Discrete Event Simulation-Based Evaluation of a Single-Lane Synchronized Dual-Traffic Light Intersections

This research involved an exploratory evaluation of the dynamics of vehicular traffic on a road network across two traffic light-controlled junctions. The study uses the case study of a one-kilometer road system modelled on Anylogic version 8.8.4. Anylogic is a multi-paradigm simulation tool that supports three main simulation methodologies: discrete event simulation, agent-based modeling, and system dynamics modeling. The system is used to evaluate the implication of stochastic time-based vehicle variables on the general efficiency of road use. Road use efficiency as reflected in this model is based on the percentage of entry vehicles to exit the model within a one-hour simulation period. The study deduced that for the model under review, an increase in entry point time delay has a domineering influence on the efficiency of road use far beyond any other consideration. This study therefore presents a novel approach that leverages Discrete Events Simulation to facilitate efficient road management with a focus on optimum road use efficiency. The study also determined that the inclusion of appropriate random parameters to reflect road use activities at critical event points in a simulation can help in the effective representation of authentic traffic models. The Anylogic simulation software leverages the Classic DEVS and Parallel DEVS formalisms to achieve these objectives.

Supply Chain Demand Forecast Based on SSA-XGBoost Model

Supply chain management usually faces problems such as high empty rate of transportation, unreasonable inventory management, and large material consumption caused by inaccurate market demand forecasts. To solve these problems, using artificial intelligence and big data technology to achieve market demand forecasting and intelligent decision-making is becoming a strategic technology trend of supply chain management in the future. Firstly, this paper makes a visual analysis of the historical data of the Stock Keeping Unit (SKU);Then, the characteristic factors affecting the future demand are constructed from the storage level, product level, historical usage of SKU, etc;Finally, a supply chain demand forecasting algorithm based on SSA-XGBoost model has proposed around three aspects of feature engineering, parameter optimization and model integration, and is compared with other machine learning models. The experiment shows that the forecasting result of SSA-XGBoost forecasting model is highly consistent with the actual value, so it is of practical significance to adopt this forecasting model to solve the supply chain demand forecasting problem.

A New Method Based on Association Rules Mining and Geo-filter for Mining Spatial Association Knowledge

Association rule mining methods, as a set of important data mining tools, could be used for mining spatial association rules of spatial data. However, applications of these methods are limited for mining results containing large number of redundant rules. In this paper, a new method named Geo-Filtered Association Rules Mining(GFARM) is proposed to effectively eliminate the redundant rules. An application of GFARM is performed as a case study in which association rules are discovered between building land distribution and potential driving factors in Wuhan, China from 1995 to 2015. Ten sets of regular sampling grids with different sizes are used for detecting the influence of multi-scales on GFARM. Results show that the proposed method can filter 50%–70% of redundant rules. GFARM is also successful in discovering spatial association pattern between building land distribution and driving factors.

Multi-scale visual analysis of cycle characteristics in spatially-embedded graphs

We present a visual analysis environment based on a multi-scale partitioning of a 2d domain intoregions bounded by cycles in weighted planar embedded graphs.The work has been inspired by anapplication in granular materials research,where the question of scale plays a fundamental role inthe analysis of material properties.We propose an efficient algorithm to extract the hierarchical cyclestructure using persistent homology.The core of the algorithm is a filtration on a dual graph exploitingAlexander’s duality.The resulting partitioning is the basis for the derivation of statistical properties thatcan be explored in a visual environment.We demonstrate the proposed pipeline on a few syntheticand one real-world dataset.

Quantitative data graphics in 3D desktop-based virtual environments-an evaluation

3D desktop-based virtual environments provide a means for displaying quantitative data in context.Data that are inherently spatial in three dimensions may benefit from visual exploration and analysis in relation to the environment in which they were collected and to which they relate.We empirically evaluate how effectively and efficiently such data can be visually analyzed in relation to location and landform in 3D versus 2D visualizations.In two experiments,participants performed visual analysis tasks in 2D and 3D visualizations and reported insights and their confidence in them.The results showed only small differences between the 2D and 3D visualizations in the performance measures that we evaluated:task completion time,confidence,complexity,and insight plausibility.However,we found differences for different datasets and settings suggesting that 3D visualizations or 2D representations,respectively,may be more or less useful for particular datasets and contexts.

You get by with a little help:The effects of variable guidance degrees on performance and mental state

Since it can be challenging for users to effectively utilize interactive visualizations,guidance is usually provided to assist users in solving tasks.Guidance is mentioned as an effective mean to overcome stall situations occurring during the analysis.However,the effectiveness of a peculiar guidance solution usually varies for different analysis scenarios.The same guidance may have different effects on users with(1)different levels of expertise.The choice of the appropriate(2)degree of guidance and the type of(3)task under consideration also affect the positive or negative outcome of providing guidance.Considering these three factors,we conducted a user study to investigate the effectiveness of variable degrees of guidance with respect to the user’s previous knowledge in different analysis scenarios.Our results shed light on the appropriateness of certain degrees of guidance in relation to different tasks,and the overall influence of guidance on the analysis outcome in terms of user’s mental state and analysis performance.