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Boosting the rail freight modal share is an ambitious target in Europe and North America. Yards, where freight trains are arranged, can be crucial in realizing this target by reliable dispatching to the network. This paper predicts freight train departures by developing a simulation‐assisted machine learning model with two concepts: general (adding all predictors at once) and step‐wise (adding predictors as they become available in sub‐yard operations) for hump yards with the conventional layout to provide a generalized model for European and North American contexts. The developed model is a decision tree algorithm, validated via 10‐fold cross‐validation. The model's performance on three data sets—a real‐world European yard, a baseline simulation, and an ultimate randomness simulation for a comparable North American yard—shows a respective R2$R^2$ of 0.90, 0.87, and 0.70. Step‐wise inclusion of the predictors results differently for the real‐world and simulation data. The global feature importance highlights maximum planned length, departure weekday, the number of arriving trains, and minimum arrival deviation as key predictors for the real‐world data. For the simulation data, the most significant predictors are departure yard predictors, the number of arriving trains, and the maximum hump duration. Additionally, utilization rates—except for the receiving yard—enhance the predictions. [ABSTRACT FROM AUTHOR]

With the rapid development of logistics, the categories of goods and the frequencies of train transportation in railway freight have increased significantly. The volatility and uncertainty of railway freight transportation have become even greater. Accurately predicting railway freight volume in the medium to long term has become increasingly challenging. On the basis of traditional prediction models, this paper introduces the concepts of interval and probability prediction, and proposes a temporal convolutional network (TCN)‐bi‐directional long short‐term memory (BiLSTM) interval prediction method for medium and long‐term railway freight volume. The method uses grey relational analysis for data dimensionality reduction and feature extraction, and TCN, BiLSTM, and quantile regression for modelling. Through a case study of freight transportation on the Shuohuang Railway, the results show that the TCN‐BiLSTM model achieves higher accuracy in point prediction and better performance in interval prediction compared to other general prediction models. The interval prediction can provide references for freight volume fluctuations in periods with significant volatility, which can assist railway transportation companies in better scheduling and planning based on such information. [ABSTRACT FROM AUTHOR]

Abstract Boosting the rail freight modal share is an ambitious target in Europe and North America. Yards, where freight trains are arranged, can be crucial in realizing this target by reliable dispatching to the network. This paper predicts freight train departures by developing a simulation‐assisted machine learning model with two concepts: general (adding all predictors at once) and step‐wise (adding predictors as they become available in sub‐yard operations) for hump yards with the conventional layout to provide a generalized model for European and North American contexts. The developed model is a decision tree algorithm, validated via 10‐fold cross‐validation. The model's performance on three data sets—a real‐world European yard, a baseline simulation, and an ultimate randomness simulation for a comparable North American yard—shows a respective R2 of 0.90, 0.87, and 0.70. Step‐wise inclusion of the predictors results differently for the real‐world and simulation data. The global feature importance highlights maximum planned length, departure weekday, the number of arriving trains, and minimum arrival deviation as key predictors for the real‐world data. For the simulation data, the most significant predictors are departure yard predictors, the number of arriving trains, and the maximum hump duration. Additionally, utilization rates—except for the receiving yard—enhance the predictions.

Abstract With the rapid development of logistics, the categories of goods and the frequencies of train transportation in railway freight have increased significantly. The volatility and uncertainty of railway freight transportation have become even greater. Accurately predicting railway freight volume in the medium to long term has become increasingly challenging. On the basis of traditional prediction models, this paper introduces the concepts of interval and probability prediction, and proposes a temporal convolutional network (TCN)‐bi‐directional long short‐term memory (BiLSTM) interval prediction method for medium and long‐term railway freight volume. The method uses grey relational analysis for data dimensionality reduction and feature extraction, and TCN, BiLSTM, and quantile regression for modelling. Through a case study of freight transportation on the Shuohuang Railway, the results show that the TCN‐BiLSTM model achieves higher accuracy in point prediction and better performance in interval prediction compared to other general prediction models. The interval prediction can provide references for freight volume fluctuations in periods with significant volatility, which can assist railway transportation companies in better scheduling and planning based on such information.

