Provision Of Real-time Information Research Articles

The value of real-time continuous glucose monitoring in premature infants of diabetic mothers.

To determine the feasibility of using a real-time continuous glucose monitoring system (RTGMS) in intensive care units, our study focus on preterm infants with diabetic mothers owing to their high risk of blood sugar abnormalities. Thirty preterm babies (M = 15 and F = 15; ≤ 36 week gestation age) were studied from within 72 hours of delivery. These babies were admitted to the newborn intensive care and were further categorized into groups based on whether their mothers with or without diabetic mellitus. Blood sugar levels were monitored by both RTGMS and the traditional intermittent arterial line (A-Line) glucose method. Continuous glucose monitoring were well tolerated in 30 infants. There were good consistency between RTGMS and A-Line glucose concentration measurements. Of the preterm infants, 33.33% experienced abnormal glucose levels (hypoglycemia or hyperglycemia) between the checkpoint intervals of the intermittent A-Line blood sugar measurements. RTGM showed advantages with regards to reduced pain, greater comfort, the provision of real-time information, high sensitivity (94.59%) and specificity (97.87%) in discovering abnormalities of blood sugar, which are especially valuable for premature infants of diabetic mothers. RTGMS is comparable to A-line measurement for identifying fluctuations in blood glucose in premature infants. RTGMS detects more episodes of abnormal glucose concentration than intermittent A-line blood glucose measurement. High risk infants, especially premature infants with diabetic mothers, should receive more intensive blood sugar level checks by using continuous RTGMS.

Simulation study of the impact of local real-time traffic information provision strategy in connected vehicle systems

Abstract Current vehicle navigation systems usually rely on global real-time traffic information. They suggest the best paths to travelers through an independent and selfish-routing mechanism. Existing studies show that such systems have a great potential to cause traffic congestions oscillating among several alternative corridors, i.e., iteratively leading exceeding traffic into light traffic corridors and making congestion on one and then another within a short time period. To address this issue, this research proposes a local information provision strategy, given travelers are still guided by an independent and selfish-routing mechanism. The coverage of the real-time information provision is identified by the minimum bounding rectangle, and the ellipse method respectively. The benefits of the proposed strategy in system level are investigated by the simulation studies built upon the Borman expressway network. The simulation results indicate that as the local travel time information provision strategy is used, it may save 10–25% of the system travel time resulting from global information provision strategy under medium or high network traffic loads. Moreover, the sensitivity analyses indicate that the merits of the local travel time information provision strategy are more significant as the penetration of smart vehicles increases over the network. Thus, the proposed local information provision strategy outperforms global information provision strategy in system level network performance.

Effectiveness of Real Time Information Provision with Time of Use Pricing

Real time information feedback combined with various pricing schemes has been found to reduce residential energy consumption more than information and pricing policies alone. I examine the effect of information provision with bi-monthly, monthly, and real time pricing with in-home displays with a time-of-use pricing scheme on consumption over each month of the Irish Consumer Behavior Trial. I find that time-of-use pricing with real time pricing information reduce electricity usage up to 8.7 percent during peak times at the start of the trial but the effect decays over the first three months and after three months the trial group is indistinguishable from the control group. I do not find statistically significant improvements in energy savings when comparing monthly and bi-monthly billing treatments. These findings suggest that increasing billing reports to the monthly level or a web application providing real time information may be more cost effective than providing in-home displays.

