Disaggregated Crash Prediction Models for Different Crash Types using Joint Probability Model

ICTIS 2013 ◽  
2013 ◽  
Author(s):  
Xin Pei ◽  
N. N. Sze ◽  
S. C. Wong ◽  
Ling Huang ◽  
Danya Yao
Keyword(s):  
Prediction Models ◽  
Probability Model ◽  
Joint Probability ◽  
Crash Prediction ◽  
Crash Prediction Models ◽  
Crash Types ◽  
Joint Probability Model

scholarly journals A Variable-Correlation Model to Characterize Asymmetric Dependence for Postprocessing Short-Term Precipitation Forecasts

2019 ◽  
Vol 148 (1) ◽  
pp. 241-257 ◽  
Author(s):  
Wentao Li ◽  
Quan J. Wang ◽  
Qingyun Duan
Keyword(s):  
Correlation Coefficient ◽  
Extreme Events ◽  
Prediction Models ◽  
Probability Model ◽  
Weather Prediction ◽  
Joint Probability ◽  
Correlation Model ◽  
Asymmetric Dependence ◽  
Huai River ◽  
Joint Probability Model

Abstract Statistical postprocessing methods can be used to correct bias and dispersion error in raw ensemble forecasts from numerical weather prediction models. Existing postprocessing models generally perform well when they are assessed on all events, but their performance for extreme events still needs to be investigated. Commonly used joint probability postprocessing models are based on the correlation between forecasts and observations. Because the correlation may be lower for extreme events as a result of larger forecast uncertainty, the dependence between forecasts and observations can be asymmetric with respect to the magnitude of the precipitation. However, the constant correlation coefficient in the traditional joint probability model lacks the flexibility to model asymmetric dependence. In this study, we formulated a new postprocessing model with a decreasing correlation coefficient to characterize asymmetric dependence. We carried out experiments using Global Ensemble Forecast System reforecasts for daily precipitation in the Huai River basin in China. The results show that, although it performs well in terms of continuous ranked probability score or reliability for all events, the traditional joint probability model suffers from overestimation for extreme events defined by the largest 2.5% or 5% of raw forecasts. On the contrary, the proposed variable-correlation model is able to alleviate the overestimation and achieves better reliability for extreme events than the traditional model. The proposed variable-correlation model can be seen as a flexible extension of the traditional joint probability model to improve the performance for extreme events.

Traffic Safety Implications of Travel Demand Management Policies

2014 ◽  
pp. 115-140
Author(s):  
Ali Pirdavani ◽  
Tom Bellemans ◽  
Tom Brijs ◽  
Bruno Kochan ◽  
Geert Wets
Keyword(s):  
Spatial Correlation ◽  
Traffic Safety ◽  
Travel Demand ◽  
Prediction Models ◽  
Demand Management ◽  
Primary Objective ◽  
Travel Demand Management ◽  
Crash Prediction ◽  
Crash Prediction Models ◽  
Crash Types

Travel Demand Management (TDM) consists of a variety of policy measures that affect the transportation system’s effectiveness by changing travel behavior. Although the primary objective to implement such TDM strategies is not to improve traffic safety, their impact on traffic safety should not be neglected. The main purpose of this study is to investigate differences in the traffic safety consequences of two TDM scenarios: a fuel-cost increase scenario (i.e. increasing the fuel price by 20%) and a teleworking scenario (i.e. 5% of the working population engages in teleworking). Since TDM strategies are usually conducted at a geographically aggregated level, crash prediction models that are used to evaluate such strategies should also be developed at an aggregate level. Moreover, given that crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is also examined. The results indicate the necessity of accounting for the spatial correlation when developing crash prediction models. Therefore, Zonal Crash Prediction Models (ZCPMs) within the geographically weighted generalized linear modeling framework are developed to incorporate the spatial variations in association between the Number Of Crashes (NOCs) (including fatal, severe, and slight injury crashes recorded between 2004 and 2007) and a set of explanatory variables. Different exposure, network, and socio-demographic variables of 2200 traffic analysis zones in Flanders, Belgium, are considered as predictors of crashes. An activity-based transportation model is adopted to produce exposure metrics. This enables a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models. In this chapter, several ZCPMs with different severity levels and crash types are developed to predict the NOCs. The results show considerable traffic safety benefits of conducting both TDM scenarios at an average level. However, there are certain differences when considering changes in NOCs by different crash types.

Multivariate Poisson Lognormal Modeling of Weather-Related Crashes on Freeways

2018 ◽  
Vol 2672 (38) ◽  
pp. 184-198 ◽  
Author(s):  
Kai Wang ◽  
Shanshan Zhao ◽  
Eric Jackson
Keyword(s):  
Model Prediction ◽  
Prediction Models ◽  
Weather Conditions ◽  
Parameter Estimates ◽  
Prediction Ability ◽  
Crash Prediction ◽  
Adverse Weather Conditions ◽  
Adverse Weather ◽  
Crash Prediction Models ◽  
Crash Types

