Impacts of Unattended Train Operations (UTO) on Productivity and Efficiency in Metropolitan Railways

Urban metro subway systems (metros) around the world are choosing increasing levels of automation for new and existing lines: the global length of metro lines capable of unattended train operation (UTO) is predicted to triple in the next 10 years. Despite significant investment in this technology, empirical evidence for the financial and service quality impacts of UTO in metros remains scarce. This study used questionnaires and semistructured interviews with the Community of Metros and Nova Group benchmarking groups to assemble emerging evidence of how automation affected costs, staffing, service capacity, and reliability. The results from an analysis of data from 23 lines suggested that UTO could reduce staff numbers by 30% to 70%, with the amount of wage cost reduction depending on whether staff on UTO lines were paid more. On the basis of the experience of seven metros, the capital costs of lines capable of UTO were higher, but the internal rate of return had been estimated by two metros at 10% to 15%. Automated lines were capable of operating at the highest service frequencies of up to 42 trains per hour, and the limited available data suggested that automated lines were more reliable. The findings indicated that UTO was a means to a more flexible and reliable operating model that could increase metro productivity and efficiency. The study identified important work needed to understand the impacts of UTO and identify where statistical analyses would add value once sufficiently large data sets became available

Development of a key performance indicator system to benchmark relative paratransit performance

The Americans with Disabilities Act of 1990 prohibits discrimination against people with disabilities. US transit agencies are therefore required to offer services to eligible customers that complement the mobility opportunities provided to the general public on fixed-route public transit. While these paratransit services are necessary and just, they represent a proportionally large cost to agencies: approximately eight times the cost per boarding compared to fixed-route bus service. To be able to identify opportunities for (cost) efficiencies, and to further improve the quality of paratransit services offered, the twenty agencies of the American Bus Benchmarking Group (ABBG) decided to benchmark their relative performance in paratransit management and operations. To ensure comparability of agencies’ performance and hence ensure the usefulness of the benchmarking program, a key performance indicator system was developed and associated data items were defined in detail. The scope of this system went beyond the data already provided to the National Transit Database, both in amount and granularity of data collected, as well as the detail of definitions. This paper describes the challenges, respective solutions, and other lessons identified during four years of paratransit benchmarking development led by Imperial College London, the ABBG facilitators. The paper provides transit agencies and authorities as well as benchmarking practitioners and academics an opportunity to apply these lessons for the further benefit of paratransit services and their customers around the U.S

CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering

Intrinsic image decomposition is a challenging, long-standing computer vision problem for which ground truth data is very difficult to acquire. We explore the use of synthetic data for training CNN-based intrinsic image decomposition models, then applying these learned models to real-world images. To that end, we present \ICG, a new, large-scale dataset of physically-based rendered images of scenes with full ground truth decompositions. The rendering process we use is carefully designed to yield high-quality, realistic images, which we find to be crucial for this problem domain. We also propose a new end-to-end training method that learns better decompositions by leveraging \ICG, and optionally IIW and SAW, two recent datasets of sparse annotations on real-world images. Surprisingly, we find that a decomposition network trained solely on our synthetic data outperforms the state-of-the-art on both IIW and SAW, and performance improves even further when IIW and SAW data is added during training. Our work demonstrates the suprising effectiveness of carefully-rendered synthetic data for the intrinsic images task.Comment: Paper for 'CGIntrinsics: Better Intrinsic Image Decomposition through Physically-Based Rendering' published in ECCV, 201

Joint Learning of Intrinsic Images and Semantic Segmentation

Semantic segmentation of outdoor scenes is problematic when there are variations in imaging conditions. It is known that albedo (reflectance) is invariant to all kinds of illumination effects. Thus, using reflectance images for semantic segmentation task can be favorable. Additionally, not only segmentation may benefit from reflectance, but also segmentation may be useful for reflectance computation. Therefore, in this paper, the tasks of semantic segmentation and intrinsic image decomposition are considered as a combined process by exploring their mutual relationship in a joint fashion. To that end, we propose a supervised end-to-end CNN architecture to jointly learn intrinsic image decomposition and semantic segmentation. We analyze the gains of addressing those two problems jointly. Moreover, new cascade CNN architectures for intrinsic-for-segmentation and segmentation-for-intrinsic are proposed as single tasks. Furthermore, a dataset of 35K synthetic images of natural environments is created with corresponding albedo and shading (intrinsics), as well as semantic labels (segmentation) assigned to each object/scene. The experiments show that joint learning of intrinsic image decomposition and semantic segmentation is beneficial for both tasks for natural scenes. Dataset and models are available at: https://ivi.fnwi.uva.nl/cv/intrinsegComment: ECCV 201