Real-Time Bus Arrival Information System: An Empirical Evaluation

The provision of real-time information concerning bus arrival times could potentially reduce the uncertainty associated with public transport trips and improve the overall level of service. In addition, real-time predictions might enable operators to apply proactive control strategies. Even though considerable research efforts were devoted to the development of bus arrival prediction schemes, there is a lack of knowledge on the performance of real-world operational systems. This article aims to investigate the performance of a commonly deployed real-time information generation scheme. A conventionally used scheme is implemented and evaluated based on an empirical analysis. Performance metrics concerning the prediction error accuracy and reliability and their impact on expected waiting time were formulated from both passengers’ and operators’ perspectives. The real-time information generator was applied on the trunk line network in Stockholm, Sweden. The accuracy and reliability of the prediction scheme was analyzed by comparing the generated predictions against vehicle positioning data. This scheme was found to systematically underestimate the remaining waiting time by 6.2% on average. The provision of real-time information yields a waiting time estimate that is more than twice as close to the actual waiting times than the timetable. This difference in waiting time expectations is equivalent to 30% of the average waiting time.

Beyond instrument: smartphone app and sustainable mobility

The rise of smartphones and mobile applications (apps) is of major importance to multiple recent innovations in sustainable urban mobility, including car sharing schemes and real-time information provision in public transport, as well as the recent surge in urban cycling. Yet, exactly how apps feature in these innovations and trends remains largely unclear. This paper argues that this lack of understanding reflects not only the rapid pace of developments in apps and their technical functionalities but also gaps in academics’ conceptualization of the nature of apps and their effects. Too easily and often are apps seen as mere instruments for the realisation of human-centred goals and intentions, or are their capacities and effects assumed to emerge from the webs of relations in which apps and smartphones are enmeshed. An alternative conceptualisation is therefore proposed, one that is informed principally by the object-oriented approach developed by philosopher Graham Harman. After summarising some of Harman’s original concepts and developing his account of power, the paper elaborates a series of ideas and recommendations about how the developed conceptual framework can be deployed in empirical research on the interactions between apps and physical mobility in the city.

Modeling the impacts of public transport reliability and travel information on passengers’ waiting-time uncertainty

Public transport systems are subject to uncertainties related to traffic dynamic, operations, and passenger demand. Passenger waiting time is thus a random variable subject to day-to-day variations and the interaction between vehicle and passenger stochastic arrival processes. While the provision of real-time information could potentially reduce travel uncertainty, its impacts depend on the underlying service reliability, the performance of the prognosis scheme, and its perceived credibility. This paper presents a modeling framework for analyzing passengers’ learning process and adaptation with respect to waiting-time uncertainty and travel information. The model consists of a within-day network loading procedure and a day-to-day learning process, which are implemented in an agent-based simulation model. Each loop of within-day dynamics assigns travelers to paths by simulating the progress of individual travelers and vehicles as well as the generation and dissemination of travel information. The day-to-day learning model updates the accumulated memory of each traveler and updates consequently the credibility attributed to each information source based on the experienced waiting time. A case study in Stockholm demonstrates model capabilities and emphasizes the importance of behavioral adaptation when evaluating alternative measures which aim to improve service reliability.

Distributed Regret Matching Algorithm for Dynamic Congestion Games with Information Provision

The advances in adaptive learning dynamics to pure Nash equilibria in game theory provide promising results for the modeling of selfish agents with limited information in congestion games. In this study, a distributed game-theoretical learning algorithm with real-time information provision for dynamic congestion games is proposed. The learning algorithm is based on the regret matching process by considering a user's previously realised payoffs and real-time information. The numerical studies show that the proposed algorithm can converge to a non-cooperative Nash equilibrium in both static and dynamic congestion networks. Moreover, the proposed algorithm leads to a plausible real-time route choice modeling framework based on a user's perception being updated by incorporating the user's past experience, real-time information and behaviour inertia.

Attitudes of bus operators towards real-time transit information tools

Although it is apparent that providing useful information has a positive effect on transit riders, no studies to date have investigated bus operators’ reactions to real-time arrival information and other potential rider information tools. In this study, the project team surveyed 253 bus operators to determine their views and values concerning the existing use of real-time information and to ask about future transit rider information applications. Almost all operators (93 and 91 % on two separate questions) were positive or neutral to the provision of real-time information. In addition, operators were receptive to building other new information applications, with all applications in the survey being supported by at least 60 % of the bus operators. The two most widely supported potential applications in the survey were additional tools to help blind and deaf-blind riders (89 % of bus operators favored) and an application that would aid riders in identifying physical stop, shelter and bus issues such as graffiti, broken parts or a need for lights (88 % of bus operators). Applications displaying data about past performance or current bus capacity received the least support (66 and 61 % respectively). This research gives a better understanding of the impact of rider information tools on bus operators, including the views and values of the operators, and the harms and benefits of such tools.

