effects exceeding 25% under high-rebound conditions and threshold ranges around $P^{*}\approx 0.3$–0.4 for plausible parameters. The results provide interpretable guidance for coordinating autonomous truck deployment with intermodal logistics design and decarbonization strategies. Full article

To assess the market competitiveness of high-speed rail (HSR) express and forecast its freight volume, this paper develops an integrated framework combining strategic analysis, market forecasting, and competition assessment. A hybrid SWOT-AHP model identifies and quantifies key strategic factors, clarifying HSR express positioning. Considering macroeconomic and consumption factors, a GM(1,N) model forecasts intercity express volume. Based on a generalized cost function covering timeliness, economy, safety, and stability, an improved Logit model calculates HSR’s mode share against road and air express, deriving future HSR freight volume. Using China as a case study, results show: (1) A proactive strategy leveraging intrinsic strengths is recommended, supported by rapid intercity express growth; (2) HSR can capture over 20% mode share initially, showing strong competitiveness in medium-long distance transport; (3) Transport cost is the most sensitive factor, a 20% reduction raises mode share by 10%, while rising timeliness demands enhance long distance advantages. This study offers a quantitative basis for HSR express strategic planning. Full article

Background: The rapid digital transformation in logistics requires the adaptation of transport companies to electronic information management, particularly through the implementation of electronic consignment notes (e-CMR). This study examines how the digitization of transport documentation affects the competitiveness, operational efficiency, and environmental performance of road freight transport companies. Methods: A questionnaire survey was conducted among Slovak and Czech carriers to analyze their experience and readiness for adopting e-CMR. The collected data were evaluated using descriptive and comparative methods to quantify economic and ecological impacts, focusing mainly on invoicing efficiency and paper consumption. Results: The results show that only a small share of carriers currently use e-CMR, primarily due to high software costs and the lack of partner participation. Nevertheless, digitization can significantly shorten the average invoicing delay by approximately 11.5 days, releasing around 7% of tied-up working capital and improving cash flow. From an environmental perspective, the replacement of paper CMR forms could save millions of sheets annually, leading to a substantial reduction in CO₂ emissions and paper waste within the V4 region. Conclusions: The findings confirm that the adoption of e-CMR improves economic performance, increases transparency, and contributes to sustainability, representing a crucial step toward a more competitive and environmentally responsible road freight transport sector in Europe. Full article

To identify the emission reduction potential and policy synergies of Tokyo’s road passenger and urban road freight transport under the “carbon neutrality target,” this paper constructs an assessment framework for megacities. First, based on macroeconomic socioeconomic variables (population, GDP, road length, and employment), regression equations are used to predict traffic turnover for different modes of transport from 2021 to 2050. Then, the prediction results are imported into the LEAP (Long-range Energy Alternatives Planning) model. By adjusting three policy levers—vehicle technology substitution (ZEV: EV/FCEV), energy intensity improvement, and upstream electricity and hydrogen supply decarbonization—a “single-factor vs. multi-factor (policy synergy)” scenario matrix is designed for comparison. The results show that the emission reduction potential of a single measure is limited; upstream decarbonization yields the greatest independent emission reduction effect, while the emission reduction effect of deploying zero-emission vehicles and improving energy efficiency alone is small. In the most ambitious composite scenario, emissions will decrease by approximately 83% by 2050 compared to the baseline scenario, with cumulative emissions decreasing by over 35%. Emissions from rail and taxis will approach zero, while buses and freight will remain the primary residual sources. This indicates that achieving net zero emissions in the transportation sector requires not only accelerated ZEV penetration but also the simultaneous decarbonization of electricity and hydrogen, as well as policy timing design oriented towards fleet replacement cycles. The integrated modeling and scenario analysis presented in this paper provide quantifiable evidence for the formulation of a medium- to long-term emissions reduction roadmap and the optimization of policy mix in Tokyo’s transportation sector. Full article

