The effects of local interventions on global technological change through spillovers: A modeling framework and application to the road-freight sector.

To address global sustainability challenges, (public) policy interventions are needed to induce or accelerate technological change. While most policy interventions occur on the local level, their innovation effects can spill over to other jurisdictions, potentially having global impact. These spillovers can increase or reduce the incentive for interventions. Lacking to date are computational models that capture these spillover dynamics. Here, we devise a conceptual and methodological approach to quantify ex ante the effects of local demand-side interventions on global competition between incumbent and novel technologies. We introduce two factors that moderate global spillovers-relative size of selection environments and relative innovation potential of competing technologies. Our approach incorporates both factors in a techno-economic discrete choice model that evaluates technology competition over time through endogenized technological learning. We apply this modeling framework to the case of road freight. Different demand-pull interventions and shocks are modeled to assess spillover effects. In the case of road freight, electric vehicles experience growth in most application segments but can still be accelerated substantially through public policy intervention-spillovers occur if strong public interventions are introduced in large regions or in multiple combined regions under club policy interventions. These findings are discussed in the context of club policy interventions and a modeled geopolitical shock in China. A full sensitivity analysis of model input parameters and intervention or shock dynamics reveals high model robustness. Finally, we discuss the implications of the road-freight case study as it might inform the progress of other niche technologies in transitioning sectors.

Corridor-Level Impacts of Battery-Electric Heavy-Duty Trucks and the Effects of Policy in the United States.

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.

Environmental Justice and Systems Analysis for Air Quality Planning in the Port of Oakland in California.

Many frontline communities experience adverse health impacts from living in proximity to high-polluting industrial sources. Securing environmental justice requires, in part, a comprehensive set of quantitative indicators. We incorporate environmental justice and life-cycle thinking into air quality planning to assess fine particulate matter (PM2.5) exposure and monetized damages from operating and maintaining the Port of Oakland, a major multimodal marine port located in the historically marginalized West Oakland community in the San Francisco Bay Area. The exposure domain for the assessment is the entire San Francisco Bay Area, a home to more than 7.5 million people. Of the more than 14 sources included in the emissions inventory, emissions from large container ships, or ocean-going vessels (OGVs), dominate the PM2.5 intake, and supply chain sources (material production and delivery, fuel production) represent between 3.5% and 7.5% of annual intake. Exposure damages, which model the costs from excess mortalities resulting from exposure from the study's emission sources, range from USD 100 to 270 million per annum. Variations in damages are due to the use of different concentration-response relationships, hazard ratios, and Port resurfacing area assumptions. Racial and income-based exposure disparities are stark. The Black population and people within the lowest income quintile are 2.2 and 1.9 times more disproportionately exposed, respectively, to the Port's pollution sources relative to the general population. Mitigation efforts focused on electrifying in-port trucking operations yield modest reductions (3.5%) compared to strategies that prioritize emission reductions from OGVs and commercial harbor craft operations (8.7-55%). Our recommendations emphasize that a systems-based approach is critical for identifying all relevant emission sources and mitigation strategies for improving equity in civil infrastructure systems.

The relationship between freight train length and the risk of derailment.

In recent years, longer and heavier trains have become more common, primarily driven by efficiency and cost-saving measures in the railroad industry. Regulation of train length is currently under consideration in the United States at both the federal and state levels, because of concerns that longer trains may have a higher risk of derailment, but the relationship between train length and risk of derailment is not yet well understood. In this study, we use data on freight train accidents during the 2013-2022 period from the Federal Railroad Administration (FRA) Rail Equipment Accident and Highway-Rail Grade Crossing Accident databases to estimate the relationship between freight train length and the risk of derailment. We determine that longer trains do have a greater risk of derailment. Based on our analysis, running 100-car trains is associated with 1.11 (95% confidence interval: 1.10-1.12) times the derailment odds of running 50-car trains (or a 11% increase), even accounting for the fact that only half as many 100-car trains would need to run. For 200-car trains, the odds increase by 24% (odds ratio 1.24, 95% confidence interval: 1.20-1.28), again accounting for the need for fewer trains. Understanding derailment risk is an important component for evaluating the overall safety of the rail system and for the future development and regulation of freight rail transportation. Given the limitations of the current data on freight train length, this study provides an important step toward such an understanding.

Human performance in the rail freight yard.