Buying from the Babbling Retailer? The Impact of Availability Information on Customer Behavior

Provision of real-time information by a firm to its customers has become prevalent in recent years in both the service and retail sectors. In this paper, we study a retail operations model where customers are strategic in both their actions and in the way they interpret information, whereas the retailer is strategic in the way it provides information. This paper focuses on the ability (or the lack thereof) to communicate unverifiable information and influence customers' actions. We develop a game-theoretic framework to study this type of communication and discuss the equilibrium language emerging between the retailer and its customers. We show that for a single retailer and homogeneous customer population setting, the equilibrium language that emerges carries no information. In this sense, a single retailer providing information on its own cannot create any credibility with the customers. We study how the results are impacted due to the heterogeneity of the customers. We provide conditions under which the firm may be able to influence the customer behavior. In particular, we show that the customers' willingness to pay and willingness to wait cannot be ranked in an opposite manner. However, even when the firm can influence each customer class separately, the effective demand is not impacted. This paper was accepted by Yossi Aviv, operations management.

Experimental analysis of a hybrid route choice model to capture dynamic behavioral phenomena under advanced information systems

The driver route choice problem under real-time information provision is characterized by subjectively interpreted and/or linguistically expressed data in addition to quantitative inputs. In previous work, the authors propose a hybrid probabilistic-possibilistic model which treats qualitative and quantitative data simultaneously in a single framework. This paper presents experimental insights on the performance of the hybrid model based on extensive simulation analysis under potential real-world scenarios. It performs sensitivity analyses on the associated driver behavior model parameters, and investigates the model’s ability to capture dynamic qualitative phenomena under information provision. The results highlight the flexibility of the hybrid model in addressing a broad range of driver behavior characteristics and network conditions. They also suggest that the hybrid model can capture the evolution of the driver behavior characteristics over time and the influence of the randomness in network conditions on route choice behavior.

Buying from the Babbling Newsvendor: Availability Information and Cheap Talk

Provision of real-time information by a firm to its customers has become prevalent in recent years in both the service and retail sectors. In this paper, we study a retail operations model where customers are strategic in both their actions and in the way they interpret information, while the retailer is strategic in the way it provides information. This paper focuses on the ability (or the lack thereof) to communicate credibly unverifiable information. We develop a game-theoretic framework to study this type of communication and discuss the equilibrium language emerging between the retailer and its customers. We show that for a single-retailer setting, the equilibrium language that emerges carries no information. In this sense, a single-retailer providing information on its own cannot create any credibility with the customers. We explore several remedies so that the firm can credibly disclose availability information to its customers. While in these remedies we show that the firm may be able to reveal complete information, the firm would prefer to shade some information and use intentional vagueness.

Towards Anonymous Mobile Community services

In the current market conditions, network operators are in search of novel value-added services that will increase their revenue. This paper introduces the innovative concept of Anonymous Mobile Community (AMC) services and thoroughly defines and describes a robust platform targeted for their deployment. AMC services take advantage of the terminals’ capabilities to collect information and deliver it to the network. In this context, terminals are enabled to form communities that serve as sources of information. In these communities, the anonymity and privacy of the end-users are respected and guarded. Several examples of promising AMC services are presented and categorized. An indicative example application is the provision of real-time information regarding road-traffic conditions, based on the location and speed of mobile terminals. A system aiming at the provision of diverse AMC services is proposed, and its requirements, architecture and functionality are described in detail. The related scalability issues and business models are carefully outlined, and a use case scenario as well as trial results are presented.

