Search

This study provides an approach to identify driving style of truck drivers by using GPS, load condition, and in-vehicle monitoring data and investigates the association of driving styles with risky driving behaviors from macro and micro perspectives. The naturalistic driving data used in this study were collected from 4,357 trucks in Hangzhou, China over three months in 2021. Six driving volatility parameters and six warning parameters were used to characterize the driving styles. Then, three driving styles under the two load conditions were identified using k-means clustering methods and principal component analysis. Finally, one-way MANOVA and ANOVA were used to analyze the relationship between driving styles and driving risk. It was found that truck drivers have different thresholds for aggressive and cautious driving style under different load conditions. Truck drivers who exhibited aggressive driving under both load conditions exhibited high driving risk. Although most truck drivers exhibited safe or normal driving under both conditions, the few who exhibited aggressive driving contribute to a disproportionate driving risk. These results can help distinguish differences in truck drivers' driving styles under different load conditions, thus providing a more comprehensive safety assessment of truck drivers' performance for monitoring purposes. [ABSTRACT FROM AUTHOR]

Aggressive driving significantly impacts traffic safety, and heavy‐duty vehicle drivers are more liable for causing serious crashes. This paper analyzes drivers' aggressive driving behavior from the vehicle type perspective and identifies the influencing factors of aggressive driving behavior through artificial neural network (ANN) and Shapley additive explanations (SHAPs). Using Kaggle's open‐source aggressive driving data, we establish an ANN model to identify driving styles, where road conditions, environmental conditions, and vehicle parameters are independent variables and driving style is a dependent variable. The following measurements, including accuracy, recall, precision, and F1 score, are used to evaluate the model's performance, and the neural network got 85.33%, 82.32%, 84.16%, and 0.8308, respectively. To illustrate the influence of independent variables, the SHAP algorithm is used to analyze the model's feature importance. It was found that illumination and weather conditions influenced the model's performance along with the vehicle length. The number of lanes relates to driving style, and there were more aggressive driving behaviors on two‐lane roads than on single‐lane roads. Besides, heavy‐duty vehicle drivers were more likely to drive aggressively in wet road conditions and indulge in aggressive driving behaviors at night. Particularly, drivers of heavy‐duty vehicles were more likely to drive aggressively, provided that the vehicle in front was also a heavy‐duty vehicle. These findings inform heavy‐duty vehicle drivers to reduce aggressive driving behavior. The information is suitable for inclusion in driver education programs, thus improving traffic safety. [ABSTRACT FROM AUTHOR]

Featured Application: In this study, the impact of errors and missing information in the inventory data of highway–rail grade crossings (HRGCs) on the crash frequency prediction models currently used in the US is investigated. Transportation authorities and safety regulators can apply the insights gained from this research to enhance the accuracy of crash frequency prediction models for HRGCs. By utilizing more accurate and complete inventory data, these models provide more reliable crash frequency predictions, enabling better resource allocation and more targeted safety interventions. The highway–rail grade crossings (HRGCs) crash frequency models used in the US are based on the Federal Railroad Administration's (FRA) database for highway–rail crossing inventory. Inaccuracies or missing values within this database directly impact the estimated parameters of the crash models and subsequent crash predictions. Utilizing a set of 560 HRGCs in Nebraska, this research demonstrates variations in crash predictions estimated by the FRA's 2020 Accident Prediction (AP) model under two scenarios: firstly, employing the unchanged, original FRA HRGCs inventory dataset as the input, and secondly, utilizing a field-validated inventory dataset for the same 560 HRGCs as input to the FRA's 2020 Accident Prediction (AP) model. The findings indicated a significant statistical disparity in the predictions made with the two input datasets. Furthermore, two new Zero-inflated Negative Binomial (ZINB) models were estimated by employing 5-year reported HRGCs crashes and the two inventory datasets for the 560 HRGCs. These models facilitated the comparison of model parameter estimates and estimated marginal values. The results indicated that errors and missing values in the original FRA HRGCs inventory dataset resulted in crash predictions that statistically differed from those made using the more accurate and complete (validated in the field) HRGCs inventory dataset. Furthermore, the crash prediction model estimated upon the corrected inventory data demonstrated enhanced prediction performance, as measured by the statistical fitness criteria. The findings emphasize the importance of collecting complete and accurate inventory data when developing HRGCs crash frequency models. This will enhance models' precision, improve their predictive capabilities to aid in better resource allocation, and ultimately reduce HRGCs crashes. [ABSTRACT FROM AUTHOR]

This paper describes a new method of safety assessment of highway stopping and passing sight distances. Topographic data were collected by flying a plane equipped with a light detection and ranging (LiDAR) system on a section of two-lane rural highway. The collected data were manipulated in a geographic information system (GIS) to obtain three-dimensional models of the various highway subsections. These models were visually analyzed to check for stopping and passing sight distances, thus narrowing the list of locations with potential safety issues. Subsequent line-of-sight analysis in a GIS indicated the presence of several obstructions, prompting a field visit for validation. Field inspection of the locations verified the presence of the obstructions detected during the analysis. Overall, this research indicated the viability of successful visualization of LiDAR data for highway sight distance evaluations and is indicative of LiDAR's potential for highway safety-related applications. Comments on the resources needed to embrace this technology and future research improvements are part of the paper. CE Database subject headings: Geographic information systems; Highway safety; Sight distances; Data collection; Imaging techniques.

