A foundational dwell time model for regional railways

This paper describes how accurately estimating station dwell time is critical for timetable planning. Its importance has increased as railways seek to improve timetable stability and network efficiency, while serving more passengers and different types of transport services. This research consisted of developing a station dwell time model in cooperation with the Swiss Federal Railways (SBB). The proposed model estimates dwell times based on the input parameters: vehicle type (number, position, width and level of doorways), infrastructure (platform level) and demand (number and distribution of passengers). The research divides dwell time into five sub-processes: door-unblocking, opening doors, passenger boarding/alighting, closing doors and train dispatching. Each sub-process was evaluated separately to understand its influence on dwell time. The SBB’s automatic passenger counting system was used to record the number of passengers boarding and alighting at each door and the beginning/ending time of each sub-process. During eight months over three million measurements were made on four different vehicle types operating on 20 different routes. These data were analyzed and used to develop the dwell time model. This paper describes the research methodology, the structure of the dwell time model, the data collection system and presents a summary of results including statistical distribution and influence factors of sub-process times.

Transit service reliability is important for transit planning and operations as well as passenger experience. Large travel time variations increase operating costs and negatively affect passenger satisfaction. Existing literature focuses on specific aspects of transit travel times but less on how these aspects interact with each other. This paper proposes to combine previous research efforts by further decomposing observed trip travel times into four elements using 3 months of archived vehicle location and fare transaction data. Departure times and inter-stop travel times are obtained from vehicle locations. Dwell times at stops are estimated from fare transaction data using a dwell time model. Red-light waiting times are calculated using the vehicle locations and estimated signal timing plans. Then, using these as inputs, we identify important trip elements affecting the overall travel time variation, as well as how much variation can be attributed to each trip element using variance-based and one-at-a-time sensitivity analyses. The overall travel times and red-light waiting times are more affected by interaction effects between trip elements, whereas the overall inter-stop times and dwell times are mainly affected by large individual variations. The results suggest that planners must consider potential chain reactions where small variations in one trip element can lead to significant changes in the overall trip times as a result of interaction effects with varying cycle lengths in fixed signal timing plans. These findings will help planners better integrate available data sets, carry out comprehensive analyses, and pinpoint the determinants affecting travel time variation on each route.

The dwell time distribution which represents a higher level of abstraction than mobility modeling is often required for the performance evaluation of mobile communication systems. This paper deals with the interrelation between the mobility behavior of the subscribers and the probability density function (PDF) of the travel length of a subscriber in the radio cell. Although this problem is very important it seems to be little-known up to now. In order to get more insights into the abovementioned interrelation we classify the mobility behavior of the subscribers concerning the covered travel length into three categories: type A-completely aimless motion; type B-preferred direction with a certain deviation; and type C-completely directed motion. A realistic motion can be characterized by a linear combination of the defined types. Finally, a few examples are given emphasizing the applicability of these dwell time models.

In this paper, the sensitivity of the handoff dwell time (HDT) model on wireless mobile network performance is studied. We present an approach on the basis of the universal approximation property of phase-type (PH) distribution in order to address the intractable numerical issue caused by HDT. We first obtain the steady state probability under the general HDT. The performance metrics from the perspective of base station (BS) as well as from the point view of call are developed. The PH-type HDT is then assumed to derive the numerically tractable expressions for the performance metrics. Finally, the truncated Gaussian HDT is approximately fitted by the PH distribution via expectation maximization algorithm to reuse the closed form analytical expression. The comparison shows that, in terms of the BS performance, the handoff blocking probability exhibits a significant discrepancy whenever one uses the exponential or truncated Gaussian HDT distribution, while the new call blocking probability is less sensitive to different HDT. In addition, HDT has little impact on the call performance with respect to the call completion probability and total waiting time. Both the analytical model and the approximation approach are verified by simulation.

This paper describes how traffic simulation models in metro lines are widely used in predictive control algorithms for traffic regulation and robustness analysis of timetables. The simulation results are highly dependent on the uncertainty modeling. The two main parameters in the simulation models are running times and dwell times. In lines operated with Automatic Train Operation (ATO) systems, running times are more deterministic than random (consequence of control actions), while dwell times show a higher stochastic behavior due to the influence of passengers and drivers. Typically, simulation models do not include a realistic modeling of dwell time uncertainty, and the confidence on results is affected. This paper focused on the stochastic component of dwell times in order to obtain a realistic model suitable for traffic simulation of metro lines. For this purpose, several statistical studies have been developed considering peak and off-peak hours, incidences, relations with other operation variables, etc. Models have been obtained and validated using data of different days and Metro de Madrid lines.

