Missouri-Specific Crash Prediction Model for Signalized Intersections

2018 ◽  
Vol 2672 (30) ◽  
pp. 32-42 ◽  
Author(s):  
Boris Claros ◽  
Carlos Sun ◽  
Praveen Edara
Keyword(s):  
Prediction Models ◽  
Calibration Factor ◽  
Predictor Variables ◽  
Local Conditions ◽  
Left Turn ◽  
Crash Prediction ◽  
Crash Prediction Model ◽  
Cumulative Residuals ◽  
Crash Prediction Models ◽  
Exclusive Right

The Highway Safety Manual (HSM) provides guidance and tools to conduct quantitative safety analysis. Crash prediction models are used to estimate the expected number of crashes per year, by facility type, severity, and crash type. There are two approaches for applying the HSM crash prediction methodology to local conditions: (1) calibration of models provided in the HSM; or (2) development of jurisdiction-specific models. There are some instances in which model calibration may not be appropriate. To illustrate this case, 601 urban signalized four-leg intersections (U4SG) in Missouri were used to obtain the calibration factor, assess the quality of the calibration factor, and develop jurisdiction-specific models. For U4SG total crashes, the calibration factor for Missouri conditions was 3.98 (standard deviation, 0.13). The assessment of the calibration factor showed a disproportional difference between the observed data in Missouri and the HSM model. Thus, the calibration was deemed inappropriate and the development of Missouri-specific models was supported. The models were developed for severities Fatal and Injury (FI) and Property Damage Only (PDO) crashes. The predictor variables considered were intersection AADT, posted speed limit, signal control type, exclusive left turn lanes, exclusive right turn lanes, right turn on red prohibited, and facilities of interest within 1,000 ft from the intersection (bus stops, schools, and alcohol sale establishments). Functional forms for all predictor variables were optimized. The log-likelihood, inverse overdispersion, and Cumulative Residuals (CURE) plots showed satisfactory measures of model accuracy.

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.

scholarly journals Investigation of surrogate measures for safety assessment of two-way stop controlled intersections.

2021 ◽  
Author(s):  
Adrian Lorion
Keyword(s):  
Goodness Of Fit ◽  
Prediction Models ◽  
Traffic Volume ◽  
Left Turn ◽  
Crash Prediction ◽  
Greater Toronto Area ◽  
Crash Prediction Models ◽  
Surrogate Measures ◽  
Surrogate Safety Measures ◽  
Volume Intersection

Crash prediction models used to estimate safety at intersections, road, and highway segments are traditionally developed using various traffic volume measures. There are issues with this approach and surrogate safety measures such as conflicts and delays can overcome them. This study investigates the relationships between crash frequencies and traffic volume, intersection delay, and conflicts to explore the viability of these models for estimating safety at two-way stop controlled intersections. The database used includes 78 three leg and 55 four leg intersections within the Greater Toronto Area. Crash prediction models were developed and evaluated based on goodness-of-fit measures and CURE plots. Two conflict estimation techniques are compared in order to determine which is best suited for two-way stop controlled intersection simulations. This study also investigates the use of the models for estimating the safety impact of implementing a left turn lane on a major approach of a three leg intersection.

scholarly journals Crash Prediction on Expressway Incorporating Traffic Flow Continuity Parameters Based on Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Tian Lei ◽  
Jia Peng ◽  
Xingliang Liu ◽  
Qin Luo
Keyword(s):  
Prediction Model ◽  
Real Time ◽  
Traffic Flow ◽  
Prediction Models ◽  
Traffic Data ◽  
Crash Prediction ◽  
Model Based ◽  
Crash Prediction Model ◽  
Variable Space ◽  
Crash Prediction Models

Real-time crash prediction helps identify and prevent the occurrence of traffic crash. For years, various real-time crash prediction models have been investigated to provide effective information for proactive traffic management. When building real-time crash prediction model, a suitable variable space together with a specific time interval for traffic data aggregation and an appropriate modelling algorithm should be applied. Regarding the intercorrelation problem with variable space, comprehensive real-time crash prediction model considering available traffic data characteristics in applicable circumstances needs to be explored. Taking Xi’an G3001 Expressway as study area, real road traffic and accident data during the period from January 2014 to January 2019 on this expressway are applied for real-time crash prediction. To better capture traffic flow characteristics on expressway and improve the practicality of real-time crash prediction model, two new variables (segment difference coefficient and lane difference coefficient) describing the smoothness and continuity of traffic flow in spatial dimension are developed and incorporated in building the crash prediction model to solve the intercorrelation problem with variable space. Random forest (RF) is then adopted to specify the quantitative relationship between specific variable and crash risk. Real-time crash prediction model based on support vector machine (SVM) using new composed variable space is built. The results show that simplified variable space could contribute to the same classification power in currently used real-time crash prediction models compared with traditional variable space. Moreover, the prediction model based on SVM reaches an accuracy level of 0.9, which performs better than other currently used prediction models.

scholarly journals Investigation of surrogate measures for safety assessment of two-way stop controlled intersections.

