Journal ArticleDOI
Spatial heterogeneity analysis of macro-level crashes using geographically weighted Poisson quantile regression.
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TLDR
This study confirms that the influencing factors have varying effects on different quantiles of distribution and on different regions, which could be helpful to provide support for making safety countermeasures and policies at urban regional level.About:
This article is published in Accident Analysis & Prevention.The article was published on 2020-10-22. It has received 14 citations till now. The article focuses on the topics: Quantile regression & Quantile.read more
Citations
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Journal ArticleDOI
Geographically weighted poisson regression under linear model of coregionalization assistance: Application to a bicycle crash study.
TL;DR: In this article, a semi-parametric Geographically Weighted Poisson Regression (sGWPR) model was proposed to deal with the issue of spatial correlation and spatial non-stationarity simultaneously.
Journal ArticleDOI
A hybrid neural network for driving behavior risk prediction based on distracted driving behavior data
TL;DR: A new neural network named Driving Behavior Risk Prediction Neural Network (DBRPNN) is developed for prediction based on the distracted driving behavior data and has better prediction performance compared with traditional models.
Journal ArticleDOI
Modeling Road Safety in Car-Dependent Cities: Case of Jeddah City, Saudi Arabia
Mohammed Aljoufie,Alok Tiwari +1 more
TL;DR: The proposed model can inform transport policies in Jeddah in prioritizing more safety measures for the pedestrians, including expanding pedestrians’ infrastructure, and cautious monitoring of pedestrian footpaths and can facilitate the analysis and improvement of road safety for pedestrians in car-dependent cities.
Journal ArticleDOI
Predicting intersection crash frequency using connected vehicle data: A framework for geographical random forest.
TL;DR: Wang et al. as discussed by the authors investigated a new Artificial Intelligence technique called Geographical Random Forest (GRF) that can address spatial heterogeneity and retain all potential predictors, which can proactively identify at-risk intersections and alert drivers when leading indicators of driving volatility tend to worsen.
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Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression
TL;DR: Wang et al. as mentioned in this paper presented a systematic method based on multi-scale geographically weighted regression (MGWR) to explore the influence of reclassified points-of-interest (POI) on traffic crashes occurring on weekdays and weekends.
References
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Journal ArticleDOI
Local least absolute deviation estimation of spatially varying coefficient models: robust geographically weighted regression approaches
Huiguo Zhang,Changlin Mei +1 more
TL;DR: Results demonstrate that the GWR approaches to handle outliers are quite robust to outliers and can retrieve the underlying coefficient surfaces satisfactorily even though the data are seriously contaminated or contain severe outliers.
Journal ArticleDOI
A geographically weighted regression to estimate the comprehensive cost of traffic crashes at a zonal level
TL;DR: This study proposes a method to estimate the comprehensive crash cost at the zonal level by using person-injury cost, and indicates population of people over 60-years-old, the proportion of residents that use non-motorized transportation, household income, population density, household size, and metropolitan indicator have a negative association with CCCAZ.
Microlevel Traffic Simulation Method for Assessing Crash Potential at Intersections
TL;DR: In this paper, a micro-level behavioral method for estimating crash potential at intersections for different traffic and geometric attributes is introduced, where safety in real time is represented by a "Crash Potential Index" that is estimated as a function of three types of individual vehicle and time-specific deceleration rates.
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Evaluating the road safety effects of a fuel cost increase measure by means of zonal crash prediction modeling
TL;DR: The results show a considerable traffic safety benefit of conducting the fuel-cost increase scenario apart from its impact on the reduction of the total vehicle kilometers traveled (VKT).
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A Copula-Based Approach for Accommodating the Underreporting Effect in Wildlife‒Vehicle Crash Analysis
TL;DR: In this paper, a Gaussian copula regression model linking wildlife-vehicle collisions and the underreporting outcome was proposed to consider the underreported in WVC data, and the proposed copula model may be a better alternative to the conventional negative binomial (NB) model for modeling underreported WVC.
Related Papers (5)
Modeling crash spatial heterogeneity: random parameter versus geographically weighting.
Pengpeng Xu,Hongwei Huang +1 more