The world’s largest coal export operation is located in New South Wales, Australia. The state has more than 87% of the coal transportation done through railways, and one of the strategies to increase ...throughput is the use of sophisticated computational techniques for rail traffic optimisation. The current state of the art shows a lack of practical applications, thus making scalability, decentralisation and real-world commitment three key research directions. Towards that, this research presents a simulation-based machine learning approach for the railway traffic management problem, in the context of the Hunter Valley Coal Chain (HVCC). We modelled trains, load points and terminals as autonomous intelligent agents that interact, learn and act independently—thus constituting a multi-agent system (MAS). The MAS is implemented on top of a rail network simulation model currently in use at the HVCC. The model is adapted as a decentralised partially-observed Markov decision process environment that allows multi-agent learning via a genetic algorithm. We present experiments with scenarios based on the actual rail network data, which show that the MAS outperforms the heuristic approach embedded in the HVCC simulation tool by up to 81% (in terms of the schedule’s total dwell time). Further to those experiments, a comparison analysis evaluates the relevance of specific state features (e.g. track length, train conflicts, etc.). Finally, an important outcome was that the agents have learned to overcome very complex traffic situations that appear in train scheduling operations and that sometimes result in unnecessarily long dwell times. This type of high level learning represents a significant step forward in the use of complex computational techniques for rail transportation problems.
Compared with public transport operations, urban freight traffic and its associated delivery operations seem to be frequently overlooked in urban traffic management and traffic flow theory. One ...explanation for this is certainly the lack of available data, as the competitive freight transport market is fragmented and several actors are unwilling to collect or share tactical and operational data. In this study, we use the unique pNEUMA drone data set from Athens, Greece, to shed light on urban freight operations. We discuss macroscopic traffic indicators in a multimodal context. As the vehicle stopping behavior can adversely influence traffic flow, we reveal the stopping behavior of the different modes represented in the data set using clustering techniques. We find that urban freight vehicles’ stopping frequency lies between the stopping frequencies of cars and buses. We reveal the distribution of stopping times for loading and unloading stops in Athens to have a mean of around 380 seconds. Clustering all loading and unloading stops further reveals three groups of loading and unloading stops that could be labeled by incorporating knowledge and expertise about local particularities. The limited flight time of drones, owing to their battery capacities, did not allow reconstruction of longer vehicle routes, such as an entire vehicle tour within the network. However, this could be addressed in future research by realizing continuous large-scale monitoring routines. The revealed vehicle behavior parameters can be used in traffic models to generate further insights into the impacts of urban freight transport to inform public sector decision makers.
A continuous increase in the worldwide demand for high-speed traffic, freight tonnage as well as of the train operating frequency is worsening the decay conditions of many railway infrastructures. ...This occurrence affects economy-related business as well as contributing to rising maintenance costs. It is known that a failure of a railway track may result in tremendous economic losses, legal liabilities, service interruptions and, eventually, fatalities. Parallel to this, requirements to maintain acceptable operational standards are very demanding. In addition to the above, a main issue nowadays in railway engineering is a general lack of funds to allow safety and comfort of the operations as well as a proper maintenance regime of the infrastructures. This is mostly the result of a traditional approach that, on average, tends to invest in high-priority costs, such as safety-related costs, compromising lower-priority interventions (e.g., quality and comfort of the operations). A solution to correct this trend can be moving from a reactive to a proactive action planning approach in order to limit more effectively the likelihood of progressive rail track decay. Within this context, this paper reports a review on the use of traditional and non-destructive testing (NDT) methods for the assessment and health monitoring of railway infrastructures. State-of-the-art research on a stand-alone use of NDT methods or a combination of them for quality control, inspection and maintenance tasks in this subject area is discussed.
Activity changes during the COVID-19 lockdown present an opportunity to understand the effects that prospective emission control and air quality management policies might have on reducing air ...pollution. Using a regression discontinuity design for causal analysis, we show that the first UK national lockdown led to unprecedented decreases in road traffic, by up to 65%, yet incommensurate and heterogeneous responses in air pollution in London. At different locations, changes in air pollution attributable to the lockdown ranged from -50% to 0% for nitrogen dioxide (NO
), 0% to +4% for ozone (O
), and -5% to +0% for particulate matter with an aerodynamic diameter less than 10 μm (PM
), and there was no response for PM
. Using explainable machine learning to interpret the outputs of a predictive model, we show that the degree to which NO
pollution was reduced in an area was correlated with spatial features (including road freight traffic and proximity to a major airport and the city center), and that existing inequalities in air pollution exposure were exacerbated: pollution reductions were greater in places with more affluent residents and better access to public transport services.
