Investigation on the wrong way driving crash patterns using multiple correspondence analysis

Lessons learned from characteristics of extraordinarily severe traffic crashes in China, 2004-2019

China has experienced remarkable achievements in terms of reducing the number of extraordinarily severe traffic crashes (ESTCs) that cause more than 10 deaths each crash. However, ESTCs still occur occasionally and result in extremely adverse social impacts. This study aims at investigating the common characteristics, characteristic patterns, and changes of characteristics of ESTCs in China with the expectation to learn from the past and act for the future. A total of 373 ESTCs occurred in 2004-2019 were collected, and characteristics of driver factors, road factors, vehicle factors, environment factors, and other factors were analyzed through the multiple correspondence analysis (MCA). The results show that run off road crashes, not qualified drivers, improper driving, large bus, overload, class II highway, and straight road sections are the most common categories of characteristics. In addition, four underlying characteristic patterns are identified through the MCA. Significant changes in characteristics and characteristic patterns are also found, and these changes are the results of various law enforcement, safety policies, educational interventions, and engineering interventions. It is also inferred that the specific law enforcement targeting to certain category of characteristics is more effective than the corresponding safety campaigns or policies in terms of ESTC prevention.

Stability of factors influencing walking-along-the-road pedestrian injury severity outcomes under different lighting conditions: A random parameters logit approach with heterogeneity in means and out-of-sample predictions

Pedestrians walking along the road's edge are more exposed and vulnerable than those on designated crosswalks. Often, they remain oblivious to the imminent perils of potential collisions with vehicles, making crashes involving these pedestrians relatively unique compared to others. While previous research has recognized that the surrounding lighting conditions influence traffic crashes, the effect of different lighting conditions on walking-along-the-road pedestrian injury severity outcomes remains unexplored. This study examines the variations in the impact of risk factors on walking-along-the-road pedestrian-involved crash injury severity across various lighting conditions. Preliminary stability tests on the walking-along-the-road pedestrian-involved crash data obtained from Ghana revealed that the effect of most risk factors on injury severity outcomes is likely to differ under each lighting condition, warranting the estimation of separate models for each lighting condition. Thus, the data were grouped based on the lighting conditions, and different models were estimated employing the random parameter logit model with heterogeneity in the means approach to capture different levels of unobserved heterogeneity in the crash data. From the results, heavy vehicles, shoulder presence, and aged drivers were found to cause fatal pedestrian walking-along-the-road severity outcomes during daylight conditions, indicators for male pedestrians and speeding were identified to have stronger associations with fatalities on roads with no light at night, and crashes occurring on Tuesdays and Wednesdays were likely to be severe on lit roads at night. From the marginal effect estimates, although some explanatory variables showed consistent effects across various lighting conditions in pedestrian walking-along-the-road crashes, such as pedestrians aged < 25 years and between 25 and 44 years exhibited significant variations in their impact across different lighting conditions, supporting the finding that the effect of risk factors are unstable. Further, the out-of-sample simulations underscored the shifts in factor effects between different lighting conditions, highlighting that enhancing visibility could play a pivotal role in significantly reducing fatalities associated with pedestrians walking along the road. Targeted engineering, education, and enforcement countermeasures are proposed from the interesting insights drawn to improve pedestrian safety locally and internationally.

Psychological predictors of risky driving: the role of age, gender, personality traits (Zuckerman's and Gray's models), and decision-making styles

The present study was planned to study the relationships between age, personality (according to Zuckerman's and Gray's psychobiological models) and decision-making styles in relation to risky driving behaviors. The participants were habitual drivers, 538 (54.3%) men and 453 (45.7%) women, with a mean age around 45 years and mainly of middle socioeconomic status. The results indicate that the youngest men and women reported more Lapses, Ordinary violations, and Aggressive violations than the oldest men and women. Women reported more Lapses (d = -0.40), and men more Ordinary (d = 0.33) and Aggressive violations (d = 0.28) when driving. Linear and non-linear analysis clearly support the role of both personality traits and decision-making styles in risky driving behaviors. Aggressiveness, Sensitivity to Reward, Sensation Seeking played the main role from personality traits, and Spontaneous and Rational decision-making style also accounted for some variance regarding risky driving behaviors. This pattern was broadly replicated in both genders. The discussion section analyses congruencies with previous literature and makes recommendations on the grounds of observed results.

