A multivariate spatial approach to model crash counts by injury severity

Xie, Kun; Özbay, Kaan; Yang, Hong

doi:10.1016/j.aap.2018.10.009

Cited by 48 publications

(15 citation statements)

References 54 publications

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“…A possible explanation is that the elderly may have weak eyesight and might usually take longer to cross a street, thus increasing their exposure to vehicle traffic [90]. C peakday is associated with resident population (POP), which was consistent with the research of Abdel-Aty et al [10,91], Hadayeghi et al [21], and Xie et al [89]. The male population (MaPop) variable affects CPTW.…”

Section: Resultssupporting

confidence: 76%

“…This suggested that the average weighted V/C value for a given traffic zone could be used as a surrogate indicator of road safety. Xie et al [89] found that street length has a positive impact on crash occurrence.…”

Section: Resultsmentioning

confidence: 99%

“…A larger number of subway stations were found to increase traffic crashes. Bus stops attract pedestrian activities, and an increase of such nodes would increase the possibility of conflict between pedestrians and vehicle traffic, and at bus stops, between buses, other vehicles and pedestrians [89]. Pedestrian traffic is most likely unprotected, and therefore pedestrian routes must be improved.…”

Section: Resultsmentioning

confidence: 99%

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Development of Macro-Level Safety Performance Functions in the City of Naples

et al. 2019

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This paper presents macro-level safety performance functions and aims to provide empirical tools for planners and engineers to conduct proactive analyses, promote more sustainable development patterns, and reduce road crashes. In the past decade, several studies have been conducted for crash modeling at a macro-level, yet in Italy, macro-level safety performance functions have neither been calibrated nor used, until now. Therefore, for Italy to be able to fully benefit from applying these models, it is necessary to calibrate the models to local conditions. Generalized linear modelling techniques were used to fit the models, and a negative binomial distribution error structure was assumed. The study used a sample of 15,254 crashes which occurred in the period of 2009–2011 in Naples, Italy. Four traffic analysis zones (TAZ) levels were used, as one of the aims of this paper is to check the extent to which these zoning levels help in addressing the issue. The models were developed by the stepwise forward procedure using explanatory Socio-Demographic (S-D), Transportation Demand Management (TDM), and Exposure variables. The most significant variables were: children and young people placed in re-education projects, population, population aged 65 and above, population aged 25 to 44, male population, total vehicle kilometers traveled, average congestion level, average speed, number of trips originating in the TAZ, number of trips ending in the TAZ, number of total trips and, number of bus stops served per hour. An important result of the study is that children and young people placed in re-education projects negatively affects the frequency of crashes, i.e., it has a positive safety effect. This demonstrates the effectiveness of education projects, especially on children from disadvantaged neighbourhoods.

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Section: Resultssupporting

confidence: 76%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Development of Macro-Level Safety Performance Functions in the City of Naples

et al. 2019

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“…The same uninformative priors used in the previous simulation study were assumed for model parameters. The deviance information criterion (DIC), which is widely used as a Bayesian measure of model fitting and complexity (Spiegelhalter, Best, Carlin, & van der Linde, 2002;Xie, Ozbay, & Yang, 2019), was computed for each model. The model with smaller DIC is preferred.…”

Section: Modeling Resultsmentioning

confidence: 99%

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

Xie

Özbay

Yang³

et al. 2018

Risk Analysis

Self Cite

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The widely used empirical Bayes (EB) and full Bayes (FB) methods for before–after safety assessment are sometimes limited because of the extensive data needs from additional reference sites. To address this issue, this study proposes a novel before–after safety evaluation methodology based on survival analysis and longitudinal data as an alternative to the EB/FB method. A Bayesian survival analysis (SARE) model with a random effect term to address the unobserved heterogeneity across sites is developed. The proposed survival analysis method is validated through a simulation study before its application. Subsequently, the SARE model is developed in a case study to evaluate the safety effectiveness of a recent red‐light‐running photo enforcement program in New Jersey. As demonstrated in the simulation and the case study, the survival analysis can provide valid estimates using only data from treated sites, and thus its results will not be affected by the selection of defective or insufficient reference sites. In addition, the proposed approach can take into account the censored data generated due to the transition from the before period to the after period, which has not been previously explored in the literature. Using individual crashes as units of analysis, survival analysis can incorporate longitudinal covariates such as the traffic volume and weather variation, and thus can explicitly account for the potential temporal heterogeneity.

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“…In other words, the distributions of road accidents are uneven in space, which leads to the black spots. This phenomenon has been explained by combining accident records with other sources of data, including land use, social-economic, demographic, traffic, road network, human activity, point-of-interest and so forth [27] [35]. Their Bayesian CAR model demonstrated that higher population, road density, length of arterials, trip frequency and shorter intersection spacing are associated with the greater number of crashes.…”

Section: Literature Reviewmentioning

confidence: 99%

Spatial Regression Analysis of Pedestrian Crashes Based on Point-of-Interest Data

Chen¹,

Ma²,

Wang³

2020

JDAIP

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Pedestrian safety has recently been considered as one of the most serious issues in the research of traffic safety. This study aims at analyzing the spatial correlation between the frequency of pedestrian crashes and various predictor variables based on open source point-of-interest (POI) data which can provide specific land use features and user characteristics. Spatial regression models were developed at Traffic Analysis Zone (TAZ) level using 10,333 pedestrian crash records within the Fifth Ring of Beijing in 2015. Several spatial econometrics approaches were used to examine the spatial autocorrelation in crash count per TAZ, and the spatial heterogeneity was investigated by a geographically weighted regression model. The results showed that spatial error model performed better than other two spatial models and a traditional ordinary least squares model. Specifically, bus stops, hospitals, pharmacies, restaurants, and office buildings had positive impacts on pedestrian crashes, while hotels were negatively associated with the occurrence of pedestrian crashes. In addition, it was proven that there was a significant sign of localization effects for different POIs. Depending on these findings, lots of recommendations and countermeasures can be proposed to better improve the traffic safety for pedestrians.

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A multivariate spatial approach to model crash counts by injury severity

Cited by 48 publications

References 54 publications

Development of Macro-Level Safety Performance Functions in the City of Naples

Development of Macro-Level Safety Performance Functions in the City of Naples

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

Spatial Regression Analysis of Pedestrian Crashes Based on Point-of-Interest Data

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