Adverse weather conditions are one of the primary causes of motor vehicle crashes. To identify the factors contributing to crashes during adverse weather conditions and recommend cost-effective countermeasures, it is necessary to develop reliable crash prediction models to estimate weather-related crash frequencies. To account for the variations in crash count among different adverse weather conditions, crash types, and crash severities for both rain- and snow-related crashes, crash data on freeways was collected from the State of Connecticut, and crash prediction models were developed to estimate crash counts by crash type and severity for each weather condition. To account for the potential correlations among crash type and severity counts due to the common unobserved factors, integrated nested Laplace approximation (INLA) multivariate Poisson lognormal (MVPLN) models were developed to estimate weather-related crashes counts by crash type and severity simultaneously (four MVPLN models were estimated in total). To verify the model prediction ability, univariate Poisson lognormal (UPLN) models were estimated and compared with the MVPLN models. The results show that the effects of factors contributing to crashes, including median width, horizontal curve, lane width, and shoulder width, vary not only among different adverse weather conditions, but also among different crash types and severities. The crash types and severities are shown to be highly correlated and the model comparison verifies that the MVPLN models significantly improve the model prediction accuracy compared with the UPLN models. Therefore, the MVPLN model is recommended to provide more unbiased parameter estimates when estimating weather-related crashes by crash type and severity.

Traffic Safety Implications of Travel Demand Management Policies

2015 ◽  
pp. 1082-1107 ◽  
Author(s):  
Ali Pirdavani ◽  
Tom Bellemans ◽  
Tom Brijs ◽  
Bruno Kochan ◽  
Geert Wets
Keyword(s):  
Spatial Correlation ◽  
Traffic Safety ◽  
Travel Demand ◽  
Prediction Models ◽  
Demand Management ◽  
Primary Objective ◽  
Travel Demand Management ◽  
Crash Prediction ◽  
Crash Prediction Models ◽  
Crash Types

Travel Demand Management (TDM) consists of a variety of policy measures that affect the transportation system's effectiveness by changing travel behavior. Although the primary objective to implement such TDM strategies is not to improve traffic safety, their impact on traffic safety should not be neglected. The main purpose of this study is to investigate differences in the traffic safety consequences of two TDM scenarios: a fuel-cost increase scenario (i.e. increasing the fuel price by 20%) and a teleworking scenario (i.e. 5% of the working population engages in teleworking). Since TDM strategies are usually conducted at a geographically aggregated level, crash prediction models that are used to evaluate such strategies should also be developed at an aggregate level. Moreover, given that crash occurrences are often spatially heterogeneous and are affected by many spatial variables, the existence of spatial correlation in the data is also examined. The results indicate the necessity of accounting for the spatial correlation when developing crash prediction models. Therefore, Zonal Crash Prediction Models (ZCPMs) within the geographically weighted generalized linear modeling framework are developed to incorporate the spatial variations in association between the Number Of Crashes (NOCs) (including fatal, severe, and slight injury crashes recorded between 2004 and 2007) and a set of explanatory variables. Different exposure, network, and socio-demographic variables of 2200 traffic analysis zones in Flanders, Belgium, are considered as predictors of crashes. An activity-based transportation model is adopted to produce exposure metrics. This enables a more detailed and reliable assessment while TDM strategies are inherently modeled in the activity-based models. In this chapter, several ZCPMs with different severity levels and crash types are developed to predict the NOCs. The results show considerable traffic safety benefits of conducting both TDM scenarios at an average level. However, there are certain differences when considering changes in NOCs by different crash types.

scholarly journals The crash performance of seagull intersections and intersections with left turn slip lanes

2019 ◽  
Vol 30 (3) ◽  
pp. 37-47 ◽  
Author(s):  
Shane Turner ◽  
Fergus Tate ◽  
Graham Wood
Keyword(s):  
Road Safety ◽  
Prediction Models ◽  
Left Turn ◽  
Crash Prediction ◽  
Crash Rates ◽  
Research Priority ◽  
Traffic Delays ◽  
Crash Prediction Models ◽  
Crash Types ◽  
Two Alternatives

Alternative intersection layouts may reduce traffic delays and/or improve road safety. Two alternatives are reviewed in this research: ‘priority-controlled Seagull intersections’ and ‘priority-controlled intersections with a Left Turn Slip Lane’. Seagull intersections are used to reduce traffic delays. Some do experience high crash rates, however. Left Turn Slip Lanes allow turning traffic to move clear of the through traffic before decelerating, thereby reducing the risk of rear-end crashes. Although there is debate about the safety problems that occur at Seagull intersections and Left Turn Slip Lanes there has been very little research to quantify the safety impact of different layouts. In this study, crash prediction models have been developed to quantify the effect of various Seagull intersection and Left Turn Slip Lane designs on the key crash types that occur at priority intersections. The analysis showed that seagulls are not safe on 4-lane roads, that roadway features like kerb-side parking and nearby intersections can increase crash rates and that left turners in LTSLs can restrict visibility and create safety problems.

3-Dimensional direct sampling-based environmental contours using a semi-parametric joint probability model

2021 ◽  
Vol 112 ◽  
pp. 102710
Author(s):  
Xiaoyu Bai ◽  
Hui Jiang ◽  
Xiaoyu Huang ◽  
Guangsong Song ◽  
Xinyi Ma
Keyword(s):  
Probability Model ◽  
Joint Probability ◽  
3 Dimensional ◽  
Direct Sampling ◽  
Joint Probability Model

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.

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