Identification of high-risk roadway segments for wrong-way driving crash using rare event modeling and data augmentation techniques

Wrong-Way Driving (WWD) crashes are relatively rare but more likely to produce fatalities and severe injuries than other crashes. WWD crash segment prediction task is challenging due to its rare nature, and very few roadway segments experience WWD events. WWD crashes involve complex interactions among roadway geometry, vehicle, environment, and drivers, and the effect of these complex interactions is not always observable and measurable. This study applied two advanced Machine Learning (ML) models to overcome the imbalanced dataset problem and identified local and global factors contributing to WWD crash segments. Five years (2015-2019) of WWD crash data from Florida state were used in this study for WWD model development. The first modeling approach applied four different hybrid data augmentation techniques to the training dataset before applying the XGBoost classification algorithm. In the second model, a rare event modeling approach using the Autoencoder-based anomaly detection method was applied to the original data to identify WWD roadway segments. A third model was applied based on the statistical method to compare the performance of ML models in predicting the WWD segments. The performance comparison of the adopted models showed that the XGBoost model with the Adaptive Synthetic Sampling (ADASYN) method performed best in terms of precision and recall values compared to the autoencoder-based anomaly detection method. The best-performing model was used for the feature analysis with an interpretable machine-learning technique. The SHapley Additive exPlanations (SHAP) values showed that high-intensity developed land use, length of roadway, log of Annual Average Daily traffic (AADT), and lane width were positively associated with WWD roadway segments. The results of this study can be used to deploy WWD countermeasures effectively.

Understanding the drowsy driving crash patterns from correspondence regression analysis

Drowsy driving-related crashes have been a key concern in transportation safety. In Louisiana, 14% (1,758 out of 12,512) of police-reported drowsy driving-related crashes during 2015-2019 resulted in injury (fatal, severe, or moderate). Amid the calls for action against drowsy driving by national agencies, it is of paramount importance to explore the key reportable attributes of drowsy driving behaviors and their potential association with crash severity.

METHOD

This study used 5-years (2015-2019) of crash data and utilized the correspondence regression analysis method to identify the key collective associations of attributes in drowsy driving-related crashes and interpretable patterns based on injury levels.

RESULTS

Several drowsy driving-related crash patterns were identified through crash clusters - afternoon fatigue crashes by middle-aged female drivers on urban multilane curves, crossover crashes by young drivers on low-speed roadways, crashes by male drivers during dark rainy conditions, pickup truck crashes in manufacturing/industrial areas, late-night crashes in business and residential districts, and heavy truck crashes on elevated curves. Several attributes - scattered residential areas indicating rural areas, multiple passengers, and older drivers (aged more than 65 years) - showed a strong association with fatal and severe injury crashes.

PRACTICAL APPLICATIONS

The findings of this study are expected to help researchers, planners, and policymakers in understanding and developing strategic mitigation measures to prevent drowsy driving.

Identifying Potential Factors Associated with High HIV viral load in KwaZulu-Natal, South Africa using Multiple Correspondence Analysis and Random Forest Analysis

Background

Sustainable Human Immunodeficiency Virus (HIV) virological suppression is crucial to achieving the Joint United Nations Programme of HIV/AIDS (UNAIDS) 95–95-95 treatment targets to reduce the risk of onward HIV transmission. Exploratory data analysis is an integral part of statistical analysis which aids variable selection from complex survey data for further confirmatory analysis.

Methods

In this study, we divulge participants’ epidemiological and biological factors with high HIV RNA viral load (HHVL) from an HIV Incidence Provincial Surveillance System (HIPSS) sequential cross-sectional survey between 2014 and 2015 KwaZulu-Natal, South Africa. Using multiple correspondence analysis (MCA) and random forest analysis (RFA), we analyzed the linkage between socio-demographic, behavioral, psycho-social, and biological factors associated with HHVL, defined as ≥400 copies per m/L.

