Investigating factors of crash frequency with random effects and random parameters models: New insights from Chinese freeway study

Hou, Qinzhong; Tarko, Andrew P.; Meng, Xianglong

doi:10.1016/j.aap.2018.07.010

Cited by 57 publications

(47 citation statements)

References 45 publications

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“…Statistical counting models have been widely utilized for crash frequency prediction. e Poisson regression model is a basic model to analyse the impact of potential factors on crash frequency and requires the mean and variance of crash frequency to be equal [31]. However, the dispersion and low probability of traffic accidents do not satisfy the assumption of Poisson's distribution.…”

Section: Modellingmentioning

confidence: 99%

Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data

Zhang

et al. 2020

Journal of Advanced Transportation

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Current studies on traffic crash prediction mainly focus on the crash frequency and crash severity of freeways or arterials. However, collision type for urban expressway crash is rarely considered. Meanwhile, with the rapid development of urban expressway systems in China in recent years, traffic safety problems have attracted more attention. In addition, the traffic characteristics are considered to be a potentially important predictor of traffic accidents; however, their impact on crashes has been controversial. Therefore, a crash frequency predicting model for urban expressway considering collision types is proposed in this study. The loop detector traffic data and historical crash data were aggregated based on the similarities of the traffic conditions 5 minutes before crash occurrence, among which crashes were divided by collision type (rear-end collision and side-impact collision). The impact of traffic characteristics along with weather variables as well as their interactions on crash frequency was modelled by using negative binomial regression model. The results indicated that the influence of traffic and weather factors on two collision types shared similar trend, but different level. For rear-end collisions, crash frequency increased with lower average speed and high traffic volume under low speed limit. And when the speed limit is high, higher average speed coupled with larger volume increases the probability of crash. Higher average speed and traffic volume increase the probability of side-impact collisions, without being affected by the speed limit. The findings of the present study could help to determine efficient safety countermeasures aimed at improving the safety performance of urban expressway.

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Section: Modellingmentioning

confidence: 99%

Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data

Zhang

et al. 2020

Journal of Advanced Transportation

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“…The segment length was positively associated with the crash frequency indicating that the number of crashes increased on longer segments. Note that the parameter of this variable (0.971) is not significantly different from 1.0, which indicates that the crash frequency increased almost linearly with a homogeneous segment length (29). This is apparently true because if a homogeneous segment is divided into two segments, the number of crashes on the original segment must be the sum of crashes on the two segments.…”

Section: Discussion Of Explanatory Variablesmentioning

confidence: 92%

A Correlated Random Parameters Model with Heterogeneity in Means to Account for Unobserved Heterogeneity in Crash Frequency Analysis

Huo

Leng

Hou

et al. 2020

Transportation Research Record

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Unobserved heterogeneity induced by omitted variables is a major challenge in developing reliable road safety models. In recent years, the random parameters negative binomial (RPNB) model has been used frequently in crash frequency analysis to account for unobserved heterogeneity. However, the majority of past studies of the RPNB model assumed that there was no correlation between different sources of unobserved heterogeneity, which is not always true given the complex interactions of safety factors. Compared with the RPNB model, a more flexible random parameters model that is the correlated random parameters negative binomial with heterogeneity in means (CRPNBHM) model was proposed in this study. Results indicate that the CRPNBHM model could not only capture the otherwise unobserved heterogeneity, but also track the underlying correlation among different sources of unobserved heterogeneity, thus outperforming the RPNB model. In addition, new insights into the interactions of safety factors (e.g., the joint safety effects of heavy trucks and pavement rutting depth) were obtained from the CRPNBHM model and these are expected to be beneficial in developing effective safety countermeasures. Results from this study demonstrated the CRPNBHM model to be a good alternative for crash frequency analysis, particularly when unobserved heterogeneity was detected.

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“…collisions [13]. Advanced statistical models that consider the unobserved spatial heterogeneity have also been used to improve the SPF [28,29]. However, the infrequent occurrence of fatal collisions causes a large variance in estimations of their frequency, with distributions of collision frequency skewing excessively toward zero, resulting in erroneous estimations [30].…”

Section: Plos Onementioning

confidence: 99%

Application of naïve Bayesian approach in detecting reproducible fatal collision locations on freeway

et al. 2021

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Detecting high-collision-concentration locations based solely on collision frequency may produce different results compared to those considering the severities of the collisions. In particular, it can lead government agencies focusing sites with a high collision frequency while neglecting those with a lower collision frequency but a higher percentage of injury and fatal collisions. This study developed systematic ways of detecting reproducible fatal collision locations (R) using the naïve Bayes approach and a continuous risk profile (CRP) that estimates the true collision risk by filtering out random noise in the data. The posterior probability of fatal collisions being reproducible at a location is estimated by the relationship between the spatial distribution of fatal-collision locations (i.e., likelihood) and the CRP (i.e., prior probability). The proposed method can be used to detect sites with the highest proxy measure of the posterior probability (PMP) of observing R. An empirical evaluation using 5-year traffic collision data from six routes in California shows that detecting R based on the PMP outperform those based on the SPF-based approaches or random selection, regardless of various conditions and parameters of the proposed method. This method only requires traffic collision and annual traffic volume data to estimate PMP that prioritize sites being R and the PMPs can be compared across multiple routes. Therefore, it helps government agencies prioritizing sites of multiple routes where the number of fatal collisions can be reduced, thus help them to save lives with limited resources of data collection.

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Investigating factors of crash frequency with random effects and random parameters models: New insights from Chinese freeway study

Cited by 57 publications

References 45 publications

Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data

Predicting Crash Frequency for Urban Expressway considering Collision Types Using Real-Time Traffic Data

A Correlated Random Parameters Model with Heterogeneity in Means to Account for Unobserved Heterogeneity in Crash Frequency Analysis

Application of naïve Bayesian approach in detecting reproducible fatal collision locations on freeway

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