Modeling Vehicle-pedestrian Crashes With Excess Zero Along Malaysia Federal Roads

Pour, Mehdi Hossein; Prasetijo, Joewono; Yahaya, Ahmad Shukri; Ghadiri, Seyed Mohammadreza

doi:10.1016/j.sbspro.2012.09.970

Cited by 10 publications

(9 citation statements)

References 20 publications

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“…Many other studies also investigated factors affecting pedestrian and bicyclist safety risk exposure and modeled pedestrian- and bicyclist-involved crash frequency. The key contributing factors to pedestrian/bicyclist safety exposure and crash frequency that emerge from the literature include the following: sociodemographic and socioeconomic factors, such as proportion of the population by race or age group ( 9 , 13 , 14 , 16 , 17 ); land use and built environment factors, such as population density, employment density, activity diversity, bus stop density, and the ratio of residential, industrial, and commercial uses ( 9 , 14–16 ); and traffic- and travel-related factors, such as vehicle, pedestrian, and bicycle volumes as exposure measures ( 7 – 12 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Further, the literature review reveals that the most prominent methodologies that have been applied to pedestrian and bicyclist crash frequency analysis are Poisson regression, NB regression, zero-inflated Poisson (ZIP) regression, and ZINB regression ( 7 , 16 , 24–26 ). The Poisson regression is usually considered the starting point in crash frequency modeling ( 7 ).…”

Section: Literature Reviewmentioning

confidence: 99%

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Modeling the Frequency of Pedestrian and Bicyclist Crashes at Intersections: Big Data-driven Evidence From Maryland

Mahmoudi

Xiong

Yang

et al. 2022

Transportation Research Record: Journal of the Transportation R

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This study leverages big location-based service data collected from mobile devices in 2019 to conduct a pedestrian and bicyclist safety analysis. Statistical models are estimated for pedestrian and bicyclist crash frequency at Maryland intersections using location-based service data as risk exposure data. The analysis is performed by employing prominent frequency modeling methodologies, including Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial regression techniques. The findings indicate that inclusion of big location-based service exposure data in the analysis improves the performance of the models. Further, the results suggest that key contributing factors to pedestrian and bicyclist crashes at Maryland intersections include the following: (i) intersection design- and traffic-related attributes, such as the number of intersection legs, presence of a traffic signal, and average level of traffic stress rating, as well as safety risk exposure measures, such as the average daily pedestrian, bicyclist, and vehicle volumes at the intersection; (ii) travel-related attributes, including public transportation and nonmotorized mode shares within the intersection’s census block group; (iii) land use and built environment attributes, such as road network density, activity density, and extent of walkability within the census block group; (iv) socioeconomic and sociodemographic attributes, including the percentage of low-income workers, households with no vehicles, African American population, and senior population within the census block group. The findings of the study show how big location-based service exposure data can be utilized to identify pedestrian and bicyclist safety risks and guide data-driven, evidence-based policy decision-making to improve the safety of vulnerable road users.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Modeling the Frequency of Pedestrian and Bicyclist Crashes at Intersections: Big Data-driven Evidence From Maryland

Mahmoudi

Xiong

Yang

et al. 2022

Transportation Research Record: Journal of the Transportation R

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show abstract

“…Due to a random, discrete, and positive nature of crash data, count data modelling techniques such as Poisson and negative binomial (NB) regression models are used in such cases. The Poisson regression model has been traditionally considered as the starting point in modelling crash data, with assumption of the mean of crash counts being equal to its variance (that is, equal-dispersion) [8]. In the Poisson regression model, the probability of n i vehicle crashes occurring at a given road section i, Pr(n i |μ i ), can be estimated by…”

Section: Poisson Regression Model Negative Binomial Model and Zero Imentioning

confidence: 99%

Modeling Zero – Inflated Regression of Road Accidents at Johor Federal Road F001

Prasetijo

Musa

2016

MATEC Web of Conferences

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Abstract. This study focused on the Poisson regression with excess zero outcomes on the response variable. A generalized linear modelling technique such as Poisson regression model and Negative Binomial model was found to be insignificant in explaining and handle over dispersion which due to high amount of zeros thus Zero Inflated model was introduced to overcome the problem. The application work on the number of road accidents on F001 Jalan Jb -Air Hitam. Data on road accident were collected for five-year period from 2010 through 2014. The result from analysis show that ZINB model performed best, in terms of the comparative criteria based on the P value less than 0.05.

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“…To capture the effects of unobserved heterogeneity of several variables across sites researchers have suggested a random parameter NB model instead of a fixed-parameter NB model ( Ukkusuri et al, 2011 ). Further, the Zero Inflated Negative Binomial model has also been utilized by the investigators to model pedestrian crash incidence when crash data are characterized by a preponderance of zero ( Pour et al, 2012 , Shankar et al, 2003 ).…”

Section: Introductionmentioning

confidence: 99%

Investigating the fatal pedestrian crash occurrence in urban setup in a developing country using multiple-risk source model

Mukherjee

Mitra

2021

Accident Analysis & Prevention

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Modeling Vehicle-pedestrian Crashes With Excess Zero Along Malaysia Federal Roads

Cited by 10 publications

References 20 publications

Modeling the Frequency of Pedestrian and Bicyclist Crashes at Intersections: Big Data-driven Evidence From Maryland

Modeling the Frequency of Pedestrian and Bicyclist Crashes at Intersections: Big Data-driven Evidence From Maryland

Modeling Zero – Inflated Regression of Road Accidents at Johor Federal Road F001

Investigating the fatal pedestrian crash occurrence in urban setup in a developing country using multiple-risk source model

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