Electrifying freight trucks will be key to alleviating air pollution burdens on disadvantaged communities and mitigating climate change. The United States plans to pursue this aim by adding vehicle charging infrastructure along specific freight corridors. This study explores the coevolution of the electricity grid and freight trucking landscape using an integrated assessment framework to identify when each interstate and drayage corridor becomes advantageous to electrify from a climate and human health standpoint. Nearly all corridors achieve greenhouse gas emission reductions if electrified now. Most can reduce health impacts from air pollution if electrified by 2040 although some corridors in the Midwest, South, and Mid-Atlantic regions remain unfavorable to electrify from a human health standpoint, absent policy support. Recent policy, namely, the Inflation Reduction Act, accelerates this timeline to 2030 for most corridors and results in net human health benefits on all corridors by 2050, suggesting that near-term investments in truck electrification, particularly drayage corridors, can meaningfully reduce climate and health burdens.

In our study, we describe how the performance of certain modes of goods and passenger transport, as well as the postal service, has developed over the past hundred years, and how the stock of individual vehicles has changed. Technical development and methodological changes have significantly influenced the measurement and publication of certain areas of transport statistics. Based on the collection and processing of data from the Hungarian Statistical Yearbooks, we can state that natural metrics are capable of capturing long-term trends and well characterize the transport policy decisions of individual eras, as well as economic business cycles. [ABSTRACT FROM AUTHOR]

This paper presents the results of a ranking-type Delphi study on the critical risk factors for the adoption of an electric road system (e-road) for trucks on the main motorways in Sweden. The investment cost of such a system is high, necessitating an upfront evaluation of the adoption risk factors to reduce the likelihood of budget overruns and project delays. Participating Swedish e-road experts (N = 52) from the public sector, private sector, and academia identified 32 unique risk factors, which were divided into five categories. The three most critical risk factors, as ranked by the experts, were 'low expansion rate,' 'low utilization rate,' and 'lengthy public-sector evaluation.' Overall, market and financial risks were ranked as more important than institutional, technological, and sustainability risks. This study has important implications for policymakers in countries considering e-road adoption. [ABSTRACT FROM AUTHOR]

Historically, Brazil has always stood out in exports of commodities, especially the agricultural ones. Currently, among the commodities exported by the country, stand out the products of the soy complex, (soybean, soymeal and oil). Given the tax structure of the country, there is a predominance in soybean exports. This work initially presents an overview of the international soybean market, involving production, export, import and crushing of this commodity. Also, data on soybean production, area and productivity in Brazil by regions are presented. Next, the issue of logistics for soybean exports is addressed, and the routes for soybean exports were mapped and the Decomposition Method X13-ARIMA-SEATS was applied to remove the seasonality from the series of road freight and heavy vehicle flows on the highways of São Paulo and Paraná. Finally, an Engle- Granger cointegration model was estimated to estimate the long-term elasticities between the value of road freight and international oil and soybean prices.

The paper deals with the extension of the logistics simulation within an agent-based simulation framework to be able to consider the delivery across several supply chains. The logistics simulation is placed in the context of the underlying freight transportation simulation. The use of multiple possible logistics chains addresses a key limitation of previous studies. Logistics chains represent the various transportation options of a Logistic Service Provider (LSP) and are made up of resources such as depots, logistics hubs and carriers that the logistics service provider can commission. When implementing the new functionality, the existing flow of the simulation process must be considered. In particular, changes need to be made to the assignment of freight requests to logistics chains, the iterative optimization of this assignment and scheduling. Appropriate assignment methods and rescheduling strategies are developed and presented. These innovations reveal compatibility problems within the existing simulation framework. The functionality developed is presented in constructed scenarios, highlighting the advantages of using multiple logistics chains. In addition, the applicability is demonstrated in a comprehensive real scenario that includes the delivery of supermarkets. [ABSTRACT FROM AUTHOR]

We examined how the unprecedented rise in freight rates in 2021 affected US containerized agricultural trade. We considered alternative measures of trade (value, volume, container numbers), as well as product, port, and partner‐country characteristics, and conducted separate analyses for exports and imports. Overall, we find that shipping costs have a substantial trade‐reducing effect. On average, 10% higher shipping costs reduce US agricultural export values by 0.58% and import values by 1.72%. For exports, we find a significant negative impact for several product categories. Our results suggest that policies addressing port congestion and shipping rate issues will have heterogenous outcomes. [ABSTRACT FROM AUTHOR]

