Bayesian spatial correlation, heterogeneity and spillover effect modeling for speed mean and variance on urban road networks

Zhou, Yue; Jiang, Xinguo; Fu, Chuanyun; Liu, Haiyue; Zhang, Guopeng

doi:10.1016/j.aap.2022.106756

Cited by 14 publications

(11 citation statements)

References 60 publications

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“…Nonetheless, considering that there are a variety of hazardous action types, further efforts can be directed to explore the different classifications of hazardous actions for the nests to achieve better goodness of fit. To further understand the characteristics of hazardous actions, it also calls for developing advanced analytic methods to identify the heterogeneity [ 54 ] and spatial effects of hazardous actions (e.g., spatial correlation, spatial heterogeneity, and spillover effect) [ 55 ]. In addition, the real-time safety evaluation method is encouraged to be explored to diminish the hazardous actions in time [ 56 ].…”

Section: Discussionmentioning

confidence: 99%

The difference in quasi-induced exposure to crashes involving various hazardous driving actions

Zhang

Cai

2023

PLoS ONE

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In quasi-induced exposure (QIE) theory, the presence of hazardous driving action is the typical determinant of the driver’s responsibility for a crash. However, there is a lack of effort available to analyze the impacts of hazardous actions on the QIE estimate, which may result in estimation bias. Thus, the study aims to explore the difference in QIE to crashes involving various hazardous driving actions. Chi-square test is conducted to examine the consistency of non-responsible party distributions among the crashes involving various hazardous actions. Multinomial logit model and nested logit model are employed to identify the disparities of contributing factors to the actions. Results indicate that: 1) the estimated exposures appear to be inconsistent among the crashes with different hazardous actions, 2) driving cohorts have differential propensities of performing various hazardous actions, and 3) factors such as driver-vehicle characteristics, time, area, and environmental condition significantly affect the occurrence of hazardous actions while the directions and magnitude of the effects show great disparities for various actions. It can be concluded that the QIE estimates are significantly different for crashes involving various hazardous actions, which serves to highlight the importance of clarifying the specific hazardous actions for responsibility assignment in QIE theory.

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Section: Discussionmentioning

confidence: 99%

The difference in quasi-induced exposure to crashes involving various hazardous driving actions

Zhang

Cai

2023

PLoS ONE

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“…One of the main reasons is indicated in Glaser ( 31 ) as the absence of direct functional relationships between the dependent variable and the regressors in classical count data models. In addition, a recent study by Zhou et al ( 18 ) witnessed the inadequacy of existing studies of spatial dependence and the likeliness of bias in conclusions. Despite that, there are a few exemplary works that significantly contributed to the study of spatial dependence of traffic crash data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Despite that, there are a few exemplary works that significantly contributed to the study of spatial dependence of traffic crash data. These include the auto-Poisson model ( 32 ), the two-step estimator for the spatial lag of counts ( 33 ), the Poisson spatial interaction model with spatial effects ( 34 ), the Gibbs sampler approach for spatial Poisson regression models ( 35 ), the accurate likelihood evaluation of a broad class of spatial models ( 36 ), spatial analysis of taxi speeding ( 37 ), and a three spatial effect Bayesian modeling for speed mean and variance ( 18 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Consequently, spatial effects in crash count data are under investigated, relative to the application in continuous dependent variable data modeling. Despite that, it needs to be noted that indispensable attempts were made in previous studies to model different forms of spatial effects at various spatial conceptualizations, for example, spatial heterogeneity at the level of traffic analysis zones (TAZ) ( 6 – 8 ), spatial autoregression of endogenous interaction of dependent variables ( 9 , 10 ), the spatial dependence of the error terms ( 11 , 12 ), combined effects based on macro-level aggregation ( 13 – 15 ), and spatial dependence effects of spatial spillovers under different modeling scenarios and spatial units ( 16 – 19 ). Yet there is a paucity of literature on different spatial effects and aggregation units.…”

