A Novel Approach to Modeling and Predicting Crash Frequency at Rural Intersections by Crash Type and Injury Severity Level
Title | A Novel Approach to Modeling and Predicting Crash Frequency at Rural Intersections by Crash Type and Injury Severity Level PDF eBook |
Author | Jun Deng (Writer on transportation) |
Publisher | |
Pages | 112 |
Release | 2013 |
Genre | |
ISBN |
Safety at intersections is of significant interest to transportation professionals due to the large number of possible conflicts that occur at those locations. In particular, rural intersections have been recognized as one of the most hazardous locations on roads. However, most models of crash frequency at rural intersections, and road segments in general, do not differentiate between crash type (such as angle, rear-end or sideswipe) and injury severity (such as fatal injury, non-fatal injury, possible injury or property damage only). Thus, there is a need to be able to identify the differential impacts of intersection-specific and other variables on crash types and severity levels. This thesis builds upon the work of Bhat et al., (2013b) to formulate and apply a novel approach for the joint modeling of crash frequency and combinations of crash type and injury severity. The proposed framework explicitly links a count data model (to model crash frequency) with a discrete choice model (to model combinations of crash type and injury severity), and uses a multinomial probit kernel for the discrete choice model and introduces unobserved heterogeneity in both the crash frequency model and the discrete choice model, while also accommodates excess of zeros. The results show that the type of traffic control and the number of entering roads are the most important determinants of crash counts and crash type/injury severity, and the results from our analysis underscore the value of our proposed model for data fit purposes as well as to accurately estimate variable effects.
A Novel Approach to Modeling and Predicting Crash Frequency at Rural Intersections by Crash Type and Injury Severity Level
Title | A Novel Approach to Modeling and Predicting Crash Frequency at Rural Intersections by Crash Type and Injury Severity Level PDF eBook |
Author | Jun Deng (Writer on transportation) |
Publisher | |
Pages | 48 |
Release | 2015 |
Genre | Roads |
ISBN |
Advanced Statistical Modeling of the Frequency and Severity of Traffic Crashes on Rural Highways
Title | Advanced Statistical Modeling of the Frequency and Severity of Traffic Crashes on Rural Highways PDF eBook |
Author | Irfan Uddin Ahmed |
Publisher | |
Pages | 222 |
Release | 2022 |
Genre | Automobile driving in bad weather |
ISBN |
The primary objective of practitioners working on traffic safety is to reduce the number and severity of crashes. The Highway Safety Manual (HSM) provides practitioners with analytical tools and techniques to estimate the expected crash frequency and severity with the aim to identify and evaluate safety countermeasures. Expected crash frequency can be estimated using Safety Performance Functions (SPFs) provided in Part C of the HSM. The HSM provides simple SPFs which are developed using the most frequently used crash counts model, the negative binomial regression model. The rural nature of Wyoming highways coupled with the mountainous terrain (i.e., challenging roadway geometry) make the HSM basic SPFs unsuitable to determine crash contributing factors for Wyoming conditions. In this regard, the objective of this study is to implement advanced statistical methods such as the different functional forms of Negative Binomial, and Bayesian approach, to develop crash prediction models, investigate crash contributing factors, and determine the impact of safety countermeasures. Bayesian statistics in combination with the power of Markov Chain Monte Carlo (MCMC) sampling techniques provide frameworks to model small sample datasets and complex models at the same time, where the traditional Maximum Likelihood Estimation (MLE) based methods tend to fail. As such, a novel No-U-Turn Sampler for Hamiltonian Monte Carlo (NUTS HMC) sampling technique in a Bayesian framework was utilized to investigate the crash frequency, injury severity of crashes on the interstate freeways and some rural highways in Wyoming. The Poisson and the Negative Binomial (NB) models are the most commonly used regression models in traffic safety analysis. The advantage of the NB model can be further enhanced by providing different functional forms of the variance and the dispersion structure. The NB-2 is the most common form of the NB model, typically used in developing safety performance functions (SPFs) largely due to the mean-variance quadratic relationship. However, studies in the literature have shown that the mean-variance relationship could be unrestrained. Another introduced formulation of the NB model is NB-1, which assumes that there is a constant ratio linking the mean and the variance of the crash frequencies. A more general type of the NB model is the NB-P model, which does not constrain the mean-variance relationship. Thus, leveraging the power of this unrestrained mean-variance relationship, more accurate safety models could be developed, and these would lead to more accurate estimation of crash risk and benefits of potential solutions. This study will help practitioners to implement advanced methodologies to solve traffic safety problems of rural highways that have plagued the researchers for a long time now. The methodologies proposed in this study will help practitioners to replace the outdated and inefficient traditional models and obtain more accurate traffic safety models to predict crashes and the resulting crash injury severity. Moreover, this research quantified the safety effectiveness of some unique countermeasures on rural highways.
Highway Safety Manual
Title | Highway Safety Manual PDF eBook |
Author | |
Publisher | AASHTO |
Pages | 886 |
Release | 2010 |
Genre | Technology & Engineering |
ISBN | 1560514779 |
"The Highway Safety Manual (HSM) is a resource that provides safety knowledge and tools in a useful form to facilitate improved decision making based on safety performance. The focus of the HSM is to provide quantitative information for decision making. The HSM assembles currently available information and methodologies on measuring, estimating and evaluating roadways in terms of crash frequency (number of crashes per year) and crash severity (level of injuries due to crashes). The HSM presents tools and methodologies for consideration of 'safety' across the range of highway activities: planning, programming, project development, construction, operations, and maintenance. The purpose of this is to convey present knowledge regarding highway safety information for use by a broad array of transportation professionals"--p. xxiii, vol. 1.