As in many countries, rail electrification in Australia is largely confined to the major cities, and elsewhere most trains are powered by diesel. Electrification is expensive and only viable on lines where traffic density is high. Having adopted the 2050 Zero CO2 emissions target, there is limited time to find alternative power sources or repower all diesels. Hydrogen is one of the most promising alternatives, and a few hydrogen-powered trains are already in service in Europe. However, hydrogen must be stored as a compressed gas or a liquid, and either way the storage volume is much greater than diesel fuel, and handling is difficult. This paper looks at the feasibility of hydrogen for locomotive-hauled freight applications, with a focus on Australia. [ABSTRACT FROM AUTHOR]

Data on the location and size of logistics sites is essential for the accurate system-level modelling of transport and logistics operations. This is becoming increasingly important to support governments and industry transition to a net zero future which will feature new operating models and vehicle technologies, particularly for electric vehicle operations. In this work we present a dataset of logistics sites across England and Wales categorised into warehouses, retail sites, and factories. There are 47,683 rows of data in total, comprising 27,691 warehouse sites, 6,441 retail sites, and 13,551 factory sites. Each row contains the site's category, location (latitude and longitude), size (in square meters), and modelled number of heavy goods vehicle loading bays. Raw data on non-domestic properties in England and Wales were sourced from the UK's Valuation Agency Office database. Addresses were geocoded to determine the coordinates of each site, floor area was determined for each site via a web crawler script, and the type of site was derived using a keyword-based categorisation process. The size of the site gives an indication of the expected transport activity (i.e. volume of goods handled) and is a useful proxy to estimate the number of loading bays which, in turn, is a useful proxy for the number of electric heavy goods vehicle charging points the site may have to accommodate to support electric vehicle operations. Models relating the floor area to the number of loading bays were developed using satellite imagery of a sample of data from each category. Uncertainty in the geolocation, category and floor area data is deemed to be very low

This paper introduces an innovative methodology for route prediction in multimodal transport networks (MTNs) utilizing Artificial Intelligence (AI). While LSTM is a well-established technique for sequence prediction, our approach uniquely leverages Monte Carlo simulations to generate a diverse and comprehensive training dataset that captures the stochastic nature of transport networks. Firstly, Monte Carlo simulations generate a comprehensive set of training samples. These samples are then processed and used to effectively train an LSTM network and predict routes upon interrogation of the obtained model. A distinctive feature of this methodology is its ability to address the issue of unavailability of the route, which could occur at any time due to unforeseen circumstances, such as weather-related disruptions. A key innovation is the introduction of a placeholder notation system within the training data to effectively model route unavailability, enabling AI to recognize and adjust to these dynamic changes adaptively. Validation through various experiments is done to demonstrate the model’s performance in accurately predicting routes, particularly in scenarios involving route unavailability. The integration of Monte Carlo simulations with LSTM networks, coupled with the unique handling of unavailable routes, marks an incremental advancement in multimodal transport route prediction and is scalable to include other variables, such as heterogeneous agent systems.

Abstract The shift in consumer behaviour from in‐person to online shopping has led to an increase in parcel delivery volume and its associated negative impacts, such as CO2 emissions in cities. With the emergence of drone‐delivery technologies, the authors analyzed a joint delivery method using drones and trucks, which is an emerging alternative solution for last‐mile delivery in terms of CO2 emissions and delivery time. The analysis verified the highest possible level of reduction in CO2 emissions for the simultaneous and strategic operation of drones and trucks compared to diesel‐ or electric‐only truck operations. Moreover, this approach leads to reduced delivery times. A sensitivity analysis was performed to optimize the delivery‐drone flight performance in a drone‐and‐truck delivery strategy. It was found that a 1.5‐km drone flight performance was sufficient when considering the reasonable assumptions adopted in this study. Furthermore, based on the analysis results, drone‐and‐truck cooperative delivery strategies may not provide a significant advantage in terms of CO2 emissions compared with alternative transportation modes, such as fuel‐cell trucks with sufficiently low emissions. It has been empirically verified that changes in CO2 emissions are proportional to the number of clusters. However, there is a risk of local optima due to microscopic fluctuations among neighbouring cluster numbers, which occurs during the search for the optimal number of clusters.

