Analytic Methods in Accident Research最新文献

With rapid growth in motorcycle use and relatively low helmet-wearing usage rates, injuries and fatalities resulting from motorcycle crashes in Pakistan are a critical concern. To investigate possible temporal instability and differences in the factors that determine resulting injury severities between male and female non-helmet wearing motorcyclists, this study estimated male and female injury severity models using a random parameter logit approach with heterogeneity in means and variances. Motorcycle crash data between 2017 and 2019 from Rawalpindi, Pakistan, were used for the model estimation. With four possible crash injury severity outcomes (injury, minor injury, severe injury, and fatal injury), a wide variety of explanatory variables were considered, including the characteristics of riders, vehicles, roadways, environments, crashes, and temporal considerations. Temporal shifts in the effects of explanatory variables were confirmed using a series of likelihood ratio tests. While the effects of several explanatory variables are relatively temporally stable, those of most variables vary considerably across the years. In addition, out-of-sample simulations underscore the temporal shifts from year to year and the differences between male and female motorcyclist-injury severity. The findings suggest that factors such as effective enforcement countermeasures and relevant educational campaigns can be implemented to reduce injury severity. The statistically significant differences between male and female non-helmeted injury severity models underscore the importance of policies that separately target male and female motorcycle rider safety.

Toll plaza is a designated area of controlled-access roads like expressway, bridge, and tunnel for toll collection. A number of toll booths are often placed at the toll plaza accommodating high passing traffic and multiple payment methods. Traffic and safety characteristics of toll plazas are different from that of other road entities. Different conflict risk indicators, which are usually longitudinal, have been adopted for real-time safety assessment. In this study, correlated grouped random parameter logit models with heterogeneity in the means are established to capture the unobserved heterogeneity, with additional flexibility, at road user level for the association between conflict risk and influencing factors. In addition, modified conflict risk indicator is developed to assess the safety of diverging, merging, and weaving movements of traffic, with which vehicles’ dimensions (width and length), and longitudinal and angular movements are considered. Also, prevalence and severity of both rear-end and sideswipe conflicts are assessed. Results indicate that toll collection type, vehicle’s location, average longitudinal speed, angular speed, acceleration, and vehicle class all affect the risk of traffic conflicts. Furthermore, there are significant correlation among the random parameters of severe traffic conflicts. Proposed analytic method can accommodate the conflict risk analysis for different conflict types and account for the correlation of unobserved heterogeneity. Findings should shed light on appropriate remedial measures like traffic signs, road markings, and advanced traffic management system that can improve the safety at tunnel toll plazas.

The real-time crash potential prediction model is one of the important components of proactive traffic management systems. Over the years numerous models have been proposed to predict crash potential and achieved promising results using input data from roadside detectors. However, the detectors are normally installed at certain locations with limited coverage, while the connected vehicle data can provide city-wide mobility information. Previous studies have found that driver event variables such as hard braking, hard accelerations, etc. are correlated with crash potential on the road segments. Nevertheless, the existing studies are mostly conducted at the aggregated level, and the data are mostly collected from commercial vehicles such as taxis or buses traveling in the urban areas. This paper proposes a bidirectional long short-term memory (LSTM) model with two convolutional layers to predict real-time crash potential on freeways. The input data including traffic flow variables from detectors, and driver event variables from connected vehicle (CV) data, are aggregated at the one-minute level. The model achieves a recall value of 0.772 and an AUC value of 0.857. Moreover, to investigate the transferability of the proposed model, the original data are aggregated at the hourly level. The transferred model is developed with fine tuning two convolutional layers of the established model. And the transferred model achieves a recall value of 0.715 and an AUC value of 0.763. This proves that the proposed model can be successfully applied to another similar data set, or when the connected vehicles have lower penetration rate. In this study, we proved the usefulness of the connected vehicle data in the prediction of real-time crash potential, and the possibility of using it without detector data once the penetration rate increases to a reasonable level.

