Effect of geometric design consistency on road safety

2004 ◽  
Vol 31 (2) ◽  
pp. 218-227 ◽  
Author(s):  
Joanne C.W Ng ◽  
Tarek Sayed
Keyword(s):  
Road Safety ◽  
Prediction Models ◽  
Model Development ◽  
Geometric Design ◽  
Highway Design ◽  
Rural Highways ◽  
Accident Prediction ◽  
Design Characteristics ◽  
Design Consistency ◽  
The Impact

Geometric design consistency is emerging as an important rule in highway design. Identifying and treating any inconsistency on a highway can significantly improve its safety performance. Considerable research has been undertaken to explore this concept including identifying potential consistency measures and developing models to estimate them. However, little work has been carried out to quantify the safety benefits of geometric design consistency. The objectives of this study are to investigate and quantify the relationship between design consistency and road safety. A comprehensive accident and geometric design database of two-lane rural highways is used to investigate the effect of several design consistency measures on road safety. Several accident prediction models that incorporate design consistency measures are developed. The generalized linear regression approach is used for model development. The models can be used as a quantitative tool for the evaluation of the impact of design consistency on road safety. An application is presented where the ability of accident prediction models that incorporate design consistency measures is compared with those that rely on geometric design characteristics. It is found that models that explicitly consider design consistency may identify the inconsistencies more effectively and reflect the resulting impacts on safety more accurately than those that do not.Key words: geometric design consistency, road safety, quantification, accident prediction models.

Continuous Speed Profiles to Investigate Drivers' Behavior on Two-Lane Rural Highways

2015 ◽  
Vol 2521 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Alfonso Montella ◽  
Francesco Galante ◽  
Filomena Mauriello ◽  
Massimo Aria
Keyword(s):  
Driving Simulator ◽  
Prediction Models ◽  
Piecewise Linear ◽  
Small Radius ◽  
Operating Speed ◽  
Data Set ◽  
Highway Design ◽  
Rural Highways ◽  
Acceleration And Deceleration ◽  
Design Consistency

To improve highway design consistency, several studies developed operating speed prediction models and investigated drivers' speed behavior. Most existing models are based on spot speed data that assume constant operating speed throughout the horizontal curves and occurrence of acceleration and deceleration only on tangents. To overcome limitations associated with existing models, this study investigated continuous speed profiles with an experiment that used a high-fidelity dynamic-driving simulator on a two-lane highway. A piecewise linear regression model and locally weighted regression scatter-plot smoothing were used to remove noise in the data set while preserving underlying patterns and to identify significant changes in the speed profile. Based on the smoothed speed profiles, models to predict operating speed in curves and in tangents, deceleration and acceleration rates to be used in the operating speed profiles, and starting and ending points of constant operating speed in curve were developed. Radius of the curve notably affected not only the operating speed in the curve but also the operating speed of the tangent following the curve: the smaller the radius, the lower the operating speed of the exit tangent. Both acceleration and deceleration rates increased with curvature. This study found that operating speed was not constant along curves. On small radius curves, deceleration ended close to the center of the curve, and acceleration starts, close to the end of the curve. Increasing the curve radius, the end point of deceleration moves toward the curve's beginning, whereas the start of acceleration moves toward the center of the curve.

scholarly journals Evaluation of driver visual demand at different design speeds on complex two-dimensional rural highway alignments

2021 ◽  
Author(s):  
Ahmad Muneeb
Keyword(s):  
Human Life ◽  
Geometric Design ◽  
Two Dimensional ◽  
Design Features ◽  
Highway Design ◽  
Complex Curves ◽  
Design Characteristics ◽  
Design Speed ◽  
Design Consistency ◽  
Demand Profile

Road crashes are a major cause of loss of human life, property and money throughout the world. One of the reasons behind these crashes is the interaction between drivers and road alignments. The need to understand the factors that affect drivers has become obvious and is now being addressed by researchers. Moreover, driver workload is gaining attention as a measure of highway-design consistency as it directly reveals design features to the driver. This research focuses on evaluating driver visual demand at different design speeds along with other geometric design features for two-dimensional rural horizontal roadway alignments. Twelve such alignments having simple and complex curves were designed following the standards of the American Association of Highway and Transportation Officials (AASHTO) and the Transportation Association of Canada (TAC). The driver simulator at Ryerson University, Toronto, recently modified after the integration of a car, was used for the simulation of roadway alignments. Scenario Definition Language (SDL) was used to develop Event files for simulation and to save the required data. Twelve drivers drove the simulated alignments. The output data relating to driver visual demand were processed using MS Notepad and MS Excel. The visual demand calculations for full-element length (VDF), half-element length (VDH) and the first 30 m of element length (VD30) for curve and tangent sections of alignments were done using MS Excel. Statistical Analysis Software (SAS) was used to anlayze and develop models for VDF, VDH and VD30 for curve and tangent sections, first considering design speed only as explanatory variable and then considering design speed along with other geometric design characteristics as explanatory variables. It has been observed that visual demand increases with the increase in design speed. Besides, the combined effect of design speed an other geometric design characteristics (e.g., the type of preceding element, the turning direction of a curve) has significant effect on visual demand. It was also found that visual demand followed a Log Normalized distribution which was also observed by previous research. The developed models were used to establish the visual demand profile for highway design consistency evaluation. The comparison of visual demand profile and operating speed profile has shown that the visual demand can be an acceptable measure for evaluating the highway design consistency.