2021 ◽  
Author(s):  
Adrian Lorion
Keyword(s):  
Goodness Of Fit ◽  
Prediction Models ◽  
Traffic Volume ◽  
Left Turn ◽  
Crash Prediction ◽  
Greater Toronto Area ◽  
Crash Prediction Models ◽  
Surrogate Measures ◽  
Surrogate Safety Measures ◽  
Volume Intersection

Crash prediction models used to estimate safety at intersections, road, and highway segments are traditionally developed using various traffic volume measures. There are issues with this approach and surrogate safety measures such as conflicts and delays can overcome them. This study investigates the relationships between crash frequencies and traffic volume, intersection delay, and conflicts to explore the viability of these models for estimating safety at two-way stop controlled intersections. The database used includes 78 three leg and 55 four leg intersections within the Greater Toronto Area. Crash prediction models were developed and evaluated based on goodness-of-fit measures and CURE plots. Two conflict estimation techniques are compared in order to determine which is best suited for two-way stop controlled intersection simulations. This study also investigates the use of the models for estimating the safety impact of implementing a left turn lane on a major approach of a three leg intersection.

scholarly journals Evaluation of the Predictive Capabilities of Simulated Peak Hour conflict Based Crash Prediction Models

2021 ◽  
Author(s):  
Taha Saleem
Keyword(s):  
Prediction Models ◽  
Road Traffic ◽  
Traffic Crashes ◽  
Road Traffic Crashes ◽  
Left Turn ◽  
Crash Prediction ◽  
Traffic Conflicts ◽  
Traffic Volumes ◽  
Causes Of Deaths ◽  
Crash Prediction Models

Road traffic crashes are one of the major causes of deaths worldwide. A safety prediction model is designed to estimate the safety of a road entity and in most cases these models link traffic volumes to crashes. A major problem with such models is that crashes are rare events and that crash statistics do not take into account everything that may have contributed to the crashes. The use of traffic conflicts to measure safety can overcome these problems as conflicts occur more frequently than crashes and can be easily estimated using micro simulation. For the purpose of this thesis, simulated peak hour conflict based crash prediction models are developed for 113 Toronto signalized intersections and their predictive capabilities are evaluated. The effects of a hypothetical left turn treatment on crashes and conflicts are also explored and compared to the study conducted by Srinivasan et al (2012). Lastly, the transferability of SSAM prediction models is evaluated to explore how well the models predict crashes for Toronto intersections.

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.

scholarly journals Evaluation of the Predictive Capabilities of Simulated Peak Hour conflict Based Crash Prediction Models

2021 ◽  
Author(s):  
Taha Saleem
Keyword(s):  
Prediction Models ◽  
Road Traffic ◽  
Traffic Crashes ◽  
Road Traffic Crashes ◽  
Left Turn ◽  
Crash Prediction ◽  
Traffic Conflicts ◽  
Traffic Volumes ◽  
Causes Of Deaths ◽  
Crash Prediction Models

Road traffic crashes are one of the major causes of deaths worldwide. A safety prediction model is designed to estimate the safety of a road entity and in most cases these models link traffic volumes to crashes. A major problem with such models is that crashes are rare events and that crash statistics do not take into account everything that may have contributed to the crashes. The use of traffic conflicts to measure safety can overcome these problems as conflicts occur more frequently than crashes and can be easily estimated using micro simulation. For the purpose of this thesis, simulated peak hour conflict based crash prediction models are developed for 113 Toronto signalized intersections and their predictive capabilities are evaluated. The effects of a hypothetical left turn treatment on crashes and conflicts are also explored and compared to the study conducted by Srinivasan et al (2012). Lastly, the transferability of SSAM prediction models is evaluated to explore how well the models predict crashes for Toronto intersections.

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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