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Given the enormous losses to society resulting from large truck involved crashes, a comprehensive understanding of the effects of highway geometric design features on the frequency of truck involved crashes is needed. To better predict the occurrence probabilities of large truck involved crashes and gain direction for policies and countermeasures aimed at reducing the crash frequencies, it is essential to examine truck involved crashes categorized by collision vehicle types, since passenger cars and large trucks differ in dimensions, size, weight, and operating characteristics. A data set that includes a total of 1310 highway segments with 1787 truck involved crashes for a 4-year period, from 2004 to 2007 in Tennessee is employed to examine the effects that geometric design features and other relevant attributes have on the crash frequency. Since truck involved crash counts have many zeros (often 60–90% of all values) with small sample means and two established categories, car-truck and truck-only crashes, are not independent in nature, the zero-inflated negative binomial (ZINB) models are developed under the bivariate regression framework to simultaneously address the above mentioned issues. In addition, the bivariate negative binomial (BNB) and two individual univariate ZINB models are estimated for model validation. Goodness of fit of the investigated models is evaluated using AIC, SBC statistics, the number of identified significant variables, and graphs of observed versus expected crash frequencies. The bivariate ZINB (BZINB) models have been found to have desirable distributional property to describe the relationship between the large truck involved crashes and geometric design features in terms of better goodness of fit, more precise parameter estimates, more identified significant factors, and improved predictive accuracy. The results of BZINB models indicate that the following factors are significantly related to the likelihood of truck involved crash occurrences: large truck annual average daily traffic (AADT), segment length, degree of horizontal curvature, terrain type, land use, median type, lane width, right side shoulder width, lighting condition, rutting depth (RD), and posted speed limits. Apart from that, passenger car AADT, lane number, and indicator for different speed limits are found to have statistical significant effects on the occurrences of car-truck crashes and international roughness index (IRI) is significant for the predictions of truck-only crashes. [ABSTRACT FROM AUTHOR]

Vehicle speed and lane position are two of the many possible factors that lead to crashes on horizontal curves. Vehicle position and speed through horizontal curves are of great relevance to many areas of traffic engineering, including countermeasure design, design consistency, and policy. This article discusses the results of an exploratory analysis of vehicle trajectories and speed profiles at two horizontal curves located in central Iowa. The sites include an urban and rural curve. Data were collected at five stations within the horizontal curves in both directions of travel using pneumatic road tubes set up in a Z-configuration. Overall, vehicles were found to cut the horizontal curve in the inside and outside travel lanes. However, the trajectories were seen to be different at the two curve sites. Vehicle mean speed profiles indicated that the curve site selected for evaluation operate differently and are reflected in the speed profiles. Speed deviations of individual vehicles as they traversed the curve were evaluated. More vehicles were found to adjust their speeds and lateral position in the vicinity of the center of the curve, and then at the end of the horizontal curve. [ABSTRACT FROM PUBLISHER]

Abstract: Introduction: Unrestrained drivers and passengers are involved in a significant amount of fatalities and injuries in motor-vehicle crashes in the United States. While the literature documents the effectiveness of seat belt usage in reducing crash outcomes, such as fatalities and the severity of injuries, there is a need to evaluate the impact of seat belt usage by drivers and passengers in their respective vehicles. These findings could help develop effective education and enforcement strategies to enhance occupant safety. Method: This paper summarizes a study comparing seat belt usage rates of drivers and passengers based on whether or not the driver uses a seat belt. Observational data from 50 sites in the state of Nevada over 3 years are used for analyses. The data are stratified based on the gender of the driver and passengers in the front seat of the vehicle and are based on area type (rural or urban). Results: A comparison of the rates of seat belt usage across for the aggregated data and for various types of disaggregation and statistical analyses to compare the rates of seat belt use among passengers based on the use of seat belts by drivers confirms that when drivers use seat belts, their respective passengers are much more likely to use seat belts. Further, if drivers do not use seat belts, their passengers are not likely to use seat belts. This observation is comparable for male drivers and female drivers, and also for male passengers and female passengers. Further, there are no differences for combinations of the genders of the drivers and passenger (i.e., both male or female, or one male and one female). Impact on industry: These results suggest that education and enforcement strategies would benefit most by working toward increasing seat belt usage rates among drivers. [Copyright &y& Elsevier]

Presents information on a study which described the applicability of genetic algorithms (GA) in evaluating aircraft ground holding policy (GHP). Problem definition; Integer programming formulation; Comparison of GA and traditional methods; Steps in the implementation process for GHP problem using GA method; Conclusions.

Presents information on a study which proposed a genetic algorithm (GA) for evaluating ground holding policy (GHP) implications in real time. Problem definition and formulation; Proposed approach using GA; Implementation of the GHP problem using GA; Results and conclusions.