Feasibility of a real-time hand hygiene notification machine learning system in outpatient clinics

Software-Defined Networking (SDN) is revolutionizing network architecture by decoupling the control and data planes, enabling centralized control, programmability, and automation. This paper explores methods to optimize network performance through SDN, focusing on traffic engineering, load balancing, and dynamic routing. We present a comprehensive overview of existing techniques and propose an optimization framework that leverages real-time data analytics and machine learning to enhance throughput, reduce latency, and improve resource utilization. Experimental results from simulation environments demonstrate significant performance improvements when applying the proposed methods. This paper explores the role of SDN in optimizing network performance. It highlights key architectural components, examines practical implementation strategies, and evaluates performance metrics and case studies that demonstrate the effectiveness of SDN in real-world scenarios. Ultimately, the goal is to provide a comprehensive understanding of how SDN can be used to create smarter, more responsive, and efficient networks

The purpose of this research is to describe the process of dwelling time itself, to identify related parties in the case, and the preparation strategies of Semarang Container Port (TPKS) facing the AEC. The research method used is descriptive, using qualitative and quantitative data. Data collection techniques are interviews with relevant parties, observations, and literature study. The results concluded the process of handling containers in Semarang TPKS potentially huge and causing dwelling time laid in pre-clearance process, customs clearance, and post-clearance process. Parties related to the dwelling time in Semarang Container Port (TPKS) are Custom (DJBC), Importers, and freight forwarding (PPJK). Preparation of Semarang Container Port in facing AEC in order to change the dwelling time by changing a manual system to an online system for clearance process, adding facilities, and improving human resources. The research findings indicate dwelling time length in Semarang Container Port (TPKS) reached 5.6 days which is still far from the government's target of 4.7 days Semarang Container Port (TPKS) has to simplify the system of the pre-clearance process, customs clearance, and post clearance. It needs to be done in order to reduce the length of dwelling time. Optimizations of performance also need to be improved with the Standard Operating Procedure. They also have to immediately perform optimization The Integrated Physical Checking Clearance which operates for 24 hours in one week.

Precision optical figuring demands fast and accurate dwell time optimization to reach nanometer- and sub-nanometer-level accuracy in next-generation optical systems. We introduce CUDO (closed-form universal dwell-time optimization), the first, to the best of our knowledge, unified closed-form analytical framework that supports both function-form and matrix-form dwell time models in computer-controlled optical surfacing (CCOS). In contrast to traditional methods, which rely on iterative optimization and hyperparameter tuning, our framework derives direct analytical solutions with no adjustable parameters. This approach unifies the solution principles of existing methods within a single mathematical model, delivering three key advantages: (1) accuracy on par with, or superior to, iterative solvers, (2) substantial reduction in computation time, and (3) numerical robustness. Comparative studies with prior art confirm that closed-form solutions achieve equivalent residual error while removing runtime bottlenecks. By simplifying the implementation and enabling real-time, scalable deployment, CUDO establishes a practical foundation for future deterministic fabrication of large-aperture and high-performance optics.

Conjunctoids: Statistical learning modules for binary events

In the field of ultra-precision manufacturing, industrial robotic polishing has the potential to become a more economical and intelligent method than the conventional polishing machines. But the challenge of the robotic polishing lies in the low control accuracy, which seriously affects the polishing quality. In this paper a new region-adaptive path planning method is proposed, where the path is generated adaptively according to the specific form error. Each time only the regions with form error large enough are processed, thereby improving the polishing stability and efficiency. Smooth paths are generated based on the hexagonal meshing of the processing regions to avoid sharp turning, and then the dwell time is calculated by space-variant deconvolution. The PVr metric of the final form error resulting from the robotic polisher converges down to λ/15. In addition this method can reduce the polishing time by 80%, henceforth the stability and efficiency of robotic polishing can be greatly improved.

