Provision Of Real-time Information Research Articles

PERANCANGAN SISTEM ENTERPRISE RESOURCE PLANNING (ERP) TERHADAP PENGADAAN BAHAN BAKU, PENJUALAN, DAN PRODUKSI PADA CV. JAYA LOGAM MENGGUNAKAN SOFTWARE ODOO

CV. Jaya Logam faced various operational challenges caused by ineffective sales and raw material inventory recording systems, leading to production delays, errors in information delivery, and difficulties in monitoring and decision-making. This research was conducted to address these issues through system needs analysis, design, and testing of an Enterprise Resource Planning (ERP) system using Odoo software. The methodological process involved a detailed analysis of system requirements, system design using Unified Modelling Language (UML) diagrams to visualize the system architecture, and system testing using the Black Box Testing method to ensure the reliability and effectiveness of the designed system. The testing results showed that the developed ERP system successfully met all 12 test class parameters, indicating that the system is valid and can be implemented effectively. The implementation of this ERP system at CV. Jaya Logam resulted in significant improvements in operational efficiency and data accuracy, better business process integration, and the provision of real-time information that supports faster and more accurate decision-making processes.

The role of automatic pollen and fungal spore monitoring across major end-user domains

The advent of automatic pollen and fungal spore monitoring over the past few years has brought about a paradigm change. The provision of real-time information at high temporal resolution opens the door to a wide range of improvements in terms of the products and services made available to a widening range of end-users and stakeholders. As technology and methods mature, it is essential to properly quantify the impact automatic monitoring has on the different end-user domains to better understand the real long-term benefits to society. In this paper, we focus the main domains where such impacts are expected, using Europe as a basis to provide qualitative estimates and to describe research needs to better quantify impacts in future. This will, in part, also serve to justify further investment and help to expand monitoring networks.

A fast, high-precision deep learning model for regional wave prediction

Accurate prediction of regional significant wave height is crucial for safe ship navigation, route planning, and reducing carbon emissions from shipping. Physics-based wave prediction methods involve complex calculations, limiting real-time information provision. Convolutional Neural Networks (CNNs) have gained popularity for wave prediction but suffer from feature loss, positional insensitivity, and poor performance in high sea conditions. This study introduces a Vision Transformer-based regional wave prediction model (VIT-RWP) to address these issues. VIT-RWP utilizes an attention mechanism to extract the wind-wave mapping relationship. It employs convolution and transpose convolution as encoder and decoder, preserving positional information and relative point positions within the region. Evaluation in four sea areas, including comparison with CNN-RWP, demonstrates VIT-RWP's advantages. Pre-training enhances VIT-RWP's predictive accuracy by over 0.5%, surpassing CNN-RWP by over 5%. VIT-RWP maintains accuracy even with wave heights exceeding 5 m. Importantly, it exhibits remarkable robustness when subjected to Gaussian noise in input data. VIT-RWP's consistent performance across diverse seas establishes its efficacy and accuracy as a reliable wave prediction model.

The real-time information provision problem in assessing rehabilitation needs among athletes with overtraining syndrome

One of the biggest challenges in sports medicine is the return-to-play decisions, making or breaking athletic careers. Since there are no protocols to guide team physicians and consultants for athletes with fatigue syndrome, illness, injury, or overtraining syndrome, real-time monitoring plays a crucial role in such cases. By monitoring a combination of performance (e.g., maximal lactate concentration, maximal heart rate at lactate threshold), physiological (e.g., resting heart rate and maximal heart rate), biochemical (e.g., glucose) and hormonal (e.g., cortisol) variables, there should be objective indices determining eligibility or disqualification for the ill or injured athletes, allowing rehabilitation practitioners to improve and adjust their plan accordingly on a real-time information provision basis.

Evaluating the cognitive and psychological effects of real-time auditory travel information on drivers using EEG

ABSTRACT Real-time travel information design with inadequate consideration of human factors can lead to driver distraction and diminish road safety. This study measures drivers’ brain electrical activity patterns to evaluate multiple aspects of driver cognition and psychology under real-time information provision using insights from the neuroscience domain on the localisation of brain functions. The brain electrical activity patterns of 84 participants are collected using an electroencephalogram (EEG) in an interactive driving simulator environment. The impacts of real-time auditory travel information characteristics (amount, sufficiency, and content) and different time stages of interaction with information provision (before, during, and after) on the frequency band powers of EEG signals in different brain regions are analyzed using linear mixed models. Study results illustrate that drivers exert more cognitive effort to perceive/process information on routes with complex driving environments. Insufficient information may evoke increased attention to internal processing and memory processing on routes characterised by higher travel time uncertainty, while route recommendation to switch to such routes may increase drivers’ stress and anxiety. The study findings can aid information providers, both private and public, as well as auto manufacturers to incorporate driver cognition in designing safer real-time information and their delivery systems.

