Exploring the modeling and site-ranking performance of Bayesian spatiotemporal crash frequency models with mixture components

Many neighborhood weight matrices have been adopted for modeling crash spatial heterogeneity. However, there has been little evaluation of their influence on crash prediction modeling performance. This study investigated 17 spatial-proximity matrices for development of spatial crash prediction models and site ranking with county-level data in California. Of the group of matrices being evaluated, traffic exposure–weighted and population-weighted distance-based matrices were first proposed in the traffic safety field. Bayesian spatial analysis was conducted with a combination of a first-order autoregressive error process and time trend for crashes to address the serial correlation of crashes in successive years. Two diagnostic measures were used for assessment of goodness of fit and complexity of models, and seven evaluation criteria were employed to assess the benefits associated with better-fitting models in site ranking. The results showed that modeling performance improved with an increase in number of neighbors considered in the weight matrix. However, a larger number of neighbors also led to greater variability of modeling performance. Specifically, Queen-2 and Decay-50 models proved to be superior among the adjacency- and distance-based models, respectively. The significance of incorporating spatial correlations was highlighted by the consistently poor performance of the base model, which included only the heterogeneity random effect. Finally, model-fitting performance seems to be strongly correlated with site-ranking performance. The models with closer goodness of fit tend to yield more consistent ranking results.

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Identifying Temporal Aggregation Effect on Crash-Frequency Modeling

Sustainability ◽

10.3390/su13116214 ◽

2021 ◽

Vol 13 (11) ◽

pp. 6214

Author(s):

Bumjoon Bae ◽

Changju Lee ◽

Tae-Young Pak ◽

Sunghoon Lee

Keyword(s):

Traffic Safety ◽

Negative Binomial ◽

Random Effect ◽

Temporal Correlation ◽

Negative Binomial Regression ◽

Spatiotemporal Analysis ◽

Parameter Estimates ◽

Crash Frequency ◽

Crash Frequency Modeling ◽

Using Data

Aggregation of spatiotemporal data can encounter potential information loss or distort attributes via individual observation, which would influence modeling results and lead to an erroneous inference, named the ecological fallacy. Therefore, deciding spatial and temporal resolution is a fundamental consideration in a spatiotemporal analysis. The modifiable temporal unit problem (MTUP) occurs when using data that is temporally aggregated. While consideration of the spatial dimension has been increasingly studied, the counterpart, a temporal unit, is rarely considered, particularly in the traffic safety modeling field. The purpose of this research is to identify the MTUP effect in crash-frequency modeling using data with various temporal scales. A sensitivity analysis framework is adopted with four negative binomial regression models and four random effect negative binomial models having yearly, quarterly, monthly, and weekly temporal units. As the different temporal unit was applied, the result of the model estimation also changed in terms of the mean and significance of the parameter estimates. Increasing temporal correlation due to using the small temporal unit can be handled with the random effect models.

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Investigation of the Factors Influencing the Crash Frequency in Expressway Tunnels: Considering Excess Zero Observations and Unobserved Heterogeneity

IEEE Access ◽

10.1109/access.2021.3072914 ◽

2021 ◽

Vol 9 ◽

pp. 58549-58565

Author(s):

Feng Tang ◽

Xinsha Fu ◽

Mingmao Cai ◽

Yue Lu ◽

Shiyu Zhong

Keyword(s):

Unobserved Heterogeneity ◽

Crash Frequency ◽

Factors Influencing

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Factors Contributing to the Relationship between Driving Mileage and Crash Frequency of Older Drivers

Sustainability ◽

10.3390/su11236643 ◽

2019 ◽

Vol 11 (23) ◽

pp. 6643 ◽

Cited By ~ 3

Author(s):

Lee ◽

Guldmann ◽

Choi

Keyword(s):

Negative Binomial ◽

Age Groups ◽

Urban Environments ◽

Vehicle Miles Traveled ◽

Crash Frequency ◽

Rural Environments ◽

Lower Likelihood ◽

Conditional Autoregressive ◽

The Relationship ◽

Binomial Models

As a characteristic of senior drivers aged 65 +, the low-mileage bias has been reported in previous studies. While it is thought to be a well-known phenomenon caused by aging, the characteristics of urban environments create more opportunities for crashes. This calls for investigating the low-mileage bias and scrutinizing whether it has the same impact on other age groups, such as young and middle-aged drivers. We use a crash database from the Ohio Department of Public Safety from 2006 to 2011 and adopt a macro approach using Negative Binomial models and Conditional Autoregressive (CAR) models to deal with a spatial autocorrelation issue. Aside from the low-mileage bias issue, we examine the association between the number of crashes and the built environment and socio-economic and demographic factors. We confirm that the number of crashes is associated with vehicle miles traveled, which suggests that more accumulated driving miles result in a lower likelihood of being involved in a crash. This implies that drivers in the low mileage group are involved in crashes more often, regardless of the driver’s age. The results also confirm that more complex urban environments have a higher number of crashes than rural environments.