This study investigates the influence of roadway geometry and environmental conditions on traffic crash frequency along a 57 km mountainous segment of the BR-116/SP (Régis Bittencourt Highway), one of Brazil’s most critical freight and passenger corridors. A Generalized Linear Mixed Model (GLMM) with a Negative Binomial distribution was developed using monthly data aggregated by highway segment. Explanatory variables included traffic exposure, geometric design characteristics, and meteorological factors. The results revealed that horizontal curvature and longitudinal grade are key determinants of crash occurrence and that the interaction between these factors substantially amplifies crash risk. Specifically, segments with combined tight curvature (radius < 500 m) and moderate-to-steep grades showed up to a 4.3-fold increase in expected crash frequency compared with straight or flat sections. The model achieved satisfactory fit (RMSE = 1.273) and provided a robust framework for identifying high-risk locations. The findings highlight the importance of geometric consistency and integrated safety management strategies, contributing to sustainable transport management and offering methodological and practical contributions to data-driven road safety policies in Brazil. Full article

Road transport constitutes a crucial element of the European economy, but it also generates significant external costs. In the process of reducing the impact of road transport on the environment and society, numerous actions are being undertaken to implement the concept of sustainable transport development in the Member States of the European Union. A key measure in this area is the introduction of low- and zero-emission propulsion systems in vehicles intended for passenger and freight transport. This article focuses on electric vehicles powered by battery energy storage systems. An essential component of these efforts is the development of alternative fuels infrastructure, which is expected to enable the operation of such vehicles by providing access to battery charging facilities. The development of infrastructure in the form of electric vehicle charging stations, initially concentrated in urban areas, has been extended to the network of European roads. The driving force behind this expansion is the European Parliament and the Council of the EU, which, on the basis of the Alternative Fuels Infrastructure Regulation (AFIR), stimulate the development of alternative fuels infrastructure along the TEN-T network. The aim of the article is to present selected challenges related to the electrification of road transport infrastructure in the context of the sustainable transport development concept and the construction of alternative fuels infrastructure along the TEN-T network. The research focuses on forecasting the demand for alternative fuels infrastructure along the A1 and A2 motorways, which form part of the TEN-T network within the territory of Poland. The research process stems from the implementation of the AFIR in the EU Member States. Full article

Urban freight pickup-and-delivery services operate in road networks where travel times are highly variable due to congestion, incidents, and operational restrictions. Such variability threatens the punctuality of deliveries and complicates the design of reliable service schedules. This paper examines an urban pickup-and-delivery vehicle routing problem with soft time windows under travel-time uncertainty and provides an empirical comparison of robust and deterministic planning approaches on a real road network. The problem is formulated as a time-dependent pickup-and-delivery VRP with soft time windows, where link travel times are represented by a finite set of scenarios calibrated from observed network conditions. The objective function combines four components that are central to urban freight operations: total travel time, total distance, and penalties for earliness and lateness relative to customer time windows. This structure captures the trade-off between routing efficiency and service quality. On this basis, a robust model is constructed that optimises tour plans with respect to scenario-based worst-case or risk-aggregated costs, while a standard deterministic model minimises the same objective using nominal (average) travel times only. An empirical study on a real urban network compares the deterministic and robust solutions with respect to delivery punctuality, tour length, and time-window violations across a range of demand and variability settings. The results show that robust routing systematically reduces the frequency and magnitude of late deliveries at the expense of only moderate increases in planned distance and travel time. Although energy use and emissions are not modelled explicitly, the improved reliability and reduced need for reactive re-routing indicate a potential to support more reliable and resource-efficient urban freight operations in the context of sustainable city logistics. Full article

