Subgroups Of Drivers Research Articles

Drivers’ long-term crash risks associated with being ticketed for speeding

Introduction: This research analyzes the relationship between police-issued tickets for speeding and the crash risk of those drivers, in New Zealand, between 2015–2019. Method: The main data are constructed through data-matching license details of crash outcomes with all officer-issued tickets for speeding between 2015–2016 (N = 534,935). The sub-group of drivers that accumulate tickets is compared to a coarsened exact matched set of drivers of the same age. Results: There is a strong relationship between the number of tickets a person has in a two-year period (2015–16) and the likelihood of a crash outcome (2017–2019). However, the accumulation of tickets is not the best predictor of crash likelihood. A combination of the excess in speed and the accumulation of tickets increases the relative odds of a subsequent crash. These results are discussed considering the threshold at which New Zealand criminalizes alcohol-relating offending (notionally 4.2 times the base rate crash risk). The same rate of elevated crash risk exists when a driver has one ticket for being 10 km/h over the speed limit and has another speeding ticket within two years.

Changes in older drivers’ risky driving behavior over time: Insights from a naturalistic study

This study aimed to investigate changes in the risky driving behavior of older drivers with age using naturalistic driving data. The driving behaviors of 44 drivers aged 60 years or older at baseline were tracked for two to five years. Harsh events characterized by elevated g-forces were used as driving outcomes. A multilevel modeling approach was used to examine (1) the heterogeneity in individual trajectories of risky driving behaviors and (2) the factors associated with the average risky driving behavior over the study period and changes in risky driving behavior over time. The results indicated that there was heterogeneity in individual trajectories of risky driving behaviors. Older female drivers and a higher level of sensation seeking (i.e., the tendency to pursue new and different sensations, feelings, and experiences) were associated with higher rates of harsh events over the study period. A reduction in driving exposure was associated with an increase in harsh event rates, and driving exposure can partly explain the sex difference in risky driving behaviors. The cluster analysis indicated that the heterogeneity in individual trajectories of risky driving behaviors can be explained by the presence of three subgroups of drivers, with each group exhibiting specific trajectories. Initial levels of risky driving behaviors were much more important than time slopes in identifying subgroups of drivers, indicating that differences between drivers existed early and remained relatively consistent over the study period. A multinomial logistic regression was used to examine the predictors of class membership identified by the clustering algorithm, and the findings were similar to those from the multilevel regression analysis. The findings of this study enhance our understanding of how older drivers’ risky driving behaviors change with age and indicate the distinctive individual processes of change in driving behaviors with age.

Effects of Behavior-Based Driver Feedback Systems on the Speeding Violations of Commercial Long-Haul Truck Drivers

Effects of Behavior-Based Driver Feedback Systems on the Speeding Violations of Commercial Long-Haul Truck Drivers

A hierarchical cluster analysis of young drivers based on their perceived risk and frequency of texting while driving

Introduction: The present study attempted to provide a proof-of-concept of usefulness of cluster analysis for identifying distinct and practically meaningful subgroups of drivers who differed in their perceived risk and frequency of texting while driving (TWD). Method: Using a hierarchical cluster analysis, which involves sequential steps in which individual cases are merged together one at a time based on their similarities, the study first attempted to identify distinct subgroups of drivers who differed in their perceived risk and frequency of TWD. To further evaluate the meaningfulness of the subgroups identified, the subgroups were compared in terms of levels of trait impulsivity and impulsive decision making for each gender. Results: The study identified the following three distinct subgroups: (a) drivers who perceive TWD as risky but frequently engage in TWD; (b) drivers who perceive TWD as risky and infrequently engage in TWD; and (c) drivers who perceive TWD as not so risky and frequently engage in TWD. The subgroup of male, but not female, drivers who perceive TWD as risky but frequently engage in TWD showed significantly higher levels of trait impulsivity, but not impulsive decision making, than the other two subgroups. Discussion: This is the first demonstration that drivers who frequently engage in TWD can be categorized into two distinct subgroups that differ in terms of the perceived risk of TWD. Practical applications: For drivers who perceived TWD as risky yet frequently engage in TWD, the present study suggests that different intervention strategies may be needed for each gender.