Section: Introductionmentioning

confidence: 99%

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Segment-Level Spatial Spillover Effects of Exogenous Characteristics of Arterials on Crash Frequency

Atumo

Zhang²,

et al. 2023

Transportation Research Record: Journal of the Transportation R

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Studies of spatial effects on road traffic safety are highly skewed in relation to aggregation units. As a result, the spatial effect of road traffic crashes at segment-level aggregation is rarely investigated. In light of that, this study investigates spatial spillover effects to determine the effects of independent variable changes initiated in one segment on the dependent variable of its neighboring segments. Additionally, the performance of the implemented models is evaluated under five different spatial weighting approaches. Spatial spillover effect of fatal and injury crashes aggregated at the segment level is estimated with the spatial lag of explanatory variables model under the general framework of Poisson and negative binomial models. The results indicate (i) spatial spillover effects are better modeled with spatial relation conceptualized by inverse distance spatial weighting schemes; (ii) spatial lag of explanatory variables under negative binomial and inverse distance spatial weighting provides the best model fitting; (iii) exogenous interactions among influence factors of fatal and injury crashes have a noteworthy effect in determining the crash frequency of neighboring segments. Convincingly, spatial spillover effect seems to significantly affect the results of conventional count modeling at the segment levels. To that effect, this study expects to provide empirical evidence and complement the existing literature on the impact of changes in explanatory variables initiated in a given segment on the safety of segments in proximity.

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“…Hence, if the confounding variable is overlooked, the statistical analysis would conclude that “the higher the speed, the lower the crash risk”. Moreover, recent studies illustrated the significance of spatial correlations in causing heterogeneity in the distribution of speed and speed variation in a road network [ 12 ]. The heterogeneity that affects the relationship between speed and crashes might come from those unobserved effects.…”

Section: Introductionmentioning

confidence: 99%

Investigating speed-safety association: Considering the unobserved heterogeneity and human factors mediation effects

Nassiri

Mohammadpour

2023

PLoS ONE

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The relationship between mean speed and crash likelihood is unclear in the literature. The contradictory findings can be attributed to the masking effects of the confounding variables in this association. Moreover, the unobserved heterogeneity has almost been criticized as a reason behind the current inconclusive results. This research provides an effort to develop a model that analyzes the mean speed-crash frequency relationship by crash severity and type. Also, the confounding and mediation effects of the environment, driver, and traffic-related attributes have been considered. To this end, the loop detector and crash data were aggregated daily for rural multilane highways of Tehran province, Iran, covering two years, 2020–2021. The partial least squares path modeling (PLS-PM) was employed for crash causal analysis along with the finite mixture partial least squares (FIMIX-PLS) segmentation to account for potential unobserved heterogeneity between observations. The mean speed was negatively and positively associated with the frequency of property damage-only (PDO) and severe accidents, respectively. Moreover, driver-related variables, including tailgating, distracted driving, and speeding, played key mediation roles in associating traffic and environmental factors with the crash risk. The higher the mean speed and the lower the traffic volume, the higher odds of distracted driving. Distracted driving was, in turn, associated with the higher vulnerable road users (VRU) accidents and single-vehicle accidents, triggering a higher frequency of severe accidents. Moreover, lower mean speed and higher traffic volume were positively correlated with the percentage of tailgating violations, which, in turn, predicted multi-vehicle accidents as the main predictor of PDO crash frequency. In conclusion, the mean speed effects on the crash risk are entirely different for each crash type through distinct crash mechanisms. Hence, the distinct distribution of crash types in different datasets might have led to current inconsistent results in the literature.

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Bayesian spatial correlation, heterogeneity and spillover effect modeling for speed mean and variance on urban road networks

Cited by 14 publications

References 60 publications

The difference in quasi-induced exposure to crashes involving various hazardous driving actions

The difference in quasi-induced exposure to crashes involving various hazardous driving actions

Segment-Level Spatial Spillover Effects of Exogenous Characteristics of Arterials on Crash Frequency

Investigating speed-safety association: Considering the unobserved heterogeneity and human factors mediation effects

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