Estimation of bicycle crash modification factors (CMFs) on urban facilities using zero inflated negative binomial models

Raihan, Asif; Alluri, Priyanka; Wu, Wensong; Gan, Albert

doi:10.1016/j.aap.2018.12.009

Cited by 52 publications

(26 citation statements)

References 36 publications

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“…In order to analyze the overdispersed data, many studies proposed different mixed Poisson models, such as the Poisson-gamma model (the negative binomial (NB) model) [7][8][9][10][11][12][13], Poisson-lognormal model [14][15][16], and Poissoninverse gamma model [17]. For the data with many zeros (i.e., excess zero-count data), the zero-inflated models were applied, including the zero-inflated Poisson model [18,19], zero-inflated negative binomial model [20][21][22], and their extension models (i.e., multiple random parameter zeroinflated negative binomial regression model [20] and zero expansion Poisson regression model with random parameter effect [23]). Although rare, crash data can sometimes be characterized by underdispersion.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency

Zhang

Zou

et al. 2020

Journal of Advanced Transportation

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Annual fatal traffic accident data often demonstrate time series characteristics. The existing traffic safety analysis approaches (e.g., negative binomial (NB) model) often cannot accommodate the dynamic impact of factors in fatal traffic accident data and may result in biased parameter estimation results. Thus, a linear Poisson autoregressive (PAR) model is proposed in this study. The objective of this study is to apply the PAR model to analyze the dynamic impact of traffic laws and climate on the frequency of fatal traffic accidents occurred in a large time span (from 1975 to 2016) in Illinois. Besides, the NB model, NB with a time trend, and autoregressive integrated moving average model with exogenous input variables (ARIMAX) are also developed to compare their performances. The important conclusions from the modelling results can be summarized as follows. (1) The PAR model is more appropriate for analyzing the dynamic impacts of traffic laws on annual fatal traffic accidents, especially the instantaneous impacts. (2) The law that allows motorcycles and bicycles to proceed on a red light following the rules applicable after a “reasonable period of time” leads to an increase in the frequency of annual fatal traffic accidents by 14.98% in the short term and 30.69% in the long term. The climate factors such as average temperature and precipitation concentration period have insignificant impacts on annual fatal traffic accidents in Illinois. Thus, the modelling results suggest that the PAR model is more appropriate for annual fatal traffic accident data and has an advantage in estimating the dynamic impact of traffic laws.

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Section: Introductionmentioning

confidence: 99%

Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency

Zhang

Zou

et al. 2020

Journal of Advanced Transportation

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“…Previous AT-related studies have primarily employed traditional data sources such as cordon counts and non-spatial regression model techniques such as the Poisson (Hong, McArthur, and Livingston 2020;C. Chen et al 2020), mixed logit (Kang and Fricker 2013;Lind, Honey-Rosés, and Corbera 2020), negative binomial (NB) (C. Chen et al 2020;Raihan et al 2019) and ordinary least squares (OLS) (Hong, McArthur, and Stewart 2020;Boss et al 2018) models. However, the ubiquity of information and communications technology has enabled users to generate data that include the three Vs (volume, velocity and variety) as well as fine spatial granularity, denoted as crowdsourced datasets (Ali et al 2016).…”

Section: Questionsmentioning

confidence: 99%

Accounting for Spatial Heterogeneity Using Crowdsourced Data

2021

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Given the numerous benefits of active travel (human-powered transportation), in this paper, we argue that using crowdsourced data and a spatial heterogeneity treatment enhances the predictive performance of data modelling. Using such an approach thus increases the amount of insight that can be obtained to improve active travel decision-making. In particular, we model cyclists' route choices using data on cycling trips and street network centralities obtained from Strava and OSMnx, respectively. It was found that: i) the number of cyclist trips is spatially clustered; and ii) the spatial error model exhibits a better predictive performance than spatial lag and ordinary least squares models. The results demonstrate the ability of the fine-grained resolution of crowdsourced data to provide more insights on active travel compared to traditional data.

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“…Besides, Raihan et al [38] state that there exists the possibility that datasets with a high amount of zeros are not eligible for analysis with traditional models, while the ZINB model is ideal for datasets with excessive dispersion or zeros. Meanwhile, Lord et al [16] concluded that although this model promises a better goodness-of-fit than traditional models, it should only be used if the only objective of the research is crash frequency prediction and should not be used for crash modelling on highways.…”

Section: Models For Data Analysismentioning

confidence: 99%

“…In this framework, the dataset was rigorously revised, considering time/space scales and the exposition problem [38] in order to apply the ZINB model correctly.…”

Section: Negative Binomial Model (Nb)mentioning

confidence: 99%

Factors Influencing Crash Frequency on Colombian Rural Roads

Arévalo-Támara

Orozco-Fontalvo

Cantillo

2020

PROMET

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Traffic crashes in Colombia have become a public health problem causing about 7,000 deaths and 45,000 severe injuries per year. Around 40% of these events occur on rural roads, taking note that the vulnerable users (pedestrians, motorcyclists, cyclists) account for the largest percentage of the victims. The objective of this research is to identify the factors that influence the frequency of crashes, including the singular orography of the country. For this purpose, we estimated Negative Binomial (Poisson-gamma) regression, Zero-inflated model, and generalized the linear mixed model, thus developing a comparative analysis of results in the Colombian context. The data used in the study came from the official sources regarding records about crashes with consequences; that is, with the occurrence of fatalities or injuries on the Colombian roads. For collecting the highway characteristics, an in-field inventory was conducted, gathering information about both infrastructure and operational parameters in more than three thousand kilometres of the national network. The events were geo-referenced, with registries of vehicles, involved victims, and their condition. The results suggest that highways in flat terrain have higher crash frequency than highways in rolling or mountainous terrain. Besides, the presence of pedestrians, the existence of a median and the density of intersections per kilometre also increase the probability of crashes. Meanwhile, roads with shoulders and wide lanes have lower crash frequency. Specific interventions in the infrastructure and control for reducing crashes risk attending the modelling results have been suggested.

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Estimation of bicycle crash modification factors (CMFs) on urban facilities using zero inflated negative binomial models

Cited by 52 publications

References 36 publications

Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency

Exploring the Application of the Linear Poisson Autoregressive Model for Analyzing the Dynamic Impact of Traffic Laws on Fatal Traffic Accident Frequency

Accounting for Spatial Heterogeneity Using Crowdsourced Data

Factors Influencing Crash Frequency on Colombian Rural Roads

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