1,721,270 research outputs found
Development of Crash Prediction Models for Urban Road Segments Using Poisson Inverse Gaussian Regression
No full textTransportation safety researchers utilize crash prediction models (CPMs) to examine the safety performance of roadway facilities. Using statistical modeling, the CPMs associate traffic and roadway design elements to crash frequency. Many studies in recent years have applied relatively advanced techniques to model crash data. Regardless of this, the traditional negative binomial (NB) model remains highly popular, probably because of the ease of estimation and the ability to accommodate overdispersion. Though the NB model performs well for data with moderate to low overdispersion, its performance is compromised in the case of highly over-dispersed data. Analysts need alternative approaches to model such datasets. The Poisson-inverse Gaussian (PIG) regression modeling framework has shown the potential to model highly dispersed data more effectively due to the flexible nature of the inverse Gaussian distribution. This study applied the PIG regression framework to model a six-year crash data for urban road segments, taking the traffic volume and roadway geometric design attributes as predictor variables. We compared the PIG models with the traditional NB models for goodness-of-fit and predictive performance. Moreover, we computed the prediction intervals (PIs) for the predicted responses at new sites to examine the level of uncertainty beyond the point estimates for the two modeling frameworks. The PIG and NB models revealed a significant association between the predictor variables (i.e., traffic volume and roadway design attributes) and crash frequency. In terms of performance (i.e., goodness-of-fit, predictive performance, prediction intervals), the PIG models performed either better or equally well for the models developed in this study. In conclusion, the PIG models could be adopted as a potential alternative to the NB models, given the convenience in their estimation and the ability to accommodate overdispersion
Analysis of Factors Influencing Road Crashes in the Urban Areas: The Application of Generalized Poisson Model vs Negative Binomial Model
No full textTransportation safety researchers extensively apply the negative binomial (NB) modeling framework to analyze crash data and identify factors influencing road crashes due to its ability to accommodate overdispersion. However, a few studies have informed that crash data can sometimes exhibit under-dispersion, meaning smaller data variance than its mean. The NB model cannot accommodate under-dispersion. In statistics and econometrics, generalized Poisson (GP) regression is applied to address over- and/or underdispersion, but its application in transportation safety is scarce. Another issue that is often highlighted in safety literature is the complex and somewhat contradictory understanding of the relationship between crash frequency and other covariates in urban areas. In this study, we applied the GP modeling framework to examine the impact of various geometric design factors and traffic volume on the frequency of different crash types in an urban context. We also applied the NB model to the same data and compared its results with the GP models using goodness-of-fit and predictive performance measures. The analysis showed that roadway design characteristics, including lane width, number of lanes, road separation, on-street parking, posted speed limit, and traffic volume, contribute to urban road crashes. Besides, it was revealed that the GP models outperformed the NB models for some crash types and demonstrated almost similar performance for the remaining ones. Given the predictive performance, ease of estimation, and ability to model under- and over-dispersion, our study proposes that the GP model could be a potential alternative to the NB model in crash data analysis
Development of Crash Prediction Models for Urban Road Segments Using Poisson Inverse Gaussian Regression
No full textTransportation safety researchers utilize crash prediction models (CPMs) to examine the safety performance of roadway facilities. Using statistical modeling, the CPMs associate traffic and roadway design elements to crash frequency. Many studies in recent years have applied relatively advanced techniques to model crash data. Regardless of this, the traditional negative binomial (NB) model remains highly popular, probably because of the ease of estimation and the ability to accommodate overdispersion. Though the NB model performs well for data with moderate to low overdispersion, its performance is compromised in the case of highly over-dispersed data. Analysts need alternative approaches to model such datasets. The Poisson-inverse Gaussian (PIG) regression modeling framework has shown the potential to model highly dispersed data more effectively due to the flexible nature of the inverse Gaussian distribution. This study applied the PIG regression framework to model a six-year crash data for urban road segments, taking the traffic volume and roadway geometric design attributes as predictor variables. We compared the PIG models with the traditional NB models for goodness-of-fit and predictive performance. Moreover, we computed the prediction intervals (PIs) for the predicted responses at new sites to examine the level of uncertainty beyond the point estimates for the two modeling frameworks. The PIG and NB models revealed a significant association between the predictor variables (i.e., traffic volume and roadway design attributes) and crash frequency. In terms of performance (i.e., goodness-of-fit, predictive performance, prediction intervals), the PIG models performed either better or equally well for the models developed in this study. In conclusion, the PIG models could be adopted as a potential alternative to the NB models, given the convenience in their estimation and the ability to accommodate overdispersion