How do psychological factors sway customers' preferences towards transit service attributes?

The study aims at quantifying the influence of subjective psychological factors on shaping customers’ preferences towards transit service attributes. In addition, the study examines the association between customers’ subjective psychological tendencies and socioeconomic characteristics. A dataset of 1,241 potential transit users is explored through an Error Components (EC) logit model with systematic taste variations and multivariate analysis of variance (MANOVA). The results quantified the influence of subjective psychological aspects on customers’ preferences towards various transit service attributes and concluded the power of psychological aspects in explaining preference heterogeneity. For instance, environmentally conscious customers are more tolerant towards walking time to/from bus stops and have a higher appreciation of at-stop real-time information provision than others. Further, the multivariate analysis of variance highlighted that customers’ psychological factors vary significantly across their socioeconomic characteristics. For example, young customers are more environmentally conscious and have higher perceived behaviour control and social norms towards transit than old and middle-aged customers. Overall, the findings provide research-based evidence to practitioners and policymakers on the dire need to jointly considering both psychological and utilitarian aspects in evaluating the desired quality from public transit.

Consumers oriented investments in transit service quality improvements: The best bang for your buck

Targeted transit quality improvement is essential to maintain ridership and attract car users. However, there remain the question about what are the best investments to increase the likelihood of transit utilization. This study aims at unveiling the broad-spectrum of transit users' preferences as well as their willingness to pay (WTP) for six service attributes. The study employed multinomial logit (MNL) interaction models and a random parameter logit (RPL) model to unveil preference heterogeneity in a choice experiment. The results revealed significant heterogeneity on customers' preferences and WTP towards service improvements due to variations in customers’ socioeconomic characteristics, travel behaviour and attitudes. For instance, females are willing to pay more to reduce service headway and the number of transfers, while males would pay more for at-stop real-time information provision. In a nutshell, effective transit quality improvements cannot ignore such heterogeneity, and should be tailored based on the targeted user type.

Temporal Analysis of Influence of Resource Failures on Cyber-Physical Systems Based on Discrete Timed Petri Nets

Advancement of IoT and ICT provide infrastructure to manage, monitor and control Cyber-Physical Systems (CPS) through timely provision of real-time information from the shop floor. Although real-time information in CPS such as resource failures can be detected based on IoT and ICT, improper response to resource failures may cripple CPS and degrade performance. Effective operations of CPS relies on an effective scheme to evaluate the impact of resource failures, support decision making needed and take proper actions to respond to resource failures. This motivates us to develop a methodology to assess the impact of resource failures on operations of CPS and provide the decision support as needed. The goal of this study is to propose solution algorithms to analyze robustness of CPS with respect to resource failures in terms of the impact on temporal properties. Given CPS modeled by a class of discrete timed Petri nets (DTPNs), we develop theory to analyze robustness of CPS by transforming the models to residual spatial-temporal network (RSTN) models in which capacity loss due to resources is reflected. We formulate an optimization problem to determine the influence of resource failures on CPS based on RSTNs and analyze the feasibility to meet the order deadline. To study the feasibility to solve a real problem, we analyze the computational complexity of the proposed algorithms. We illustrate the proposed method by application scenarios. We conduct experiments to study efficiency and verify computational feasibility of the proposed method to solve a real problem.

