132571 research outputs found
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Improving Traffic Efficiency With Lane Guidance Based on Desired Speeds
No full textDrivers initiate a discretionary lane change when they perceive an anticipated improvement in their own driving condition from moving to another lane. However, such a lane change can slow down other vehicles on the target lane, and even worse initiate a disturbance. In this work, we argue that the blocking effect triggered by individual lane changes results from the heterogeneity in the desired speeds of vehicles, and thus using desired speed information of vehicles when regulating lane-changing decisions can improve traffic efficiency. In doing so, our work also exemplifies the usefulness of incorporating user preferences into control decisions. The proposed lane guidance system uses an optimization-based approach to update the target range of desired speeds on each lane in real time, and accordingly recommends individual lane changes. The control system coordinates the lane-changing decisions at the link level, for which the road stretch is subdivided into multiple sections that are controlled independently. We evaluate the performance of the lane guidance system in micro-simulation, for different network demands and desired speed distributions. The results highlight that the proposed approach utilizing the desired speed preferences of drivers results in positive efficiency gains for most traffic compositions in free flow. Moreover, the highest gains are expected in medium to high demand, and when the traffic composition includes a higher proportion of vehicles desiring higher speeds. The gains also increase when the desired speeds of vehicles that want to drive fast and those that want to drive slower are more separated.Transport and PlanningTransport and Plannin
Where the White Continent Is Blue: Deep Learning Locates Bare Ice in Antarctica
No full textIn some areas of Antarctica, blue-colored bare ice is exposed at the surface. These blue ice areas (BIAs) can trap meteorites or old ice and are vital for understanding the climatic history. By combining multi-sensor remote sensing data (MODIS, RADARSAT-2, and TanDEM-X) in a deep learning framework, we map blue ice across the continent at 200-m resolution. We use a novel methodology for image segmentation with “noisy” labels to learn an underlying “clean” pattern with a neural network. In total, BIAs cover ca. 140,000 km2 (∼1%) of Antarctica, of which nearly 50% located within 20 km of the grounding line. There, the low albedo of blue ice enhances melt-water production and its mapping is crucial for mass balance studies that determine the stability of the ice sheet. Moreover, the map provides input for fieldwork missions and can act as constraint for other geophysical mapping efforts.Mathematical Geodesy and PositioningCivil Engineering & Geoscience
Handling uncertainty in train timetable rescheduling: A review of the literature and future research directions
No full textExternal and internal factors can cause disturbances or disruptions in daily train operations, leading to deviations from official timetables and passenger delays. As a result, efficient train timetable rescheduling (TTR) methods are necessary to restore disrupted train services. Although TTR has been a popular research topic in recent years, the uncertain characteristics of railways have not been sufficiently addressed. This review first identifies the primary uncertainties of TTR and examines their impacts on both TTR and passenger routing during disturbances or disruptions. It finds that only a few uncertainties have been investigated, and the existing solution methods do not adequately meet practical requirements, such as considering the dynamic nature of disturbances or disruptions, which is crucial for real-world applications. Therefore, the review highlights problems associated with TTR uncertainties that need urgent attention and suggests promising methodologies that could effectively address these issues as future research directions. This review aims to help practitioners develop improved automatic train-dispatching systems with better train-rescheduling performance under disturbances or disruptions compared to current systems.Transport and Plannin
Assessing sampling and retrieval errors of GPROF precipitation estimates over the Netherlands
No full textThe Goddard Profiling algorithm (GPROF) converts radiometer observations from Global Precipitation Measurement (GPM) constellation satellites into precipitation estimates. Typically, high-quality ground-based estimates serve as reference to evaluate GPROF's performance. To provide a fair comparison, the ground-based estimates are often spatially aligned to GPROF. However, GPROF combines observations from various sensors and channels, each associated with a distinct footprint. Consequently, uncertainties related to the representativeness of the sampled areas are introduced in addition to the uncertainty when converting brightness temperatures into precipitation intensities. The exact contribution of resampling precipitation estimates, required to spatially and temporally align different resolutions when combining or comparing precipitation observations, to the overall uncertainty remains unknown. Here, we analyze the current performance of GPROF over the Netherlands during a 4-year period (2017-2020) while investigating the uncertainty related to sampling. The latter is done by simulating the reference precipitation as satellite footprints that vary in size, geometry, and applied weighting technique. Only GPROF estimates based on observations from the conical-scanning radiometers of the GPM constellation are used. The reference estimates are gauge-adjusted radar precipitation estimates from two ground-based weather radars from the Royal Netherlands Meteorological Institute (KNMI). Echo top heights (ETHs) retrieved from