scholarly journals Evaluation of driver visual demand at different design speeds on complex two-dimensional rural highway alignments

2021 ◽  
Author(s):  
Ahmad Muneeb
Keyword(s):  
Human Life ◽  
Geometric Design ◽  
Two Dimensional ◽  
Design Features ◽  
Highway Design ◽  
Complex Curves ◽  
Design Characteristics ◽  
Design Speed ◽  
Design Consistency ◽  
Demand Profile

Road crashes are a major cause of loss of human life, property and money throughout the world. One of the reasons behind these crashes is the interaction between drivers and road alignments. The need to understand the factors that affect drivers has become obvious and is now being addressed by researchers. Moreover, driver workload is gaining attention as a measure of highway-design consistency as it directly reveals design features to the driver. This research focuses on evaluating driver visual demand at different design speeds along with other geometric design features for two-dimensional rural horizontal roadway alignments. Twelve such alignments having simple and complex curves were designed following the standards of the American Association of Highway and Transportation Officials (AASHTO) and the Transportation Association of Canada (TAC). The driver simulator at Ryerson University, Toronto, recently modified after the integration of a car, was used for the simulation of roadway alignments. Scenario Definition Language (SDL) was used to develop Event files for simulation and to save the required data. Twelve drivers drove the simulated alignments. The output data relating to driver visual demand were processed using MS Notepad and MS Excel. The visual demand calculations for full-element length (VDF), half-element length (VDH) and the first 30 m of element length (VD30) for curve and tangent sections of alignments were done using MS Excel. Statistical Analysis Software (SAS) was used to anlayze and develop models for VDF, VDH and VD30 for curve and tangent sections, first considering design speed only as explanatory variable and then considering design speed along with other geometric design characteristics as explanatory variables. It has been observed that visual demand increases with the increase in design speed. Besides, the combined effect of design speed an other geometric design characteristics (e.g., the type of preceding element, the turning direction of a curve) has significant effect on visual demand. It was also found that visual demand followed a Log Normalized distribution which was also observed by previous research. The developed models were used to establish the visual demand profile for highway design consistency evaluation. The comparison of visual demand profile and operating speed profile has shown that the visual demand can be an acceptable measure for evaluating the highway design consistency.

The Influence of Inhomogeneous Input Data from Different Waves on Predictive Model Development for COVID-19 ICU Patients (Preprint)

2021 ◽  
Author(s):  
Sebastian Johannes Fritsch ◽  
Konstantin Sharafutdinov ◽  
Moein Einollahzadeh Samadi ◽  
Gernot Marx ◽  
Andreas Schuppert ◽  
...  
Keyword(s):  
Machine Learning ◽  
Convex Hull ◽  
Prediction Models ◽  
Model Development ◽  
Predictive Performance ◽  
Support Vector ◽  
Good Prediction ◽  
The Impact ◽  
Second Wave ◽  
Over Time