Analysis of Factors Influencing Road Crashes in the Urban Areas: The Application of Generalized Poisson Model vs Negative Binomial Model
No full textTransportation safety researchers extensively apply the negative binomial (NB) modeling framework to analyze crash data and identify factors influencing road crashes due to its ability to accommodate overdispersion. However, a few studies have informed that crash data can sometimes exhibit under-dispersion, meaning smaller data variance than its mean. The NB model cannot accommodate under-dispersion. In statistics and econometrics, generalized Poisson (GP) regression is applied to address over- and/or underdispersion, but its application in transportation safety is scarce. Another issue that is often highlighted in safety literature is the complex and somewhat contradictory understanding of the relationship between crash frequency and other covariates in urban areas. In this study, we applied the GP modeling framework to examine the impact of various geometric design factors and traffic volume on the frequency of different crash types in an urban context. We also applied the NB model to the same data and compared its results with the GP models using goodness-of-fit and predictive performance measures. The analysis showed that roadway design characteristics, including lane width, number of lanes, road separation, on-street parking, posted speed limit, and traffic volume, contribute to urban road crashes. Besides, it was revealed that the GP models outperformed the NB models for some crash types and demonstrated almost similar performance for the remaining ones. Given the predictive performance, ease of estimation, and ability to model under- and over-dispersion, our study proposes that the GP model could be a potential alternative to the NB model in crash data analysis
Comparative Evaluation of Crash Hotspot Identification Methods: Empirical Bayes vs. Potential for Safety Improvement Using Variants of Negative Binomial Models
No full textThe empirical Bayes (EB) method is widely acclaimed for crash hotspot identification (HSID), which integrates crash prediction model estimates and observed crash frequency to compute the expected crash frequency of a site. The traditional negative binomial (NB) models, often used to estimate crash predictive models, typically struggle with accounting for the unobserved heterogeneity in crash data. Complex extensions of the NB models are applied to overcome these shortcomings. These techniques also present new challenges, for instance, applying the EB procedures, especially for out-of-sample data. This study applies a random parameter negative binomial (RPNB) model within the EB framework for HSID using out-of-sample data, comparing its performance with a varying dispersion parameter NB model (VDPNB). The research also evaluates the potential for safety improvement (PSI) scores for both models and compares them with EB estimates using three generalised criteria: high crashes consistency test (HCCT), common sites consistency test (CSCT), and absolute rank differences test (ARDT). The results yield dual insights. Firstly, the study highlights associations between crash covariates and frequency, emphasising the significance of roadway geometric design characteristics (e.g., lane width, number of lanes, and parking type) and traffic volume. Some variables also influenced overdispersion parameters in the VDPNB model. In the RPNB model, annual average daily traffic (AADT) and lane width emerged as random parameters. Secondly, the HSID performance assessment revealed the superiority of the EB method over PSI. Notably, the RPNB model, compared to the VDPNB, demonstrates superior performance in EB estimates for HSID with out-of-sample data. This research recommends adopting the EB method with RPNB models for robust HSID.The authors thank Antwerp Police for providing crash data for this work, Lantis (a mobility company of Antwerp city) for providing the necessary traffic data, and the Flemish Government for providing the road infrastructure data
Examining the Effects of Geometric Features, Traffic Control, and Traffic Volume on Crash Frequency at Urban Intersections
No full textIntersections are sites where traffic crashes are highly concentrated. This is because conflicting traffic maneuvers are highest at the intersections and, therefore, they are more prone to crash occurrence. An accurate understanding of the relationships between various geometric features, traffic flow and crashes in the influence area of intersections, could help reduce these crashes by suggesting appropriate countermeasures. Safety performance functions are the tools to understand these relationships. In this study, we developed safety performance functions specific to Antwerp, Belgium, for urban intersections, using multiple functional forms of a Poisson-gamma framework considering total crash counts. A dataset consisting of six years (2010-2015) of intersection crashes, traffic flows (major and minor), and geometric features was created. This paper documents the development process of safety performance functions for signalized and un-signalized intersections, connecting roadway geometry and traffic characteristics to intersection related crashes and, thus, estimating the impact of predictor variables on the crash occurrence, so as to facilitate the safe design. The findings indicate several road geometric and traffic variables that have a significant impact on crashes at urban intersections. The results revealed that to improve intersection safety, enough attention must be paid to the number of through lanes of a minor approach, intersection skewness, size, and presence of crosswalks on the approach roads. Furthermore, it was found that an early inquiry into the model's functional form could result in more accurate estimates of crash frequency