Intelligent transportation systems (ITS) have been used to alleviate congestion problems arising due to demand during peak periods. The success of ITS strategies relies heavily on two factors: 1) the ability to accurately estimate the temporal and spatial distribution of travel demand on the transportation network during peak periods, and, 2) providing real-time route guidance to users. This paper addresses the first factor. A model to estimate time dependent origin-destination (O-D) trip tables in urban areas during peak periods is proposed. The daily peak travel period is divided into several time slices to facilitate simulation and modeling. In urban areas, a majority of the trips during peak periods are work trips. For illustration purposes, only peak period work trips are considered in this paper. The proposed methodology is based on the arrival pattern of trips at a traffic analysis zone (TAZ) and the distribution of their travel times. The travel time matrix for the peak period, the O-D trip table for the peak period, and the number of trips expected to arrive at each TAZ at different work start times are inputs to the model. The model outputs are O-D trip tables for each time slice in the peak period. 1995 data for the Las Vegas metropolitan area are considered for testing and validating the model, and its application. The model is reasonably robust, but some lack of precision was observed. This is due to two possible reasons: 1) rounding-off, and, 2) low ratio of total number of trips to total number of O-D pair combinations. Hence, an attempt is made to study the effect of increasing this ratio on error estimates. The ratio is increased by multiplying each O-D pair trip element with a scaling factor. Better estimates were obtained. Computational issues involved with the simulation and modeling process are discussed. [ABSTRACT FROM AUTHOR]

Horizontal curves present drivers with numerous tasks that, if not performed while approaching and negotiating the curve, may result in a roadway departure crash. A vehicle's lateral position within the lane and its speed are two indicators of interest from safety and operational perspectives. These can and have been measured simultaneously at multiple locations along the curve. However, researchers face the challenge of collecting operational data while minimizing impacts on driver behavior, and developing robust, efficient, and accurate means to obtain the data. This paper presents the findings of a series of pilot studies on closed and open courses that investigated the effectiveness and accuracy of pneumatic road tubes and digital video cameras for collecting such data. Closed-course studies investigated a single data collection station setup, whereas an open-course study investigated multiple data collection stations on a horizontal curve. The data were reduced manually and automatically, and tests were performed to evaluate the statistical significance of the results. In general, the pilot studies show that pneumatic road tubes provide a higher level of accuracy than video data. Other findings include that drivers moved towards the centerline of the roadway overall and even more so when the presence of video equipment. It was also found that vehicle mean speeds at the center of the curve were lower with an increase amount of data site-based vehicle data collection equipment present. [ABSTRACT FROM AUTHOR]

Abstract: Identifying and ranking high pedestrian crash zones plays a key role in developing efficient and effective strategies to enhance pedestrian safety. This paper presents (1) a Geographical Information Systems (GIS) methodology to study the spatial patterns of pedestrian crashes in order to identify high pedestrian crash zones, and (2) an evaluation of methods to rank these high pedestrian crash zones. The GIS based methodology to identify high pedestrian crash zones includes geocoding crash data, creating crash concentration maps, and then identifying high pedestrian crash zones. Two methods generally used to create crash concentration maps based on density values are the Simple Method and the Kernel Method. Ranking methods such as crash frequency, crash density, and crash rate, as well as composite methods such as the sum-of-the-ranks and the crash score methods are used to rank the selected high pedestrian crash zones. The use of this methodology and ranking methods for high pedestrian crash zones are illustrated using the Las Vegas metropolitan area as the study area. Crash data collected for a 5-year period (1998–2002) were address matched using the street name/reference street name intersection location reference system. A crash concentration map was then created using the Kernel Method as it facilitates the creation of a smooth density surface when compared to the Simple Method. Twenty-two linear high crash zones and seven circular high crash zones were then identified. The GIS based methodology reduced the subjectivity in the analysis process. Results obtained from the evaluation of methods to rank high pedestrian crash zones show a significant variation in ranking when individual methods were considered. However, rankings of high pedestrian crash zones were relatively consistent with little to no variation when the sum-of-the-ranks method and the crash score method were used. Thus, these composite methods are recommended for use in ranking high pedestrian crash zones instead of individual methods. [Copyright &y& Elsevier]

Highlights • We proposed a dynamic state-space model with deep learning for traffic safety analyses. • Three modules have been developed for traffic safety evaluation, estimation, and prediction. • Relational information and differential distribution patterns in traffic crashes have been examined. • The proposed model can better address the heterogeneity issues and has superior performances. Abstract Since traffic crashes involve complex interactions among drivers, vehicles, roadway, traffic, and environmental elements and not all of the factors that could potentially determine the occurrences of traffic crashes can be observed and measured, new methods are needed to better perform traffic crash estimations and predictions and address the unobserved heterogeneity issues in crash data. Unlike the conventional methods, which generally are the statistical models with the observed crash counts as the dependent variables and the factors affecting the likelihood of a traffic crash as the independent variables, a dynamic state-space model with deep learning is proposed to analyze the traffic crashes. The proposed model includes three modules, an unsupervised feature learning module to identify functional network between the explanatory variables and the feature representations, a supervised fine tuning module to perform crash occurrence likelihood estimations, and a dynamic state-space module to perform crash count predictions. A multivariate Tobit model is incorporated in the supervised fine tuning module as the regression layer to account for the heterogeneity issues in correlated crash data. The results of deep learning are fed to the dynamic state-space model that contains a dynamic equation governing the state dynamics to improve the performances of estimation and prediction. The proposed model was applied to the dataset that was obtained from Knox County in Tennessee to validate the model effectiveness and efficiency. The results show that the proposed model has superior performances in terms of estimation and prediction power compared to the SVM and Random Forest (RF) models. The overall performances of the proposed model for all crashes show an 50.559% RMSD improvement over the SVM models and an 57.867% RMSD improvement over the RF models. The findings indicate that the feature learning module identifies relational information between the explanatory variables and feature representations, which reduces the dimensionality of the input and preserves the original information. The proposed model that includes a multivariate Tobit regression layer in the supervised fine tuning module can better account for differential distribution patterns in traffic crashes across injury severities and provides superior crash occurrence likelihood estimation results. The findings suggest that the proposed model can better address the heterogeneity issues in correlated crash data and is a superior alternative for traffic crash estimations and predictions. [ABSTRACT FROM AUTHOR]