This paper examines the differences between weekday, weekend, and holiday crashes on the severity of motorcyclist injury using four-year motorcycle crash data in Thailand from 2016 to 2019. While also considering the temporal stability assessment of significant factors, this study adopted a random parameters logit model with possible heterogeneity in means and variances to account for unobserved heterogeneity. Three levels of motorcyclist injury severity were considered including minor injury, severe injury, and fatal injury. Two series of likelihood ratio tests clearly indicated nontransferability between weekday, weekend, and holiday crashes and substantial temporal instability over the four-year study period. Findings also revealed many statistically significant factors that affect motorcyclist injury severity probabilities in various time-of-year and yearly models. In addition, the prediction comparison results (using out-of-sample prediction simulation) clearly illustrated substantial differences between weekday, weekend, and holiday motorcyclist injury severity probabilities, and substantial changes in each injury predicted probabilities over time. This paper highlights the importance of accounting for day-of-week and holiday transferability and temporal instability with unobserved effects in the determinants that affect motorcyclist injury severity. Through the observed nontransferability and temporal instability, the findings provide valuable knowledge for practitioners, researchers, institutions, and decision-makers to enhance highway safety, specifically motorcyclist safety, and facilitate the development of more effective motorcycle crash injury mitigation policies.

Animal-vehicle collisions (AVCs) are common around the world and result in considerable loss of animal and human life, as well as significant property damage and regular insurance claims. Understanding their occurrence in relation to various contributing factors and being able to identify high-risk locations are valuable to AVC prevention, yielding economic, social, and environmental cost savings. However, many challenges exist in the study of AVC datasets. These include seasonality of animal activity, unknown exposure (i.e., the number of animal crossings), very low AVC counts across most sections of extensive roadway networks, and computational burdens that come with discrete response analysis using large datasets. To overcome these challenges, a Bayesian hierarchical model is proposed where the exposure is modeled with nonparametric Dirichlet process, and the number of segment-level AVCs is assumed to follow a binomial distribution. A Pólya-Gamma augmented Gibbs sampler is derived to estimate the proposed model. By using the AVC data of multiple years across about 85,000 segments of state-controlled highways in Texas, U.S., it is demonstrated that the model is scalable to large datasets, with a preponderance of zeros and clear monthly seasonality in counts, while identifying high-risk locations and key explanatory factors based on segment-specific factors (such as changes in speed limit). This can be done within the modelling framework, which provides useful information for policy-making purposes.

Extreme value theory is the state-of-the-art modelling technique for estimating crash risk from traffic conflicts, with two different sampling techniques, i.e. block maxima and peak-over-threshold, at its core. However, the uncertainty associated with the estimates obtained by these sampling techniques has been too large to enable its widespread practical use. A fundamental reason for this issue is the improper selection of extreme values and a lack of a suitable and efficient sampling mechanism. This study proposes a hybrid modelling framework of machine learning and extreme value theory to estimate crash risk from traffic conflicts with an efficient sampling technique for identifying extremes. More specifically, a machine learning approach replaces the conventional sampling techniques with anomaly detection techniques since an anomaly is a data point that does not conform with the rest of the data, making it very similar to the definition of an extreme value. Six representative machine learning-based unsupervised anomaly detection algorithms have been tested in this study. They include iforest, minimum covariance determinant, one-class support vector machine, k-nearest neighbours, local outlier factor, and connectivity-based outlier factor. The extremes identified by these algorithms are then fitted to extreme value distributions for both univariate and bivariate frameworks. These algorithms were tested on a large set of traffic conflict data collected for four weekdays (6 am to 6 pm) from three four-legged intersections in Brisbane, Australia. Results indicate that the proposed hybrid models consistently outperform the conventional extreme value models, which use block maxima and peak-over-threshold as the underlying sampling technique. Among the sampling algorithms, iforest has been found to perform better than other algorithms in estimating crash risks from traffic conflicts. The proposed hybrid modelling framework represents a methodological advancement in traffic conflict-based crash estimation models and opens new avenues for exploring the possibility of utilising machine learning techniques within the existing traffic conflict techniques.

