A Bayesian semi-parametric model to estimate relationships between crash counts and roadway characteristics

Shively, Thomas S.; Kockelman, Kara M.; Damien, Paul

doi:10.1016/j.trb.2009.12.019

Cited by 32 publications

(16 citation statements)

References 31 publications

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“…For the purpose of enriching the model flexibility and robustness and avoid mis-specifying parametric models, Bayesian semi-parametric or nonparametric models were introduced (Jara et al, 2011). Shively et al, (2010) adopted a Bayesian semi-parametric approach to analyze the relationships between crash counts and roadway characteristics. The nonparametric estimation procedure was employed for the model's link function and the results showed that the semi-parametric model was more robust compared to the standard parametric assumption.…”

Section: Bayesian Semi-parametric Modelsmentioning

confidence: 99%

Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Wang

Yang

et al. 2016

Accident Analysis & Prevention

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Urban expressway systems have been developed rapidly in recent years in China; it has become one key part of the city roadway networks as carrying large traffic volume and providing high traveling speed. Along with the increase of traffic volume, traffic safety has become a major issue for Chinese urban expressways due to the frequent crash occurrence and the non-recurrent congestions caused by them. For the purpose of unveiling crash occurrence mechanisms and further developing Active Traffic Management (ATM) control strategies to improve traffic safety, this study developed disaggregate crash risk analysis models with loop detector traffic data and historical crash data. Bayesian random effects logistic regression models were utilized as it can account for the unobserved heterogeneity among crashes. However, previous crash risk analysis studies formulated random effects distributions in a parametric approach, which assigned them to follow normal distributions. Due to the limited information known about random effects distributions, subjective parametric setting may be incorrect. In order to construct more flexible and robust random effects to capture the unobserved heterogeneity, Bayesian semi-parametric inference technique was introduced to crash risk analysis in this study. Models with both inference techniques were developed for total crashes; semi-parametric models were proved to provide substantial better model goodness-of-fit, while the two models shared consistent coefficient estimations. Later on, Bayesian semi-parametric random effects logistic regression models were developed for weekday peak hour crashes, weekday non-peak hour crashes, and weekend non-peak hour crashes to investigate different crash occurrence scenarios. Significant factors that affect crash risk have been revealed and crash mechanisms have been concluded.

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Section: Bayesian Semi-parametric Modelsmentioning

confidence: 99%

Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Wang

Yang

et al. 2016

Accident Analysis & Prevention

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“…Poisson regression model was widely used in the past few decades as an introductory method of modeling the highway crash prediction because it can easily handle the nature of the crash frequency data counts, which are often described as random events, discrete, and non-negative integers, and often their distributions were found to be skewed, and close to the Poisson distribution rather than other distributions such as the normal distribution [2] [6] [29]. The Poisson model can be expressed as:…”

Section: -The Poisson Regression Modelmentioning

confidence: 99%

Crash Frequency Analysis

Abdulhafedh¹

2016

JTTs

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Modeling highway traffic crash frequency is an important approach for identifying high crash risk areas that can help transportation agencies allocate limited resources more efficiently, and find preventive measures. This paper applies a Poisson regression model, Negative Binomial regression model and then proposes an Artificial Neural Network model to analyze the 2008-2012 crash data for the Interstate I-90 in the State of Minnesota in the US. By comparing the prediction performance between these three models, this study demonstrates that the Neural Network is an effective alternative method for predicting highway crash frequency. KeywordsPoisson Regression, Negative Binomial Regression, Artificial Neural Network, Crash Frequency 2011 fatalities, and an additional of 2,362,000 people were injured in crashes with an increase of 6.5% over 2011 injuries. Therefore, there should be further research studies on the risk factors associated with traffic accidents. The occurrence of crashes can be attributed to driver, vehicle, environment, and roadway characteristics. This paper begins with a literature review of modeling accident frequencies, followed by a description of the data used in the analysis, then introduces the methodological approach of evaluating Poisson regression and Negative Binomial regression, and then proposes the Artificial Neural Network approach to improve upon the A. Abdulhafedh 170 two previous methods, followed by discussion of findings, and comparison of results. The paper concludes with a summary and directions for future researches. Literature ReviewModeling of crash count data is very important topic in highway safety analysis, and in the past few decades, modelers have proposed a significant number of analysis tools for analyzing crash data. The number of crashes per year (or per more than one year, such as five years) is called the crash frequency, which has been widely used as an indicator of the crash occurrence at highways or certain segments of the roads. A variety of independent variables can affect crash frequency that are related to the driver behaviors, road geometric, vehicle, and environment. The influence of such variables on crash occurrence could significantly vary on case by case basis, but in general, past researches have shown that both behavioral factors related to the driver's errors, and nonbehavioral factors related to the road geometry, vehicle, and environment can significantly affect traffic accidents, and researchers usually extract only a limited number of variables from each class to be used as independent variables in the modeling process [1]. Previous researches in the literature that attempted to estimate crash frequency can be classified into two types. One type includes conventional univariate regression models, such as the Poisson regression model, Poisson-Gamma (Negative Binomial) model, Poisson-lognormal model, zero-inflated model, and Conway-Maxwell-Poisson model. The second type includes more specification-based models such as generalized additive models...

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“…Multivariate regression techniques are often used to describe the dependence of a variable on explanatory variables, which include the parametric, nonparametric and semiparametric methods (Shively et al 2010;Shively, Sager 1999). The parametric approach assumes that response function could be expressed parametrically and is often used to describe the dependence of a variable on explanatory variables.…”

Section: An Overview Of Parametric Nonparametric and Semiparametric mentioning

confidence: 99%

Assessing Effects of Bus Service Quality on Passengers’ Taxi-Hiring Behavior

Wang

Peng

et al. 2018

Transport

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Abstract.Due to low quality of bus service in a congested road network, some bus-waiting travelers would take taxis instead in order to save time or get to their destinations on time. However, the correlation between bus service quality and passengers' taxi-hiring behavior is essentially unknown. This paper aims to assess the effects of bus service quality on taxi-hiring behavior based on historical data from the Global Position Systems (GPS) equipped buses and taxis in the city of Shenzhen, China. The taxi-hiring behavior is captured by analyzing the taxi-data, such as the origins of passenger pick-up, destinations of passengers drop-off, and taxi paths from the taxi movement data. The quality of bus service is assessed based on the bus location information. Parametric, semiparametric and nonparametric models are developed to explore the effects of bus service quality on taxi-hiring behavior. The results indicate that bus speed, headway and stoppage time are the core factors affecting passengers' taxi-hiring behavior. Availability of metro, time of the day and bus route directions are the secondary important factors. This study shows that when buses run with relatively low and stable speed, taxi-hiring behavior is sensitive to the slight change of bus speed. More passengers would like to take taxis when bus speed starts to decline, or speed or stoppage time of buses tends to be irregular. However, the effects of bus headway on taxi-hiring behavior are more complicated. A specific turning point (coefficient of variability of mean headway ≈ 0.7) in the relationship between taxi-hiring behavior and bus headway is shown in this paper. Based on data mining and model development, this research presents details on attributes of bus service that drive passengers to switch to taxis and how changes in those attributes encourage modal shift from buses to taxis.

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A Bayesian semi-parametric model to estimate relationships between crash counts and roadway characteristics

Cited by 32 publications

References 31 publications

Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Crash risk analysis for Shanghai urban expressways: A Bayesian semi-parametric modeling approach

Crash Frequency Analysis

Assessing Effects of Bus Service Quality on Passengers’ Taxi-Hiring Behavior

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