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A Bivariate Extreme Value Model for Estimating Crash Frequency by Severity using Traffic Conflicts

Analytic Methods in Accident Research ◽

10.1016/j.amar.2021.100180 ◽

2021 ◽

pp. 100180

Author(s):

Ashutosh Arun ◽

Md. Mazharul Haque ◽

Ashish Bhaskar ◽

Simon Washington ◽

Tarek Sayed

Keyword(s):

Extreme Value ◽

Crash Frequency ◽

Traffic Conflicts ◽

Extreme Value Model ◽

Value Model

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Web ranking performance perspectives of Malaysian higher education institutions stakeholders

International Journal of Knowledge and Learning ◽

10.1504/ijkl.2021.10039142 ◽

2021 ◽

Vol 14 (3) ◽

pp. 1

Author(s):

Ilham Sentosa ◽

Baharudin Kadir ◽

Ibrahim Kamal Abdul Rahman ◽

Alexander Ugochukwu Ubaka

Keyword(s):

Higher Education ◽

Higher Education Institutions ◽

Ranking Performance

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Predicting motorcycle crash injury severity using weather data and alternative Bayesian multivariate crash frequency models

Accident Analysis & Prevention ◽

10.1016/j.aap.2017.08.032 ◽

2017 ◽

Vol 108 ◽

pp. 172-180 ◽

Cited By ~ 22

Author(s):

Wen Cheng ◽

Gurdiljot Singh Gill ◽

Taha Sakrani ◽

Mohan Dasu ◽

Jiao Zhou

Keyword(s):

Injury Severity ◽

Weather Data ◽

Crash Frequency ◽

Crash Injury Severity

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Investigating varying effect of road-level factors on crash frequency across regions: A Bayesian hierarchical random parameter modeling approach

Analytic Methods in Accident Research ◽

10.1016/j.amar.2018.10.002 ◽

2018 ◽

Vol 20 ◽

pp. 81-91 ◽

Cited By ~ 13

Author(s):

Chunyang Han ◽

Helai Huang ◽

Jaeyoung Lee ◽

Jie Wang

Keyword(s):

Random Parameter ◽

Crash Frequency ◽

Bayesian Hierarchical ◽

Modeling Approach

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Safety Evaluation of Destination Lighting Treatment at Stop Controlled Cross-Intersections

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198118774747 ◽

2018 ◽

Vol 2672 (16) ◽

pp. 113-121 ◽

Cited By ~ 4

Author(s):

Amrita Goswamy ◽

Shauna Hallmark ◽

Theresa Litteral ◽

Michael Pawlovich

Keyword(s):

Rural Areas ◽

Negative Binomial ◽

Safety Evaluation ◽

Negative Binomial Regression ◽

State Agencies ◽

Crash Frequency ◽

Binomial Regression ◽

Manual Selection ◽

Roadway Lighting ◽

Selection Of

Intersection crashes during nighttime hours may occur because of poor driver visual cognition of conflicting traffic or intersection presence. In rural areas, the only source of lighting is typically provided by vehicle headlights. Roadway lighting enhances driver recognition of intersection presence and visibility of signs and markings. Destination lighting provides some illumination for the intersection but is not intended to fully illuminate all approaches. Destination lighting has been widely used in Iowa but the effectiveness has not been well documented. This study, therefore, sought to evaluate the effect on safety of destination lighting at rural intersections. As part of an extensive data collection effort, locations with destination/street lighting were gathered with the assistance of several state agencies. After manual selection of a similar number of control intersections, propensity score matching using the caliper width technique was used to match 245 treatments with 245 control sites. Negative binomial regression was used to evaluate crash frequency data. The presence of destination lighting at stop-controlled cross-intersections generally reduced the night-to-day crash ratio by 19%. The presence of treatment or destination lighting was associated with a 33%–39% increase in daytime crashes across all models but was associated with an 18%–33% reduction in nighttime crashes. Injuries in nighttime crashes decreased by 24% and total nighttime crashes reduced by 33%. Property damage crashes were reduced by 18%.

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