Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

2021 ◽  
pp. 036119812110312
Author(s):  
Denis Elia Monyo ◽  
Henrick J. Haule ◽  
Angela E. Kitali ◽  
Thobias Sando
Keyword(s):  
Latent Class ◽  
Time Of Day ◽  
Older Drivers ◽  
Contributing Factors ◽  
Lighting Condition ◽  
Crash Data ◽  
Traffic Characteristics ◽  
Road Users ◽  
Traffic Conflicts ◽  
Penalized Logistic Regression

Older drivers are prone to driving errors that can lead to crashes. The risk of older drivers making errors increases in locations with complex roadway features and higher traffic conflicts. Interchanges are freeway locations with more driving challenges than other basic segments. Because of the growing population of older drivers, it is vital to understand driving errors that can lead to crashes on interchanges. This knowledge can assist in developing countermeasures that will ensure safety for all road users when navigating through interchanges. The goal of this study was to determine driver, environmental, roadway, and traffic characteristics that influence older drivers’ errors resulting in crashes along interchanges. The analysis was based on three years (2016–2018) of crash data from Florida. A two-step approach involving a latent class clustering analysis and the penalized logistic regression was used to investigate factors that influence driving errors made by older drivers on interchanges. This approach accounted for heterogeneity that exists in the crash data and enhanced the identification of contributing factors. The results revealed patterns that are not obvious without a two-step approach, including variables that were not significant in all crashes, but were significant in specific clusters. These factors included driver gender and interchange type. Results also showed that all other factors, including distracted driving, lighting condition, area type, speed limit, time of day, and horizontal alignment, were significant in all crashes and few specific clusters.

scholarly journals Analysis of the impacts of risk factors on teenage and older driver injury severity using random-parameter ordered probit

2020 ◽  
Vol 47 (11) ◽  
pp. 1249-1257 ◽  
Author(s):  
Sina Darban Khales ◽  
Mehmet Metin Kunt ◽  
Branislav Dimitrijevic
Keyword(s):  
Injury Severity ◽  
Weather Condition ◽  
Random Parameter ◽  
Older Drivers ◽  
Ordered Probit ◽  
Driver Model ◽  
Age Group ◽  
Contributing Factors ◽  
Lighting Condition ◽  
Older Driver

The study analyzed injury severity of teenage and older drivers using 2015–2016 crash data from New Mexico. The fitness of the random-parameter ordered probit models developed for each age group was tested using likelihood ratio, comparing them to a unified model that combines both age groups, as well as comparing the random-parameter to fixed-parameter ordered probit for each age group. In both cases separate random-parameter ordered probit provided better results. It was found that vehicle type and age, lighting condition, alcohol or drug use, speeding, and seatbelt use were significant both for the teenage and older driver injury severity. The weather condition and gender were significant only in the teenage driver model, while driver inattention was significant for older drivers. The impacts of crash factors on injury severity was analyzed using marginal effects. The results indicate notable differences in the effects of contributing factors on driver injury severity between teenage and older drivers, including the sensitivity to changes in the mutual predictor parameter values.

Assessment of Commercial Truck Driver Injury Severity as a Result of Driving Actions

2021 ◽  
pp. 036119812110098
Author(s):  
Muhammad Tahmidul Haq ◽  
Milan Zlatkovic ◽  
Khaled Ksaibati
Keyword(s):  
Injury Severity ◽  
Binary Logistic Regression ◽  
Time Of Day ◽  
Truck Drivers ◽  
Truck Driver ◽  
Lighting Condition ◽  
Crash Data ◽  
Random Intercept ◽  
Gross Vehicle Weight ◽  
And Gender

The disaggregate modeling approach is a new trend in the literature to analyze the injury severity of truck-involved crashes. The assessment of truck driver injury severity based on driver action is still missing in the literature. This paper presents an extensive exploratory analysis that highlights significant variability in the severity of truck drivers’ injuries based on various action types (i.e., aggressive driving, failure to keep proper lane, driving too fast, and no improper driving). Binary logistic regression with the Bayesian random intercept approach was developed to examine the factors contributing to fatal or any injuries of truck drivers using 10 years (2007–2016) of historical crash data in Wyoming. Log-likelihood ratio tests were performed to justify that separate models by various driving action types are warranted. The results demonstrated the effects of various vehicle, driver, crash, and roadway characteristics, combined with truck driver-specific action, on the corresponding severity of driver injury. The gross vehicle weight, age and gender of the driver, time of day, lighting condition, and the presence of junctions were found to have significantly different impacts on the severity of truck driver injury in various driving action-related crashes. With the incorporation of the random intercept in the modeling procedure, the analysis found a strong presence (27%–33%) of intra-crash correlation in driver injury severity within the same crash. Finally, based on the findings of this study, several recommendations are made.