Statistical Methods and Modeling and Safety Data, Analysis, and Evaluation
Title | Statistical Methods and Modeling and Safety Data, Analysis, and Evaluation PDF eBook |
Author | National Research Council (U.S.). Transportation Research Board |
Publisher | |
Pages | 212 |
Release | 2003 |
Genre | Traffic accident investigation |
ISBN |
Covers empirical approaches to outlier detection in intelligent transportation systems data, modeling of traffic crash-flow relationships for intersections, profiling of high-frequency accident locations by use of association rules, analysis of rollovers and injuries with sport utility vehicles, and automated accident detection at intersections via digital audio signal processing.
Modelling Crash Frequency and Severity Using Global Positioning System Travel Data
Title | Modelling Crash Frequency and Severity Using Global Positioning System Travel Data PDF eBook |
Author | Joshua Stipancic |
Publisher | |
Pages | |
Release | 2019 |
Genre | |
ISBN |
"Improving road safety requires accurate network screening methods to identify and prioritize sites to maximize effectiveness of implemented countermeasures. In screening, hotspots are commonly identified using statistical models based on historical crash data. However, collision databases are subject to errors and omissions and crash-based methods are reactive. With the arrival of Global Positioning System (GPS) trajectory data, surrogate safety methods, proactive by nature, have gained popularity. Although GPS-enabled smartphones can collect reliable and spatio-temporally rich driving data from regular drivers using an inexpensive, simple, and user-friendly tool, few studies to date have analyzed large volumes of smartphone GPS data and considered surrogate-safety modelling techniques for network screening. The main objective of this thesis is to propose and validate a GPS-based network screening modeling framework dependent on surrogate safety measures (SSMs). First, methods for collecting and processing GPS and associated data sources are presented. Data, collected in Quebec City and capturing 4000 drivers and 21,000 trips, was processed using map matching and speed filtering algorithms. Spatio-temporal congestion measures were proposed and extracted and techniques for visualizing congestion patterns at aggregate and disaggregate levels were explored. Results showed that each peak period has an onset period and dissipation period lasting one hour. Congestion in the evening is greater and more dispersed than in the morning. Congestion on motorways, arterials, and collectors is most variable during peak periods. Second, various event-based and traffic flow SSMs are proposed and correlated with historical collision frequency and severity using Spearman's correlation coefficient and pairwise Kolmogorov-Smirnov tests, respectively. For example, hard braking (HBEs) and accelerating events (HAEs) were positively correlated with crash frequency, though correlations were much stronger at intersections than at links. Higher numbers of these vehicle manoeuvres were also related to increased collision severity. Considered traffic flow SSMs included congestion index (CI), average speed (V̄), and coefficient of variation of speed (CVS). CI was positively correlated with crash frequency and showed a non-monotonous relationship with severity. V̄ was negatively correlated with crash frequency and had no conclusive statistical relationship with crash severity. CVS was positively related to increased crash frequency and severity. Third, a mixed-multivariate model was developed to predict crash frequency and severity incorporating GPS-derived SSMs as predictive variables. The outcome is estimated using two models; a crash frequency model using a Full Bayes approach and estimated using the Integrated Nested Laplace Approximation (INLA) approach and a crash severity model integrated through a fractional Multinomial Logit model. The results are combined to generate posterior expected crash frequency at each severity level and rank sites based on crash cost. Negative Binomial models outperformed alternative models based on a sample of the network, and including spatial effects showed improvement in model fit. This crash frequency model was shown to be accurate at the network scale, with the majority of proposed SSMs statistically significant at 95 % confidence. In the crash severity model, fewer variables were significant, yet the effect of all significant variables was consistent with previous results. Correlations between rankings predicted by the model and by the crash data were adequate for intersections (0.46) but were poorer for links (0.25). The inclusion of severity, which is an independent dimension of safety, is a substantial improvement over many existing studies, and the ability to prioritize sites based on GPS data and SSMs rather than historical crash data represents a substantial contribution to the field of road safety." --
Highway Safety
Title | Highway Safety PDF eBook |
Author | |
Publisher | |
Pages | 101 |
Release | 2001 |
Genre | |
ISBN |
Transportation Research Record contains the following papers: Incorporating crash risk in selecting congestion-mitigation strategies : Hampton Roads area (Virginia) case study (Garber, NJ and Subramanyan, S); Development of artificial neural network models to predict driver injury severity in traffic accidents at signalized intersections (Abdelwahab, HT and Abdel-Aty, MA); Transferability of models that estimate crashes as a function of access management (Miller, JS, Hoel, LA, Kim, S and Drummond, KP); Sensor-friendly vehicle and roadway cooperative safety systems : benefits estimation (Misener, JA, Thorpe, C, Ferlis, R, Hearne, R, Siegal, M and Perkowski, J); Interstate highway crash injuries during winter snow and nonsnow events (Khattak, AJ and Knapp, KK); Simulation of road crashes by use of systems dynamics (Mehmood, A, Saccamanno, F and Hellinga, B); Longitudinal analysis of fatal run-off-road crashes, 1975 to 1997 (McGinnis, RG, Davis, MJ and Hathaway, EA); Injury severity in multivehicle rear-end crashes (Khattack, AJ); Computing and interpreting accident rates for vehicle types driver groups (Hauer, E); Geographics information system-based accident data management for Mexican federal roads (Mendoza, A, Mayoral, EF, Vicente, JL and Quintero, FL); Bayesian identification of high-risk intersections for older drivers via gibbs sampling (Davis, GA and Yang, S); Automated accident detection system (Harlow, C and Wang, Y); Evaluation of inexpensive global positioning system units to improve crash location data (Graettinger, AJ, Rushing, TW and McFadden, J).