Human performance in the rail freight yard has been identified as a source of risk for rail freight operations. This is both within the yard itself, and also with train preparation issues leading to incidents on the network. The rail freight yard is an area that has received limited research attention. Over 30 hours of observations were conducted at five major freight yards in Great Britain, along with 30 interviews of rail freight ground staff. Task models, human performance factors and potential solutions that were further explored in a workshop with freight personnel. This analysis led to an understanding of freight yard activities, the impact of freight yard design and environment, and the role external pressures on freight yard performance including upstream planning. The implications are discussed for both current freight operations, and for future technology and process change within the rail freight sector.

Human performance in the rail freight yard is critical to safety and performance, but receives little research attention. A structured study included observations in the yard, interviews with ground staff, and a validation workshop. Results include task models, influencing factors, potential solutions and implications for future technology and process change.

Environmental Health, Racial/Ethnic Health Disparity, and Climate Impacts of Inter-Regional Freight Transport in the United States.

We quantify and compare three environmental impacts from inter-regional freight transportation in the contiguous United States: total mortality attributable to PM2.5 air pollution, racial-ethnic disparities in PM2.5-attributable mortality, and CO2 emissions. We compare all major freight modes (truck, rail, barge, aircraft) and routes (∼30,000 routes). Our study is the first to comprehensively compare each route separately and the first to explore racial-ethnic exposure disparities by route and mode, nationally. Impacts (health, health disparity, climate) per tonne of freight are the largest for aircraft. Among nonaircraft modes, per tonne, rail has the largest health and health-disparity impacts and the lowest climate impacts, whereas truck transport has the lowest health impacts and greatest climate impacts─an important reminder that health and climate impacts are often but not always aligned. For aircraft and truck, average monetized damages per tonne are larger for climate impacts than those for PM2.5 air pollution; for rail and barge, the reverse holds. We find that average exposures from inter-regional truck and rail are the highest for White non-Hispanic people, those from barge are the highest for Black people, and those from aircraft are the highest for people who are mixed/other race. Level of exposure and disparity among racial-ethnic groups vary in urban versus rural areas.

Freight Wagon Digitalization for Condition Monitoring and Advanced Operation.

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.

Research on an Identification Method for Wheelset Coaxial Wheel Diameter Difference Based on Trackside Wheelset Lateral Movement Detection.

The wheelset coaxial wheel diameter difference is one of the most common wheel faults of railway vehicles. The existence of the wheelset coaxial wheel diameter difference may lead to the off-load operation of vehicles, resulting in abnormal wheel tread wear, leading to the deterioration of the wheel-rail contact relationship, resulting in the deterioration of the vehicle's operating stability and comfort, and even leading to an increase in the derailment coefficient, affecting the running safety. In order to monitor the freight car wheelset coaxial wheel diameter difference online, a vehicle-track coupling dynamics model based on a trackside detection method was established, and the response of rail lateral displacement under the condition of the wheelset coaxial wheel diameter difference was analyzed. The results show that the existence of the wheelset coaxial wheel diameter difference can lead to a deviation in the vehicle's run, with an increase in the wheelset coaxial wheel diameter difference and an increase in the lateral offset of wheelset increases. The impact of vehicle unbalance loading on the lateral movement of the wheelset is much smaller than that of the wheelset coaxial wheel diameter difference. The existence of the wheelset coaxial wheel diameter difference can be better reflected by detecting the wheelset's lateral displacement. On straight line, the variation of lateral displacement has no infection of vehicle speed, but shows a quadratic growth trend with the wheelset coaxial wheel diameter difference. Based on this, the mapping relationship between the wheelset coaxial wheel diameter difference and wheelset lateral displacement can be obtained. Through a mapping relationship, the size of the wheelset coaxial wheel diameter difference can be reversed precisely through the detection of a trackside lateral movement monitoring system. The reliability of the identification method was verified with a specific test on the trackside monitoring system.

Early Identification of Unbalanced Freight Traffic Loads Based on Wayside Monitoring and Artificial Intelligence.