Impacts of real-time information levels in public transport: A large-scale case study using an adaptive passenger path choice model

Public transport services are often uncertain, causing passengers’ travel times and routes to vary from day to day. However, since door-to-door passenger delays depend on both intended and realised routes, they are difficult to calculate, as opposed to vehicle delays which can be derived directly from the widely available Automated Vehicle Location (AVL) data of the public transport system. In this study we use three months of such historical AVL data to calculate corresponding realised routes and passengers delays in a large-scale, multi-modal transport network by formulating and implementing an adaptive passenger path choice model in an agent-based scenario of Metropolitan Copenhagen with 801,719 daily trips. The proposed model allows analysing five different levels of real-time information provision, ranging from no information at all to global real-time information being available everywhere. The results of more than 258 million (positive or negative) passenger delays show that variability of passengers’ travel time is considerable and much larger than that of the public transport vehicles. It is also shown that obtaining global real-time information at the beginning of the trip reduces passengers delay dramatically, although still being inferior to receiving such along the trip. Additionally, being able to automatically obtain real-time passenger information while walking and being on-board public transport services is found not to lead to considerable improvements compared to acquiring such information manually while waiting at stops, although slight benefits are demonstrated in supplementary models run with pseudo-intelligent vehicle delay forecasting.

The need for Pan-European automatic pollen and fungal spore monitoring: A stakeholder workshop position paper.

BackgroundInformation about airborne pollen concentrations is required by a range of end users, particularly from the health sector who use both observations and forecasts to diagnose and treat allergic patients. Manual methods are the standard for such measurements but, despite the range of pollen taxa that can be identified, these techniques suffer from a range of drawbacks. This includes being available at low temporal resolution (usually daily averages) and with a delay (usually 3–9 days from the measurement). Recent technological developments have made possible automatic pollen measurements, which are available at high temporal resolution and in real time, although currently only scattered in a few locations across Europe.Materials & MethodsTo promote the development of an extensive network across Europe and to ensure that this network will respond to end user needs, a stakeholder workshop was organised under the auspices of the EUMETNET AutoPollen Programme. Participants discussed requirements for the groups they represented, ranging from the need for information at various spatial scales, at high temporal resolution, and for targeted services to be developed.ResultsThe provision of real‐time information is likely to lead to a notable decrease in the direct and indirect health costs associated with allergy in Europe, currently estimated between €50–150 billion/year.1 Discussion & ConclusionA European measurement network to meet end user requirements would thus more than pay for itself in terms of potential annual savings and provide significant impetus to research across a range of disciplines from climate science and public health to agriculture and environmental management.

Information-Providing Flexible and Transparent Smart Window Display.

A smart window, which can easily adjust light transmittance, can provide barrier functions, such as improvement in energy efficiency, glare prevention, and privacy protection. However, a smart window that can selectively provide real-time information and display various colorful characters and images at a desired location has not been developed. In this study, a novel smart window capable of real-time information conversion is developed by advancing the light transmittance control of the existing smart windows. A transparent and flexible window display is fabricated by synthesizing poly(N-isopropylacrylamide) (pNIPAM)-N,N-methylenebisacrylamide-crosslinked hydrogels (NBcH) and near-infrared (NIR) absorption-heating films sandwiched between two plastic substrates. When the NIR laser irradiates the window display panel surface, the temperature rises rapidly, as the NIR absorption-heating film absorbs the NIR wavelength. The generated heat is transferred to pNIPAM in contact with the NIR absorption-heating film, and an image forms in real time. In addition, if the NIR laser and projector simultaneously irradiate the window display panel surface, various colorful images can be displayed. The smart window for real-time information provision proposed in this study acts like a glass curtain that can selectively make a desired location transparent or opaque by controlling the transmittance of light and acts as a display that can present various colorful characters and images in real time. Therefore, it is expected to be highly convenient for users.

The extended theory of planned behavior considering heterogeneity under a connected vehicle environment: A case of uncontrolled non-signalized intersections

With the emergence of connected vehicle (CV) technology, there is a doubt whether CVs can improve driver intentions and behaviors, and thus protect them from accidents with the provision of real-time information. In order to understand the possible impacts of the real-time information provided by CV technology on drivers, this paper aims to develop a model which considers the heterogeneity between drivers with the aid of the extended theory of planned behavior. At the uncontrolled non-signalized intersections, a stated preference (SP) questionnaire survey was conducted to build the dataset consisting of 1001 drivers. Based on the collected dataset, the proposed model examines the relationships between subjective norms, attitudes, risk perceptions, perceived behavioral control and driving intentions, and studies how such driving intentions are simultaneously related to driver characteristics and experiences in the CV environment. Furthermore, driver groups which are homogenous with respect to personality traits are formed, and then are employed to analyze the heterogeneity in responses to driving intentions. Four key findings are obtained when analyzing driver responses to the real-time information provided by CV technology: 1) the proposed H-ETPB model is verified with a good fitness measure; 2) irrespective to driver personality traits, attitudes and perceived behavioral control have a direct and indirect association with driving intentions to accelerate; 3) driving intentions of high-neurotic drivers to accelerate are significantly related to subjective norms, while that of low-neurotic drivers are not; 4) elder high-neurotic drivers, and low-neurotic drivers who have unstable salaries or ever joined in online car hailing service have a strong intention in accelerating. The findings of this study could provide the theoretical framework to optimize traffic performance and information design, as well as provide in-vehicle personalized information service in the CV and CAV environments and assist traffic authorities to design the most acceptable traffic rules for different drivers at an uncontrolled non-signalized intersection.

