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Aggregate crash prediction models: introducing crash generation concept.
1Iran University of Science and Technology, Narmak, Tehran, Iran. Ali@Naderan.Com
Accident; Analysis and Prevention
|November 6, 2009
Summary
This study introduces crash generation models (CGMs) to predict traffic crashes proactively. By linking trip generation data to crash frequencies, urban planners can better assess safety impacts of future development.
Area of Science:
- Transportation Engineering
- Urban Planning
- Traffic Safety Analysis
Background:
- Traditional safety planning is reactive, lacking tools for proactive assessment of urban development impacts.
- Assessing safety effects of alternative urban planning scenarios requires a decision-support tool for proactive analysis.
Purpose of the Study:
- Develop aggregate crash prediction models (ACPM) aligned with the trip generation step of four-step demand models.
- Introduce and validate the concept of crash generation models (CGMs) for forecasting traffic safety.
Main Methods:
- Utilized trip generation data within a generalized linear regression framework.
- Assumed a negative binomial error structure for crash frequency modeling.
- Investigated the relationship between crash frequencies in traffic analysis zones (TAZ) and trip generation by purpose.
Main Results:
- Established a significant relationship between crash frequencies and the number of trips produced/attracted per TAZ, categorized by trip purpose.
- Demonstrated the feasibility of forecasting crashes at each time-step corresponding to trip forecasting.
Conclusions:
- Crash generation models (CGMs) offer a proactive approach to traffic safety assessment in urban planning.
- The developed ACPMs provide a valuable tool for transportation planners to forecast crash impacts linked to future travel demand.
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