Estimating heterogeneous treatment effects in road safety analysis using generalized random forests

2022 ◽  
Vol 165 ◽  
pp. 106507
Author(s):  
Yingheng Zhang ◽  
Haojie Li ◽  
Gang Ren
Keyword(s):  
Random Forests ◽  
Road Safety ◽  
Treatment Effects ◽  
Safety Analysis ◽  
Heterogeneous Treatment Effects

scholarly journals Development of Driving Simulation Scenarios Based on Building Information Modeling (BIM) for Road Safety Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 2039
Author(s):  
Juan F. Dols ◽  
Jaime Molina ◽  
F. Javier Camacho-Torregrosa ◽  
David Llopis-Castelló ◽  
Alfredo García
Keyword(s):  
Road Safety ◽  
Data Exchange ◽  
Building Information Modeling ◽  
Cost Savings ◽  
Safety Analysis ◽  
Information Modeling ◽  
Driving Simulators ◽  
The Road ◽  
Road Design ◽  
Building Information

The analysis of road safety is critical in road design. Complying to guidelines is not enough to ensure the highest safety levels, so many of them encourage designers to virtually recreate and test their roads, benefitting from the evolution of driving simulators in recent years. However, an accurate recreation of the road and its environment represents a real bottleneck in the process. A very important limitation lies in the diversity of input data, from different sources and requiring specific adaptations for every single simulator. This paper aims at showing a framework for recreating faster virtual scenarios by using an Industry Foundation Classes (IFC)-based file. This methodology was compared to two other conventional methods for developing driving scenarios. The main outcome of this study has demonstrated that with a data exchange file in IFC format, virtual scenarios can be faster designed to carry out safety audits with driving simulators. As a result, the editing, programming, and processing times were substantially reduced using the proposed IFC exchange file format through a BIM (Building Information Modeling) model. This methodology facilitates cost-savings, execution, and optimization resources in road safety analysis.

Estimating Heterogeneous Treatment Effects and the Effects of Heterogeneous Treatments with Ensemble Methods

2017 ◽  
Vol 25 (4) ◽  
pp. 413-434 ◽  
Author(s):  
Justin Grimmer ◽  
Solomon Messing ◽  
Sean J. Westwood
Keyword(s):  
Treatment Effects ◽  
Ensemble Methods ◽  
Ensemble Method ◽  
Superior Performance ◽  
Accurate Estimation ◽  
Randomized Experiments ◽  
Heterogeneous Treatment Effects ◽  
Data Set ◽  
Heterogeneous Effects ◽  
The Ensemble Method

Randomized experiments are increasingly used to study political phenomena because they can credibly estimate the average effect of a treatment on a population of interest. But political scientists are often interested in how effects vary across subpopulations—heterogeneous treatment effects—and how differences in the content of the treatment affects responses—the response to heterogeneous treatments. Several new methods have been introduced to estimate heterogeneous effects, but it is difficult to know if a method will perform well for a particular data set. Rather than using only one method, we show how an ensemble of methods—weighted averages of estimates from individual models increasingly used in machine learning—accurately measure heterogeneous effects. Building on a large literature on ensemble methods, we show how the weighting of methods can contribute to accurate estimation of heterogeneous treatment effects and demonstrate how pooling models lead to superior performance to individual methods across diverse problems. We apply the ensemble method to two experiments, illuminating how the ensemble method for heterogeneous treatment effects facilitates exploratory analysis of treatment effects.

A conflict-based approach for real-time road safety analysis: Comparative evaluation with crash-based models

2021 ◽  
Vol 161 ◽  
pp. 106382
Author(s):  
Federico Orsini ◽  
Gregorio Gecchele ◽  
Riccardo Rossi ◽  
Massimiliano Gastaldi
Keyword(s):  
Real Time ◽  
Road Safety ◽  
Comparative Evaluation ◽  
Safety Analysis

Using claims prediction model for road safety evaluation

2001 ◽  
Vol 28 (5) ◽  
pp. 804-812 ◽  
Author(s):  
Paul de Leur ◽  
Tarek Sayed
Keyword(s):  
Prediction Model ◽  
Road Safety ◽  
Claims Data ◽  
Safety Evaluation ◽  
Safety Analysis ◽  
Insurance Claims ◽  
Insurance Claim ◽  
Auto Insurance ◽  
Improvement Programs ◽  
Traffic Collision

Road safety analysis is typically undertaken using traffic collision data. However, the collision data often suffer from quality and reliability problems. These problems can inhibit the ability of road safety engineers to evaluate and analyze road safety performance. An alternate source of data that characterize the events of a traffic collision is the records that become available from an auto insurance claim. In settling an auto insurance claim, a claim adjuster must make an assessment and determination of the circumstances of the event, recording important contributing factors that led to the crash occurrence. As such, there is an opportunity to access and use the claims data in road safety engineering analysis. This paper presents the results of an initial attempt to use auto insurance claims records in road safety evaluation by developing and applying a claim prediction model. The prediction model will provide an estimate of the number of auto insurance claims that can be expected at signalized intersections in the Vancouver area of British Columbia, Canada. A discussion of the usefulness and application of the claim prediction model will be provided together with a recommendation on how the claims data could be utilized in the future.Key words: road safety improvement programs, auto insurance claims, road safety analysis, prediction models.

scholarly journals Metalearners for estimating heterogeneous treatment effects using machine learning

2019 ◽  
Vol 116 (10) ◽  
pp. 4156-4165 ◽  
Author(s):  
Sören R. Künzel ◽  
Jasjeet S. Sekhon ◽  
Peter J. Bickel ◽  
Bin Yu
Keyword(s):  
Machine Learning ◽  
Treatment Effects ◽  
Field Experiments ◽  
Average Treatment Effect ◽  
Regularity Conditions ◽  
Heterogeneous Treatment Effects ◽  
Lipschitz Continuous ◽  
Additive Regression ◽  
Underlying Mechanisms ◽  
And Control

There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms—such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks—to estimate the CATE, a function that the base algorithms are not designed to estimate directly. We introduce a metaalgorithm, the X-learner, that is provably efficient when the number of units in one treatment group is much larger than in the other and can exploit structural properties of the CATE function. For example, if the CATE function is linear and the response functions in treatment and control are Lipschitz-continuous, the X-learner can still achieve the parametric rate under regularity conditions. We then introduce versions of the X-learner that use RF and BART as base learners. In extensive simulation studies, the X-learner performs favorably, although none of the metalearners is uniformly the best. In two persuasion field experiments from political science, we demonstrate how our X-learner can be used to target treatment regimes and to shed light on underlying mechanisms. A software package is provided that implements our methods.

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