This study presents a well-to-wheel life-cycle assessment (WTW-LCA) comparing battery-electric heavy-duty trucks (BEVs) with conventional diesel trucks, utilizing real-world fleet data from Southern California’s Volvo LIGHTS project. Class 7 and Class 8 vehicles were analyzed under ISO 14040/14044 standards, combining measured diesel emissions from portable emissions measurement systems (PEMSs) with BEV energy use derived from telematics and charging records. Upstream (“well-to-tank”) emissions were estimated using USLCI datasets and the 2020 Southern California Edison (SCE) power mix, with an additional scenario for BEVs powered by on-site solar energy. The analysis combines measured real-world energy consumption data from deployed battery electric trucks with on-road emission measurements from conventional diesel trucks collected by the UCR team. Environmental impacts were characterized using TRACI 2.1 across climate, air quality, toxicity, and fossil fuel depletion impact categories. The results show that BEVs reduce total WTW CO₂-equivalent emissions by approximately 75% compared to diesel. At the same time, criteria pollutants (NO_x, VOCs, SO_x, PM_2.5) decline sharply, reflecting the shift in impacts from vehicle exhaust to upstream electricity generation. Comparative analyses indicate BEV impacts range between 8% and 26% of diesel levels across most environmental indicators, with near-zero ozone-depletion effects. The main residual hotspot appears in the human-health cancer category (~35–38%), linked to upstream energy and materials, highlighting the continued need for grid decarbonization. The analysis focuses on operational WTW impacts, excluding vehicle manufacturing, battery production, and end-of-life phases. This use-phase emphasis provides a conservative yet practical basis for short-term fleet transition strategies. By integrating empirical performance data with life-cycle modeling, the study offers actionable insights to guide electrification policies and optimize upstream interventions for sustainable freight transport. These findings provide a quantitative decision-support basis for fleet operators and regulators planning near-term heavy-duty truck electrification in regions with similar grid mixes, and can serve as an empirical building block for future cradle-to-grave and dynamic LCA studies that extend beyond the operational well-to-wheels scope adopted here. Full article

Truck platooning refers to a group of heavy-duty vehicles travelling in close succession through cooperative driving technologies and inter-vehicle communication. This transport solution is increasingly investigated as a promising strategy to enhance the efficiency and sustainability of road freight transport. The expected benefits include fuel and operational cost savings, reduced emissions, improved traffic flow and congestion mitigation, as well as enhanced safety for both platoon drivers and surrounding traffic. This paper presents a literature review of truck platooning, with a specific focus on the expected benefits and on how they are addressed across two fundamental perspectives: planning and control. Planning encompasses issues related to platoon formation, maintenance and reconfiguration during transport operations, whereas control focuses on the methods and schemes used to coordinate vehicle behaviour within and between platoons. The reviewed contributions are further analysed according to the methodology adopted, the level of vehicle automation, and the specific control approaches implemented. The resulting classification provides an integrated view of how different research streams contribute to economic, environmental, safety and social benefits. Finally, the current gaps and promising research directions are outlined to support future developments in large-scale platooning deployment. Full article

Electromobility is a key element of efforts to reduce transport emissions at points where transport tasks are carried out (e.g., along roads, in urban areas). At the same time, the implementation of electromobility, as a whole, encompasses the movement of people and cargo using electric vehicles (EVs), is strongly dependent on the deployment of EV charging points, which are part of the alternative fuel infrastructure. At the current stage of electromobility development, the process of deploying alternative fuel infrastructure along the TEN-T (Trans-European transport network) is underway, a process mandated by the AFIR (Regulation for the Deployment of Alternative Fuels Infrastructure). The AFIR regulation assumes the construction of infrastructure adapted to serve low- and zero-emission vehicles along the TEN-T network. The elements of the infrastructure under construction include a recharging pool, a recharging station, a recharging point for electric vehicles (EVs), and hydrogen refueling stations for fuel cell electric vehicles (FCEVs). It should be noted that infrastructure elements must be adapted to support light-duty electric vehicles (eLDVs) and heavy-duty electric vehicles (eHDVs). This approach expands the possibilities of using electric vehicles in passenger and freight transport within the TEN-T network. The aim of this article is to estimate the impact of electric vehicle charging points on electromobility in a selected area. During the research phase, spatial interpolation of electric vehicle charging points was conducted using GIS tools. The spatial interpolation of electric vehicle charging points presented in the article represents an innovative approach at the stage of analysis and development of alternative fuel infrastructure along the TEN-T network. Full article