scholarly journals Development and Evaluation of Geostatistical Methods for Estimating Weather Related Collisions: A Large-Scale Case Study

2021 ◽  
pp. 036119812110200
Author(s):  
Andy H. Wong ◽  
Tae J. Kwon
Keyword(s):  
Large Scale ◽  
Mean Squared Error ◽  
Hot Spot ◽  
High Ratio ◽  
Contributing Factors ◽  
Geostatistical Methods ◽  
Road Users ◽  
Statistical Measures ◽  
Weather Events

Winter driving conditions pose a real hazard to road users with increased chance of collisions during inclement weather events. As such, road authorities strive to service the hazardous roads or collision hot spots by increasing road safety, mobility, and accessibility. One measure of a hot spot would be winter collision statistics. Using the ratio of winter collisions (WC) to all collisions, roads that show a high ratio of WC should be given a high priority for further diagnosis and countermeasure selection. This study presents a unique methodological framework that is built on one of the least explored yet most powerful geostatistical techniques, namely, regression kriging (RK). Unlike other variants of kriging, RK uses auxiliary variables to gain a deeper understanding of contributing factors while also utilizing the spatial autocorrelation structure for predicting WC ratios. The applicability and validity of RK for a large-scale hot spot analysis is evaluated using the northeast quarter of the State of Iowa, spanning five winter seasons from 2013/14 to 2017/18. The findings of the case study assessed via three different statistical measures (mean squared error, root mean square error, and root mean squared standardized error) suggest that RK is very effective for modeling WC ratios, thereby further supporting its robustness and feasibility for a statewide implementation.

scholarly journals Association of Time-of-Day Energy Intake Patterns with Nutrient Intakes, Diet Quality, and Insulin Resistance

Nutrients ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 725
Author(s):  
Xiaoyun Song ◽  
Huijun Wang ◽  
Chang Su ◽  
Zhihong Wang ◽  
Feifei Huang ◽  
...  
Keyword(s):  
Insulin Resistance ◽  
Energy Intake ◽  
Diet Quality ◽  
Latent Class ◽  
Asian Population ◽  
Time Of Day ◽  
Quality Score ◽  
Nutrient Intakes ◽  
Nutrition Survey ◽  
Dominant Pattern

Evidence shows time-of-day of energy intake are associated with health outcomes; however, studies of time-of-day energy patterns and their health implication are still lacking in the Asian population. This study aims to examine the time-of-day energy intake pattern of Chinese adults and to examine its associations with nutrient intakes, diet quality, and insulin resistance. Dietary data from three 24-h recalls collected during the 2015 China Health and Nutrition Survey (CHNS) were analyzed (n = 8726, aged ≥ 18 years). Time-of-day energy intake patterns were determined by latent class analysis (LCA). General Linear Models and Multilevel Mixed-effects Logistic Regression Models were applied to investigate the associations between latent time-of-day energy intake patterns, energy-adjusted nutrient intakes, diet quality score, and insulin resistance. Three time-of-day energy intake patterns were identified. Participants in the “Evening dominant pattern” were younger, had higher proportions of alcohol drinkers and current smokers. The “Evening dominant pattern” was associated with higher daily energy intake and a higher percentage of energy from fat (%) (p < 0.001), as well as higher insulin resistance risk (OR = 1.21; 95% CI: 1.05, 1.40), after adjusting for multivariate covariates. The highest diet quality score was observed in participants with “Noon dominant pattern” (p < 0.001). A higher proportion of energy in the later of the day was associated with insulin resistance in free-living individuals.

scholarly journals Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

2021 ◽  
Vol 11 (7) ◽  
pp. 101
Author(s):  
Andrew Paul Morris ◽  
Narelle Haworth ◽  
Ashleigh Filtness ◽  
Daryl-Palma Asongu Nguatem ◽  
Laurie Brown ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Red Light ◽  
Road User ◽  
Red Light Running ◽  
Crash Data ◽  
Passenger Vehicles ◽  
Road Users ◽  
Vulnerable Road User ◽  
Vulnerable Road Users ◽  
Road Intersections