Abstract Multimodal Freight Transport (MFT) has been introduced as a solution for reducing the external costs of freight transport while achieving cost improvements. Despite the MFT benefits, its share has been low in practice, and transport by trucks remains the most preferred transport mode. A few works have recently investigated this issue by discussing various barriers to MFT. However, little conceptual work comprehensively examines the barriers that organizations may face during MFT applications. To address this gap, this paper has reviewed 104 studies and identified 31 barriers and possible strategies for overcoming them. To clarify the nature of these barriers, we developed a conceptual barrier framework that positions the identified barriers within the overall MFT chain. This framework categorizes the barriers into six categories: MFT terminal, MFT network, management, regulations and subsidies, delivery characteristics, and interoperability. The findings provide decision-makers and practitioners with theoretical and practical insights into the barriers to transition toward MFT and will assist them in implementing MFT successfully.

Regionalization and digitalization are significant factors in the development of international transport, including road transport. Liberalization of mutual trade, creation of a single customs territory and gradual elimination of non-tariff barriers within integration unions have generally led to an increase in the speed of international movement of goods. Today the EAEU is the most advanced integration project in the post-Soviet space. Transport as an area of Eurasian integration occupies a special place in the integration process due to the spatial characteristics of the association and its transit potential. It is expected that the common transport space (CTS) will be created without exceptions and limitations by 2025. Trade relations of EAEU member states are provided by all modes of transport (including pipeline transport, but this study focuses only on long-distance modes of transport). In terms of countries, road transport plays the largest role in transport support of foreign trade in the Kyrgyz Republic, and the smallest role in the Russian Federation, where other modes of transport are more involved, mainly sea and rail transport. At the same time, the Russian and Kazakh segments account for more than 95 % of the freight transportations of the EAEU countries carried out by road transport, which determined the relevance of the research topic. This investigation presents the role of road transport in ensuring the international transport of goods of the EAEU countries. The results of the study, aimed at determining the role of road transport in the provision of international freight transportation of EAEU countries, were obtained through an empirical analysis of the road transport industry. The analysis identified the key features of the development of this segment in the EAEU countries. In turn, the assessment of the place of road transport in ensuring foreign trade of EAEU countries was carried out on the basis of two quantitative indicators of transport as an industry - the volume of freight traffic and cargo turnover.

This research conducted a probabilistic life-cycle assessment (pLCA) into the greenhouse gas (GHG) emissions performance of nine combinations of truck size and powertrain technology for a recent past and a future (largely decarbonised) situation in Australia. This study finds that the relative and absolute life-cycle GHG emissions performance strongly depends on the vehicle class, powertrain and year of assessment. Life-cycle emission factor distributions vary substantially in their magnitude, range and shape. Diesel trucks had lower life-cycle GHG emissions in 2019 than electric trucks (battery, hydrogen fuel cell), mainly due to the high carbon-emission intensity of the Australian electricity grid (mainly coal) and hydrogen production (mainly through steam–methane reforming). The picture is, however, very different for a more decarbonised situation, where battery electric trucks, in particular, provide deep reductions (about 75–85%) in life-cycle GHG emissions. Fuel-cell electric (hydrogen) trucks also provide substantial reductions (about 50–70%), but not as deep as those for battery electric trucks. Moreover, hydrogen trucks exhibit the largest uncertainty in emissions performance, which reflects the uncertainty and general lack of information for this technology. They therefore carry an elevated risk of not achieving the expected emission reductions. Battery electric trucks show the smallest (absolute) uncertainty, which suggests that these trucks are expected to deliver the deepest and most robust emission reductions. Operational emissions (on-road driving and vehicle maintenance combined) dominate life-cycle emissions for all vehicle classes. Vehicle manufacturing and upstream emissions make a relatively small contribution to life-cycle emissions from diesel trucks (<5% each), but these are important aspects for electric trucks (5% to 30%). [ABSTRACT FROM AUTHOR]