the same radars are used to classify the precipitation as shallow, medium, or deep. Spatial averaging methods (Gaussian weighting vs. arithmetic mean) minimally affect the magnitude of the precipitation estimates. Footprint size has a higher impact but cannot explain all discrepancies between the ground- and satellite-based estimates. Additionally, the discrepancies between GPROF and the reference are largest for low ETHs, while the relative bias between the different footprint sizes and implemented weighting methods increase with increasing ETHs. Lastly, our results do not show a clear difference between coastal and land simulations. We conclude that the uncertainty introduced by merging different channels and sensors cannot fully explain the discrepancies between satellite- and ground-based precipitation estimates. Hence, uncertainties related to the retrieval algorithm and environmental conditions are found to be more prominent than resampling uncertainties, in particular for shallow and light precipitation.Water Resource
An adaptive agent-based approach for instant delivery order dispatching: Incorporating task buffering and dynamic batching strategies
No full textThe volume of instant delivery has witnessed a significant growth in recent years. Given the involvement of numerous heterogeneous stakeholders, instant delivery operations are inherently characterized by dynamics and uncertainties. This study introduces two order dispatching strategies, namely task buffering and dynamic batching, as potential solutions to address these challenges. The task buffering strategy aims to optimize the assignment timing of orders to couriers, thereby mitigating demand uncertainties. On the other hand, the dynamic batching strategy focuses on alleviating delivery pressure by assigning orders to couriers based on their residual capacity and extra delivery distances. To model the instant delivery problem and evaluate the performances of order dispatching strategies, Adaptive Agent-Based Order Dispatching (ABOD) approach is developed, which combines agent-based modelling, deep reinforcement learning, and the Kuhn-Munkres algorithm. The ABOD effectively captures the system's uncertainties and heterogeneity, facilitating stakeholders learning in novel scenarios and enabling adaptive task buffering and dynamic batching decision-makings. The efficacy of the ABOD approach is verified through both synthetic and real-world case studies. Experimental results demonstrate that implementing the ABOD approach can lead to a significant increase in customer satisfaction, up to 275.42%, while simultaneously reducing the delivery distance by 11.38% compared to baseline policies. Additionally, the ABOD approach exhibits the ability to adaptively adjust buffering times to maintain high levels of customer satisfaction across various demand scenarios. As a result, this approach offers valuable support to logistics providers in making informed decisions regarding order dispatching in instant delivery operations.Transport and Plannin
Thermally self-sufficient process for single-step coproduction of methanol and dimethyl ether by CO<sub>2</sub> hydrogenation
No full textMethanol and DME are highly efficient fuels and relevant building blocks that can be synthesized by CO2 hydrogenation. While several alternatives for methanol production by CO2 hydrogenation have already been developed at a commercial scale, DME production is still based on methanol dehydration. In this sense, the development of bifunctional methanol synthesis/dehydration catalysts is a clear opportunity for the simultaneous coproduction of methanol and DME in a single-step process. Although a few alternatives for DME-methanol coproduction have been proposed, either they need external fuels or refrigerants, or part of the CO2 used as raw material is purged, resulting in a loss of methanol and DME yields. This work presents a novel thermally self-sufficient process that hydrogenates CO2 into methanol and DME in a single reactor at 100 % yield (only water as a byproduct at 0.94 kgwater/kgproduct), that only consumes air, cooling water (0.006 m3 water/kgproducts) and electricity (net CO2 emissions of −1.20 or 0.64 kgCO2eq/kgproducts when the plant is operated with green or grey electricity, respectively). The innovative design, based on the combination of a top-divided wall column, an integrated heat network, and limited pressure drop in the reaction-separation loop, results in a thermally self-sufficient process that uses only 0.76 kWh per kg products.ChemE/Product and Process Engineerin
On Understanding Environmental Inefficiencies in Air Traffic Management: A Causal Inference Approach
No full textAddressing the increasingly urgent need for sustainable aviation solutions, this study explores operational innovations as a quicker and more scalable addition to novel zero-emission propulsion systems. Through the use of regression-based causal inference methods, this study aims to understand the relationship between flight fuelburn inefficiency and the factors causing these inefficiencies. Such an approach allows for the attribution of inefficiencies to factors on an overall scale, requiring less specific domain knowledge for initial results. A case study, involving a sample of 100,000 flights, representative of European operations, reveals that airspace structure (3.2% increase in inefficiency) and turbulence along the flight plan (2.5% increase) are the leading causes, while variations in average airspeed, congestion, and crosswind contribute the least to flight inefficiency. A compilation of the results shows that the performed analysis leaves 61% of the observed flight inefficiency unaccounted for. Future work would include the exploration of different metrics even closer to actual climate and air quality effects, as well as detailed uncertainty quantification. The developed flight inefficiency prediction model allows experimentation with counterfactual scenarios, contributing to the global transition towards more sustainable air transport networks.Aerospace Engineerin
