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 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.

Safety evaluation of freeway acceleration lanes based on crashes and simulated conflicts

2018 ◽  
Vol 45 (1) ◽  
pp. 51-60 ◽  
Author(s):  
Lei Qin ◽  
Bhagwant Persaud ◽  
Taha Saleem
Keyword(s):  
Prediction Models ◽  
Safety Evaluation ◽  
Crash Prediction ◽  
Time To Collision ◽  
Design Changes ◽  
Traffic Volumes ◽  
Conventional Models ◽  
Crash Prediction Models ◽  
Surrogate Safety Measures ◽  
Positive Coefficients

Crash-based safety performance functions (SPFs) typically cannot account for all design and operational factors that contribute to crash frequency in assessing the safety impacts of these factors. An approach using surrogate safety measures can address this issue, providing these measures can be linked to crashes. This approach was explored by using microsimulation to generate and analyze conflicts for merge areas on Ontario freeways. Crash–conflict integrated SPFs with different time to collision thresholds were then developed and compared. The results of the conflict-based SPFs are in agreement with those from crash-based SPFs developed for the same sample, with logically negative coefficients for acceleration lane length and positive coefficients for traffic volumes. This suggests that the crash–conflict approach is a reasonable substitute for conventional crash prediction models in assessing the safety effects of design changes, especially for those changes that cannot be captured in the conventional models.

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.

scholarly journals COMPARISON OF ADJACENCY AND DISTANCE-BASED APPROACHES FOR SPATIAL ANALYSIS OF MULTIMODAL TRAFFIC CRASH DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1157-1161 ◽  
Author(s):  
G. Gill ◽  
T. Sakrani ◽  
W. Cheng ◽  
J. Zhou
Keyword(s):  
Prediction Accuracy ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Influential Factors ◽  
Simultaneous Estimation ◽  
Spatial Correlations ◽  
Crash Prediction ◽  
Traffic Crash ◽  
Crash Data ◽  
Crash Prediction Models

Many studies have utilized the spatial correlations among traffic crash data to develop crash prediction models with the aim to investigate the influential factors or predict crash counts at different sites. The spatial correlation have been observed to account for heterogeneity in different forms of weight matrices which improves the estimation performance of models. But very rarely have the weight matrices been compared for the prediction accuracy for estimation of crash counts. This study was targeted at the comparison of two different approaches for modelling the spatial correlations among crash data at macro-level (County). Multivariate Full Bayesian crash prediction models were developed using Decay-50 (distance-based) and Queen-1 (adjacency-based) weight matrices for simultaneous estimation crash counts of four different modes: vehicle, motorcycle, bike, and pedestrian. The goodness-of-fit and different criteria for accuracy at prediction of crash count reveled the superiority of Decay-50 over Queen-1. Decay-50 was essentially different from Queen-1 with the selection of neighbors and more robust spatial weight structure which rendered the flexibility to accommodate the spatially correlated crash data. The consistently better performance of Decay-50 at prediction accuracy further bolstered its superiority. Although the data collection efforts to gather centroid distance among counties for Decay-50 may appear to be a downside, but the model has a significant edge to fit the crash data without losing the simplicity of computation of estimated crash count.

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.

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