Application of local conditional autoregressive models for development of zonal crash prediction models and identification of crash risk boundaries

2019 ◽  
Vol 15 (2) ◽  
pp. 1102-1123 ◽  
Author(s):  
Emad Soroori ◽  
Abolfazl Mohammadzadeh Moghaddam ◽  
Mahdi Salehi
Keyword(s):  
Prediction Models ◽  
Autoregressive Models ◽  
Crash Risk ◽  
Crash Prediction ◽  
Conditional Autoregressive ◽  
Crash Prediction Models ◽  
Conditional Autoregressive Models

Access Separation at Interchanges: Examining Crash Rates on the Cross-Street and in the Transition Areas from Full to Partial Access Control

2018 ◽  
Vol 2672 (17) ◽  
pp. 1-10 ◽  
Author(s):  
Timothy P. Barrette ◽  
Jacob Warner ◽  
Patricia Thompson ◽  
Peter T. Savolainen
Keyword(s):  
Access Control ◽  
Prediction Models ◽  
Access Point ◽  
Crash Risk ◽  
Exit Point ◽  
Crash Prediction ◽  
Access Points ◽  
Important Concern ◽  
Crash Prediction Models ◽  
Transition Areas

Access control remains an important concern to roadway agencies as the spacing of at-grade access points significantly affects both the safety and operational performance of highways. Significant variability exists with respect to policies dictating where the first access points may occur in the vicinity of interchanges. This study examines two important spacing criteria, which include: (1) the distance from the freeway off-ramp to the first access on the interchanging arterial roadway; and (2) the distance from a freeway on-ramp to the first at-grade intersection in areas with partial access control. To investigate these relationships, a series of crash prediction models were estimated that examined how crash risk changed with respect to access spacing while controlling for the effects of traffic volume and other pertinent characteristics. The results illustrate that at cross-streets near ramp terminal intersections, the crash rate generally increases as the distance to the nearest access point decreases. The same trend was also true of freeway transition areas. Ultimately, this research illustrates a complex relationship that exists among the proximity of the exit point of the controlled access facility and the adjacent access point, the volume of traffic along the roadway, and the volume of traffic at the access point.

Exploring a multilevel approach with spatial effects to model housing price in San José, Costa Rica

2021 ◽  
pp. 239980832110411
Author(s):  
Eduardo Pérez-Molina
Keyword(s):  
Costa Rica ◽  
Multilevel Models ◽  
Housing Prices ◽  
Housing Price ◽  
Autoregressive Models ◽  
Metropolitan Region ◽  
Spatial Effects ◽  
San Jose ◽  
Conditional Autoregressive ◽  
Conditional Autoregressive Models

A multilevel model of the housing market for San José Metropolitan Region (Costa Rica) was developed, including spatial effects. The model is used to explore two main questions: the extent to which contextual (of the surroundings) and compositional (of the property itself) effects explain variation of housing prices and how does the relation between price and key covariates change with the introduction of multilevel effects. Hierarchical relations (lower level units nested into higher level) were modeled by specifying multilevel models with random intercepts and a conditional autoregressive term to include spatial effects from neighboring units at the higher level (districts). The random intercepts and conditional autoregressive models presented the best fit to the data. Variation at the higher level accounted for 16% of variance in the random intercepts model and 28% in the conditional autoregressive model. The sign and magnitude of regression coefficients proved remarkably stable across model specifications. Travel time to the city center, which presented a non-linear relation to price, was found to be the most important determinant. Multilevel and conditional autoregressive models constituted important improvements in modeling housing price, despite most of the variation still occurring at the lower level, by improving the overall model fit. They were capable of representing the regional structure and of reducing sampling bias in the data. However, the conditional autoregressive specification only represented a limited advance over the random intercepts formulation.

New Intersection Crash Prediction Models for the Second Edition of the Highway Safety Manual

2021 ◽  
pp. 036119812110398
Author(s):  
Darren J. Torbic ◽  
Daniel Cook ◽  
Joseph Grotheer ◽  
Richard Porter ◽  
Jeffrey Gooch ◽  
...  
Keyword(s):  
Traffic Control ◽  
Prediction Models ◽  
Highway Safety ◽  
Crash Severity ◽  
Crash Prediction ◽  
Part C ◽  
Highway Safety Manual ◽  
Rural And Urban ◽  
Diamond Interchanges ◽  
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.

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

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.

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

2018 ◽  
Vol 43 (10) ◽  
pp. 5645-5656 ◽  
Author(s):  
Khaled Al-Sahili ◽  
Mohammed Dwaikat ◽  
Sameer Abu-Eisheh ◽  
Wael Alhajyaseen
Keyword(s):  
Prediction Models ◽  
Crash Prediction ◽  
Crash Prediction Models

Calibration of Predictive Models for Estimating Safety of Ramp Design Configurations

2005 ◽  
Vol 1908 (1) ◽  
pp. 88-95 ◽  
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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