Dataset Information

On the existence of network Macroscopic Safety Diagrams: Theory, simulation and empirical evidence.

ABSTRACT: Recent studies have proposed using well-defined relationships between network productivity and accumulation-otherwise known as Network or Macroscopic Fundamental Diagrams (network MFDs)-to model the dynamics of large-scale urban traffic networks. Network MFDs have been used to develop a variety of network-wide traffic control policies to improve a network's operational efficiency. However, the relationship between a network's MFD and its safety performance has not been well explored. This study proposes the existence of a Macroscopic Safety Diagram (MSD) that relates safety performance (e.g., likelihood of a crash occurring or number of vehicle conflicts observed) with the current network state (i.e., average density) in an urban traffic network. We theoretically posit a relationship between a network's MSD and its MFD based on the average maneuver envelop of vehicles traveling within the network. Based on this model, we show that the density associated with maximum crash propensity is always expected to be larger than the density associated with maximum network productivity. This finding suggests that congested states are not only inefficient, but they might also be associated with more crashes, which can be both more unsafe and lead to decreased network reliability. These theoretical results are validated using surrogate safety assessment metrics in microsimulation and limited field empirical data from a small arterial network in Riyadh, Kingdom of Saudi Arabia. The existence of such MSDs can be used to develop more comprehensive network-wide control policies that can ensure both safe, efficient and reliable network operations.

SUBMITTER: Alsalhi R

PROVIDER: S-EPMC6080770 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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On the existence of network Macroscopic Safety Diagrams: Theory, simulation and empirical evidence.

Alsalhi Raed R Dixit Vinayak V VV Gayah Vikash V VV

PloS one 20180807 8

Recent studies have proposed using well-defined relationships between network productivity and accumulation-otherwise known as Network or Macroscopic Fundamental Diagrams (network MFDs)-to model the dynamics of large-scale urban traffic networks. Network MFDs have been used to develop a variety of network-wide traffic control policies to improve a network's operational efficiency. However, the relationship between a network's MFD and its safety performance has not been well explored. This study ...[more]

PMID: 30086157

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Project description:I study the co-evolution between public opinion and party policy in situations of crises by investigating a policy U-turn of a major Austrian right-wing party (FPÖ) during the Covid-19 pandemic. My analysis suggests the existence of both i) a “Downsian” effect, which causes voters to adapt their party preferences based on policy congruence and ii) a “party identification” effect, which causes partisans to realign their policy preferences based on “their” party's platform. Specifically, I use individual-level panel data to show that i) “corona skeptical” voters who did not vote for the FPÖ in the pre-Covid-19 elections of 2019 were more likely to vote for the party after it embraced “corona populism”, and ii) beliefs of respondents who declared that they voted for the FPÖ in 2019 diverged from the rest of the population in three out of four health-related dimensions only after the turn, causing them to underestimate the threat posed by Covid-19 compared to the rest of the population. Using aggregate-level panel data, I study whether the turn has produced significant behavioral differences which could be observed in terms of reported cases and deaths per capita. Paradoxically, after the turn the FPÖ vote share is significantly positively correlated with deaths per capita, but not with the reported number of infections. I hypothesize that this can be traced back to a self-selection bias in testing, which causes a correlation between the number of “corona skeptics” and the share of unreported cases after the turn. I find empirical support for this hypothesis in individual-level data from a Covid-19 prevalence study that involves information about participants’ true vs. reported infection status. I finally study a simple heterogeneous mixing epidemiological model and show that a testing bias can indeed explain the apparent paradox of an increase in deaths without an increase in reported cases. My results can, among others, be used to enrich formal analyses regarding the co-evolution between voter and party behavior.

Project description:BackgroundDigital education tools (e-learning, technology-enhanced learning) can be defined as any educational intervention that is electronically mediated. Decveloping and applying such tools and interventions for postgraduate medical professionals who work and learn after graduation can be called postgraduate medical digital education (PGMDE), which is increasingly being used and evaluated. However, evaluation has focused mainly on reaching the learning goals and little on the design. Design models for digital education (instructional design models) help educators create a digital education curriculum, but none have been aimed at PGMDE. Studies show the need for efficient, motivating, useful, and satisfactory digital education.ObjectiveOur objective was (1) to create an empirical instructional design model for PGMDE founded in evidence and theory, with postgraduate medical professionals who work and learn after graduation as the target audience, and (2) to compare our model with existing models used to evaluate and create PGMDE.MethodsPreviously we performed an integrative literature review, focus group discussions, and a Delphi procedure to determine which building blocks for such a model would be relevant according to experts and users. This resulted in 37 relevant items. We then used those 37 items and arranged them into chronological steps. After we created the initial 9-step plan, we compared these steps with other models reported in the literature.ResultsThe final 9 steps were (1) describe who, why, what, (2) select educational strategies, (3) translate to the real world, (4) choose the technology, (5) complete the team, (6) plan the budget, (7) plan the timing and timeline, (8) implement the project, and (9) evaluate continuously. On comparing this 9-step model with other models, we found that no other was as complete, nor were any of the other models aimed at PGMDE.ConclusionsOur 9-step model is the first, to our knowledge, to be based on evidence and theory building blocks aimed at PGMDE. We have described a complete set of evidence-based steps, expanding a 3-domain model (motivate, learn, and apply) to an instructional design model that can help every educator in creating efficient, motivating, useful, and satisfactory PGMDE. Although certain steps are more robust and have a deeper theoretical background in current research (such as education), others (such as budget) have been barely touched upon and should be investigated more thoroughly in order that proper guidelines may also be provided for them.

Dataset Information

On the existence of network Macroscopic Safety Diagrams: Theory, simulation and empirical evidence.

Publications

On the existence of network Macroscopic Safety Diagrams: Theory, simulation and empirical evidence.

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