Random Parameter Negative Binomial Model of Signalized Intersections

Park, Minho; Lee, Dongmin; Jeon, Jinwoo

doi:10.1155/2016/1436364

Cited by 12 publications

(5 citation statements)

References 26 publications

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“…This phenomenon strongly verifies that the random parameters method can effectively capture the inter-samples heterogeneity and thus obtain a better goodness of fit. We are not surprised by this result because too many studies [13]- [18], [61]- [63] have shown that the random parameters models outperform the traditional fixed parameters models in fitting urban road crash data, rural road crash data, and expressway crash data. The superiority of the spatial-temporal model for addressing spatial-temporal heterogeneity existing in the tunnel crash dataset can be reflected by comparing the goodness of fit of the RPNBL, the SP-RPNBL, and the ST-RPNBL models.…”

Section: B Model Comparisonmentioning

confidence: 98%

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

Tang

Cai

et al. 2021

IEEE Access

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The existing crash modeling techniques for expressway tunnels must overcome the following difficulties: 1) The collected risk factors contributing to the tunnel crashes include narrow ranges, especially the pavement conditions and weather conditions of the tunnels are rarely taken into account. 2) Most researchers ignored the estimation deviation caused by the excess zero observations of tunnel crash datasets.3) No existing tunnel crash model can combine the random-parameters approach and spatial-temporal approach to solve the estimation deviation caused by the inter-samples and spatial-temporal heterogeneity.To address these problems, this study presents an investigation of the safety effects of risk factors of tunnel design features, traffic conditions, pavement conditions and weather conditions utilizing a 12-quarter period (3 years) of data as well as five crash frequency models: 1) a fixed parameters negative binomial model (FPNB), 2) a random parameters negative binomial model (RPNB), 3) a random parameters negative binomial Lindley model (RPNBL), 4) a spatial and random parameters negative binomial Lindley model (SP-RPNBL), and 5) a spatial-temporal and random parameters negative binomial Lindley model (ST-RPNBL). The results showed that the ST-RPNBL model solves the deviation that arises from excess zero observations by introducing the Lindley distribution and considers the unobserved heterogeneity by introducing both the random parameters and spatial-temporal parameters that provided better goodness of fit and offered more insights into the factors that contribute to tunnel safety. Furthermore, the ST-RPNBL model detected 16 variables that were significantly correlated with tunnel crash frequency, of which 12 variables were associated with a higher crash frequency and four variables were associated with a lower crash frequency. The random variables of the curvature, the steep downgrade indicator, the proportion of class 5 vehicle and the skidding resistance index (SRI) were identified, and the influence of each significant variable on the crash frequency was analyzed.INDEX TERMS Crash modeling techniques, random parameters approach, spatial-temporal approach, Lindley distribution, tunnel design features, traffic conditions, pavement conditions, weather conditions.

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Section: B Model Comparisonmentioning

confidence: 98%

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

Tang

Cai

et al. 2021

IEEE Access

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“…Safety engineers and planners have utilized the crash prediction model/safety performance function (SPF) as a beneficial tool to analyse and enhance the level of road safety. In recent years, using these methods, intensive studies have been conducted to investigate the impact of various geometric design parameters and traffic volume at intersections on safety (Park et al 2016;Anjana & Anjaneyulu 2015;Abdul Manan et al 2013;HSM 2010;Yan et al 2005). However, the influences of these parameters have not been explicitly quantified in roundabout vicinity, especially in non-lane-based traffic conditions.…”

Section: Introductionmentioning

confidence: 99%

Safety evaluation of urban roundabouts in India: a safety performance function-based approach