Urban mobility in the digital era: An exploration of travellers' expectations of MaaS mobile-technologies

Mobility-as-a-Service (MaaS) represents a technological innovation with the potential to revolutionise the urban mobility paradigm, triggering a societal shift towards more sustainable travel behaviours. Successful implementation of these novel mobility solutions will largely rely on travellers' adoption of MaaS mobile-technologies. As with any new technology, the qualitative, user-centric research approaches focusing on the individuals' point of view may be significantly beneficial for the initial stages of development.Within this context, the paper explores which services should be offered by MaaS technologies to provide travellers with tailored mobility solutions that satisfy their daily needs. To that end, six Focus Groups were conducted in the metropolitan area of Madrid (Spain). We examined the services expected by travellers when adopting MaaS and compared them with those already identified from a comprehensive literature review. Our analysis revealed six new services that had not been recognised previously, including route optimisation and the provision of real-time information on the following five categories: passenger crowding, pollution levels, route facilities, vehicle conditions, and urban security. Overall, our results also highlighted the relevance given to the availability of information on environmental and health implications of travel behaviours.We believe that our findings provide valuable guidelines for the development of successful MaaS mobile-technologies. The incorporation of the travellers' perspective enables a more thoughtful design of these innovations and, thus, maximises their potential for addressing the limitations of our complex mobility-systems in meeting urban sustainability goals.

Understanding the Transit Market: A Persona-Based Approach for Preferences Quantification

The study aims at utilizing a persona-based approach in understanding, and further quantifying, the preferences of the key transit market groups and estimating their willingness to pay (WTP) for service improvements. The study adopted an Error Component (EC) interaction choice model to investigate personas’ preferences in a bus service desired quality choice experiment. Seven personas were developed based on four primary characteristics: travel behaviour, employment status, geographical distribution, and Perceived Behavioural Control (PBC). The study utilized a dataset of 5238 participants elicited from the Hamilton Street Railway Public Engagement Survey, Ontario, Canada. The results show that all personas, albeit significantly different in magnitude, are negatively affected by longer journey times, higher trip fares, longer service headways, while positively affected by reducing the number of transfers per trip and real-time information provision. The WTP estimates show that, in general, potential users are more likely to have higher WTP values compared to current users except for at-stop real-time information provision. Also, there is no consensus within current users nor potential users on the WTP estimates for service improvements. Finally, shared and unique preferences for service attributes among personas were identified to help transit agencies tailor their marketing/improvement plans based on the targeted segments.

The quality of information desired by paratransit users in Medan, Indonesia

In many developing cities, like Medan, the state of public transportation is considered inadequate and limited to paratransit, locally known as ‘angkot.’ While advanced mass transit systems are still far in the future, improving the paratransit is arguably the most plausible supporting solution at the moment. Paratransit users in Medan have reported to our study that reliability is one of their sources of disappointment. Technological development nowadays should ease the problem through the quality of real-time information provision. In this preliminary study, we conducted surveys to explore users’ perspective and desire for transit information services. Questionnaire responses from 350 tech-savvy users were collected from several centers of activity and terminals. It is as expected that most respondents experienced uncertainty using the current paratransit, and acknowledged the importance of having information services. Further result shows the user’s high expectancy towards information related to fleet location and arriving time estimation. In general, users were willing to use information services if provided regardless of the additional costs. This preliminary study gives a meaningful view of the opportunity to improve public likeness to this local paratransit service. This research is part of research on perception and preference of paratransit user on real-time information provision.

