AIMC Topic: Environment Design
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Cooperative control of self-learning traffic signal and connected automated vehicles for safety and efficiency optimization at intersections.
Accident; analysis and prevention
Dec 19, 2024
Cooperative control of intersection signals and connected automated vehicles (CAVs) possess the potential for safety enhancement and congestion alleviation, facilitating the integration of CAVs into urban intelligent transportation systems. This rese...
Does road environment aesthetics influence risky driving behavior of autonomous vehicles? An evaluation on road readiness using explainable machine learning and random parameters multinomial logit with heterogeneity.
Accident; analysis and prevention
Dec 9, 2024
Aesthetics has always been an advanced requirement in road environment design, because it can provide a pleasant driving experience and guide better driving behavior for human drivers. However, it remains unknown whether aesthetics-based road environ...
A machine learning approach to quantify effects of geometric design features and traffic control devices on wrong-way driving incidents at partial cloverleaf interchange terminals.
Accident; analysis and prevention
Nov 28, 2024
This study addresses the issue of wrong-way driving (WWD) incidents at partial cloverleaf (parclo) interchange terminals in the United States. These incidents are a safety concern, often attributed to geometric design features and inadequate traffic ...
Investigating streetscape environmental characteristics associated with road traffic crashes using street view imagery and computer vision.
Accident; analysis and prevention
Nov 23, 2024
Examining the relationship between streetscape features and road traffic crashes is vital for enhancing roadway safety. Traditional field surveys are often inefficient and lack comprehensive spatial coverage. Leveraging street view images (SVIs) and ...
Nonlinear effects of traffic statuses and road geometries on highway traffic accident severity: A machine learning approach.
PloS one
Nov 22, 2024
The purpose of this study is to explore nonlinear and threshold effects of traffic statuses and road geometries, as well as their interactions, on traffic accident severity. In contrast to earlier research that primarily defined road alignment qualit...
Predicting pedestrian-vehicle interaction severity at unsignalized intersections.
Traffic injury prevention
Oct 15, 2024
OBJECTIVES: This study aims to develop and validate a novel deep-learning model that predicts the severity of pedestrian-vehicle interactions at unsignalized intersections, distinctively integrating Transformer-based models with Multilayer Perceptron...
Unraveling the determinants of traffic incident duration: A causal investigation using the framework of causal forests with debiased machine learning.
Accident; analysis and prevention
Oct 7, 2024
Predicting the duration of traffic incidents is challenging due to their stochastic nature. Accurate predictions can greatly benefit end-users by informing their route choices and safety warnings, while helping traffic operation managers more effecti...
Investigation of a surrogate measure-based safety index for predicting injury crashes at signalized intersections.
Traffic injury prevention
Sep 26, 2024
OBJECTIVES: The paper develops a machine learning-based safety index for classifying traffic conflicts that can be used to estimate the frequency of signalized intersection crashes, with a focus on the more severe ones that result in fatal and severe...
Evaluating the effectiveness of safety countermeasures at highway-railway grade crossing based on a machine learning framework.
Traffic injury prevention
Sep 26, 2024
OBJECTIVE: This research aims to cluster similar highway-railway grade crossings (HRGCs) to examine the safety countermeasures at HRGCs.
Assessing the nonlinear impact of green space exposure on psychological stress perception using machine learning and street view images.
Frontiers in public health
Sep 18, 2024
INTRODUCTION: Urban green space (GS) exposure is recognized as a nature-based strategy for addressing urban challenges. However, the stress relieving effects and mechanisms of GS exposure are yet to be fully explored. The development of machine learn...
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