Results

Out of 3956 in 2014 and 3868 in 2015, 50.1% and 41% of participants, respectively, had HHVL. MCA and RFA revealed that knowledge of HIV status, ART use, ARV dosage, current CD4 cell count, perceived risk of contracting HIV, number of lifetime HIV tests, number of lifetime sex partners, and ever diagnosed with TB were consistent potential factors identified to be associated with high HIV viral load in the 2014 and 2015 surveys. Based on MCA findings, diverse categories of variables identified with HHVL were, did not know HIV status, not on ART, on multiple dosages of ARV, with less likely perceived risk of contracting HIV and having two or more lifetime sexual partners.

Conclusion

The high proportion of individuals with HHVL suggests that the UNAIDS 95–95-95 goal of HIV viral suppression is less likely to be achieved. Based on performance and visualization evaluation, MCA was selected as the best and essential exploration tool for identifying and understanding categorical variables’ significant associations and interactions to enhance individual epidemiological understanding of high HIV viral load. When faced with complex survey data and challenges of variables selection in research, exploratory data analysis with robust graphical visualization and reliability that can reveal divers’ structures should be considered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12874-022-01625-6.

Temporal stability of associations between crash characteristics: A multiple correspondence analysis

Understanding the associations between crash characteristics facilitates the development of traffic safety policies for improving traffic safety. This study investigates the temporal stability of associations between crash characteristics at different temporal levels using multiple correspondence analysis (MCA). For each date in 2020, crash data from the previous week, month, season, half year, one year, two years, three years, and four years are collected respectively as eight temporal levels. MCA plots and chi-square distance analysis are used to assess the temporal stability of associations between crash characteristics across dates in 2020 with data from various temporal levels. The key findings of this study demonstrate that associations between crash characteristics at lower temporal levels show notable and potential cyclical variations across dates, while more stable and long-term trend of associations between crash characteristics may be identified as the temporal level increases, especially at the two-year level and higher temporal levels at which temporal stability may be expected. The study contributes to the literature by presenting a challenge for traffic analysts in that both temporally stable and unstable associations between crash characteristics may be observed at any point in time when different temporal levels are considered as study periods. Therefore, it may serve as a foundation for future research and practical works to identify traffic safety issues and optimal policies as well as facilitate the interpretation of statistical modeling in the presence of temporally unstable data.

The Use of Multiple Correspondence Analysis to Explore Associations Between Categories of Qualitative Variables and Cancer Incidence

BACKGROUND

Previous works have shown that risk factors for some kinds of cancer depend on people's lifestyle (e.g. rural or urban residence). This article looks into this, seeking relationships between cancer, age group, gender and population in the region of Lleida (Catalonia, Spain) using Multiple Correspondence Analysis (MCA).

METHODS

The dataset analysed was made up of 3408 cancer episodes between 2012 and 2014, extracted from the Population-based Cancer Registry (PCR) for Lleida province. The cancers studied were colon and rectal (1059 cases), lung (551 cases), urinary bladder (446 cases), prostate (609 cases) and breast (743 cases). The MCA technique was applied and used to search relationships among the main qualitative features. The basic statistics were the percentage explaining (variance), the inertia and the contribution of each qualitative variable.

RESULTS

General outcomes showed a low and moderate contribution of living in rural areas to colorectal and male prostate cancer. Males in urban areas were slightly and heavily affected by lung and urinary bladder cancer respectively. The analysis of each cancer provided additional information. Colorectal cancer greatly affected males aged <60, urban residents aged 70-79, and rural females aged ≥ 80. The impact of lung cancer was high among urban females <60, moderate among males aged 70-79 and high among rural females aged ≥ 80. The results for urinary bladder cancer results were similar to those for lung cancer. Prostate cancer affected both the <60 and ≥ 80 age groups significantly in rural areas. Breast cancer hit the 70-79 group significantly and, somewhat less so, rural females aged ≥ 80.