Fatigue driving of freight vehicles is a major threat to transport safety, often causing heavy casualties and property losses. However, existing studies only focus on superficial correlations between fatigue driving and influencing factors, failing to reveal intrinsic causal mechanisms, which limits practical guidance for prevention. To address this gap, this study, focusing on safety performance analysis in intelligent transportation systems and machine learning applications for sustainable transport management, uses monitoring data of “two types of passenger vehicles and one type of hazardous materials transport vehicle” in Shanghai. It identifies causal relationships between fatigue driving and 19 key factors (vehicle speed, driving time period, etc.) via a causal inference framework. Results show that 10 factors (including driving during specific periods) positively affect fatigue driving, while 9 factors (including vehicle speed) have negative effects. A Causal-GBDT Hybrid Model is built by weighting causal core factors into XGBoost (1.7.6) and CatBoost (1.2). Results show causal weights raise XGBoost accuracy from 90% to 93% and CatBoost from 89% to 94%. This clarifies fatigue triggers, provides technical support for targeted prevention, and advances machine learning in freight safety risk management. The research results can provide technical support for the development of real-time fatigue warning systems for freight vehicle and traffic safety management policies, contributing to the sustainable improvement of road transport safety. 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

Current research on the performance and emissions of vehicles and internal combustion engines should include analysis of efficiency-enhancing technologies and emission reduction strategies across a variety of vehicle systems. To improve both performance and emission control, it is necessary to examine advanced heavy-duty driveline technologies, considering their real-world impact on fuel economy and emission reduction under various driving conditions. This article will deal with predictive cruise control (PCC) and its influence on the operating characteristics of a truck, specifically a semi-trailer combination. The measurement was carried out using dynamic driving tests of a truck on a selected road. The use of electronic systems for automatically maintaining the vehicle’s motion states (especially speed) based on the specified conditions most often has several benefits for the driver not only from the point of view of vehicle operation but also from the point of view of transport companies (cost reduction). It is generally known that the use of these electronic systems reduces the vehicle’s fuel consumption and therefore also reduces the amount of exhaust gases. Comparing the individual directions of the road tests, the difference in relative maximum power utilization between the driver and the PCC system was 26.42% in the ST-MY direction and 23.81% in the MY-ST direction. The use of PCC also results in fuel savings of up to 17.11%. This study provides new insights into the quantification of the impact of PCC on fuel consumption in real operating conditions and highlights the potential for integrating PCC into driver assistance systems and logistics planning to reduce costs and emissions in freight transport. Further research could focus on applying this system in specific road conditions. Full article

Liquefied natural gas (LNG) container trucks are regarded as clean energy vehicles with the potential to reduce air pollution. However, their CO₂ emissions remain relatively high and are not yet well understood. In this study, the actual CO₂ emissions of LNG container trucks in Shanghai were measured using a portable emissions measurement system (PEMS). This study quantitatively analyzed the relationship between traffic congestion levels and CO₂ emissions on elevated roadways, providing new insights into the impact of urban traffic conditions. In addition, distinct emission patterns were revealed under different uphill, downhill, and level road conditions, highlighting the substantial effects of roadway geometry on vehicle carbon emissions. Based on these findings, engine-related factors were identified as the dominant contributors, explaining 74% of the emission variance, while road slope analysis showed that uphill driving increased emissions by 13.41% compared with flat roads, whereas downhill driving reduced them by 76.22%. Finally, an efficient carbon emission prediction model for LNG container trucks was developed using machine learning methods. This study enriches the understanding of carbon emissions from LNG container trucks and provides theoretical support for their future applications in sustainable freight transportation. Full article

Despite continued efforts by the Korean government to improve road safety, truck-related accidents remain disproportionately fatal, with a rate approximately 2.6 times higher than that of passenger vehicles. Although legal regulations prohibit overloading and improper loading, existing enforcement practices—primarily dependent on low-speed weigh-in-motion (WIM) systems—are limited in coverage and responsiveness. This study develops and validates standardized test procedures for detecting overloading and improper loading in commercial freight vehicles using a high-speed weigh-in-motion (HS-WIM) system. The HS-WIM system offers advanced sensing capabilities, including vehicle speed, length, axle configuration, and weight measurement at highway speeds. However, Korean HS-WIM performance standards currently lack detailed guidance, especially concerning group axle load testing and asymmetric cargo detection. To address these regulatory and technical gaps, a comprehensive set of test scenarios was designed based on domestic and international standards. A dedicated testbed was constructed, and 12 commercial vehicle types were tested under varied speeds and loading conditions. The proposed procedures reliably detect violations, and the study introduces evaluation criteria that improve HS-WIM system accuracy and support future enforcement and policy development in Korea. Full article