BACKGROUND During the course of the COVID-19 pandemic, a variety of machine learning models were developed to predict different aspects of the disease, such as long-term causes, organ dysfunction or ICU mortality. The number of training datasets used has increased significantly over time. However, these data now come from different waves of the pandemic, not always addressing the same therapeutic approaches over time as well as changing outcomes between two waves. The impact of these changes on model development has not yet been studied. OBJECTIVE The aim of the investigation was to examine the predictive performance of several models trained with data from one wave predicting the second wave´s data and the impact of a pooling of these data sets. Finally, a method for comparison of different datasets for heterogeneity is introduced. METHODS We used two datasets from wave one and two to develop several predictive models for mortality of the patients. Four classification algorithms were used: logistic regression (LR), support vector machine (SVM), random forest classifier (RF) and AdaBoost classifier (ADA). We also performed a mutual prediction on the data of that wave which was not used for training. Then, we compared the performance of models when a pooled dataset from two waves was used. The populations from the different waves were checked for heterogeneity using a convex hull analysis. RESULTS 63 patients from wave one (03-06/2020) and 54 from wave two (08/2020-01/2021) were evaluated. For both waves separately, we found models reaching sufficient accuracies up to 0.79 AUROC (95%-CI 0.76-0.81) for SVM on the first wave and up 0.88 AUROC (95%-CI 0.86-0.89) for RF on the second wave. After the pooling of the data, the AUROC decreased relevantly. In the mutual prediction, models trained on second wave´s data showed, when applied on first wave´s data, a good prediction for non-survivors but an insufficient classification for survivors. The opposite situation (training: first wave, test: second wave) revealed the inverse behaviour with models correctly classifying survivors and incorrectly predicting non-survivors. The convex hull analysis for the first and second wave populations showed a more inhomogeneous distribution of underlying data when compared to randomly selected sets of patients of the same size. CONCLUSIONS Our work demonstrates that a larger dataset is not a universal solution to all machine learning problems in clinical settings. Rather, it shows that inhomogeneous data used to develop models can lead to serious problems. With the convex hull analysis, we offer a solution for this problem. The outcome of such an analysis can raise concerns if the pooling of different datasets would cause inhomogeneous patterns preventing a better predictive performance.

Traffic accident modeling: some statistical issues

2006 ◽  
Vol 33 (9) ◽  
pp. 1115-1124 ◽  
Author(s):  
Z Sawalha ◽  
T Sayed
Keyword(s):  
Road Safety ◽  
Regression Models ◽  
Model Building ◽  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Outlier Analysis ◽  
Accident Prediction ◽  
Binomial Regression ◽  
Statistical Issues

Accident prediction models are invaluable tools that have many applications in road safety analysis. However, there are certain statistical issues related to accident modeling that either deserve further attention or have not been dealt with adequately in the road safety literature. This paper discusses and illustrates how to deal with two statistical issues related to modeling accidents using Poisson and negative binomial regression. The first issue is that of model building or deciding which explanatory variables to include in an accident prediction model. The study differentiates between applications for which it is advisable to avoid model over-fitting and other applications for which it is desirable to fit the model to the data as closely as possible. It then suggests procedures for developing parsimonious models, i.e., models that are not over-fitted, and best-fit models. The second issue discussed in the paper is that of outlier analysis. The study suggests a procedure for the identification and exclusion of extremely influential outliers from the development of Poisson and negative binomial regression models. The procedures suggested for model building and conducting outlier analysis are more straightforward to apply in the case of Poisson regression models because of an added complexity presented by the shape parameter of the negative binomial distribution. The paper, therefore, presents flowcharts detailing the application of the procedures when modeling is carried out using negative binomial regression. The described procedures are then applied in the development of negative binomial accident prediction models for the urban arterials of the cities of Vancouver and Richmond located in the province of British Columbia, Canada. Key words: accident prediction models, overfitting, parsimony, outlier analysis, Poisson regression, negative binomial regression.

Evaluation of accident prediction for rural highways

2008 ◽  
Vol 35 (6) ◽  
pp. 647-651 ◽  
Author(s):  
Eric Hildebrand ◽  
Karen Robichaud ◽  
Hong Ye
Keyword(s):  
New Brunswick ◽  
Prediction Models ◽  
Retrospective Evaluation ◽  
Rural Highways ◽  
Accident Prediction ◽  
Predictive Variables ◽  
Accident Rates ◽  
Historical Accident

This paper evaluates the accuracy of three commonly used models that predict accidents on two-lane, rural, arterial highways. The retrospective evaluation compared model outputs with empirical collision results for a sample of highway sections in the Province of New Brunswick. The analysis determined historical accident rates, identified key predictive variables, and compared the observed results with estimates from each safety model. All three models were found to significantly overestimate accident frequencies on the highway sections under study. The model generally employed in New Brunswick, MicroBENCOST, was found to yield the highest errors in estimated collisions. These findings suggest that the benefits from accident reduction are generally overestimated on highway improvement projects analyzed with these accident prediction models.

scholarly journals Studies on the impact of the technical characteristics of local roads on road safety

2019 ◽  
Vol 262 ◽  
pp. 05006
Author(s):  
Stanisław Gaca ◽  
Mariusz Kieć
Keyword(s):  
Road Safety ◽  
Road Traffic ◽  
Road Accidents ◽  
Safety Measures ◽  
Accident Prediction ◽  
The Road ◽  
Access Road ◽  
Local Roads ◽  
On The Road ◽  
The Impact