CONCLUSIONS

MCA was a significant help for detecting the contributions of qualitative variables and the associations between them. MCA has proven to be an effective technique for analyzing the incidence of cancer. The outcomes obtained help to corroborate suspected trends, as well as detecting and stimulating new hypotheses about the risk factors associated with a specific area and cancer. These findings will be helpful for encouraging new studies and prevention campaigns to highlight observed singularities.

Space-Time Cluster's Detection and Geographical Weighted Regression Analysis of COVID-19 Mortality on Texas Counties

As COVID-19 run rampant in high-density housing sites, it is important to use real-time data in tracking the virus mobility. Emerging cluster detection analysis is a precise way of blunting the spread of COVID-19 as quickly as possible and save lives. To track compliable mobility of COVID-19 on a spatial-temporal scale, this research appropriately analyzed the disparities between spatial-temporal clusters, expectation maximization clustering (EM), and hierarchical clustering (HC) analysis on Texas county-level. Then, based on the outcome of clustering analysis, the sensitive counties are Cottle, Stonewall, Bexar, Tarrant, Dallas, Harris, Jim hogg, and Real, corresponding to Southeast Texas analysis in Geographically Weighted Regression (GWR) modeling. The sensitive period took place in the last two quarters in 2020 and the first quarter in 2021. We explored PostSQL application to portray tracking Covid-19 trajectory. We captured 14 social, economic, and environmental impact's indices to perform principal component analysis (PCA) to reduce dimensionality and minimize multicollinearity. By using the PCA, we extracted five factors related to mortality of COVID-19, involved population and hospitalization, adult population, natural supply, economic condition, air quality or medical care. We established the GWR model to seek the sensitive factors. The result shows that adult population, economic condition, air quality, and medical care are the sensitive factors. Those factors also triggered high increase of COVID-19 mortality. This research provides geographical understanding and solution of controlling COVID-19, reference of implementing geographically targeted ways to track virus mobility, and satisfy for the need of emergency operations plan (EOP).

Modeling Wrong-way Driving (WWD) crash severity on arterials in Florida

Wrong-way Driving (WWD) is the movement of a vehicle in a direction opposite to the one designated for travel. WWD studies and mitigation strategies have exclusively been focused on limited-access facilities. However, it has been established that WWD crashes on arterial corridors are also severe and relatively more common. As such, this study focused on determining factors influencing the severity of WWD crashes on arterials. The analysis was based on five years of WWD crashes (2012-2016) that occurred on state-maintained arterial corridors in Florida. Police reports of 2,879 crashes flagged as "wrong-way" were downloaded and individually reviewed. The manual review of the police reports revealed that of the 2,879 flagged WWD crashes, only 1,890 (i.e., 65.6 %) occurred as a result of a vehicle traveling the wrong way. The Bayesian partial proportional odds (PPO) model was used to establish the relationship between the severity of these WWD crashes and different driver attributes, temporal factors, and roadway characteristics. The following variables were significant at the 90 % Bayesian Credible Interval (BCI): day of the week, lighting condition, presence of work zone, crash location, age and gender of the wrong-way driver, airbag deployment, alcohol use, posted speed limit, speed ratio (i.e., driver's speed over the posted speed limit), and the manner of collision. Based on the model results, specific countermeasures on Education, Engineering, Enforcement, and Emergency response are discussed. Potential Transportation Systems Management and Operations (TSM&O) strategies for WWD detection systems on arterials to minimize WWD frequency and severity are also proposed.

Characterizing phone usage while driving: Safety impact from road and operational perspectives using factor analysis

Phone use while driving (PUWD) is one of the most crucial factors of distraction related traffic crashes. This study utilized an unsupervised learning method, known as factor analysis, on a unique distracted driving dataset to understand PUWD behavior from the roadway geometry and operational perspectives. The results indicate that the presence of a shoulder, median, and access control on the relatively higher functional class roadways could encourage more PUWD events. The roadways with relatively lower speed limits could have high PUWD event occurrences if the variation in operating speed is high. The results also confirm the correlations between the frequency of PUWD events and the frequency of distracted crashes. This relationship is strong on urban roadways. For rural roadways, this correlation is only strong on the roadways with a large amount of PUWD events. The findings could help transportation agencies to identify suitable countermeasures in reducing distraction related crashes. Moreover, this study provides researchers a new perspective to study PUWD behavior rather than only focus on drivers' personalities.