The population of the U.S. is aging, and the number of older persons licensed to drive keeps increasing. With the aging population and increase in the number of older licensed drivers, addressing safety issues related to older drivers is becoming more crucial every day. This paper reports on the analysis of 1990-1999 crash data from the State of Iowa in which an older driver (age [greater than or equal to] 65 years) was injured. The main focus of the study was to isolate factors that contribute to more severe injuries to older drivers involved in traffic crashes. The ordered probit modeling technique was used to investigate factors from vehicle, roadway, driver, crash, and environmental characteristics that can potentially contribute to older driver crash injury severity. Model findings were primarily as expected. New findings from this study were that older drivers who consumed alcohol were more likely to be seriously injured and older driver injuries in farm vehicles were more severe as compared with other types of vehicles. The writers discuss implications of the findings for the safety of older drivers. CE Database keywords: Driver behavior; Age factors; Traffic accidents.

To meet the growing demand of accurate and reliable travel time information in intelligent transportation systems, this a develops a deep architecture incorporating contextual information to estimate travel time in urban road network from a citywide perspective. First, several categories of features that affect travel time significantly are analyzed and extracted. On this basis, a deep architecture, which utilizes sparse denoising auto-encoders as building blocks, is proposed to learn the feature representations for travel time estimation. To train the deep architecture successfully, a greedy layer-wise semi-unsupervised learning algorithm is devised. The proposed approach inherently incorporates both the geographical features and contextual features, and accounts for the spatial correlation of adjacent road segments. It is a deep architecture with powerful modeling capabilities for the complex nonlinear phenomena in transportation. The information contained in the huge amount of unlabeled data are fully extracted and utilized. The feature representations for estimation are adaptively learned layer by layer from the input with an unsupervised fashion. The proposed model is applied to the real case study of the road network in Beijing, China, based on the large-scale GPS trajectories collected from a sample of taxicabs. Empirical results on extensive experiments demonstrate that the novel deep architecture provides a promising and robust approach for citywide travel time estimation, and outperforms the competing methods. [ABSTRACT FROM AUTHOR]

This research studied potential factors associated with pedestrian injury severity levels sustained in train-pedestrian crashes at highway-rail grade crossings (HRGCs) using the Federal Railroad Administration's ten-year data. The analysis focused on nonsuicide pedestrian crashes and took into consideration the unobserved heterogeneity. Latent class clustering (LCC) addressed unobserved heterogeneity by creating distinct subgroups with relatively homogeneous attributes within each subgroup. HRGC inventory variables were the basis for the LCC; the process split the dataset into five distinguished clusters. Binary logit models for each cluster and the complete data set were estimated. A generalized linear mixed model, based on the complete data set, allowed examination of the clustering and comparison of the modeling results. Findings provided justification for the use of LCC as the first step in accounting for unobserved heterogeneity. Different HRGC, pedestrian, and crash characteristics were associated with pedestrian injury severity across different clusters. Higher train speed was associated with more severe injury propensity, regardless of the conditions of the HRGCs. Other variables including freight train involvement, train hitting pedestrian, HRGCs with the absence of flashing lights, advance warnings, rural areas, lower visibility conditions, and older pedestrians increased pedestrian injury severity levels with varying effects in different clusters. [ABSTRACT FROM AUTHOR]

Mobile mapping systems (MMSs) are based on advanced technologies for automatically collecting roadway inventory data. A host of advanced technologies, such as the Global Positioning System for collecting georeferencing data and digital cameras for collecting roadway data, are used in current MMSs. An MMS is driven on a subject roadway, collecting positional data and digital images of the roadway. Through photogrammetric software packages, roadway data are extracted from images. Although there have been studies evaluating the accuracy of positional data collected by MMSs, there have been limited studies of the descriptive data accuracy they provide. In this paper the results of an evaluation of the accuracy of descriptive inventory data collected by three different MMSs are discussed. Each system was tested in three different roadway environments, and five different types of inventory elements were included in each test. The accuracy of collected data was compared with ground truth data. Analysis of these data indicated that the roadway environment and the type of inventory element affect the accuracy of descriptive inventory data collected by MMSs.

The objective of this article is to quantitatively investigate the impact of motor vehicle driver inattention on severity of driver's injuries sustained in crashes reported at or near highway-rail grade crossings (HRGCs). The Nebraska Department of Roads (USA) supplied 2002 to 2013 data on reported motor vehicle crashes at or near HRGCs. Besides information on driver behavior, the data set included information on seatbelt usage, presence of passengers, driver's age, gender, weather, train involvement, highway speed limit, road surface condition, and lighting condition. Random parameters binary logit regression models were used to investigate of the role of inattentive driving in severity of injuries sustained by motor vehicle drivers. The main findings were that driver inattention led to more severe injuries compared to attentive driving in vicinity of HRGCs. The probability of a driver suffering an injury in a single-vehicle crash increased by 9.7% when the driver was inattentive; in two-vehicle crashes the probability of at least one injured driver increased by 14.6% when any one of the involved drivers was inattentive to the driving task. The effects of inattentive driving on driver injury outcomes were not statistically different than the effects of driving under influence of alcohol/drugs or aggressive driving. [ABSTRACT FROM AUTHOR]