Local roads (district roads) constitute an important part of the road network in Poland, making up around 29.7 % (124,945 km) of all public roads. In 2017, 10,578 accidents, which is 35.7% of all accidents in Poland, took place on local roads. These roads are used primarily by regular users who are very familiar with the defects of these roads. This means that the effects of the low technical standard of local roads and the insufficient number of road traffic devices on the safety on the road can be partly compensated for by the fact that drivers adjust their behaviour to the conditions on the road. This hypothesis can be verified through developing dependency models of road safety measures of local roads’ and technical characteristics. The article presents the research carried out based on regression models of accident prediction. The models were developed with the use of the data on the road surroundings arrangement (built-up areas, access), road condition and the extent of signposting, including data on speed limits and overtaking as well as risk exposure variables. Due to the incomplete data on accidents and the small number of accidents, different approaches to the modelling of the number of road accidents were applied.

Establishing highway horizontal alignment to maximize design consistency

2007 ◽  
Vol 34 (9) ◽  
pp. 1159-1168 ◽  
Author(s):  
Said M Easa ◽  
Atif Mehmood
Keyword(s):  
Geometric Features ◽  
Trial And Error ◽  
Highway Design ◽  
Rural Highways ◽  
Physical Constraints ◽  
Horizontal Alignment ◽  
Horizontal Curves ◽  
Decision Variables ◽  
Acceleration And Deceleration ◽  
Design Consistency

Highway design consistency is one of the important criteria in selecting the geometric features of proposed or existing alignments of two-lane rural highways. Operating-speed (OS) profile models have been used to evaluate design consistency by trial and error. For a proposed new highway, however, there may be geometric and physical constraints, and selection of these elements by trial and error to achieve optimal design consistency would be difficult, if not impossible. This paper presents an optimization model that establishes highway horizontal alignment to achieve maximum design consistency based on the OS profile. The decision variables of the model include radius of horizontal curves, spiral curve lengths, length of speed-change (SC) segments, and acceleration and deceleration rates. The objective function of the model minimizes the mean OS difference or the maximum OS difference for successive geometric features along the highway section. Application examples and sensitivity analysis are presented to illustrate the capabilities of the model in evaluating improvement strategies and to ensure that the model produces sound optimum alignments. The proposed model, which complements existing optimization models that mainly address highway construction cost, should be of interest to highway practitioners and engineers.Key words: design consistency, highway, geometric, horizontal alignment, optimization modeling, speed profile.

Collisions on local roads: model development and policy level scenario analysis

2020 ◽  
Vol 47 (1) ◽  
pp. 77-87
Author(s):  
Ali Farhan ◽  
Lina Kattan ◽  
Richard Tay
Keyword(s):  
Land Use ◽  
Scenario Analysis ◽  
Transportation Planning ◽  
Prediction Models ◽  
Model Development ◽  
Fuel Price ◽  
Development Zone ◽  
Policy Level ◽  
Local Roads ◽  
The Impact

The problem of collisions on local roads has received little specific attention despite the considerable number of such collisions that occur each year. First part of this study identifies the factors that influence local road collision frequency at traffic analysis zone (TAZ) level with a particular focus on the planning and policy related variables. The City of Calgary is used as a case study, where we focus on the impacts of land use, demographic characteristics, and travel characteristics. We also investigate the effects of some key transportation planning parameters for which there have been very limited studies, including the number of personal and commercial trips and the employment numbers in various categories. This study examines the impact of the number of trips made by automobile versus more sustainable transport modes like transit, walking, and biking for personal travel. It also examines the impact of commercial truck movement on the number of collisions on local roads in a TAZ. The impact of transit-oriented development zone initiatives is explored, as is the relationship between the predominant land use type (e.g., residential, commercial, industrial) and the number of collisions on local roads. In the second part, collision prediction models were linked with regional transportation model (RTM), which is calibrated and modeled in EMME. Since the choice of transportation mode is explicitly modeled through utility functions in the RTM, the proposed approach will allow us to do scenario analysis for planning and policy level issues proactively such as impact on local collisions due to change in fuel price, parking cost, transit headway, and transit fare. Results showed that property damage only (PDO) and fatal and injury (FI) collisions decreased by 13% and 6%, respectively, when fuel price was doubled. It was also observed that PDO and FI collisions decreased by 8% and 5%, respectively, when parking cost was doubled. PDO and FI collisions decreased by 7% and 4%, respectively, when transit headway was reduced to half. When transit fare was reduced to half, PDO and FI collisions decreased by 5% and 2%, respectively. PDO and FI collisions decreased by 10% and 5%, respectively, when transit fare was set to zero. These scenario analyses demonstrate how the impact of transportation planning or policy level issues on the collision count on local roads can be incorporated in our proposed model.

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