A Review of Data Analytic Applications in Road Traffic Safety. Part 1: Descriptive and Predictive Modeling

This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream.

Investigation of pedestrian crashes using multiple correspondence analysis in India

Pedestrian safety is of growing concern with an increasing number of traffic accidents, especially in developing economies like India. In 2017, there were 20,457 pedestrian fatalities in India. Pedestrian crashes have also become a key concern in the state of Tamilnadu, India, due to the high percentage of deaths. If the available datasets are large and complex, identifying key factors is a challenging task. In this study, Multiple Correspondence Analysis (MCA), an exploratory data analysis technique was used to explore the roadway, traffic, crash, and pedestrian-related variables influencing pedestrian crashes. This study used the data from Government of Tamilnadu Road Accident Traffic Management System (RADMS) database, to analyse accident data of nine years (2009-2017) related to pedestrian crashes. The results of the study show that crashes occurring on the express highways on a multilane road are often associated with hit-and-run behaviour among drivers. Factors such as lighting conditions, location, pedestrian behaviour, crossings, and physical separation are also significantly contributing to pedestrian crashes. The key advantage of MCA is that it identifies a possible association between various contributing factors. The findings from this study will be useful for state transport authorities to improve countermeasures for mitigating pedestrian crashes and fatalities.

Understanding crash potential associated with teen driving: Survey analysis using multivariate graphical method

INTRODUCTION

Teen crash involvement is usually higher than other age groups, and they are typically overrepresented in car crashes. To infer teen drivers' understanding of crash potentials (factors that are associated with crash occurrence), two sources of data are generally used: retrospective data and prospective data. Retrospective data sources contain historical crash data, which have limitations in determining teen drivers' knowledge of crash potentials. Prospective data sources, like surveys, have more potential to minimize the research gap. Prior studies have shown that teen drivers are more likely to be involved in crashes during their early driving years. Thus, there is a benefit in examining how teen drivers' understanding of crash potentials change during their transition through licensing stages (i.e., no licensure to unrestricted licensure).

METHOD

This study used a large set of teen driver survey data (a dataset from approximately 88,000 respondents) of Texas teens to answer the research question. Researchers provided rankings of the crash potentials by gender and licensure stages using a multivariate graphical method named taxicab correspondence analysis (TCA).

RESULTS

The findings show that driving behavior and understanding of crash potentials differ among teens based upon various licensing stages. Practical applications: Findings from this study can help government authorities to refine policies of teen driver licensing and implement potential countermeasures for safety improvement.

An evaluation of wrong-way crashes from highway ramps in Kansas, USA

Wrong-way crashes on limited access highways continue to be a serious problem for state highway agencies and communities. These crashes are more likely to result in fatalities or serious injuries than other traffic incidents, due to vehicles travelling at high speeds, often involved on a roadway with limited vertical space and time to avoid a crash. This research study focused on wrong-way crashes that occurred on highways in the State of Kansas. Although these crashes represent a very small portion of crashes, wrong-way crashes were found to have a higher rate of fatalities and injuries as compared to other crash types. Using ten years of crash data, it was found that a typical wrong-way crash occurred under no adverse weather conditions, at a non-intersection location, in daylight or with streetlights present, involved alcohol or drugs, and resulted in a fatality or serious injury. An ordinary logistic model was developed to identify significant characteristics of wrong-way crashes. The model indicated that drivers under the influence of alcohol or drugs was found to be a significant in both fatal and injury wrong-way crashes. It was also found that certain lighting conditions were also significant, along with use of safety equipment and drivers over the age of 55 years old. Additional research is needed to further investigate wrong-way crashes and their causalities in Kansas on other roadway facilities including rural at-grade divided intersections.