Related Experiment Video
Updated: Apr 5, 2026
11:41
Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
Published on: February 1, 2020
21.1K
A random parameters probit model of urban and rural intersection crashes
1School of Business IT and Logistics, RMIT University, Melbourne, Victoria, Australia.
Accident; Analysis and Prevention
|August 28, 2015
Summary
Urban and rural intersection crashes differ significantly. Urban crashes involve more hit-and-runs and higher traffic, while rural crashes have increased fatalities and are linked to speed limits and road features. Tailored safety measures are crucial.
Area of Science:
- Transportation Engineering
- Traffic Safety Research
- Road Safety Analysis
Background:
- Intersections are high-risk locations for traffic crashes.
- Existing research often overlooks distinct urban and rural intersection crash factors.
- Urban and rural environments present unique road, traffic, and environmental characteristics influencing crash dynamics.
Purpose of the Study:
- To identify and compare the factors contributing to crash frequency and severity at urban versus rural intersections.
- To provide a data-driven basis for developing targeted intersection safety strategies.
Main Methods:
- Utilized a random parameters probit model.
- Analyzed intersection crash data from Alberta, Canada, spanning 2008-2012.
- Differentiated analysis between urban and rural intersection crash characteristics.
Main Results:
- Urban crashes correlate with hit-and-run, high traffic volume, wet surfaces, multiple lanes, and skewed designs, occurring during weekdays/off-peak hours.
- Rural crashes are associated with higher fatalities/injuries, speed limits, specific road features, weekend/night-time occurrences, and run-off-road events.
Conclusions:
- Recognizing distinct urban and rural intersection crash factors is vital for effective road safety.
- Safety professionals should tailor interventions, including audits, enforcement, and education, to specific urban and rural contexts and vulnerable times/locations.
Related Concept Videos
01:16Hypothesis Test for Test of Independence
8.4K
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)...
8.4K
01:23Mechanistic Models: Compartment Models in Individual and Population Analysis
335
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...
335
01:09Pharmacodynamic Models: Additive and Proportional Drug Effect Model
69
Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
69
01:08Determination of Expected Frequency
2.7K
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...
2.7K
01:21Causality in Epidemiology
2.1K
Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
2.1K
01:21Introduction to Test of Independence
3.1K
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:
3.1K