A multivariate spatial model of crash frequency by transportation modes for urban intersections

Huang, Helai; Zhou, Hanchu; Wang, Jie; Chang, Fangrong; Ma, Ming

doi:10.1016/j.amar.2017.01.001

Cited by 95 publications

(51 citation statements)

References 42 publications

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“…Current crash prediction modelling suggests that separate models for different crash types are not adequate; and therefore, multivariate modelling approaches came into application (e.g. Huang et al, 2017;Imprialou et al, 2016b;Lord and Mannering, 2010;Martensen and Dupont, 2013). Though there are various studies on crash contributory factors by severity levels, there are very few studies focusing on crashes by vehicle type and these are mostly focused on urban environments without making a distinction between heavy and light vehicles (Huang et al, 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

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Impacts of speed variations on freeway crashes by severity and vehicle type

Choudhary

Imprialou

Velaga

et al. 2018

Accident Analysis & Prevention

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Speed variations are identified as potentially important predictors of freeway crash rates; however, their impacts on crashes are not entirely known. Existing findings tend to be inconsistent possibly because of the different definitions for speed variations, different crash type consideration or different modelling and data aggregation approaches. This study explores the relationships of speed variations with crashes on a freeway section in the UK. Crashes split by vehicle type (heavy and light vehicles) and by severity mode (killed/serious injury and slight injury crashes) are aggregated based on the similarities of the conditions just before their occurrence (condition-based approach) and modelled using Multivariate Poisson lognormal regression. The models control for speed variations along with other traffic and weather variables as well as their interactions. Speed variations are expressed as two separate variables namely the standard deviations of speed within the same lane and between-lanes over a fiveminute interval. The results, similar for all crash types (by coefficient significance and sign), suggest that crash rates increase as the within lane speed variations raise, especially at higher traffic volumes. Higher speeds coupled with greater volume and high between-lanes speed variation also increase crash likelihood. Overall, the results suggest that specific combinations of traffic characteristics increase the likelihood of crash occurrences rather than their individual effects. Identification of these specific crash prone conditions could improve our understanding of crash risk and would support the development of more efficient safety countermeasures.

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Section: Literature Reviewmentioning

confidence: 99%

“…Additionally, research has shown that independent variables in crash modelling have unique effects on different crash types and these are more accurately estimated when the correlations between the examined crash types are taken into consideration (i.e. multivariate count models) (Huang et al, 2017;Lord and Mannering, 2010).…”

Section: Introductionmentioning

confidence: 99%

Impacts of speed variations on freeway crashes by severity and vehicle type

Choudhary

Imprialou

Velaga

et al. 2018

Accident Analysis & Prevention

View full text Add to dashboard Cite

show abstract

“…The multivariate conditional autoregressive (CAR) model is one of the most advanced methods for multivariate spatial modeling under the Bayesian framework. It has been successfully applied to analyze crash frequency by severity or transportation mode at the macro level (e.g., canton and census tract) [24][25][26] and the micro level (e.g., roadway segment and intersection) [1,[27][28][29]. With the ability of accounting for heterogeneous and spatial effects and their correlations among crash severities, the multivariate CAR model is expected to improve the accuracy of identifying the contributing factors to freeway crash frequency by severity.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Traffic Composition on Freeway Crash Frequency by Injury Severity: A Bayesian Multivariate Spatial Modeling Approach

Wen

Sun

Zeng

et al. 2018

Journal of Advanced Transportation

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This study sets out to investigate the effects of traffic composition on freeway crash frequency by injury severity. A crash dataset collected from Kaiyang Freeway, China, is adopted for the empirical analysis, where vehicles are divided into five categories and crashes are classified into no injury and injury levels. In consideration of correlated spatial effects between adjacent segments, a Bayesian multivariate conditional autoregressive model is proposed to link no-injury and injury crash frequencies to the risk factors, including the percentages of different vehicle categories, daily vehicle kilometers traveled (DVKT), and roadway geometry. The model estimation results show that, compared to Category 5 vehicles (e.g., heavy truck), larger percentages of Categories 1 (e.g., passenger car) and 3 (e.g., medium truck) vehicles would lead to less no-injury crashes and more injury crashes. DVKT, horizontal curvature, and vertical grade are also found to be associated with no-injury and/or injury crash frequencies. The significant heterogeneous and spatial effects for no-injury and injury crashes justify the applicability of the proposed model. The findings are helpful to understand the relationship between traffic composition and freeway safety and to provide suggestions for designing strategies of vehicle safety improvement.

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“…14 As an important class of MIMO systems, the multivariate systems are often encountered in practice. 15,16 The multivariate systems are a kind of MIMO systems in which the subsystem parameter vectors are coupled and the subsystem information vectors are independent. Many identification theories and methods have been developed for scalar and multivariate systems.…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood gradient identification for multivariate equation‐error moving average systems using the multi‐innovation theory

Liu

Ding

Hayat

2019

Adaptive Control & Signal

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For the multivariate equation-error moving average system, a multivariate maximum likelihood multi-innovation extended stochastic gradient (M-ML-MIESG) algorithm is delivered. The key is to decompose the system into several regressive identification subsystems according to the number of the system outputs. Then, a multivariate maximum likelihood extended stochastic gradient algorithm is presented to estimate the parameters of these subsystems. The M-ML-MIESG algorithm has higher parameter estimation accuracy than the multivariate extended stochastic gradient algorithm. The simulation examples indicate that the proposed methods work well.

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A multivariate spatial model of crash frequency by transportation modes for urban intersections

Cited by 95 publications

References 42 publications

Impacts of speed variations on freeway crashes by severity and vehicle type

Impacts of speed variations on freeway crashes by severity and vehicle type

The Effects of Traffic Composition on Freeway Crash Frequency by Injury Severity: A Bayesian Multivariate Spatial Modeling Approach

Maximum likelihood gradient identification for multivariate equation‐error moving average systems using the multi‐innovation theory

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