Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression

Wang, Ce; Li, Shuo; Shan, Jie

doi:10.3390/ijgi10050286

Cited by 7 publications

(4 citation statements)

References 54 publications

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“…Wang et al [6] discussed the problem of different factors regarding traffic accidents in other locations. The authors developed an approach that uses geographically weighted regression to deal with these differences.…”

Section: Analysis and Evaluation Of Factors Involving Accidentsmentioning

confidence: 99%

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A Machine Learning Approach for Classifying Road Accident Hotspots

Amorim

Firmino

Baptista

et al. 2023

IJGI

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Road accidents are a worldwide problem, affecting millions of people annually. One way to reduce such accidents is to predict risk areas and alert drivers. Advanced research has been carried out on identifying accident-influencing factors and potential highway risk areas to mitigate the number of road accidents. Machine learning techniques have been used to build prediction models using a supervised classification based on a labeled dataset. In this work, we experimented with many machine learning algorithms to discover the best classifier for the Brazilian federal road hotspots associated with severe or nonsevere accident risk using several features. We tested with SVM, random forest, and a multi-layer perceptron neural network. The dataset contains a ten-year road accident report by the Brazilian Federal Highway Police. The feature set includes spatial footprint, weekday and time when the accident happened, road type, route, orientation, weather conditions, and accident type. The results were promising, and the neural network model provided the best results, achieving an accuracy of 83%, a precision of 84%, a recall of 83%, and an F1-score of 82%.

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Section: Analysis and Evaluation Of Factors Involving Accidentsmentioning

confidence: 99%

“…Several studies are addressing this problem since it does not affect only Brazil, making the accident data analysis field more popular [3,4,6,7]. The main objectives of these studies include understanding the risk factors contributing to accidents and creating measures to reduce such accidents.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Approach for Classifying Road Accident Hotspots

Amorim

Firmino

Baptista

et al. 2023

IJGI

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“…Geographically weighted regression (GWR) [13] is one of the most popular local regression methods. At present, GWR [14,15] and its variant models, such as geographically weighted Poisson regression (GWPR) [16][17][18][19], geographically weighted negative binomial regression (GWNBR) [20][21][22][23], and geographically weighted Poisson quantile regression (GWPQR) [24], have been widely used in the field of traffic safety. But GWR still has limitations.…”

Section: Related Workmentioning

confidence: 99%

Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression

Zhu

Xiao

et al. 2021

IJGI

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Some studies on the impact of traditional land use factors on traffic crashes do not take into account the limitations of spatial heterogeneity and spatial scale. To overcome these limitations this study presents a systematic method based on multi-scale geographically weighted regression (MGWR), which considers spatial heterogeneity and spatial scale differences of different influencing factors, to explore the influence of reclassified points-of-interest (POI) on traffic crashes occurring on weekdays and weekends. Experiments were conducted on 442 communities in Hankou, Wuhan, and the performance of the proposed method was compared against traditional methods based on ordinary least squares (OLS), spatial lag model (SLM), spatial error model (SEM), and geographically weighted regression (GWR). The experiments show that the proposed method yielded the best fitness of models and more accurate model results of local coefficient estimates. The highlights of the results are as follows: There are differences in the scale of the predictor variables. Residential POI, scenic POI, and transportation POI have a global effect on traffic crashes. Commercial service POI and industrial POI affects traffic crashes at the regional scale, while public service POI affects crashes at the local scale. The local coefficient estimates from residential POI and scenic POI have little impact on traffic crashes. During weekdays, more transportation POI in the entire study area leads to more traffic crashes. While on weekends, transportation POI has a significant positive effect on crashes only in some communities. The local coefficient estimates for industrial POI vary at different periods. Commercial service POI and public service POI may increase the risk of crashes in some communities, which can be observed on weekdays and weekends. Exploring the influence of POI on traffic crashes at different periods is helpful for traffic management strategies and in reducing traffic crashes.

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“…is the gamma-distributed error (e) term with mean and variance considered as 1 and α (Chang, 2005;Wang et al, 2021).…”

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confidence: 99%

Examining the status of forest fire emission in 2020 and its connection to COVID-19 incidents in West Coast regions of the United States

Sannigrahi

Pilla

Maiti

et al. 2022

Environmental Research

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Forest fires impact on soil, water, and biota resources. The current forest fires in the West Coast of the United States (US) profoundly impacted the atmosphere and air quality across the ecosystems and have caused severe environmental and public health burdens. Forest fire led emissions could significantly exacerbate the air pollution level and, therefore, could play a critical role if the same occurs together with any epidemic and pandemic health crisis. Limited research is done so far to examine its impact in connection to the current pandemic. As of October 21, nearly 8.2 million acres of forest area were burned, with more than 25 casualties reported so far. In-situ air pollution data were utilized to examine the effects of the 2020 forest fire on atmosphere and coronavirus (COVID-19) casualties. The spatial-temporal concentrations of particulate matter (PM 2.5 and PM 10 ) and Nitrogen Dioxide (NO 2 ) were collected from August 1 to October 30 for 2020 (the fire year) and 2019 (the reference year). Both spatial (Multiscale Geographically Weighted Regression) and non-spatial (Negative Binomial Regression) analyses were performed to assess the adverse effects of fire emission on human health. The in-situ data-led measurements showed that the maximum increases in PM 2.5 , PM 10 , and NO 2 concentrations (μg/m 3 ) were clustered in the West Coastal fire-prone states during August 1 – October 30, 2020. The average concentration (μg/m 3 ) of particulate matter (PM 2.5 and PM 10 ) and NO 2 was increased in all the fire states severely affected by forest fires. The average PM 2.5 concentrations (μg/m 3 ) over the period were recorded as 7.9, 6.3, 5.5, and 5.2 for California, Colorado, Oregon, and Washington in 2019, increasing up to 24.9, 13.4, 25.0, and 17.0 in 2020. Both spatial and non-spatial regression models exhibited a statistically significant association between fire emission and COVID-19 incidents. Such association has been demonstrated robust and stable by a total of 30 models developed for analyzing the spatial non-stationary and local association. More in-depth research is needed to better understand the complex relationship between forest fire emission and human health.

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Non-Stationary Modeling of Microlevel Road-Curve Crash Frequency with Geographically Weighted Regression

Cited by 7 publications

References 54 publications

A Machine Learning Approach for Classifying Road Accident Hotspots

A Machine Learning Approach for Classifying Road Accident Hotspots

Exploring the Influences of Point-of-Interest on Traffic Crashes during Weekdays and Weekends via Multi-Scale Geographically Weighted Regression

Examining the status of forest fire emission in 2020 and its connection to COVID-19 incidents in West Coast regions of the United States

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