Spatial heterogeneity analysis of macro-level crashes using geographically weighted Poisson quantile regression.
TL;DR: 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.
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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.
8 citations
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.
Abstract: Distracted driving behavior is one of the main factors of road accidents. Accurately predicting the risk of driving behavior is of great significance to the active safety of road transportation. The large amount of information collected by the sensors installed on the vehicle can be identified by the algorithm to obtain the distracted driving behavior data, which can be used to predict the driving behavior risk of the vehicle and the area. In this paper, a new neural network named Driving Behavior Risk Prediction Neural Network (DBRPNN) is developed for prediction based on the distracted driving behavior data. The network consists of three modules: the Feature Processing Module, the Memory Module, and the Prediction Module. In this process, attribute data (time in a day, daily driving time, and daily driving mileage) that can reflect external factors and driver statuses, are added to the network to increase the accuracy of the model. We predicted the driving behavior risk of different objects (Vehicle and Area). For the applicability improvement of the model, we further classify the distracted driving behavior categories, and DBRPNN can provide more accurate risk prediction. The results show that compared with traditional models (Classification and Regression Tree, Support Vector Machines, Recurrent Neural Network, and Long Short-Term Memory), DBRPNN has better prediction performance. The method proposed in this paper has been fully verified and may be transplanted into active safety early warning system for more accurate and flexible application.
6 citations
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.
Abstract: Investigating the connections between pedestrian crashes and various urban variables is critical to ameliorate the prediction of pedestrian fatalities, formulate advisories for the stakeholders, and provide an evidence base for policy change to mitigate the occurrence and intensity of pedestrian fatalities. In this paper, we aim to explore the geographically varying association between the pedestrian fatalities and other associated factors of an urban environment in Jeddah city, which is a car-dependent city in Saudi Arabia. At first, Global Moran’s I and Local Indicators of Spatial Association (LISA) were applied to visualize the clustering of pedestrian fatalities in the various districts of Jeddah. Subsequently, we developed Poisson regression models based on their geographically weighted indicators. Both the global and geographically weighted regression models attempt to assess the association between the pedestrian fatalities and the geographically relevant land use and transport infrastructure factors. The results indicate that geographically weighted Poisson regression (GWPR) performed better than the global Poisson counterparts. It is also revealed that the existing transportation infrastructure in Jeddah was significantly associated with the higher pedestrian fatalities. The results have shown that the proposed model in this study can inform transport policies in Jeddah in prioritizing more safety measures for the pedestrians, including expanding pedestrians’ infrastructure, and cautious monitoring of pedestrian footpaths. It can facilitate the analysis and improvement of road safety for pedestrians in car-dependent cities.
6 citations
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.
5 citations
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.
Abstract: 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.
5 citations
References
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Book•
01 Jan 2014
TL;DR: The Second Edition of this practical guide to partial least squares structural equation modeling is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.
Abstract: With applications using SmartPLS (www.smartpls.com)—the primary software used in partial least squares structural equation modeling (PLS-SEM)—this practical guide provides concise instructions on how to use this evolving statistical technique to conduct research and obtain solutions. Featuring the latest research, new examples, and expanded discussions throughout, the Second Edition is designed to be easily understood by those with limited statistical and mathematical training who want to pursue research opportunities in new ways.
13,621 citations
TL;DR: Two problems arising in the two and three-dimensional cases of stochastic phenomena which are distributed in space of two or more dimensions are considered.
Abstract: The study of stochastic processes has naturally led to the consideration of stochastic phenomena which are distributed in space of two or more dimensions. Such investigations are, for instance, of practical interest in connexion with problems concerning the distribution of soil fertility over a field or the relations between the velocities at different points in a turbulent fluid. A review of such work with many references has recently been given by Ghosh (1949) (see also Matern, 1947). In the present note I consider two problems arising in the twoand three-dimensional cases.
5,771 citations
Book•
11 Oct 2002
TL;DR: In this paper, the basic GWR model is extended to include local statistics and local models for spatial data, and a software for Geographically Weighting Regression is described. But this software is not suitable for the analysis of large scale data.
Abstract: Acknowledgements.Local Statistics and Local Models for Spatial Data. Geographically Weighted Regression: The Basics. Extensions to the Basic GWR Model. Statistical Inference and Geographically Weighted Regression. GWR and Spatial Autocorrelation. Scale Issues and Geographically Weighted Regression. Geographically Weighted Local Statistics. Extensions of Geographically Weighting. Software for Geographically Weighted Regression. Epilogue. Bibliography.Index.
2,845 citations
TL;DR: The most important empirical studies into speed and crash rate with an emphasis on the more recent studies found evidence that crash rate increases faster with an increase in speed on minor roads than on major roads.
1,087 citations
TL;DR: It was found that pedestrian and driver demographic factors, and road geometric, traffic and environment conditions are closely related to the frequency and injury severity of pedestrian crashes.
544 citations