The identification of instability problems in freight trains circulation such as unbalanced loads is of particular importance for railways management companies and operators. The early detection of unbalanced loads prevents significant damages that may cause service interruptions or derailments with high financial costs. This study aims to develop a methodology capable of automatically identifying unbalanced vertical loads considering the limits proposed by the reference guidelines. The research relies on a 3D numerical simulation of the train-track dynamic response to the presence of longitudinal and transverse scenarios of unbalanced vertical loads and resorting to a virtual wayside monitoring system. This methodology is based on measured data from accelerometers and strain gauges installed on the rail and involves the following steps: (i) feature extraction, (ii) features normalization based on a latent variable method, (iii) data fusion, and (iv) feature discrimination based on an outlier and a cluster analysis. Regarding feature extraction, the performance of ARX and PCA models is compared. The results prove that the methodology is able to accurately detect and classify longitudinal and transverse unbalanced loads with a reduced number of sensors.

An optimal control theory approach for freight structure path evolution post-COVID-19 pandemic.

After the outbreak of COVID-19, the freight demand fell briefly, and as production resumed, the trucking share rate increased again, further increasing energy consumption and environmental pollution. To optimize the sudden changing freight structure, the study aims on developing an evolution model based on Markov's theory to estimate the freight structure post-COVID-19. The current study applies economic cybernetics to establish a freight structural adjustment path optimization model and solve the problem of how much freight transportation should increase each year under the premise that the total turnover of the freight industry continues to grow, and how many years it will take at least to reach a reasonable freight structure. The freight transport structure of China is used to examine the feasibility of the proposed model. The finding indicates that the development of China's freight transport structure is at an adjustment period and should enter a stable period by 2035 and the COVID-19 makes it harder to adjust the freight structure. Increasing the growth rate of the freight volume of railway and waterway transportation is the key to realizing the optimization of the freight structure, and the freight structure path optimization method can realize the rationalization of the freight structure in advance.

How Well Did U.S. Rail and Intermodal Freight Respond to the COVID-19 Pandemic versus the Great Recession?

This paper analyzes and compares patterns of U.S. domestic rail freight volumes during and after the disruptions caused by the 2007-2009 Great Recession and the COVID-19 pandemic in 2020. Trends in rail and intermodal (IM) shipment data are examined in conjunction with economic indicators, focusing on the extent of drop and recovery of freight volumes of various commodities and IM shipments, and the lead/lag time with respect to economic drivers. Impacts of the Great Recession and the rebound from it were slow to develop, whereas COVID-19 produced both profound disruptions in the freight market and rapid rebound, with important variations across commodity types. Demand for energy-related commodities (coal, petroleum, and fracking sand) dropped during the pandemic whereas demand for other commodities (grain products and lumber, and IM freight) rebounded rapidly and in some cases grew. Overall, rail freight experienced a rapid rebound following the precipitous drop in traffic in March and April, 2020, achieving a near-full recovery in 5 months. As the recovery proceeded through 2020, IM flow, containers moving by rail for their longest overland trips, rebounded strongly, some exceeding 2019 levels. In contrast, rail flows during the Great Recession changed slowly with the onset and recovery extending over multiple years. Pandemic response reflected the impacts of quick shutdowns and a rapid shift in consumer purchasing patterns. Results for the pandemic illustrate the resilience of the U.S. rail freight industry and the multifaceted role it plays in the overall logistics system. Amid a challenging logistic environment, freight rail kept goods moving when other methods of transport were constrained.

Determining the Impacts of COVID-19 on Urban Deliveries in the Metropolitan Region of Belo Horizonte Using Spatial Analysis.

The COVID-19 pandemic has changed lifestyles, with consequent impacts on urban freight movements. This paper analyzes the impacts of COVID-19 on urban deliveries in the Belo Horizonte Metropolitan Region, Brazil. The Lee index and the Local Indicator of Spatial Association were calculated using data on urban deliveries (retail and home deliveries) and COVID-19 cases. The results confirmed the negative impacts on retail deliveries and the positive impacts on home deliveries. The spatial analysis demonstrated that the most interconnected cities presented more similar patterns. At the beginning of the pandemic, consumers were considerably concerned about the virus spread, and the changes in consumption behavior were slow. The findings suggest the importance of alternative strategies to traditional retail. In addition, the local infrastructure should adapt to the increased demand for home deliveries during pandemics.

Multiperspective Analysis of Pandemic Impacts on U.S. Import Trade: What Happened, and Why?

