Understanding and Communicating Reliability of Crash Prediction Models

The objective of this research was to develop new intersection crash prediction models for consideration in the second edition of the Highway Safety Manual (HSM), consistent with existing methods in HSM Part C and comprehensive in their ability to address a wide range of intersection configurations and traffic control types in rural and urban areas. The focus of the research was on developing safety performance functions (SPFs) for intersection configurations and traffic control types not currently addressed in HSM Part C. SPFs were developed for the following general intersection configurations and traffic control types: rural and urban all-way stop-controlled intersections; rural three-leg intersections with signal control; intersections on high-speed urban and suburban arterials (i.e., arterials with speed limits greater than or equal to 50 mph); urban five-leg intersections with signal control; three-leg intersections where the through movements make turning maneuvers at the intersections; crossroad ramp terminals at single-point diamond interchanges; and crossroad ramp terminals at tight diamond interchanges. Development of severity distribution functions (SDFs) for use in combination with SPFs to estimate crash severity as a function of geometric design elements and traffic control features was explored; but owing to challenges and inconsistencies in developing and interpreting the SDFs, it was recommended for the second edition of the HSM that crash severity for the new intersection configurations and traffic control types be addressed in a manner consistent with existing methods in Chapters 10, 11, and 12 of the first edition, without use of SDFs.

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Crash Prediction Models for Horizontal Curve Segments on Two-Lane Rural Roads in Thailand

Sustainability ◽

10.3390/su13169011 ◽

2021 ◽

Vol 13 (16) ◽

pp. 9011

Author(s):

Nopadon Kronprasert ◽

Katesirint Boontan ◽

Patipat Kanha

Keyword(s):

Prediction Models ◽

Safety Performance ◽

Horizontal Curve ◽

Crash Frequency ◽

Rural Roads ◽

Crash Prediction ◽

Road Crashes ◽

Highway Safety Manual ◽

Safety Performance Functions ◽

Crash Prediction Models

The number of road crashes continues to rise significantly in Thailand. Curve segments on two-lane rural roads are among the most hazardous locations which lead to road crashes and tremendous economic losses; therefore, a detailed examination of its risk is required. This study aims to develop crash prediction models using Safety Performance Functions (SPFs) as a tool to identify the relationship among road alignment, road geometric and traffic conditions, and crash frequency for two-lane rural horizontal curve segments. Relevant data associated with 86,599 curve segments on two-lane rural road networks in Thailand were collected including road alignment data from a GPS vehicle tracking technology, road attribute data from rural road asset databases, and historical crash data from crash reports. Safety Performance Functions (SPFs) for horizontal curve segments were developed, using Poisson regression, negative binomial regression, and calibrated Highway Safety Manual models. The results showed that the most significant parameter affecting crash frequency is lane width, followed by curve length, traffic volume, curve radius, and types of curves (i.e., circular curves, compound curves, reverse curves, and broken-back curves). Comparing among crash prediction models developed, the calibrated Highway Safety Manual SPF outperforms the others in prediction accuracy.

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Effectiveness of Consistency Measures in Crash Prediction Models for Two-Lane Highways in Palestine

Arabian Journal for Science and Engineering ◽

10.1007/s13369-018-3285-0 ◽

2018 ◽

Vol 43 (10) ◽

pp. 5645-5656 ◽

Cited By ~ 1

Author(s):

Khaled Al-Sahili ◽

Mohammed Dwaikat ◽

Sameer Abu-Eisheh ◽

Wael Alhajyaseen

Keyword(s):

Prediction Models ◽

Crash Prediction ◽

Crash Prediction Models

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Developing crash prediction models using parametric and nonparametric approaches for rural mountainous freeways: A case study on Wyoming Interstate 80

Accident Analysis & Prevention ◽

10.1016/j.aap.2018.10.011 ◽

2019 ◽

Vol 123 ◽

pp. 176-189 ◽

Cited By ~ 9

Author(s):

Sherif M. Gaweesh ◽

Mohamed M. Ahmed ◽

Annalisa V. Piccorelli

Keyword(s):

Prediction Models ◽

Crash Prediction ◽

Crash Prediction Models ◽

Parametric And Nonparametric Approaches

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Calibration of Predictive Models for Estimating Safety of Ramp Design Configurations

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198105190800111 ◽

2005 ◽

Vol 1908 (1) ◽

pp. 88-95 ◽

Cited By ~ 15

Author(s):

Dominique Lord ◽

James A. Bonneson

Keyword(s):

Predictive Models ◽

Prediction Models ◽

Free Flow ◽

Flow Loop ◽

Local Conditions ◽

Crash Prediction ◽

Calibration Process ◽

Low Costs ◽

Crash Prediction Models ◽

Transportation Agencies

The goal for the calibration process is to use predictive models developed with data collected from other jurisdictions and apply them to the jurisdiction of interest by adapting the models for local conditions and characteristics. Given the large costs associated with data collection, this process is often the only method available to transportation agencies for estimating the safety of different transportation facilities. Thus, recalibrating models produced from other jurisdictions allows agencies to produce their own models at relatively low costs. The objective for the research was to recalibrate a set of crash prediction models for different ramp design configurations. The ramp design configurations addressed included diagonal ramps, non-free-flow loop ramps, free-flow loop ramps, and outer connection ramps. A total of 44 ramps located in and around Austin, Texas, were used in the calibration process. The results of the study showed that more crashes occur on exit ramps than entrance ramps by a ratio of about 6 to 4. The results also showed that the non-free-flow ramp experiences twice as many crashes as other types of ramp. Similarly, more crashes occur on rural than urban ramps.

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