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Author: Jun Deng (Writer on transportation)
Publisher:
ISBN:
Category :
Languages : en
Pages : 112
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Book Description
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.
Author: Jun Deng (Writer on transportation)
Publisher:
ISBN:
Category :
Languages : en
Pages : 112
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Book Description
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.
Author: Jun Deng (Writer on transportation)
Publisher:
ISBN:
Category : Roads
Languages : en
Pages : 48
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Book Description
Author: Irfan Uddin Ahmed
Publisher:
ISBN:
Category : Automobile driving in bad weather
Languages : en
Pages : 222
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Book Description
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.
Author:
Publisher: AASHTO
ISBN: 1560514779
Category : Technology & Engineering
Languages : en
Pages : 886
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Book Description
"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.
Author: National Research Council (U.S.). Transportation Research Board
Publisher:
ISBN:
Category : Traffic accident investigation
Languages : en
Pages : 212
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Book Description
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.
Author: Joshua Stipancic
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
"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." --
Author: National Research Council (U.S.). Transportation Research Board
Publisher:
ISBN:
Category : Traffic accidents
Languages : en
Pages : 148
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Book Description
Transportation Research Record contains the following papers: Method for identifying factors contributing to driver-injury severity in traffic crashes (Chen, WH and Jovanis, PP); Crash- and injury-outcome multipliers (Kim, K); Guidelines for identification of hazardous highway curves (Persaud, B, Retting, RA and Lyon, C); Tools to identify safety issues for a corridor safety-improvement program (Breyer, JP); Prediction of risk of wet-pavement accidents : fuzzy logic model (Xiao, J, Kulakowski, BT and El-Gindy, M); Analysis of accident-reduction factors on California state highways (Hanley, KE, Gibby, AR and Ferrara, T); Injury effects of rollovers and events sequence in single-vehicle crashes (Krull, KA, Khattack, AJ and Council, FM); Analytical modeling of driver-guidance schemes with flow variability considerations (Kaysi, I and Ail, NH); Evaluating the effectiveness of Norway's speak out! road safety campaign : The logic of causal inference in road safety evaluation studies (Elvik, R); Effect of speed, flow, and geometric characteristics on crash frequency for two-lane highways (Garber, NJ and Ehrhart, AA); Development of a relational accident database management system for Mexican federal roads (Mendoza, A, Uribe, A, Gil, GZ and Mayoral, E); Estimating traffic accident rates while accounting for traffic-volume estimation error : a Gibbs sampling approach (Davis, GA); Accident prediction models with and without trend : application of the generalized estimating equations procedure (Lord, D and Persaud, BN); Examination of methods that adjust observed traffic volumes on a network (Kikuchi, S, Miljkovic, D and van Zuylen, HJ); Day-to-day travel-time trends and travel-time prediction form loop-detector data (Kwon, JK, Coifman, B and Bickel, P); Heuristic vehicle classification using inductive signatures on freeways (Sun, C and Ritchie, SG).
Author:
Publisher:
ISBN:
Category :
Languages : en
Pages : 101
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Book Description
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).
Author: Kai Wang
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages :
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Book Description
This report first describes the use of different copula based models to simultaneously estimate the two crash indicators: injury severity and vehicle damage. The Gaussian copula model outperforms the other copula based model specifications (i.e. Gaussian, Farlie-Gumbel-Morgenstern (FGM), Frank, Clayton, Joe and Gumbel copula models), and the results indicate that injury severity and vehicle damage are highly correlated, and the correlations between injury severity and vehicle damage varied with different crash characteristics including manners of collision and collision types. This study indicates that the copula-based model can be considered to get a more accurate model structure when simultaneously estimating injury severity and vehicle damage in crash severity analyses. The second part of this report describes estimation of cluster based SPFs for local road intersections and segments in Connecticut using socio-economic and network topological data instead of traffic counts as exposure. The number of intersections and the total local roadway length were appropriate to be used as exposure in the intersection and segment SPFs, respectively. Models including total population, retail and non-retail employment and average household income are found to be the best both on the basis of model fit and out of sample prediction. The third part of this report describes estimation of crashes by both crash type and crash severity on rural two-lane highways, using the Multivariate Poisson Lognormal (MVPLN) model. The crash type and crash severity counts are significantly correlated; the standard errors of covariates in the MVPLN model are slightly lower than the other two univariate crash prediction models (i.e. Negative Binomial model and Univariate Poisson Lognormal model) when the covariates are statistically significant; and the MVPLN model outperforms the UPLN and NB models in crash count prediction accuracy. This study indicates that when simultaneously predicting crash counts by crash type and crash severity for rural two-lane highways, the MVPLN model should be considered to avoid estimation error and to account for the potential correlations among crash type counts and crash severity counts.
Author: Asif Mahmud
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Individual crash types have different underlying causes and thus the relationships between roadway/traffic characteristics and crash frequency are likely to differ across unique crash types. Two statistical methods -- univariate and multivariate formulations -- have been widely used so far by researchers in estimating the impact of contributing factors on different crash types. Addressing the limitations of these methods, recently a two-stage approach has been proposed in which one model is estimated to predict the total crash frequency and its prediction is combined with another model which predicts the proportions of different crash types. More efficient one-stage joint models, in which both the frequency and proportion models are estimated simultaneously and predictions are provided more directly, have also been proposed for macro-level analysis. This study investigates the performance of this joint modeling paradigm in analyzing unique crash type frequencies on individual road segments. Moreover, this study also proposes the use of a multinomial logit (MNL) model to estimate the proportion of different collision types, which has never been done in safety literature. This study compares the performance of all these methods in predicting crash frequency by crash type on two-way two-lane urban-suburban collector roadway segments in Pennsylvania. While the methodologies of crash type frequency estimation are well-established, less focus has been given on the quality of the crash dataset they are applied on. Crash misclassification (MC) -- e.g., a crash of one type or severity being mistakenly miscategorized as another -- is a relatively common problem in transportation safety. Crash frequency models for individual crash categories estimated using datasets with MC errors could result in biased parameter estimates and thus lead to ineffective countermeasure planning. This study proposes a novel methodological formulation to directly account for this MC error and incorporates it into the two most common count data models used for crash frequency prediction: Poisson and Negative Binomial (NB) regression. The proposed framework introduces probabilistic MC rates among different crash types and modifies the likelihood function of the count models accordingly. The study also demonstrates how this approach can be integrated into reformulated models that express each count model as a discrete choice model. The capability of the proposed models to estimate true parameters, given the existence of MC error, is examined via simulation analysis. Then, the proposed models are applied to empirical data to examine the presence of MC in crash data and further examine the robustness of the proposed models. Lastly, the ability of the proposed models in accounting for underreporting, another acute problem in crash data, is examined through comparing its performance with that from established frameworks.