Since the development of transport systems, humans have exploited ground-level, below-ground, and high-altitude spaces for transportation purposes. However, with the increasing burden of expanding ...populations and rapid urbanization in recent decades, public transportation systems and freight traffic are suffering huge pressure, plaguing local governments and straining economies. Engineers and researchers have started to re-examine, propose, and develop the underused near-ground spaces (NGS) for transportation purposes. For instance, flying cars, which are not a totally novel idea, aim at solving the traffic congestion problem and releasing the strains on existing city transport networks by utilizing unoccupied NGS. Flying cars differ from traditional grounded transportation systems that are entirely limited by their physical space, such as trains on tracks or automobiles on roads. Flying cars do not occupy or compete for high-altitude spaces used by air traffic for long-distance transfer. As there is limited specific literature on flying cars and flying car transportation systems (FCTS), this paper aims to describe the modern advances, techniques, and challenges of FCTS. We explore the inherent nature of NGS transportation and devise useful proposals to facilitate the construction and commercialization of FCTS. We begin with an introduction to the increasing need for NGS transportation and we address the advantages of using flying cars. Next, we present a brief overview of the history of the development of flying cars in terms of the historic timeline and technique development. Then, we discuss and compare the state of the art in the design of flying cars, including the take-off & landing (TOL) modes, pilot modes, operation modes, and power types, which are related to the adaptability, flexibility & comfort, stability & complexity, and environmental friendliness of flying cars, respectively. Additionally, since large-scale operations of flying cars can improve current transportation problems, we also introduce different facets of the various designs of FCTS, including path and trajectory planning, supporting facilities, and commercial designs. Finally, we discuss the challenges that might arise while developing and commercializing FCTS in terms of safety issues, commercial issues, and ethical issues.
•Steel girders bridges with cast-in-place concrete decks may exhibit a certain degree of composite action, even when there are no shear connectors. Consideration of this composite deck and girder ...behavior can improve load ratings.•The AASHTO LLDFs can be quite conservative for some bridge geometries, and more accurate LLDFs can lead to improved bridge load ratings.•Refined analysis can provide a more accurate prediction of the live load effects on individual girders of a bridge.•Load testing results can also be used to determine load distribution and to verify FEM models. These approaches can produce improved load ratings for some steel girder bridges and possibly the removal of load postings.
Bridges that are posted for load can cause a variety of issues for the people that use them and the entities that manage them. Load postings generally create detours for routes between origins and destinations of freight trucks, thereby causing additional traffic and a major impact on economic vitality. Load-posted bridges also create management issues for the state departments of transportation as they may require more stringent monitoring, inspection, and maintenance. For these reasons, it is desirable for states to have as few load-posted bridges in their inventory as possible. This paper examines an individual steel beam bridge and presents a methodology that led to the improvement of the load rating of the bridge in a safe and appropriate manner through load testing and refined modeling. The results of the load tests were used to develop new rating factors for the test vehicle and finite element method analysis models were used to develop new rating factors for the HS-20 design truck. It was found that the bridge exhibited rating factors much higher than what it was posted for, and therefore the posting could be removed.
The recycling of concrete waste is becoming increasingly important as a contribution to conserving resources and reducing mineral construction waste. Currently, concrete is mainly reused in a ...subordinate application area (downcycling), such as earthworks or road construction, or disposed (Kreislaufwirtschaft Bau 2021). Due to this, natural raw materials have to be mined for the production of new concrete, although there are processing techniques that allow the use of concrete waste for concrete production.
This paper deals with the optimization of plant locations for the recycling of concrete waste. Taking the city of Cologne as an example, the recycling process is balanced with regard to the use of natural resources and CO2 emissions of the freight transport. This makes an important contribution to the conservation of natural resources and to climate protection.
Accurate rail vehicle positioning is crucial for railroad operational safety. Modern light detection and ranging (LiDAR) simultaneously localization and mapping (SLAM) systems have delivered ...excellent results in real-world scenarios. However, it still lacks well investigation for rail vehicle applications. In this paper, we propose to achieve real-time accurate and robust positioning and mapping for rail vehicles utilizing LiDAR SLAM. Our framework tightly couples one non-repetitive scanning LiDAR with IMU, wheel odometer, and global navigation satellite system (GNSS) into pose estimation and simultaneous global map generation. As frontend, the IMU/odometer preintegration data de-skews the denoised point clouds and produces initial guess for LiDAR odometry. Besides, we leverage the plane constraints from extracted rail tracks and the height descriptor to further improve the system accuracy. As backend, a sliding window based factor graph is constructed to jointly optimize multi-modal information. To ensure a globally-consistent and less blurry mapping result, we develop a two-stage mapping method to register the submaps to the global. The proposed method is extensively evaluated on real-world datasets of numerous scenarios, including general-speed and high-speed ones, both freight traffic and passenger traffic is covered. The results show that our system delivers meter-level localization accuracy even in large or degenerated environments.
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