Related Experiment Video
Updated: Nov 2, 2025
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
3.5K
Exploring temporal interactions of crash counts in California using distinct log-linear contingency table models
Wen Cheng1, Mankirat Singh1, Edward Clay1
1Department of Civil Engineering, California State Polytechnic University, Pomona, Pomona, CA, USA.
Summary
Analyzing road crash data across multiple time units reveals significant temporal patterns. Peak crash occurrences are identified during specific hours, weekdays, and months, emphasizing the need for comprehensive temporal analysis in road safety.
Area of Science:
- Road Safety
- Transportation Science
- Statistical Modeling
Background:
- Road crash occurrence is significantly influenced by temporal factors.
- Limited research has explored multiple, short-term time units (e.g., hourly, daily, monthly) for crash analysis.
- Understanding temporal crash patterns is crucial for effective safety interventions.
Purpose of the Study:
- To investigate the temporal distribution of road crash counts using multiple time spans (hour, weekday, month).
- To identify significant associations between time variables and crash occurrences.
- To evaluate the predictive performance of different statistical models for temporal crash data.
Main Methods:
- Employed Chi-square and Cochran-Mantel-Haenzel tests for analyzing contingency tables.
- Developed and evaluated eight contingency table models based on independence and association.
- Utilized a set of evaluation criteria to assess model performance.
Main Results:
- Confirmed significant associations between all time variables (hour, weekday, month) and crash counts.
- The model incorporating main and interactive effects of time variables demonstrated the best predictive performance.
- Identified peak crash occurrences at Hour 18, weekdays 1, 6, 7 (Friday and Weekends), and month 8 (August).
Conclusions:
- Both main and interactive temporal effects are essential for accurate road crash modeling.
- Excluding these effects can lead to misleading safety insights.
- Findings aid safety professionals in understanding temporal crash patterns and optimizing resource allocation.
Related Concept Videos
01:21Introduction to Test of Independence
2.6K
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:
2.6K
01:29Contingency Table
2.9K
A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
2.9K
01:16Hypothesis Test for Test of Independence
5.5K
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)...
5.5K
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
01:20Comparing the Survival Analysis of Two or More Groups
369
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
369
01:19Survival Tree
200
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
200