Analysis of Road Infrastructure and Traffic Factors Influencing Crash Frequency: Insights from Generalised Poisson Models
No full textThis research utilises statistical modelling to explore the impact of roadway infrastructure elements, primarily those related to cross-section design, on crash occurrences in urban areas. Cross-section design is an important step in the roadway geometric design process as it influences key operational characteristics like capacity, cost, safety, and overall functionality of the transport system entity. Evaluating the influence of cross-section design on these factors is relatively straightforward, except for its impact on safety, especially in urban areas. The safety aspect has resulted in inconsistent findings in the existing literature, indicating a need for further investigation. Negative binomial (NB) models are typically employed for such investigations, given their ability to account for over-dispersion in crash data. However, the low sample mean and under-dispersion occasionally exhibited by crash data can restrict their applicability. The generalised Poisson (GP) models have been proposed as a potential alternative to NB models. This research applies GP models for developing crash prediction models for urban road segments. Simultaneously, NB models are also developed to enable a comparative assessment between the two modelling frameworks. A six-year dataset encompassing crash counts, traffic volume, and cross-section design data reveals a significant association between crash frequency and infrastructure design variables. Specifically, lane width, number of lanes, road separation, on-street parking, and posted speed limit are significant predictors of crash frequencies. Comparative analysis with NB models shows that GP models outperform in cases of low sample mean crash types and yield similar results for others. Overall, this study provides valuable insights into the relationship between road infrastructure design and crash frequency in urban environments and offers a statistical approach for predicting crash frequency that maintains a balance between interpretability and predictive power, making it more viable for practitioners and road authorities to apply in real-world road safety scenarios.The authors thank Antwerp Police for providing crash data for this work. Lantis (a mobility company of Antwerp city) for providing the necessary traffic data and the Flemish Government for the road infrastructure data is further acknowledged
Estimation of safety performance functions for urban intersections using various functional forms of the negative binomial regression model and a generalized Poisson regression model
No full textIntersections are established dangerous entities of a highway system due to the challenging and unsafe roadway environment they are characterized for drivers and other road users. In efforts to improve safety, an enormous interest has been shown in developing statistical models for intersection crash prediction and explanation. The selection of an adequate form of the statistical model is of great importance for the accurate estimation of crash frequency and the correct identification of crash contributing factors. Using a six-year crash data, road infrastructure and geometric design data, and traffic flow data of urban intersections, we applied three different functional forms of negative binomial models (i.e., NB-1, NB-2, NB-P) and a generalized Poisson (GP) model to develop safety performance functions (SPF) by crash severity for signalized and unsignalized intersections. This paper presents the relationships found between the explanatory variables and the expected crash frequency. It reports the comparison of different models for total, injury & fatal, and property damage only crashes in order to obtain ones with the maximum estimation accuracy. The comparison of models was based on the goodness of fit and the prediction performance measures. The fitted models showed that the traffic flow and several variables related to road infrastructure and geometric design significantly influence the intersection crash frequency. Further, the goodness of fit and the prediction performance measures revealed that the NB-P model outperformed other models in most crash severity levels for signalized intersections. For the unsignalized intersections, the GP model was the best performing model. When only the NB models were compared, the functional form NB-P performed better than the traditional NB-1 and, more specifically, the NB-2 models. In conclusion, our findings suggest a potential improvement in the estimation accuracy of the SPFs for urban intersections by applying the NB-P and GP models.The authors would like to thank the Police of Antwerp for providing the crash data, the Lantis, an Antwerp-based mobility management company, providing the necessary traffic data, and the Flemish government for the road infrastructure data used in this researc
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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