Severity of pedestrian injuries due to traffic crashes at signalized intersections in Hong Kong: a Bayesian spatial logit model

As the basis of traffic safety management, crash prediction models have long been a prominent focus in the field of freeway safety research. Studies usually take years or seasons as the observed time units, which may result in heterogeneity in crash frequency. To eliminate that heterogeneity, this study analyzes monthly crash counts and develops Bayesian hierarchical models with random effects, lag-1 autoregression (AR-1), and both (REAR-1) to accommodate the multilevel structure and temporal correlation in crash data. The candidate models are estimated and evaluated in the freeware WinBUGS using a crash dataset obtained from the Kaiyang Freeway in Guangdong Province, China. Significant temporal effects are found in the three models, and Deviance Information Criteria (DIC) results show that taking temporal correlation into account considerably improves the model fit compared with the Poisson model. The hierarchical models also avoid any misidentification of the factors with significant safety effects, because their variances are greater than in the Poisson model. The DIC value of the AR-1 model is substantially lower than that of the random effect model and equivalent to that of the REAR-1 model, which indicates the superiority of the lag-1 autoregressive structure in accounting for the temporal effects in crash frequency.

Section: Model Comparisonmentioning

confidence: 99%

Bayesian Hierarchical Modeling Monthly Crash Counts on Freeway Segments with Temporal Correlation

Zeng

Sun

Wen

2017

“…For example, Barua and Tay [20] developed an ordered logit model to study the injury severities of bus crashes in Bangladesh. Xu et al [21] used spatial logit model to examine the impact of possible risk factor on the injury severity of pedestrians in the crashes which occurred at signalized intersections.…”

Section: Mixed Logit Modelmentioning

confidence: 99%

Investigating the Differences of Single-Vehicle and Multivehicle Accident Probability Using Mixed Logit Model

Dong

Chen

et al. 2018

Road traffic accidents are believed to be associated with not only road geometric feature and traffic characteristic, but also weather condition. To address these safety issues, it is of paramount importance to understand how these factors affect the occurrences of the crashes. Existing studies have suggested that the mechanisms of single-vehicle (SV) accidents and multivehicle (MV) accidents can be very different. Few studies were conducted to examine the difference of SV and MV accident probability by addressing unobserved heterogeneity at the same time. To investigate the different contributing factors on SV and MV, a mixed logit model is employed using disaggregated data with the response variable categorized as no accidents, SV accidents, and MV accidents. The results indicate that, in addition to speed gap, length of segment, and wet road surfaces which are significant for both SV and MV accidents, most of other variables are significant only for MV accidents. Traffic, road, and surface characteristics are main influence factors of SV and MV accident possibility. Hourly traffic volume, inside shoulder width, and wet road surface are found to produce statistically significant random parameters. Their effects on the possibility of SV and MV accident vary across different road segments.

“…References [34,35] also applied nested logit techniques to analyze crash severity at unsignalized intersections and at roundabouts, respectively. Other logit modeling techniques that have been used in injury severity studies include binary logistic models [36][37][38][39][40], ordered logit models [41][42][43][44], multinomial logit [45,46], mixed logit [5,47], and heterogeneous models [44]. Logit models are however not able to handle random variations and are not applicable to panel data with temporally correlated errors.…”

Section: Crash Injury Severity Modelsmentioning

confidence: 99%

“…Two categories of severity were adopted as is often done in crash injury severity studies [e.g., [16,[36][37][38][39][40]]. Serious injury crashes (defined as fatal or incapacitating injury, where an incapacitating injury implies that the victim is unable to leave the scene of the crash without physical assistance to do so) comprised 30% of the data and minor injury crashes (defined as nonincapacitating injury or possible injury) made up 70% of the crash observations.…”

Section: Data Descriptionmentioning

confidence: 99%

Effects of Human-Centered Factors on Crash Injury Severities

Adanu

Jones

2017

Factors related to drivers and their driving habits dominate the causation of traffic crashes. An in-depth understanding of the human factors that influence risky driving could be of particular importance to facilitate the application of effective countermeasures. This paper sought to investigate effects of human-centered crash contributing factors on crash outcomes. To select the methodology that best accounts for unobserved heterogeneity between crash outcomes, latent class (LC) logit model and random parameters logit (RPL) model were developed. Model estimation results generally show that serious injury crashes were more likely to involve unemployed drivers, no seatbelt use, old drivers, fatigued driving, and drivers with no valid license. Comparison of model fit statistics shows that the LC logit model outperformed the RPL model, as an alternative to the traditional multinomial logit (MNL) model.