The shift in consumer behaviour from in‐person to online shopping has led to an increase in parcel delivery volume and its associated negative impacts, such as CO2 emissions in cities. With the emergence of drone‐delivery technologies, the authors analyzed a joint delivery method using drones and trucks, which is an emerging alternative solution for last‐mile delivery in terms of CO2 emissions and delivery time. The analysis verified the highest possible level of reduction in CO2 emissions for the simultaneous and strategic operation of drones and trucks compared to diesel‐ or electric‐only truck operations. Moreover, this approach leads to reduced delivery times. A sensitivity analysis was performed to optimize the delivery‐drone flight performance in a drone‐and‐truck delivery strategy. It was found that a 1.5‐km drone flight performance was sufficient when considering the reasonable assumptions adopted in this study. Furthermore, based on the analysis results, drone‐and‐truck cooperative delivery strategies may not provide a significant advantage in terms of CO2 emissions compared with alternative transportation modes, such as fuel‐cell trucks with sufficiently low emissions. It has been empirically verified that changes in CO2 emissions are proportional to the number of clusters. However, there is a risk of local optima due to microscopic fluctuations among neighbouring cluster numbers, which occurs during the search for the optimal number of clusters. [ABSTRACT FROM AUTHOR]

In order to promote the internal cooperation and coordinated development of Guangdong-Hong Kong-Macao Greater Bay Area (GBA), it is necessary to coordinate and unify the distinct legal systems involved, especially to provide uniform transportation laws and regulations between ports. However, if the GBA wants to establish a coordinated and unified shipping legal system, there are challenges due to interregional legal differences. These differences are rooted in legal systems of the three regions under the basic national policy of 'one country, two systems' and apply differently, leading the difficulties in balancing different interests. To achieve the strategic position and target of the GBA, a key area to improve is freight among the ports in the GBA which form an international shipping hub. There are many ports with large throughput in the GBA, and they are also big ports for cargos import and export or transit. However, the cargoes are without deregulated movement among them. Therefore, the problems of different cabotage laws in the three places need to be solved to make the positive effects of cargoes' free movement be realized in the GBA. A coordinated and unified special shipping scheme can be established in GBA by application of the regional cabotage law. This has the potential to deliver consistency in cabotage law, thereby eliminating restrictions and thresholds, simplifying procedures and reducing costs, so as to enhance the overall strength of the GBA and help it to become an international shipping super zone. [ABSTRACT FROM AUTHOR]

There is a clear and present desire from government authorities to actualize processes and procedures that place equity at the forefront of decision-making and improve supply chain resilience. Following a federal executive order, several agencies including the Federal Emergency Management Agency have instituted initiatives focused on equity in resilience planning, recognizing the variance in how different populations are impacted during disturbances. To actualize policy and programmatic priorities, decision makers need tools that are theoretically grounded yet computationally simplistic and adoptable for participants outside of academia. Thus, developing methodology that is deployable at scale within the framework of average agency capabilities remains critical. This research seeks to leverage network science, transportation theory, and social vulnerability analysis to explore the relationship between the exposure of counties to food supply disruptions through the road and highway transportation network and their community social vulnerability. This result highlights that in our food transportation network, targeted attacks are not more impactful than random removals when fewer nodes are removed. Our results also found that the most exposed counties are generally in the southeastern portion of the region. In our study area, a large part of the exposure is due to the high food-inflow demand. The framework presented is both theoretically grounded and computationally simplistic and presents a potential strategy for understanding social vulnerability alongside critical infrastructure resilience. [ABSTRACT FROM AUTHOR]

Multimodal Freight Transport (MFT) has been introduced as a solution for reducing the external costs of freight transport while achieving cost improvements. Despite the MFT benefits, its share has been low in practice, and transport by trucks remains the most preferred transport mode. A few works have recently investigated this issue by discussing various barriers to MFT. However, little conceptual work comprehensively examines the barriers that organizations may face during MFT applications. To address this gap, this paper has reviewed 104 studies and identified 31 barriers and possible strategies for overcoming them. To clarify the nature of these barriers, we developed a conceptual barrier framework that positions the identified barriers within the overall MFT chain. This framework categorizes the barriers into six categories: MFT terminal, MFT network, management, regulations and subsidies, delivery characteristics, and interoperability. The findings provide decision-makers and practitioners with theoretical and practical insights into the barriers to transition toward MFT and will assist them in implementing MFT successfully. [ABSTRACT FROM AUTHOR]

