A Quasi-Poisson Approach on Modeling Accident Hazard Index for Urban Road Segments

Ma, Lu; Yan, Xuedong; Qiao, Wenxin

doi:10.1155/2014/489052

Cited by 20 publications

(11 citation statements)

References 20 publications

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“…The negative binomial regression model is popular for traffic safety modeling [32]. In this section, we only list the main related equations, and for model derivation see reference [33].…”

Section: Negative Binomial Modelmentioning

confidence: 99%

Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model

Naji

Xue

Zheng

et al. 2020

Sensors

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Driving risk varies substantially according to many factors related to the driven vehicle, environmental conditions, and drivers. This study explores the contributing historical factors of driving risk with hierarchical clustering analysis and the quasi-Poisson regression model. The dataset of the study was collected from two sources: naturalistic driving experiments and self-reports. The drivers who participated in the naturalistic driving experiment were categorized into four risk groups according to their near-crash frequency with the hierarchical clustering method. Moreover, a quasi-Poisson model was used to identify the essential factors of individual driving risk. The findings of this study indicated that historical driving factors have substantial impacts on individual risk of drivers. These factors include the total number of miles driven, the driver’s age, the number of illegal parking (past three years), the number of over-speeding (past three years) and passing red lights (past three years). The outcome of the study can help transportation officials, educators, and researchers to consider the influencing factors on individual driving risk and can give insights and provide suggestions to improve driving safety.

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“…The negative binomial regression model is popular for traffic safety modeling [32]. In this section, we only list the main related equations, and for model derivation see reference [33].…”

Section: Negative Binomial Modelmentioning

confidence: 99%

Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model

Naji

Xue

Zheng

et al. 2020

Sensors

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“…Some researchers have analyzed both intersections and road segments (Erdogan et al, 2008;Castro et al, 2012;Ma et al, 2014). However, Moore et al (2011) mentioned that intersections and roads were not analyzed together, because factors related to the accidents that occurred at junctions were different from factors on the road segment.…”

Section: Scope Of the Studymentioning

confidence: 99%

“…Over the last two decades, the road accidents studies have been done by using GIS tools, for example, to examine the spatial distribution and pattern of their accidents (Kim & Nitz, 1995;Gundogdu, 2010;Truong & Somenahalli, 2011;Budiharto & Saido, 2012), and to investigate the accident-prone locations (Rankavat & Tiwari, 2013). In the recent years, the combination of GIS and statistical analysis is increasingly more used by many researchers for assessing the road accidents (Steenberghen et al, 2004;Erdogan et al, 2008;Erdogan, 2009, Ma et al, 2014Tortum & Atalay, 2015;Yalcin & Duzgun, 2015;Benedek et al, 2016).…”

Section: Accident Data In the Analysismentioning

confidence: 99%

Gis Tools for Analyzing Accidents and Road Design: A Review

Satria

2016

Libro De Actas CIT2016. XII Congreso De Ingeniería Del Transporte

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A significant unexpected outcome of transportation systems is road accidents with injuries and loss of lives. In recent years, the number of studies about the tools for analyzing accidents and road design has increased considerably. Among these tools, Geographical Information Systems (GIS) stand out for their ability to perform complex spatial analyses. However, sometimes the GIS, has been used only as a geographical database to store and represent data about accidents and road characteristics. It has also been used to represent the results of statistical studies of accidents but, these statistical studies have not been carried out with GIS. Owing to its integrated statistical-analysis capabilities GIS provides several advantages. First, it allows a more careful and accurate data selection, screening and reduction. Also, it allows a spatial analysis of the results in pre and post-processing. Second, GIS allows the development of spatial statistics that rely on geographically-referenced data. In this paper, several GIS tools used to model accidents have been examined. The understanding of these tools will help the analyst to make a better decision about which tool could be applied in each particular condition and context.

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“…When the load factor of each basic activity component is explicitly described by external factors, such as day of the week and weather conditions, it enables us to realize predictive analysis in urban dynamics. From this view-point, a discriminative model is used to model and predict the number of activities [6,9,17]. The crowd counting prediction [6], hazard index for urban roads [17], and traffic flow [9] were modeled using regression models in previous studies.…”

Section: Related Work and Their Limitationsmentioning

confidence: 99%

“…From this view-point, a discriminative model is used to model and predict the number of activities [6,9,17]. The crowd counting prediction [6], hazard index for urban roads [17], and traffic flow [9] were modeled using regression models in previous studies. The regression models can predict the future from related external factors.…”

Section: Related Work and Their Limitationsmentioning

confidence: 99%

Forecasting urban dynamics with mobility logs by bilinear Poisson regression

Shimosaka

Maeda

Tsukiji

et al. 2015

Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing

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Understanding people flow in a city (urban dynamics) is of great importance in urban planning, emergency management, and commercial activity. With the spread of smart devices, many studies on urban dynamics modeling with mobility logs have been conducted. It is predictive analysis, not analysis of the past, that enables various applications contributing to a more prosperous society. To deal with the non-linear effects on urban dynamics from external factors, such as day of the week, national holiday, or weather, we propose a low-rank bilinear Poisson regression model, for a novel and flexible representation of urban dynamics predictive analysis. The results obtained from an experiment with one year's worth of mobility records suggest the high prediction accuracy of the proposed model. We also introduce the following applications: regional event detection via irregularities, visualization of urban dynamics corresponding to urban demographics, and extraction of urban demographics of unknown point of interests.

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A Quasi-Poisson Approach on Modeling Accident Hazard Index for Urban Road Segments

Cited by 20 publications

References 20 publications

Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model

Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model

Gis Tools for Analyzing Accidents and Road Design: A Review

Forecasting urban dynamics with mobility logs by bilinear Poisson regression

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