Mohamed Abdel-Aty

MODELING TRAFFIC ACCIDENT OCCURRENCE AND INVOLVEMENT

The Negative Binomial modeling technique was used to model the frequency of accident occurrence and involvement. Accident data over a period of 3 years, accounting for 1,606 accidents on a principal arterial in Central Florida, were used to estimate the model. The model illustrated the significance of the Annual Average Daily Traffic (AADT), degree of horizontal curvature, lane, shoulder and median widths, urban/rural, and the sections length, on the frequency of accident occurrence. Several Negative Binomial models of the frequency of accident involvement were also developed to account for the demographic characteristics of the driver (age and gender). The results showed that heavy traffic volume, speeding, narrow lane width, larger number of lanes, urban roadway sections, narrow shoulder width and reduced median width increase the likelihood for accident involvement. Subsequent elasticity computations identified the relative importance of the variables included in the models. Female drivers experience more accidents than male drivers in heavy traffic volume, reduced median width, narrow lane width, and larger number of lanes. Male drivers have greater tendency to be involved in traffic accidents while speeding. The models also indicated that young and older drivers experience more accidents than middle aged drivers in heavy traffic volume, and reduced shoulder and median widths. Younger drivers have a greater tendency of being involved in accidents on roadway curves and while speeding.

Development of Artificial Neural Network Models to Predict Driver Injury Severity in Traffic Accidents at Signalized Intersections

The relationship between driver injury severity and driver, vehicle, roadway, and environment characteristics was examined. The use of two well-known neural network paradigms, the multilayer perceptron (MLP) and fuzzy adaptive resonance theory (ART) neural networks, was investigated. The use of artificial neural networks can lead to greater understanding of the relationship between the aforementioned factors and driver injury severity. Accident data for 1997 for the Central Florida area, which consists of Orange, Osceola, and Seminole Counties, were used. The analysis focuses on two-vehicle accidents that occurred at signalized intersections. The MLP neural network has a better generalization performance of 65.6 and 60.4 percent for the training and testing phases, respectively. The performance of the MLP was compared with that of an ordered logit model. The ordered logit model was able to correctly classify only 58.9 and 57.1 percent for the training and testing phases, respectively. A simulation experiment was then carried out to understand the MLP neural network model. Results show that rural intersections are more dangerous in terms of driver injury severity than urban intersections. Also, female drivers are more likely to experience a severe injury than are male drivers. Speed ratio increases the likelihood of injury severity. Drivers at fault are less likely to experience severe injury than are those not at fault. Wearing a seat belt decreases the chance of sustaining severe injuries. Vehicle type plays a role in driver injury severity. Drivers in passenger cars are more likely to experience a greater injury severity level than are drivers of vans or pickup trucks. Finally, drivers exposed to impact at their side experience greater injury severity than those exposed to impact elsewhere.

Predicting Freeway Crashes from Loop Detector Data by Matched Case-Control Logistic Regression

Growing concern over traffic safety has led to research into prediction of freeway crashes in an advanced traffic management and information systems environment. A crash likelihood prediction model was developed by using real-time traffic flow variables (measured through a series of underground sensors) potentially associated with crash occurrence. The issues related to real-time application, including range of stations and time slice duration to be examined, were also addressed. The methodology used, matched case-control logistic regression, was adopted from epidemiological studies in which every crash is a case and corresponding noncrashes act as controls. The 5-min average occupancy observed at the upstream station during the 5 to 10 min before the crash, along with the 5-min coefficient of variation in speed at the downstream station during the same time, was found to affect crash occurrence most significantly and hence was used to calculate the corresponding log-odds ratio. A threshold value for this ratio may then be set to determine whether the location must be flagged as a potential crash location. It was shown that by using 1.0 as the threshold for the log-odds ratio, more than 69% crash identification was achieved.