Long-haul truck electrification has attracted nascent policy support, but the potential health and climate impacts remain uncertain. Here, we developed an integrated assessment approach with high spatial-temporal (km and hourly) resolution to characterize the causal chain from truck operation to charging loads, electricity grid response, changes in emissions and atmospheric concentrations, and the resulting health and climate impacts across the United States. Compared to future diesel trucks, electrified trucking's net health benefits are concentrated only along the West Coast with a business-as-usual electricity grid. However, with an 80%-renewable electricity grid, most regions would experience net health benefits, and the economic value of avoided climate and health damages exceeds $5 billion annually, an 80% reduction relative to future diesel trucks. Electric trucks with larger batteries may increase health and climate impacts due to additional trips needed to compensate for the payload penalty, but a 2× improvement in the battery specific energy (to ∼320 Wh/kg) could eliminate the additional trips.

This study sheds light on the multicriteria mode choice selection process of freight transport in the Kingdom of Bahrain from the perspective of top decision-makers. A questionnaire-based interview survey was carried out to determine the factors influencing this choice. The responses were analyzed using FAHP method, which resulted in a prioritized list of factors affecting mode choice for freight transport and the preferred mode choices for different future routes. Results indicated that the criteria for mode choice can be ranked as follows in order of decreasing significance: safety, CO2 emission, cost, reliability, speed, flexibility, and capacity. The rail mode is the preferable one for 2, out of the 3 proposed routes, while the dedicated truck lane is the most convenient choice because of the short distance and the nature of the commodities moved along this corridor. Some of the results from this study suggested that the top decision-makers could be somewhat different in the criteria for mode choice and more specific in terms of route conditions in this regard. This research offers meaningful insights into the mode selection process, classified on the basis of different routes, of traders as well as providing valuable inputs for transport planners and decision-makers. This research provides a unique perspective into the decision-making process from top management’s perspective which was found to be different from the general understanding related to mode choice. The results of the study could be used to devise policies to attract more traders, consequently, increasing economic activities in the country.

For several decades, modal shift within the freight transport sector has been promoted by policy makers as an important basis for achieving lower transport emission targets. Even though the literature on mode choice is well established, there is less consensus about the volume of freight with the potential to shift and, more importantly, the actual contribution of freight modal shift to achieving less climate and air pollution impacts. The climate contribution of freight modal shift can be increasingly questioned, as decarbonization is taking place at increasing rates within road freight transport. In this paper, the role of modal shift policies in realizing climate objectives is scrutinized by focusing on the case of Sweden, which serves to illustrate some general insights. We highlight how modal shift is often analyzed and discussed in isolation, even though it forms an important part of a policy mix in which it may contribute to achieving climate objectives. Treating modal shift as an objective in its own right may render less effective and cost-efficient policy instruments. • The climate contribution of freight modal shift is increasingly questioned. • The role of modal shift policies in realizing climate objectives is scrutinized by focusing on the case of Sweden. • Modal shift is often analyzed and discussed in isolation. • Treating modal shift as an objective in its own right may render policy instruments less effective and cost-efficient, [ABSTRACT FROM AUTHOR]

Copyright of Management & Quality / Zarządzanie i Jakość is the property of Scientific Society of Organization & Management / Towarzystwo Naukowe Organizacji i Kierownictwa and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditionally, freight wagon technology has lacked digitalization and advanced monitoring capabilities. This article presents recent advancements in freight wagon digitalization, covering the system's definition, development, and field tests on a commercial line in Sweden. A number of components and systems were installed on board on the freight wagon, leading to the intelligent freight wagon. The digitalization includes the integration of sensors for different functions such as train composition, train integrity, asset monitoring and continuous wagon positioning. Communication capabilities enable data exchange between components, securely stored and transferred to a remote server for access and visualization. Three digitalized freight wagons operated on the Nässjo–Falköping line, equipped with strategically placed monitoring sensors to collect valuable data on wagon performance and railway infrastructure. The field tests showcase the system's potential for detecting faults and anomalies, signifying a significant advancement in freight wagon technology, and contributing to an improvement in freight wagon digitalization and monitoring. The gathered insights demonstrate the system's effectiveness, setting the stage for a comprehensive monitoring solution for railway infrastructures. These advancements promise real-time analysis, anomaly detection, and proactive maintenance, fostering improved efficiency and safety in the domain of freight transportation, while contributing to the enhancement of freight wagon digitalization and supervision. [ABSTRACT FROM AUTHOR]