Vinayaraj

Vedagiri

2022

TSR

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There is a lack of comprehensive research in roundabout safety under non-lane-based traffic conditions, mainly due to the absence of relevant crash data and effective tools for safety evaluation. Safety Performance Function (SPF) is a suitable tool for providing valuable information related to factors which can potentially contribute to the likelihood of increasing traffic crashes. Currently, very limited studies are available to explain the usefulness of SPF in the vicinity of roundabouts, especially at non-lane-based traffic conditions. This study aims to develop an SPF model for assessing the safety evaluation at roundabouts as a whole (intersection level) and the approach level. Data corresponding to crashes in nineteen roundabouts with different geometric and traffic characteristics was used for model formulation. Crash data for five years (2015–2019) was obtained from the State Crime Records Bureau. An SPF model was developed using a negative binomial model with a log-link function based on the number of crashes, traffic characteristics, and geometry characteristics of the roundabouts. The proportion of powered two-wheelers, percentage of heavy vehicles, entry-angle, and weaving-length were all significantly related with higher crash occurrences at roundabouts, according to the findings. In contrast, the number of circulatory lanes, inscribed circle diameter, and presence of road lane marking were negatively associated with the increased crash occurrences at the roundabout vicinity. In addition to this, the overall crash rate significantly varies across roundabout sections due to the asymmetric effects of geometric and traffic characteristics. The developed SPF would best explain the relationship between geometric and traffic characteristics and the crash occurrence rate in non-lane traffic conditions. The findings of this study support the need to relook at design parameters for better movement at the roundabouts, thereby improving the existing facilities to enhance road users' safety, especially in developing countries. The proposed SPF tool would help engineers examine the safety of roundabouts in terms of design adequacy, quantifying the risk factors, and future crash predictions.

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“…Using the GPS data and the advance warning message data from Hong Kong-Zhuhai-Macau Bridge (HZMB) shuttle buses, this study first analysed the temporal and spatial distribution of the risky behaviour of bus drivers, then developed random parameters negative binomial models to provide a deep understanding of contributing factors to risky behaviours, such as driving close to the front vehicle, and lane departure. This model is statistically superior to the fixed parameters negative binomial approach, as it captures the possible unobserved factors [23,24]. The findings of this study will shed light on bus operators to better understand their risky behaviour, and, therefore, allow special training in driving behaviour to provide a better and safer service.…”

Section: Introductionmentioning

confidence: 89%

“…To identify the contributing factors to risky behaviours, count data models were used in this study. As two categories of the most frequently used approach, a Poisson model and negative binomial model have been commonly applied in count data modelling [23,26,27]. Poisson distribution is relatively easy to interpret.…”

Section: Methodsmentioning

confidence: 99%

Driving Performance Evaluation of Shuttle Buses: A Case Study of Hong Kong–Zhuhai–Macau Bridge

Luo¹,

Shao

Li³

et al. 2022

IJERPH

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The risky behaviours of bus drivers are of great concern to public health and environmental sustainability, especially for the buses operated between cities. With this in mind, the present study examined the distribution of risky behaviours among bus drivers, and the contributing factors to risky performance. To achieve this, 1648 records of GPS trajectory data and 8281 records of advance warning message data from Hong Kong–Zhuhai–Macau Bridge shuttle buses were obtained. The temporal and spatial distribution of risky behaviours was analysed. A random parameters negative binomial model was developed to further investigate the relationship between speed-related factors and risky behaviours. The results indicated that the warning of safety distance, lane departure, forward collision, and distraction were more likely to occur on weekdays. The period between 14 and 16 o’clock obtained the highest frequency of safety distance and lane departure warnings. Regarding the model estimation results, indicators reflecting average speed, acceleration, and number of trips per day showed a statistically significant impact on safety distance and lane departure warnings. Also, the acceleration of bus drivers showed a mixed impact on lane departure warnings. Corresponding implications were discussed according to the findings to reduce the frequency of risky behaviours in shuttle bus operations.

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Random Parameter Negative Binomial Model of Signalized Intersections

Cited by 12 publications

References 26 publications

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

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

Safety evaluation of urban roundabouts in India: a safety performance function-based approach

Driving Performance Evaluation of Shuttle Buses: A Case Study of Hong Kong–Zhuhai–Macau Bridge

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