Analysis of left-turn crash injury severity by conflicting pattern using partial proportional odds models

The purpose of this study is to examine left-turn crash injury severity. Left-turning traffic colliding with opposing through traffic and with near-side through traffic are the two most frequently occurring conflicting patterns among left-turn crashes (Patterns 5 and 8 in the paper, respectively), and they are prone to be severe. Ordered probability models with either logit or probit function is commonly applied in crash injury severity analyses; however, its critical assumption that the slope coefficients do not vary over different alternatives except the cut-off points is usually too restrictive. Partial proportional odds models are generalizations of ordered probability models, for which some of the beta coefficients can differ across alternatives, were applied to investigate Patterns 5 and 8, and the total left-turn crash injuries. The results show that partial proportional odds models consistently perform better than ordered probability models. By focusing on specific conflicting patterns, locating crashes to the exact crash sites and relating approach variables to crash injury in the analysis, researchers are able to investigate how these variables affect left-turn crash injuries. For example, opposing through traffic and near-side crossing through traffic in the hour of collision were identified significant for Patterns 5 and 8 crash injuries, respectively. Protected left-turn phasing is significantly correlated with Pattern 5 crash injury. Many other variables in driver attributes, vehicular characteristics, roadway geometry design, environmental factors, and crash characteristics were identified. Specifically, the use of the partial proportional formulation allows a much better identification of the increasing effect of alcohol and/or drug use on crash injury severity, which previously was masked using the conventional ordered probability models.

Split Models for Predicting Multivehicle Crashes During High-Speed and Low-Speed Operating Conditions on Freeways

The future of traffic management and highway safety lies in proactive traffic management systems. Crash prediction models that use real-time traffic flow variables measured through a series of loop detectors are the most important component of such systems. A previous crash prediction model was developed with the matched case-control logistic regression technique. Although the model achieved reasonable classification accuracy, it remained open to improvement because of the limited study area, sample size, and transferability issues. Therefore, the previous work had been extended. Multivehicle freeway crashes under high- and low-speed traffic conditions were found to differ in severity and in their mechanism. The distribution of 5-min average speeds obtained immediately before the crash from the loop detector station closest to the crash shows two approximate mound-shaped distributions. This distribution is used as the basis to separate the models for crashes occurring under the two speed conditions. The results show that, as expected, variables that entered in the final models (for crashes under high and low speeds) were not the same. However, they were found to be consistent with the probable mechanisms of crashes under the respective speed conditions. A possible implementation of the separate models with the use of the odds ratios and with the balancing of the threshold between achieving high classification of crash potential and the false alarm situation is presented.

Modeling signalized intersection safety with corridor-level spatial correlations

Intersections in close spatial proximity along a corridor should be considered as correlated due to interacted traffic flows as well as similar road design and environmental characteristics. It is critical to incorporate this spatial correlation for assessing the true safety impacts of risk factors. In this paper, several Bayesian models were developed to model the crash data from 170 signalized intersections in the state of Florida. The safety impacts of risk factors such as geometric design features, traffic control, and traffic flow characteristics were evaluated. The Poisson and Negative Binomial Bayesian models with non-informative priors were fitted but the focus is to incorporate spatial correlations among intersections. Two alternative models were proposed to capture this correlation: (1) a mixed effect model in which the corridor-level correlation is incorporated through a corridor-specific random effect and (2) a conditional autoregressive model in which the magnitude of correlations is determined by spatial distances among intersections. The models were compared using the Deviance Information Criterion. The results indicate that the Poisson spatial model provides the best model fitting. Analysis of the posterior distributions of model parameters indicated that the size of intersection, the traffic conditions by turning movement, and the coordination of signal phase have significant impacts on intersection safety.

Macroscopic spatial analysis of pedestrian and bicycle crashes

This study investigates the effect of spatial correlation using a Bayesian spatial framework to model pedestrian and bicycle crashes in Traffic Analysis Zones (TAZs). Aggregate models for pedestrian and bicycle crashes were estimated as a function of variables related to roadway characteristics, and various demographic and socio-economic factors. It was found that significant differences were present between the predictor sets for pedestrian and bicycle crashes. The Bayesian Poisson-lognormal model accounting for spatial correlation for pedestrian crashes in the TAZs of the study counties retained nine variables significantly different from zero at 95% Bayesian credible interval. These variables were - total roadway length with 35 mph posted speed limit, total number of intersections per TAZ, median household income, total number of dwelling units, log of population per square mile of a TAZ, percentage of households with non-retired workers but zero auto, percentage of households with non-retired workers and one auto, long term parking cost, and log of total number of employment in a TAZ. A separate distinct set of predictors were found for the bicycle crash model. In all cases the Bayesian models with spatial correlation performed better than the models that did not account for spatial correlation among TAZs. This finding implies that spatial correlation should be considered while modeling pedestrian and bicycle crashes at the aggregate or macro-level.