Assessing Autonomous nerve function
No full textThis thesis proposes a method of detecting autonomic neuropathy in low-income countries using an infrared camera. Access to healthcare is often limited in low-income countries delaying the detection of neuropathy. If neuropathy can be detected in the earliest stage, often the autonomic state, damage to motor and sensory nerves can be prevented. To develop the method for detecting autonomic neuropathy, first the autonomic system was researched, followed by looking into the effects of neuropathy on the autonomic system to discover what physical properties might be measured with the detection device. Four affected physical properties were determined through literature research: blood flow, blood pressure, skin resistance, and skin temperature. Of these properties, skin temperature was found to be the most promising based on a literature study, as it seemed to be both easily measurable and relatively independent of other bodily functions. Then the constraints of healthcare in low-income countries were examined and devices that work within these constraints were identified. Of these, the infrared camera showed the most promise, because of its ease of use and cost to accuracy ratio.A single-subject study was performed to test the restorative capacity of the autonomous system by deliberately changing the temperature of the hand with a heating and cooling agent. Four locations were used on both hands, and both palmar and dorsal side of the hand, using different doses of the agents. The temperature change of the skin was measured using an InfraRed (IR) camera.A large variation in results was found, but the results did show some evidence for structural differences in the temperature normalization between the affected and unaffected hand. The palmar side shows a stronger reaction than the dorsal side. The cooling agent seems to be more effective, but there are some caveats attached to its use. An interesting observation is that the most noticeable difference between left and right was measured in an area of low circulation. This gives some indication that this area has the most difficulty with returning to the neutral state. Conclusion: This research shows that skin temperature variation as a result of applying heating or cooling agents to the skin can be measured using an infrared camera, suggesting that minor variations in skin temperature as a result of neuropathy can also be measured, further research with more test subjects should be done.Biomedical Engineerin
The interplay of collagen, macrophages, and microcalcification in atherosclerotic plaque cap rupture mechanics
No full textThe rupture of an atherosclerotic plaque cap overlying a lipid pool and/or necrotic core can lead to thrombotic cardiovascular events. In essence, the rupture of the plaque cap is a mechanical event, which occurs when the local stress exceeds the local tissue strength. However, due to inter- and intra-cap heterogeneity, the resulting ultimate cap strength varies, causing proper assessment of the plaque at risk of rupture to be lacking. Important players involved in tissue strength include the load-bearing collagenous matrix, macrophages, as major promoters of extracellular matrix degradation, and microcalcifications, deposits that can exacerbate local stress, increasing tissue propensity for rupture. This review summarizes the role of these components individually in tissue mechanics, along with the interplay between them. We argue that to be able to improve risk assessment, a better understanding of the effect of these individual components, as well as their reciprocal relationships on cap mechanics, is required. Finally, we discuss potential future steps, including a holistic multidisciplinary approach, multifactorial 3D in vitro model systems, and advancements in imaging techniques. The obtained knowledge will ultimately serve as input to help diagnose, prevent, and treat atherosclerotic cap rupture.Medical Instruments & Bio-Inspired Technolog
Unravelling night train travel behaviour: A stated preference survey into the influence of operational and personal factors
No full textNight trains benefit society in multiple ways. They are environmentally friendly, improve the accessibility of regions and are space-effective. Understanding traveller's preferences enables night train operators to improve night train services and harness societal benefits better. This study conducted a stated preference survey with 1031 respondents from the Netherlands to dive deeper into the importance of operational factors like booking convenience, travel costs, long travel times of up to 18 hours and accommodations. Additionally, factors that make up a convenient booking scenario were revealed. Lastly, a latent class choice model (LCCM) was applied to derive insights into heterogeneity and to determine to which extent personal factors influence class membership. Results reveal that for a convenient booking scenario, being able to book one ticket and comparing travel options are most important. However, booking convenience only plays a minor role in determining night train mode choice. Travel costs and accommodation are significantly more important. Several classes have been revealed: Environmentally conscious comfort lovers make up 13% of the respondents, experienced night train travellers 29\%, cost-sensitive travellers 37% and flight lovers 20%. Applying a scenario analysis, night train market shares vary from 20% to 71%, with significant heterogeneity among respondents. For practitioners, this implies focusing mainly on prices and accommodation while taking the significantly different preferences of the population into consideration.Transport, Infrastructure and Logistic
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