The explosion of data in the digital era has posed major challenges handling, computing and analyzing enormous and complex datasets. Cloud computing has arisen as a revolutionary solution providing scalable and elastic infrastructure necessary to deal with incoming big data workloads. This study employs an empirical approach to evaluate the performance, cost efficiency, and scalability of the three dominant cloud service models, Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS) and Function-as-a-Service (FaaS) on Amazon Web Services, Microsoft Azure and Google Cloud Platform. Standard big data analytics workloads running real-time stream processing and machine learning activities were then implemented in Apache Spark, Hadoop, and Kafka on harmonized cloud environments. Key performance metrics such as execution time, CPU utilization, memory, cost per task and throughput were taken, analyzed statistically using ANOVA and Tukey’s post hoc tests. Results show that FaaS configurations are always faster in execution speed, memory efficiency and cost compared to IaaS, while IaaS delivers better CPU usage for continual workloads. AWS and GCP platform performed relatively balanced when compared to Azure. It is concluded that serverless architecture is, in fact, optimal for modular and burst-oriented analytics, and hybrid models might be more appropriate for complex pipelines. These results can offer cloud architects practical directions towards scalable and cost-effective big data solutions.

In the era of IoT, the world of connected experiences is created by the convergence of multiple technologies including real-time analytics, machine learning, and commodity sensors and embedded systems. However, with the proliferation of these IoT technologies and devices, there are challenges in integrating, indexing and managing time-series data from multiple sources to optimize the storage of those data and/or retrieve the information from them in real-time. Many researchers have addressed the data integration issue through developing time-series data compression techniques; however, they focused mainly on the application of integer value compression to IoT data. Moreover, existing work does not focus on the issues of data and information retrieval without decompression. In this paper, we solve these issues by constructing an indexing framework within a lossless compression for floating point time-series data, where an index is based on the time-stamp from the compressed data that facilitates the search for data without full decompression. We conduct several sets of experiments and quantify the performance of our proposed approach. The experimental results, performed on IoT datasets, show a reduction in storage compared with existing compression techniques. The experimental study also demonstrates the capability of time-series data indexing and integration in real-time.

The emergence of the Data Lakehouse—fusing the scale of cloud object storage (AWS S3, Azure ADLS Gen2) with the transactional governance of a data warehouse—necessitates a robust mechanism for handling high-velocity event streams from Apache Kafka. This paper addresses the architectural challenge of reliably bridging Kafka topics to Lakehouse formats (Delta Lake, Apache Hudi, Apache Iceberg) while ensuring three critical constraints are met: consistency (ACID), timeliness (low latency), and schema integrity. We propose and evaluate two primary ingestion patterns utilizing Confluent Sink Connectors: Direct Micro-Batch Ingestion (Pattern 1) and Stream Processor-Aided Ingestion (Pattern 2). The analysis demonstrates that Pattern 2, which incorporates an intermediate stream processor for inline deduplication and enrichment, is superior for high-churn/Change Data Capture (CDC) workloads. Furthermore, we establish that transactional consistency and graceful schema evolution are maintained through the layered cooperation of the Confluent Schema Registry (enforcing data contracts pre-landing) [11] and the Lakehouse Table Format (implementing atomic commits post-landing) [4]. The approach confirms the architectural portability of this streaming integration strategy across both AWS and Azure cloud ecosystems, providing a standardized blueprint for real-time analytics and machine learning applications.

The integration of IoT and AI technologies in agriculture has revolutionized modern farming, giving rise to innovative solutions aimed at optimizing resource use, improving productivity, and enhancing sustainability. This book chapter explores the transformative impact of smart agriculture systems, focusing on precision farming practices that harness the power of real-time data, machine learning, and robotics. Key components such as IoT-based sensor networks, AI-driven decision support systems, and autonomous machinery are examined for their role in optimizing crop management, irrigation, fertilization, pest control, and livestock management. The chapter delves into how AI and IoT technologies enable farmers to make data-driven decisions, reduce environmental impact, and enhance operational efficiency. With a focus on resource optimization, the chapter also addresses challenges such as system integration, data complexity, and accessibility for small-scale farmers. The future of agriculture lies in the seamless convergence of IoT, AI, and robotics, which promises to shape a more sustainable, productive, and efficient farming landscape. This research highlights critical advancements, practical applications, and emerging trends that will drive the next generation of agricultural innovation.