Playing for Data: Ground Truth from Computer Games

Recent progress in computer vision has been driven by high-capacity models trained on large datasets. Unfortunately, creating large datasets with pixel-level labels has been extremely costly due to the amount of human effort required. In this paper, we present an approach to rapidly creating pixel-accurate semantic label maps for images extracted from modern computer games. Although the source code and the internal operation of commercial games are inaccessible, we show that associations between image patches can be reconstructed from the communication between the game and the graphics hardware. This enables rapid propagation of semantic labels within and across images synthesized by the game, with no access to the source code or the content. We validate the presented approach by producing dense pixel-level semantic annotations for 25 thousand images synthesized by a photorealistic open-world computer game. Experiments on semantic segmentation datasets show that using the acquired data to supplement real-world images significantly increases accuracy and that the acquired data enables reducing the amount of hand-labeled real-world data: models trained with game data and just 1/3 of the CamVid training set outperform models trained on the complete CamVid training set.Comment: Accepted to the 14th European Conference on Computer Vision (ECCV 2016

A tool for routine monitoring and feedback of morbidities following paediatric cardiac surgery

Short-term survival after paediatric cardiac surgery has improved significantly over the past 20 years and increasing attention is being given to measuring and reducing incidence of morbidities following surgery. How to best use routinely collected data to share morbidity information constitutes a challenge for clinical teams interested in analysing their outcomes for quality improvement. We aimed to develop a tool facilitating this process in the context of monitoring morbidities following paediatric cardiac surgery, as part of a prospective multi-centre research study in the United Kingdom. We developed a prototype software tool to analyse and present data about morbidities associated with cardiac surgery in children. We used an iterative process, involving engagement with potential users, tool design and implementation, and feedback collection. Graphical data displays were based on the use of icons and graphs designed in collaboration with clinicians. Our tool enables automatic creation of graphical summaries, displayed as a Microsoft PowerPoint presentation, from a spreadsheet containing patient-level data about specified cardiac surgery morbidities. Data summaries include numbers/percentages of cases with morbidities reported, co-occurrences of different morbidities, and time series of each complication over a time window. Our work was characterised by a very high level of interaction with potential users of the tool, enabling us to promptly account for feedback and suggestions from clinicians and data managers. The United Kingdom centres involved in the project received the tool positively, and several expressed their interest in using it as part of their routine practice

Death and Emergency Readmission of Infants Discharged After Interventions for Congenital Heart Disease: A National Study of 7643 Infants to Inform Service Improvement.

Improvements in hospital-based care have reduced early mortality in congenital heart disease. Later adverse outcomes may be reducible by focusing on care at or after discharge. We aimed to identify risk factors for such events within 1 year of discharge after intervention in infancy and, separately, to identify subgroups that might benefit from different forms of intervention.Cardiac procedures performed in infants between 2005 and 2010 in England and Wales from the UK National Congenital Heart Disease Audit were linked to intensive care records. Among 7976 infants, 333 (4.2%) died before discharge. Of 7643 infants discharged alive, 246 (3.2%) died outside the hospital or after an unplanned readmission to intensive care (risk factors were age, weight-for-age, cardiac procedure, cardiac diagnosis, congenital anomaly, preprocedural clinical deterioration, prematurity, ethnicity, and duration of initial admission; c-statistic 0.78 [0.75-0.82]). Of the 7643, 514 (6.7%) died outside the hospital or had an unplanned intensive care readmission (same risk factors but with neurodevelopmental condition and acquired cardiac diagnosis and without preprocedural deterioration; c-statistic 0.78 [0.75-0.80]). Classification and regression tree analysis were used to identify 6 subgroups stratified by the level (3-24%) and nature of risk for death outside the hospital or unplanned intensive care readmission based on neurodevelopmental condition, cardiac diagnosis, congenital anomaly, and duration of initial admission. An additional 115 patients died after planned intensive care admission (typically following elective surgery).Adverse outcomes in the year after discharge are of similar magnitude to in-hospital mortality, warrant service improvements, and are not confined to diagnostic groups currently targeted with enhanced monitoring

Noiseless nonreciprocity in a parametric active device

Nonreciprocal devices such as circulators and isolators belong to an important class of microwave components employed in applications like the measurement of mesoscopic circuits at cryogenic temperatures. The measurement protocols usually involve an amplification chain which relies on circulators to separate input and output channels and to suppress backaction from different stages on the sample under test. In these devices the usual reciprocal symmetry of circuits is broken by the phenomenon of Faraday rotation based on magnetic materials and fields. However, magnets are averse to on-chip integration, and magnetic fields are deleterious to delicate superconducting devices. Here we present a new proposal combining two stages of parametric modulation emulating the action of a circulator. It is devoid of magnetic components and suitable for on-chip integration. As the design is free of any dissipative elements and based on reversible operation, the device operates noiselessly, giving it an important advantage over other nonreciprocal active devices for quantum information processing applications.Comment: 17 pages, 4 figures + 12 pages Supplementary Informatio