U.S. container ports have experienced unpresented congestion since mid-2020. The congestion is generally attributed to import surges triggered by heavy spending on consumer goods during the COVID-19 pandemic. Port congestion has been compounded by the inability of importers to retrieve, receive, and process all the inbound goods they have ordered, resulting in supply chain shortfalls and economic disruption. How can the shipping industry and government organizations predict the end of the current surge and anticipate future surges? Expected seasonal variations in import volume are associated with peak holiday shopping periods; nonseasonal import surges are signaled by other factors. The research goes beyond transportation data sources to examine broader connections between import volume and indicators of economic and retail industry conditions. The strongest and most useful relationship appears to be between retail inventory indicators and containerized import growth. From January 2018 through July 2021, there was a relatively strong negative correlation between retail inventory- and import TEU indices with a 4-month lag (corresponding roughly to the time between import orders and -arrival). In the 2020 to 2021 pandemic period the negative correlation was stronger, again with a 4-month lag. These findings suggest that observers might anticipate import surges after marked, nonseasonal drops in retail inventories, and that import surges are likely to last until target inventory levels are restored. In a broader sense, an awareness of the linkages between consumer demand, retail chain responses, and containerized import volumes could better inform port, freight transportation, and government planning and policy choices.

Optimization of Joint Decision of Transport Mode and Path in Multi-Mode Freight Transportation Network.

This paper mainly studies the joint decision of transportation mode and path in the multi-mode transportation network to provide the optimal plan for freights. This paper constructs a multi-mode transportation network system by setting virtual connections between networks with different transportation modes. The Dijkstra and multi-objective optimization algorithms are used to select the path in the network. After determining the optimal path, the paths' time, cost, and risk functions are established. The multi-objective function is converted into a single objective function by setting constraint conditions through the analytic hierarchy process. Then, the function is optimized by using the gradient descent method. Finally, the transportation plan for the case of chemical freights is formulated by using the above algorithms. The results show that the proposed algorithm can successfully find the solution for the joint decision of transportation mode and path in the complex network. After a quantitative analysis of the planned effect, the optimization actions of changing the initial transportation time and adjusting the upper limit of resources are proposed. The study findings provide a theoretical basis for improving the efficiency of the comprehensive transportation network.

Applied Study of the Fluidization Model of Logistics Transportation through the Prism of the Impact Generated on the Environment.

A real problem of today's society is the loss of human lives due to road accidents and the pollution caused by freight transport through metropolitan areas. The restrictions imposed in the near future for freight transport could reduce its efficiency and create many more problems. Using data centralization and developing applications or algorithms dedicated to the freight transportation sectors, routes and emissions can be managed much more efficiently. In this work, general aspects are presented, as well as a route optimization model for freight transport, taking into account the environmental impact, based on a heuristic algorithm, that of the ant colony (ACO). A multitude of studies has focused on what represents the benefits created by the applicability of solutions rather than on generalities and perspectives. The paper aims to highlight the usefulness of an optimization model of freight transport routes and the minimization of time and social costs. The study will show us that an optimized route for freight transport has a huge impact on costs, but also on time efficiency and polluting emissions.

Energy Autonomous Wireless Sensor Nodes for Freight Train Braking Systems Monitoring.

Nowadays, railway freight transportation is becoming more and more crucial since it represents the best alternative to road transport in terms of sustainability, pollution, and impact on the environment and on public health. Upgrading the potentiality of this kind of transportation, it would be possible to avoid delays in goods deliveries due to road accidents, traffic jams, and other situation occurring on roads. A key factor in this framework is therefore represented by monitoring and maintenance of the train components. Implementing a real time monitoring of the main components and a predictive maintenance approach, it would be possible to avoid unexpected breakdowns and consequently unavailability of wagons for unscheduled repair activities. As highlighted in recent statistical analysis, one of the elements more critical in case of failure is represented by the brake system. In this view, a real time monitoring of pressure values in some specific points of the system would provide significant information on its health status. In addition, since the braking actions are related to the load present on the convoy, thanks to this kind of monitoring, it would be possible to appreciate the different behavior of the system in case of loaded and unloaded trains. This paper presented an innovative wireless monitoring system to perform brake system diagnostics. A low-power system architecture, in terms of energy harvesting and wireless communication, was developed due to the difficulty in applying a wired monitoring system to a freight convoy. The developed system allows acquiring brake pressure data in critical points in order to verify the correct behavior of the brake system. Experimental results collected during a five-month field test were provided to validate the approach.

Will multi-industry supply chains' resilience under the impact of COVID-19 pandemic be different? A perspective from China's highway freight transport.