County-Level Crash Risk Analysis in Florida: Bayesian Spatial Modeling

An increasing research effort has been made on spatially disaggregated safety analysis models to meet the needs of region-level safety inspection and recently emerging transportation safety planning techniques. However, without explicitly differentiating exposure variables and risk factors, most existing studies alternate the use of crash frequency, crash rate, and crash risk to interpret model coefficients. This procedure may have resulted in the inconsistent findings in relevant studies. This study proposes a Bayesian spatial model to account for county-level variations of crash risk in Florida by explicitly controlling for exposure variables of daily vehicle miles traveled and population. A conditional autoregressive prior is specified to accommodate for the spatial autocorrelations of adjacent counties. The results show no significant difference in safety effects of risk factors on all crashes and severe crashes. Counties with higher traffic intensity and population density and a higher level of urbanization are associated with higher crash risk. Unlike arterials, freeways seem to be safer with respect to crash risk given either vehicle miles traveled or population. Increase in truck traffic volume tends to result in more severe crashes. The average travel time to work is negatively correlated with all types of crash risk. Regarding the population age cohort, the results suggest that young drivers tend to be involved in more crashes, whereas the increase in elderly population leads to fewer casualties. Finally, it is confirmed that the safety status is worse for more deprived areas with lower income and educational level and higher unemployment rate in comparison with relatively affluent areas.

Exploring a Bayesian hierarchical approach for developing safety performance functions for a mountainous freeway.

While rural freeways generally have lower crash rates, interactions between driver behavior, traffic and geometric characteristics, and adverse weather conditions may increase the crash risk along some freeway sections. This paper examines the safety effects of roadway geometrics on crash occurrence along a freeway section that features mountainous terrain and adverse weather. Starting from preliminary exploration using Poisson models, Bayesian hierarchical models with spatial and random effects were developed to efficiently model the crash frequencies on road segments on the 20-mile freeway section of study. Crash data for 6 years (2000-2005), roadway geometry, traffic characteristics and weather information in addition to the effect of steep slopes and adverse weather of snow and dry seasons, were used in the investigation. Estimation of the model coefficients indicates that roadway geometry is significantly associated with crash risk; segments with steep downgrades were found to drastically increase the crash risk. Moreover, this crash risk could be significantly increased during snow season compared to dry season as a confounding effect between grades and pavement condition. Moreover, sites with higher degree of curvature, wider medians and an increase of the number of lanes appear to be associated with lower crash rate. Finally, a Bayesian ranking technique was implemented to rank the hazard levels of the roadway segments; the results confirmed that segments with steep downgrades are more crash prone along the study section.

Examining Traffic Crash Injury Severity at Unsignalized Intersections

INTRODUCTION This study presents multiple approaches to the analysis of crash injury severity at three- and four-legged unsignalized intersections in the state of Florida from 2003 until 2006. An extensive data collection process was conducted for this study. METHOD The dataset used in the analysis included 2,043 unsignalized intersections in six counties in the state of Florida. For the scope of this study, there were three approaches explored. The first approach dealt with the five injury levels, and an ordered probit model was fitted. The second approach was an aggregated one, and dealt with only the severe versus non-severe crash levels, and a binary probit model was used. The third approach dealt with fitting a nested logit model. Results from the three fitted approaches were shown and discussed, and a comparison between the three approaches was shown. RESULTS Several important factors affecting crash severity at unsignalized intersections were identified. These include the traffic volume on the major approach, and the number of through lanes on the minor approach (surrogate measure for traffic volume), and among the geometric factors, the upstream and downstream distance to the nearest signalized intersection, left and right shoulder width, number of left turn movements on the minor approach, and number of right and left turn lanes on the major approach. As for driver factors, young and very young at-fault drivers were associated with the least fatal probability compared to other age groups. IMPACT ON INDUSTRY The analysis identified some countermeasures to reduce injury severity at unsignalized intersections. The spatial covariates showed the importance of including safety awareness campaigns for speeding enforcement. Also, having a 90-degree intersection design is the most appropriate safety design for reducing severity. Moreover, the assurance of marking stop lines at unsignalized intersections is very essential.