Multiagent Simulation of Real-Time Passenger Information on Transit Networks

Modeling and simulation play an important role in transportation networks analysis. With the widespread use of personalized real-time information sources, the status of the simulation depends heavily on individual travelers reactions to the received information. As a consequence, it is relevant for the simulation model to be individual-centered, and agent-based simulation is the most promising paradigm in this context. Information is now personalized, and the simulations have to take into account the interaction of individually guided passengers. In this paper, we present a multiagent simulation model to observe and assess the effects of real-time information provision on the passengers in transit networks. These effects are measured by simulating several scenarios according to the ratio of connected passengers to a real-time information system. We represent the passengers and the vehicles as agents in the system. We analyze the simulated scenarios following their effect on the passengers travel times. The information provided to the connected passengers is based on a space-time representation of the transportation networks. Results show that real-time personalized information may have an increasingly positive impact on overall travel times following the increasing ratio of connected passengers. However, there is a ratio threshold after which the effect of real-time information becomes less positive.

Day-to-day evolution of departure time choice in stochastic capacity bottleneck models with bounded rationality and various information perceptions

This study investigates the dynamical evolution of travelers’ departure time choice in a bottleneck-constrained roadway system with stochastic capacity. When considering day-to-day traffic evolution, this study explores two scenarios: travelers have long-term historical knowledge and real-time information provision; and travelers have short-term travel experience and real-time information provision. In this context, this study models boundedly rational travelers. This study sheds light on the trade-off between daily (short-term) equilibrium and statistical (long-term) equilibrium.

Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation

Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low cost. Due to the development of big data mining technologies, it is now easier to extract useful disaster-related geographic information from social media big data. Additionally, many researchers have used related technology to study social media for disaster mitigation. However, few researchers have considered the extraction of public emotions (especially fine-grained emotions) as an attribute of disaster-related geographic information to aid in disaster mitigation. Combined with the powerful spatio-temporal analysis capabilities of geographical information systems (GISs), the public emotional information contained in social media could help us to understand disasters in more detail than can be obtained from traditional methods. However, the social media data is quite complex and fragmented, both in terms of format and semantics, especially for Chinese social media. Therefore, a more efficient algorithm is needed. In this paper, we consider the earthquake that happened in Ya’an, China in 2013 as a case study and introduce the deep learning method to extract fine-grained public emotional information from Chinese social media big data to assist in disaster analysis. By combining this with other geographic information data (such population density distribution data, POI (point of interest) data, etc.), we can further assist in the assessment of affected populations, explore emotional movement law, and optimize disaster mitigation strategies.

Determinants of Travel Choice Behaviour with Travel-Time Variability in the Presence of Real-Time Information

This paper provided a statistical analysis of route and departure time choice behaviour under the effects of travel-time variability and real-time information for commuters in Japan using a web-based stated preference survey data. Furthermore, we developed a model predicting the relationship between the shift in average travel time and its reliability with the average travel time and real-time information provided. A factorial ANOVA underscored a significant effect of travel-time variability and real-time information on both departure time and route choice behaviour. The proposed model estimated the expected travel cost based on the estimated optimal departure time before and after having real-time information. This model may be useful for evaluating the benefit of travel-time reliability improved by the provision of real-time information.

Individual, Travel, and Bus Stop Characteristics Influencing Travelers’ Safety Perceptions

Ensuring safety during door-to-door public transport trips is a fundamental challenge to service providers, as safety influences individuals’ mobility. Using reported safety perceptions of travelers waiting at six bus stops with different characteristics in Stockholm, this study investigates factors that have an impact on determining travelers’ perceived safety and crime perceptions. This is done by assessing the importance of real-time information provision and the environmental characteristics of bus stops during the day and at night for different types of crime, after controlling for travelers’ individual and trip characteristics, and their previous experiences of victimization. Interaction effects of age, gender, and travel frequency are also tested. The results suggest that bus shelter characteristics, natural surveillance, and trustworthy real-time information are the most important factors influencing safety and crime perceptions. Additionally, safety perceptions are strongly influenced by previous experiences of victimization. The effect of perceived feelings about crime and safety are found to be nuanced by age and gender. Unlike some common beliefs, travelers: (1) feel less worried about becoming a victim of crime at bus stops associated with high crime rates; (2) prefer opaque shelters at night; and (3) have higher safety perceptions when the stop is located in an area of mixed land use. The impact of a bus stop’s number of passers-by is found to be insignificant. No direct or indirect effects can be attributed to frequency of travel by bus, indicating that familiar places and routine behavior have no effect on declared crime and safety perceptions.