Speeding is one of the major significant causes of high crash risk and the associated injury severity outcomes. To combat such significant safety concerns, a speed limit enforcement system has been adopted widely around the world. This study aims to present an econometric approach that estimates the casual effect of speed enforcement on safety while addressing the endogeneity issue by employing an instrumental variable approach within a maximum simulated likelihood framework. In our study, safety enforcement is represented as the number of speeding tickets issued from the speed camera systems, while safety profile is presented as two dimensions of interest, including total crash risk and crashes by injury severity levels. The proposed econometric model takes the form of a correlated panel random parameters model with speed enforcement endogeneity. In estimating the joint panel model, speed enforcement and crash severity components are modeled by employing Random Parameters Ordered Logit Fractional Split technique, while ‘ is modeled by employing Random Parameters Negative Binomial regression technique. In the current study context, the ‘operational duration of speed camera’ serves as the instrumental variable for controlling the endogeneity between speed enforcement and safety. Further, the analysis is augmented by a detailed policy scenario analysis. The empirical analysis is demonstrated by employing roadway segment-level crash data and speeding tickets data from Queensland, Australia, for the years 2010 through 2013. From the policy analysis, it is found that a stricter speed enforcement for serious level of speeding offenses is likely to have greater safety benefits in reducing crash severity levels. Moreover, a targeted increase in operation duration along with stricter citations for major speeding is likely to have significant safety gain. The outcome of the study will allow the decision-makers to identify a robust resource allocation and speed camera deployment plan.

Safety literature has traditionally developed independent model systems for macroscopic and microscopic level analysis. The current research effort contributes to the literature on crash frequency by building a bridge between these two divergent streams of crash frequency research. The study proposes an integrated micro–macro level model for crash frequency estimation. Specifically, the study develops an integrated model system that allows for the influence of independent variables at the microscopic level to be incorporated within the macroscopic propensity estimation. The empirical analysis is based on the data drawn from 300 traffic analysis zones, 1818 roadway segments, and 4184 intersections from the City of Orlando, Florida for the years 2018 and 2019. The study considers a host of exogenous variables including roadway and traffic factors, land-use, built environment, and sociodemographic characteristics for the model estimation. The proposed model system can also accommodate for hierarchical correlations such as correlation between all segments or intersections in a zone. The study findings highlight the presence of common spatial unobserved factors influencing crash frequency across segment level and intersection level as well as presence of significant parameter variability across both micro and macro level in the crash frequency. The empirical analysis is further augmented by employing several goodness of fit and predictive measures. The results clearly demonstrate the improved performance offered by the proposed integrated micro–macro model relative to the non-integrated macro model. The overall model fit measures and interpretations encourage the application of the proposed model for crash frequency analysis.

Sleepiness is a common human factor among truck drivers resulting from sleep loss or time of day and causing impairment in vigilance, attention, and driving performance. While driver sleepiness may be associated with increased risk on the road, sleepy drivers may drive more cautiously as a result of risk-compensating behaviour. This endogeneity has been overlooked in the previous driver behaviour studies and may provide new insight into the effects of sleepiness on driving performance. In addition, the Karolinska Sleepiness Scale (KSS) has been widely used to quantify sleepiness. However, the KSS is a subjective self-reported measure and is reliant on honest reporting and understanding of the scale. An alternative way of quantifying sleepiness is using drivers’ heart rate and correlating it with their sleepiness. While recent advances in data collection technologies have made it possible to collect heart rate data in real-time and in an unobtrusive way, their application in measuring sleepiness particularly among truck drivers has been unexplored.

This study aims to address these gaps and contribute to analytic methods in road safety research by collecting truck drivers’ heart rate data in real-time, measuring sleepiness from those data, and using it in an instrumental variable modelling framework to investigate its effect on driving performance. To this end, a driving simulator experiment was conducted in Belgium and heart rate data were collected for 35 truck drivers via sensors installed on the steering wheel of the simulator. Additional demographic data were collected using a questionnaire before the experiment. An instrumental variable model consisting of a discrete binary logit and a continuous generalized linear model with grouped random parameters and heterogeneity in their means was then developed to study the effects of driver sleepiness on headway. Results indicate that age, years of holding driver licence, road type, type of truck transport, and weekly distance travelled are significantly associated with sleepiness among the participants of this study. Sleepy driving is associated with reduced headway for 30.5% of the drivers and increased headway for the other 69.5%, and night-time shift is associated with such varied effects. These findings indicate that there may be group- or context-specific risk patterns which cannot be explicitly addressed by hours of service regulations and therefore, transport operators, driver trainers and fleet managers should identify and handle such context-specific high risk patterns in order to ensure safe operations.