(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.

scholarly journals Lower Volumes, Higher Speeds: Changes to Crash Type, Timing, and Severity on Urban Roads from COVID-19 Stay-at-Home Policies

2021 ◽  
pp. 036119812110444
Author(s):  
Jonathan Stiles ◽  
Armita Kar ◽  
Jinhyung Lee ◽  
Harvey J. Miller
Keyword(s):  
Logistic Regression ◽  
Traffic Safety ◽  
Travel Demand ◽  
Multinomial Logistic Regression ◽  
High Volume ◽  
Time Of Day ◽  
Bayesian Hierarchical ◽  
Crash Data ◽  
The Road ◽  
At Home

Stay-at-home policies in response to COVID-19 transformed high-volume arterials and highways into lower-volume roads, and reduced congestion during peak travel times. To learn from the effects of this transformation on traffic safety, an analysis of crash data in Ohio’s Franklin County, U.S., from February to May 2020 is presented, augmented by speed and network data. Crash characteristics such as type and time of day are analyzed during a period of stay-at-home guidelines, and two models are estimated: (i) a multinomial logistic regression that relates daily volume to crash severity; and (ii) a Bayesian hierarchical logistic regression model that relates increases in average road speeds to increased severity and the likelihood of a crash being fatal. The findings confirm that lower volumes are associated with higher severity. The opportunity of the pandemic response is taken to explore the mechanisms of this effect. It is shown that higher speeds were associated with more severe crashes, a lower proportion of crashes were observed during morning peaks, and there was a reduction in types of crashes that occur in congestion. It is also noted that there was an increase in the proportion of crashes related to intoxication and speeding. The importance of the findings lay in the risk to essential workers who were required to use the road system while others could telework from home. Possibilities of similar shocks to travel demand in the future, and that traffic volumes may not recover to previous levels, are discussed, and policies are recommended that could reduce the risk of incapacitating and fatal crashes for continuing road users.

scholarly journals Association between Crash Attributes and Drivers’ Crash Involvement: A Study Based on Police-Reported Crash Data

2020 ◽  
Vol 17 (23) ◽  
pp. 9020
Author(s):  
Guofa Li ◽  
Weijian Lai ◽  
Xingda Qu
Keyword(s):  
Logistic Regression ◽  
Age Groups ◽  
Nearest Neighbors ◽  
Older Drivers ◽  
Driving Safety ◽  
Crash Data ◽  
Imbalance Problem ◽  
Different Types ◽  
Crash Types ◽  
Safety Enhancement

Understanding the association between crash attributes and drivers’ crash involvement in different types of crashes can help figure out the causation of crashes. The aim of this study was to examine the involvement in different types of crashes for drivers from different age groups, by using the police-reported crash data from 2014 to 2016 in Shenzhen, China. A synthetic minority oversampling technique (SMOTE) together with edited nearest neighbors (ENN) were used to solve the data imbalance problem caused by the lack of crash records of older drivers. Logistic regression was utilized to estimate the probability of a certain type of crashes, and odds ratios that were calculated based on the logistic regression results were used to quantify the association between crash attributes and drivers’ crash involvement in different types of crashes. Results showed that drivers’ involvement patterns in different crash types were affected by different factors, and the involvement patterns differed among the examined age groups. Knowledge generated from the present study could help improve the development of countermeasures for driving safety enhancement.

scholarly journals Association of Photopic and Mesopic Contrast Sensitivity in older drivers with risk of motor vehicle collision using naturalistic driving data

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Cynthia Owsley ◽  
Thomas Swain ◽  
Rong Liu ◽  
Gerald McGwin ◽  
Mi Young Kwon
Keyword(s):  
Older Adults ◽  
Contrast Sensitivity ◽  
Motor Vehicle ◽  
Age Groups ◽  
Time Of Day ◽  
Motor Vehicle Crash ◽  
Older Drivers ◽  
Crash Risk ◽  
Sensitivity Testing ◽  
Naturalistic Driving