Perspectives on Drug and Therapeutics Committee policy implementation

Background Drug and therapeutics committees (DTCs) are expected to make difficult decisions that may have an impact on the clinical and economic outcomes of drug use. There have been few studies investigating the barriers to DTC policy implementation, and little is known about ways to improve the process. Objectives The specific objectives of this qualitative study were to explore stakeholder opinions with respect to (1) the perceptions of barriers to Drug and Therapeutics Committee (DTC) policy implementation and (2) ways to improve DTC policy implementation. Methods Stakeholders of Australian DTCs participated in focus group discussions. Discussions were audiotaped and transcribed verbatim. Thematic content analysis was conducted. Results Six focus group discussions were conducted. A number of barriers were identified (lack of resources, lack of follow-up, lack of ownership, low DTC profile within the organization, and overreliance on pharmacy to implement policy). Participants were of the opinion that prioritizing decisions, optimizing pharmacy roles, provision of real-time information, and active procurement of organizational commitment were some of the ways to improve DTC policy implementation. Conclusions Although this study was conducted in an Australian setting, the challenges facing DTCs in other countries are likely to be similar. Ownership, social influence, and resources are important issues in the implementation of policy/guidelines for many DTCs, both in Australia and internationally. It is expected that these issues will have an impact on policy implementation. Therefore, the findings of this study may be widely applicable. This work reinforced the notion that the significance of DTCs lies beyond decision making alone. The time and expertise invested in decision making could be undermined, if DTC policies are not effectively implemented.

Context management for the provision of adaptive services to roaming users

Various types of sophisticated networks and terminals are used for the provision of thirdgeneration services, and even more complicated ones are designed for 4G systems. Roaming users will rely on new kinds of wireless networks and terminals to offer mobility and make multimedia services available everywhere. They will, of course, expect continuous access to personalized services from any place, transparently and independent of the underlying network technology and the terminal or point of access. Virtual home environment features were specified and implemented in order to satisfy these requirements in 3G systems. The enhanced role of IP in 4G systems and the assimilation of WLAN technology will enable further advances toward pervasive computing and the pursuit of corn-, plete context awareness in state-of-the-art applications and services. 4G services can he smarter and more adaptive by taking advantage of all types of context available to them within a well defined context management framework. This article presents results from work carried out over the last few years at NTUA,’ bringing us closer to the definition of such a flexible framework for cfficient management of content, which will he able to support the development of advanced telecommunications services and integrated real-time information provision and processing systems.

Adaptability of a Hybrid Route Choice Model to Incorporating Driver Behavior Dynamics Under Information Provision

This paper proposes a seamless framework to incorporate the day-to-day and within-day dynamics of driver route choice decisions under real-time information provision by adapting a hybrid probabilistic-possibilistic model previously developed by the authors. The day-to-day dynamics are captured through the update of driver perception and route choice rules based on the current day's experience. The within-day dynamics are captured through the en-route adjustment of the weights of the driver route choice rules in response to situational factors. Experiments are conducted to analyze the model's ability to capture driver behavior dynamics, and the associated prediction accuracy. The results suggest that the framework can reflect the evolution of driver route choice behavior over time, and adapt to the within-day variability in ambient driving conditions. This is illustrated by its ability to capture phenomena such as inertia, compliance, delusion, freezing, and perception update under information provision, in addition to the effects of familiarity and route complexity. In the within-day context, the results highlight the sensitivity to the situational factors unfolding in real-time. The results also illustrate the better prediction power of the hybrid model compared to that of a traditional multinomial probit model; however, this gap reduces with increasing heterogeneity in driver behavioral class fractions. Elsewhere, the authors show that the proposed framework can be used to predict the ambient driver class fractions, thereby addressing a key deployment limitation of existing dynamic network models for real-time traffic control through route guidance.