In view of the pressing need to reduce the negative environmental impact of freight transportation we argue that it is essential to take account of the organization of the freight transportation system when considering how to address various individual activities and parts of the system. To support a transition to a more sustainable freight transportation system this paper examines the way in which different parts of the system interact and the way this can impact the scope for profound change. Taking loosely coupled systems (Weick, 1976) as a starting point, we scrutinize the couplings within and between three system layers of the freight transportation system: the supply chain layer, the transportation layer, and the infrastructure layer. In addition, we address two interfaces connecting these layers: the market for transportation services, and the traffic using the infrastructure. We find that tight couplings dominate in the supply chain and infrastructure layers and that these couplings depend on loose couplings in the transportation layer and the two interfaces. The pattern of couplings identified in the freight transportation system can explain several positive outcomes, such as flexibility and efficiency. But there are also major negative aspects of the loosely coupled nature of the system that create resistance to change and present a barrier in the drive for increased sustainability. The paper concludes that the identified couplings and system features have important implications for policies aiming to change the freight transportation system in ways that lead to significant reduction in the reliance on oil. [ABSTRACT FROM AUTHOR]

Copyright of AIN Journal is the property of Arab Institute of Navigation and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Baltic Dry Index (BDI) is a unique gauge for measuring the marine transportation of major dry bulk shipments. Increased sea freight is a precursor to the increase in economic activities. The volumes of sea trade and freight rates are influenced by import–export dynamics and changes in commodity prices. So, levels of commodity prices are monitored to gain insight into the anticipated demand for bulk shipments. In this study, the causality-in-quantiles (CiQ) model is used to model the causal relationship between BDI spot values and spot price of major dry bulk commodities like iron ore, aluminum, copper, agricultural products by considering 12 years of daily data. CiQ model is superior compared to other linear causality models as it helps in capturing the asymmetry and nonlinearity in causality based on different quantiles or market conditions i.e., bearish, normal, and bullish market conditions. Also, it captures the causality-in-mean as well as variance and helps in exploring the causal relationship in returns as well as volatility transmission between BDI and commodity prices. The finding of the paper throws interesting light on the asymmetric relationship between BDI and commodity prices- commodity prices are causing BDI in all market conditions, but the influence is stronger in normal periods than bearish and bullish periods. The causality from commodity to BDI follows a common pattern across most of the commodities. However, the effect of BDI on commodities considerably varies across the range of commodities and across market conditions. So, this model provides a plethora of information that will help commodity market participants to hedge the risk of variations in commodity price and freight rates effectively. [ABSTRACT FROM AUTHOR]

An optimal freight train plan requires practitioners to find a balance between mainline and yard efficiencies by adjusting the complexity of the yard operation and evaluating trade-offs between the throughput volume, number of blocks and number of trains handled at a given classification (marshalling) yard. To increase knowledge of these trade-offs, YardSYM is used to model the Belt Railway Company of Chicago (BRC) Clearing Yard, a major gravity hump classification yard that processes approximately 3000 railcars per day. The experimental design specifies traffic patterns of varying complexity designed to determine the fundamental relationship between yard throughput volume, the total number of blocks assembled in the yard, and the level of service. Simulation results indicate that yard performance decreases as railcar volume increases and as the number of blocks increases. The simulation results also demonstrate the challenge of two diametrically opposed objectives for yard operators: maximizing railcar connections to outbound trains while departing outbound trains on schedule. Improved knowledge of these relationships can be implemented to advance network, traffic assignment, and blocking models, and allow practitioners to make more informed hump yard operating decisions that improve the efficiency of carload traffic and manifest train operations. [ABSTRACT FROM AUTHOR]