Related Experiment Video
Updated: Jul 1, 2025
11:41
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
Published on: February 1, 2020
20.4K
Assessing the Negative Binomial-Lindley model for crash hotspot identification: Insights from Monte Carlo simulation
Jhan Kevin Gil-Marin1, Mohammadali Shirazi1, John N Ivan2
1Department of Civil and Environmental Engineering, University of Maine, Orono, ME, 04469, USA.
Accident; Analysis and Prevention
|March 8, 2024
Summary
The Negative Binomial-Lindley (NB-L) model shows better specificity for identifying hazardous crash sites, while the Negative Binomial (NB) model offers higher sensitivity, especially with dispersed data.
Area of Science:
- Traffic Safety
- Statistical Modeling
- Highway Engineering
Background:
- Identifying hazardous crash sites is vital for effective highway safety management.
- The Negative Binomial (NB) model is widely used but has limitations with dispersed data and excess zeros.
- The Negative Binomial-Lindley (NB-L) model is a newer alternative addressing some NB limitations.
Purpose of the Study:
- To evaluate the performance of the NB-L model for hazardous site identification.
- To compare the NB-L model against the traditional NB model in hotspot identification.
- To analyze the trade-offs between NB and NB-L models using simulation.
Main Methods:
- Developed a Monte Carlo simulation protocol to generate diverse datasets.
- Implemented the NB-L model as a Full-Bayes hierarchical model.
- Compared Full-Bayes NB and NB-L models across various simulation scenarios.
Main Results:
- The NB-L model demonstrates superior specificity in identifying hazardous sites.
- The NB model exhibits higher sensitivity, particularly for highly dispersed crash data.
- A trade-off exists between NB and NB-L models regarding hotspot identification performance.
Conclusions:
- The NB-L model is advantageous when minimizing the misclassification of non-hazardous sites is a priority, especially under budget constraints.
- The NB model remains effective for maximizing the detection of actual hazardous sites.
- The choice between NB and NB-L depends on specific highway safety management objectives and data characteristics.
Related Concept Videos
01:16Hypothesis Test for Test of Independence
3.6K
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)...
3.6K
01:08Determination of Expected Frequency
2.2K
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...
2.2K
01:21Introduction to Test of Independence
2.2K
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:
2.2K
01:17Probability Histograms
11.5K
A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
11.5K
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
40
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
40
01:29Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
54
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
54