The pandemic caused by coronavirus disease 2019(COVID-19) continues to disrupt the global supply chain system, bringing new risks and challenges. The uncertainty created by COVID-19 makes it is difficult for various industries to deal with the pandemic. Since the pandemic, the supply chain's resilience has been discussed and examined in some studies. However, most existing works start from a single industry perspective or pay more attention to the disturbance caused by changes in the production side. Supply chain networks of different industries, mainly transport networks, are relatively limited under the epidemic's impact. In this paper, from the perspective of highway freight transport, a comprehensive competitiveness evaluation framework was proposed to reveal and the disruption and resilience of the supply chain under the outbreak based on nine indexes with five dimensions, including efficiency, capacity, activity, connectivity, and negotiability. Based on the availability of the data(Large-scale truck trajectory), we sorted out seven categories of Chinese industries(related to highway transport) and divided them into four categories respectively: (a) Slight disruption and worse resilience; (b) Slight disruption and remarkable resilience; (c) Serious disruption and worse resilience; (d) Serious disruption and remarkable resilience. The measurement results of supply chain network performance show that the industries (cold-chain, general products, and other industries) dominated by "Efficiency - Negotiability - Connectivity" are slightly disrupted (about 33%), forming a spatial diffusion with Wuhan(the city where the pandemic first broke out) as the disrupted center, spreading outward in a circle structure. Simultaneously, five urban agglomerations surrounding it have been impacted. By contrast, due to the strict isolation measures, the industries (building materials, construction, engineering, and high-value products industry) more vulnerable to be disrupted seriously (about 82%) tend to be the pattern of "Capacity - Activity". However, a large-scale centralized disruption was observed in the Triangle of Central China urban agglomeration was presented, resulting in almost stagnation of industry development. Meanwhile, as the future of the pandemic remains uncertain, the supply chain represented by the engineering industry, construction industry, etc are deserved to be paid more attention in line with they are prone to large-scale centralized damage due to the disruption of a single city node.

Health and Climate Impacts from Long-Haul Truck Electrification.

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.

Does Size Matter? The Influence of Size, Load Factor, Range Autonomy, and Application Type on the Life Cycle Assessment of Current and Future Medium- and Heavy-Duty Vehicles.

The transparent, flexible, and open-source Python library carculator_truck is introduced to perform the life cycle assessment of a series of medium- and heavy-duty trucks across different powertrain types, size classes, fuel pathways, and years in a European context. Unsurprisingly, greenhouse gas emissions per ton-km reduce as size and load factor increase. By 2040, battery and fuel cell electric trucks appear to be promising options to reduce greenhouse gas emissions per ton-km on long distance segments, even where the required range autonomy is high. This requires that various conditions are met, such as improvements at the energy storage level and a drastic reduction of the greenhouse gas intensity of the electricity used for battery charging and hydrogen production. Meanwhile, these options may be considered for urban and regional applications, where they have a competitive advantage thanks to their superior engine efficiency. Finally, these alternative options will have to compete against more mature combustion-based technologies which, despite lower drivetrain efficiencies, are expected to reduce their exhaust emissions via engine improvements, hybridization of their powertrain, as well as the use of biomass-based and synthetic fuels.

An ICT Prototyping Framework for the "Port of the Future".

Seaports are genuine, intermodal hubs connecting seaways to inland transport links, such as roads and railways. Seaports are located at the focal point of institutional, industrial, and control activities in a jungle of interconnected information systems. System integration is setting considerable challenges when a group of independent providers are asked to implement complementary software functionalities. For this reason, seaports are the ideal playground where software is highly composite and tailored to a large variety of final users (from the so-called port communities). Although the target would be that of shaping the Port Authorities to be providers of (digital) innovation services, the state-of-the-art is still that of considering them as final users, or proxies of them. For this reason, we show how a canonical cloud, virtualizing a distributed architecture, can be structured to host different, possibly overlapped, tenants, slicing the information system at the infrastructure, platform, and software layers. Resources at the infrastructure and platform layers are shared so that a variety of independent applications can make use of the local calculus and access the data stored in a Data Lake. Such a cloud is adopted by the Port of Livorno as a rapid prototyping framework for the development and deployment of ICT innovation services. In order to demonstrate the versatility of this framework, three case studies relating to as many prototype ICT services (Navigation Safety, e-Freight, and Logistics) released within three industrial tenants are here presented and discussed.