scholarly journals Applying a Correlated Random Parameters Negative Binomial Lindley Model to Examine Crash Frequency Along Highway Tunnels in China

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 213473-213488
Author(s):  
Feng Tang ◽  
Xinsha Fu ◽  
Mingmao Cai ◽  
Yue Lu ◽  
Shiyu Zhong ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Random Parameters ◽  
Crash Frequency

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

2020 ◽  
Vol 2674 (7) ◽  
pp. 312-322
Author(s):  
Xiaoyan Huo ◽  
Junqiang Leng ◽  
Qinzhong Hou ◽  
Hao Yang
Keyword(s):  
Frequency Analysis ◽  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Good Alternative ◽  
Omitted Variables ◽  
Random Parameters ◽  
Safety Factors ◽  
Crash Frequency ◽  
Study Results ◽  
Different Sources

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.

Safety Effects of Horizontal Curve Design on Motorcycle Crash Frequency on Rural, Two-Lane, Undivided Highways in Florida

2017 ◽  
Vol 2637 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Chunfu Xin ◽  
Zhenyu Wang ◽  
Pei-Sung Lin ◽  
Chanyoung Lee ◽  
Rui Guo
Keyword(s):  
Negative Binomial ◽  
Design Parameters ◽  
Negative Binomial Regression Model ◽  
Horizontal Curve ◽  
Random Parameters ◽  
Crash Frequency ◽  
Curve Design ◽  
Time Space ◽  
Curve Radius ◽  
Motorcycle Crashes

The association between horizontal curve design (e.g., radius and type) on rural, two-lane, undivided highways and motorcycle crash frequency is not well documented in existing reports and publications. This study aimed to investigate the effects of design parameters and associated factors on the occurrence of motorcycle crashes with consideration of the issue of unobserved heterogeneity. A random-parameters negative binomial regression model was developed on the basis of data on 431 motorcycle crashes, which were collected on 2,179 horizontal curves along two-lane, undivided highways in Florida for 11 years (2005 to 2015). Four normally distributed random parameters (i.e., logarithm of curve radius, reverse curves, pavement condition, and rough pavement indicator) were identified to represent their heterogeneity caused by unobserved factors over time, space, individuals, or some combination thereof. The major conclusions are the following: ( a) an increase in curve radius, on average, significantly and near-logarithmically reduced motorcycle crash frequency on rural, two-lane, undivided highways (this effect was more significant when the curve radius was less than 2,000 ft); ( b) 74.8% of reverse curves tended to reduce motorcycle crash frequency on rural, two-lane, undivided highways (for the remaining 25.2%, the effect had an opposite effect; on average, the likelihood of motorcycle crashes on reverse curves decreased by 39%); ( c) the crash modification function (CMF) for curve radius on rural, two-lane, undivided highways was established, given the radius of 5,000 ft as the baseline, as a power formula, CMF = (radius/5,000)-0.208.

scholarly journals Identifying Temporal Aggregation Effect on Crash-Frequency Modeling

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.

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

2019 ◽  
Vol 11 (23) ◽  
pp. 6643 ◽  
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.

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

2018 ◽  
Vol 2672 (16) ◽  
pp. 113-121 ◽  
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%.

scholarly journals Factors Affecting Crash Frequencies: A Negative Binomial Regression Based Analysis of Indus Highway, Pakistan

2019 ◽  
Vol 296 ◽  
pp. 01005
Author(s):  
Rafi Ullah Khan ◽  
Jingbo Yin ◽  
Faluk Shair Mustafa
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Crash Frequency ◽  
Risk Recognition ◽  
Factors Affecting ◽  
Trade Routes ◽  
Vital Importance ◽  
Behavior Understanding ◽  
Binomial Regression ◽  
Spss Software

The increase in vehicular traffic have also increased the highway crash frequency with the passage of time. Improvements in highway safety is of vital importance as it could save vast life and monetary losses. The highway crash frequency analysis of major Pakistani highways is a subject less discovered and many important strategic and trade routes are not studied in this regard. This study is aimed to analyze the crash frequency and the prominent factors that cause these crashes on a 302 km section of Indus highway; one of the most important trade routes of the country. Eight years’ data from 2011 till 2018 was arranged into 19 variables where the crash frequency is set as dependent variable, while the eighteen prominent causation factors as independent variables. The tool used for analysis was negative binomial regression being run in the SPSS software. The results indicate that the driver’s behavior, understanding & risk recognition, negligence and law adherence have a significant effect on the crash frequency. Furthermore, highway crash frequency significantly increases with increase in highway segment lengths, number of lanes and lane widths. Similarly, the highway crash frequency significantly enhances when the light, pavement surface and climate condition gets deteriorated. The results of this study are of vital importance to government, transportation companies and general public in order to recognize the most important accident causing factors and devise the transport policies, rules and behaviors accordingly.

Insights from Integrated Geo-Location Data for Pedestrian Crashes, Demographics, and Land Uses

2020 ◽  
Vol 2674 (8) ◽  
pp. 720-731
Author(s):  
Rui Guo ◽  
Zhiqiang Wu ◽  
Yu Zhang ◽  
Pei-Sung Lin ◽  
Zhenyu Wang
Keyword(s):  
Low Income ◽  
Fast Food ◽  
Negative Binomial ◽  
Unobserved Heterogeneity ◽  
Pedestrian Safety ◽  
Marginal Effect ◽  
Land Uses ◽  
Crash Frequency ◽  
Location Data ◽  
Pedestrian Crashes

This study investigates the effects of demographics and land uses on pedestrian crash frequency by integrating the contextual geo-location data. To address the issue of heterogeneity, three negative binomial models (with fixed parameters, with observed heterogeneity, and with both observed and unobserved heterogeneities) were examined. The best fit with the data was obtained by explicitly incorporating the observed and unobserved heterogeneity into the model. This highlights the need to accommodate both observed heterogeneity across neighborhood characteristics and unobserved heterogeneity in pedestrian crash frequency modeling. The marginal effect results imply that some land-use types (e.g., discount department stores and fast-food restaurants) could be candidate locations for the education campaigns to improve pedestrian safety. The observed heterogeneity of the area indicator suggests that priority should be given to more populated low-income areas for pedestrian safety, but attention is also needed for the higher-income areas with larger densities of bus stops and hotels. Moreover, three normally distributed random parameters (proportion of older adults, proportion of lower-speed roads, and density of convenience stores in the area) were identified as having random effects on the probability of pedestrian crash occurrences. Finally, the identification of pedestrian crash hot zone provides practitioners with prioritized neighborhoods (e.g., a list of areas) for developing effective pedestrian safety countermeasures.

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