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Modeling the frequency of opposing left-turn conflicts at signalized intersections using generalized linear
Xin Zhang1, Pan Liu, Yuguang Chen
1a School of Transportation , Southeast University , Nanjing , China.
Traffic Injury Prevention
|November 13, 2013
Summary
Traffic conflict frequency at signalized intersections can be modeled using negative binomial regression. Driver behavior varies with traffic conditions, impacting conflict prediction accuracy for improved road safety analysis.
Area of Science:
- Traffic Engineering
- Transportation Safety
- Predictive Modeling
Background:
- Traffic conflicts at signalized intersections are critical safety events.
- Predicting conflict frequency is essential for effective safety evaluation.
- Existing models may not fully capture the complexities of traffic interactions.
Purpose of the Study:
- To determine if traffic conflict frequency at signalized intersections can be modeled.
- To develop predictive models for opposing left-turn conflicts.
- To compare the performance of different modeling approaches.
Main Methods:
- Analyzed conflict data from 30 approaches at 20 signalized intersections.
- Examined conflict distributions under varying traffic conditions.
- Developed and compared linear regression, negative binomial, and scenario-specific models.
Main Results:
- Traffic conflict frequency follows a negative binomial distribution.
- Linear regression models are unsuitable for conflict frequency data.
- Driver behavior and traffic conditions significantly influence conflict frequency and prediction.
Conclusions:
- Generalized linear regression models can effectively model traffic conflicts at signalized intersections.
- Conflict predictive models enhance the application of surrogate safety measures for safety assessment.
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