Abstract Background Older drivers have a crash rate nearly equal to that of young drivers whose crash rate is the highest among all age groups. Contrast sensitivity impairment is common in older adults. The purpose of this study is to examine whether parameters from the photopic and mesopic contrast sensitivity functions (CSF) are associated with incident motor vehicle crash involvement by older drivers. Methods This study utilized data from older drivers (ages ≥60 years) who participated in the Strategic Highway Research Program Naturalistic Driving Study, a prospective, population-based study. At baseline participants underwent photopic and mesopic contrast sensitivity testing for targets from 1.5–18 cycles per degree. Model fitting generated area under the log CSF (AULCSF) and peak log sensitivity. Participant vehicles were instrumented with sensors that captured continuous driving data when the vehicle was operating (accelerometers, global positioning system, forward radar, 4-channel video). They participated for 1–2 years. Crashes were coded from the video and other data streams by trained analysts. Results The photopic analysis was based on 844 drivers, and the mesopic on 854 drivers. Photopic AULCSF and peak log contrast sensitivity were not associated with crash rate, whether defined as all crashes or at-fault crashes only (all p > 0.05). Mesopic AULCSF and peak log sensitivity were associated with an increased crash rate when considered for all crashes (rate ratio (RR): 1.36, 95% CI: 1.06–1.72; RR: 1.28, 95% CI: 1.01–1.63, respectively) and at-fault crashes only (RR: 1.50, 95% CI: 1.16–1.93; RR: 1.38, 95% CI: 1.07–1.78, respectively). Conclusions Results suggest that photopic contrast sensitivity testing may not help us understand future crash risk at the older-driver population level. Results highlight a previously unappreciated association between older adults’ mesopic contrast sensitivity deficits and crash involvement regardless of the time of day. Given the wide variability of light levels encountered in both day and night driving, mesopic vision tests, with their reliance on both cone and rod vision, may be a more comprehensive assessment of the visual system’s ability to process the roadway environment.

scholarly journals Analysis of Freeway Secondary Crashes in Different Traffic Flow States by Three-Phase Traffic Theory

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Bo Yang ◽  
Yao Wu ◽  
Weihua Zhang
Keyword(s):  
Traffic Flow ◽  
Safety Performance ◽  
Crash Risk ◽  
Contributing Factors ◽  
Crash Data ◽  
Conditional Logit ◽  
Three Phase ◽  
Secondary Crashes ◽  
Traffic Theory ◽  
Flow States

The objective of this study is to analyse the relationship between secondary crash risk and traffic flow states and explore the contributing factors of secondary crashes in different traffic flow states. Crash data and traffic data were collected on the I-880 freeway in California from 2006 to 2011. The traffic flow states are categorised by three-phase traffic theory. The Bayesian conditional logit model has been established to analyse the statistical relationship between the secondary crash probability and various traffic flow states. The results showed that free flow (F) state has the best safety performance of secondary crash and synchronized flow (S) state has the worst safety performance of secondary crashes. The traditional logistic regression model has been used to analyse the contributing factors of secondary crashes in different traffic flow states. The results indicated that the contributing factors in different traffic flow states are significantly different.

scholarly journals Integration of AHP and Regression Analysis in Forecasting Attendance in a Movie Theater

2020 ◽  
Vol 12 (2) ◽  
pp. 14
Author(s):  
Hossein Jamshidi
Keyword(s):  
Regression Analysis ◽  
Analytical Hierarchy Process ◽  
Null Hypothesis ◽  
Hypothesis Test ◽  
Strong Predictor ◽  
Time Of Day ◽  
Contributing Factors ◽  
Statistical Software ◽  
Movie Theater ◽  
Hierarchy Process

Forecasting the number of attendees in a motion picture has often been considered a wild guess since there are many factors or variables involved in forecasting the numbers.  There are many contributing factors to consider when attempting forecasting theatre attendance. Among many possible variables the ones with the most significant impact that are considered in this study are; the day that movie is playing, the time of day that movie plays, the ranking of the movie, the genre of movie, the length of time that the movie has been released and finally whether school is in or out. For the most part, these are quantifiable measures and thus should be able to derive an accurate forecasting module. At first, the aim of this study is to compare all the variables by the decision maker based on the Analytical Hierarchy Process (AHP) and to rank the variables based on the importance according to the AHP process. Once the variables are set, the regression analysis is applied to forecast attendance in a movie theater.  Multiple regression analysis is used in this study based on a sample of 711 observations. Using SPSS statistical software, a model is developed to forecast the number of attendees and the model provides R2 = 0.760, which is a strong predictor. Finally, the hypothesis test is conducted to verify the accuracy of the regression model with the actual data and even with a = 0.10 the null hypothesis could not be rejected.

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