Temporal stability of associations between crash characteristics: A multiple correspondence analysis
Tien-Pen Hsu1, Yuan-Wei Wu1, Albert Y Chen1
1Department of Civil Engineering, National Taiwan University, Taipei 106, Taiwan.
Accident; Analysis and Prevention
|February 12, 2022
Summary
Traffic safety analysis reveals that crash characteristics associations vary over time. Longer study periods, especially two years or more, reveal more stable trends crucial for effective traffic safety policies.
Area of Science:
- Traffic Safety
- Transportation Engineering
- Data Analysis
Background:
- Developing effective traffic safety policies requires understanding crash characteristic associations.
- Temporal stability of these associations is critical for accurate analysis and policy development.
Purpose of the Study:
- To investigate the temporal stability of associations between crash characteristics at various temporal levels.
- To identify the optimal temporal scope for analyzing traffic crash data.
Main Methods:
- Utilized multiple correspondence analysis (MCA) to examine crash data from 2020.
- Assessed temporal stability across eight different temporal levels (week to four years).
- Employed MCA plots and chi-square distance analysis for evaluation.
Main Results:
- Associations between crash characteristics exhibit cyclical variations at lower temporal levels (e.g., weekly).
- Increased temporal levels, particularly two years and above, reveal more stable, long-term trends.
- Both stable and unstable associations can be present depending on the chosen temporal scope.
Conclusions:
- Traffic safety data analysis must account for temporal variations in crash characteristic associations.
- Longer temporal levels provide more reliable insights for identifying traffic safety issues and developing policies.
- Findings challenge analysts by highlighting the coexistence of temporally stable and unstable patterns.
Related Concept Videos
01:16Hypothesis Test for Test of Independence
4.2K
The test of independence is a chi-square-based test used to determine whether two variables or factors are independent or dependent. This hypothesis test is used to examine the independence of the variables. One can construct two qualitative survey questions or experiments based on the variables in a contingency table. The goal is to see if the two variables are unrelated (independent) or related (dependent). The null and alternative hypotheses for this test are:
H0: The two variables (factors)...
H0: The two variables (factors)...
4.2K
01:08Determination of Expected Frequency
2.3K
Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...
2.3K
01:21Introduction to Test of Independence
2.5K
In statistics, the term independence means that one can directly obtain the probability of any event involving both variables by multiplying their individual probabilities. Tests of independence are chi-square tests involving the use of a contingency table of observed (data) values.
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
The test statistic for a test of independence is similar to that of a goodness-of-fit test:
2.5K
01:05Collisions in Multiple Dimensions: Introduction
5.7K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
5.7K
01:23Correlation of Experimental Data
329
Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
329
01:15Theory of Attribution I: Correspondent Inference Theory
29
Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
29