Negative mood mind wandering and unsafe driving in young male drivers

ObjectiveRoad traffic crash fatalities disproportionately affect young male drivers. Driver distraction is a leading contributor to crashes. Mind wandering (MW) is a prevalent form of driver distraction that is linked to certain unsafe driving behaviours that are associated with increased crash risk (e.g., faster driving). Negative mood can lead to MW, and thus may represent a causal pathway to MW-related unsafe driving. This preliminary pre-post (T1, T2), randomized, controlled, single-blinded experiment tested whether negative mood, compared to neutral mood, increases MW while driving as well as unsafe driving and emotional arousal during MW. It also tested the moderating contribution of trait rumination and inhibitory control to this proposed causal pathway. MethodsForty healthy male drivers aged 20 to 24 were randomly allocated to a negative or neutral mood manipulation involving deception. Individual differences in trait rumination and inhibitory control were measured at T1. At T1 and T2, participants drove in a driving simulator measuring driving speed, headway distance, steering behaviour, and overtaking. Heart rate and thought probes during simulation measured emotional arousal and MW, respectively. ResultsNegative mood exposure led to more MW while driving (Odds Ratio = 1.79, p = .022). Trait rumination positively moderated the relationship between negative mood and MW (Odds Ratio = 2.11, p = .002). Negative versus neutral mood exposure led to increases in headway variability (Cohen’s d = 1.46, p = .026) and steering reversals (Rate Ratio = 1.33, p = .032) during MW relative to focused driving. Between-group differences in emotional arousal were not significant. ConclusionResults support a causal pathway from negative mood to unsafe driving via MW, including the moderating contribution of trait rumination. If replicated, these preliminary findings may inform the development of interventions targeting this potential crash-risk pathway in vulnerable young driver subgroups.

Measuring and understanding the association between license-related infractions and road crash severity

The relationship between license-related infractions (LRIs) and the severity of road crashes has been scarcely addressed in previous research. This study estimates the association between each LRI and the severity of driver injuries and the partial severity of the crash (i.e., crash severity after excluding the severity of the driver’s own injuries) in a cohort comprising 78,720 drivers who were considered responsible for crashes in the Spanish National Register for Road Traffic Accident Victims, from 2014 to 2017. Adjusted Relative Risk Ratios for each LRI and severity level were obtained through multinomial logistic regression models. Age- and sex-adjusted estimates revealed an increased severity for almost all LRIs. Additional adjustment for seat belt use showed a decrease in the magnitude of the associations, particularly regarding driver injury severity, suggesting that part of these associations was related to increased vulnerability of the driver. Adjustment for other vehicle- and environment-related variables showed a further decrease in the associations but remained significant for “never having obtained a license” and other specific LRIs. These results support the need for maintaining police surveillance and legal measures to identify these subgroups of drivers, remove them from the road and adopt strategies for their safe return to driving.

Cardiac disease and driver fatality.

To determine the role of cardiac disease in driver fatalities, a retrospective review of autopsy files at Forensic Science SAin Adelaide, Australia, was undertaken over a 13-year-period January 2005-December 2017 for individuals aged ≥ 40 years who had died while driving a motor vehicle. The incidence of significant coronary artery atherosclerosis (CAA) and cardiomegaly was evaluated with comparisons between drivers and a control group of passengers. Autopsy examinations were performed on 303 drivers and 72 passengers who died of trauma and on 63 drivers who died of a cardiac event while driving. The average age for drivers dying of trauma was 58.5 years (range 40-93 years) with 48 (15.8%) having CAA and 31 (10.2%) having cardiomegaly. This was not statistically different to passengers (aged 63.3 years; range 40-93 years; 20.8% having CAA; 11 (15.2%) cardiomegaly; (p > 0.2). Drivers with significant cardiac disease did not, therefore, have increased rates of death in crashes, although a distinct subgroup of drivers consisted of those who had died from cardiac events and not trauma, while driving. The latter may be increasing in number given the aging population.

Homocygous PAX5Sequence Mutations Define a Novel Subtype of B Cell Precursor Acute Lymphoblastic Leukemia

IntroductionIdentification of molecular alterations in B cell precursor acute lymphoblastic leukemia (BCP-ALL) is critical for targeting leukemogenic drivers in BCP-ALL patients. Established driver lesions comprise structural or numerical chromosomal aberrations and define subgroups with specific gene expression signatures. Yet, up to one third of BCP-ALL patients lack recurrent genetic lesions to allow classification into distinct categories. We conceived a multi-omics study to characterize novel candidates in BCP-ALL patients.Patients and methodsWe analyzed diagnostic samples of 225 BCP-ALL patients (age: median 32 years, range 1-80 years), exploring sequence variants (whole exome sequencing, WES, n=50 / targeted gene panel sequencing, n=163 / variant calling from RNA-Seq, n=187), copy number alterations (WES, n=50 / multiplex ligation dependent probe amplification, n=123) and gene fusions (RNA-seq, n=187 / fusion break-point specific RT-PCR, n=225) together with their gene expression (RNA-Seq) and DNA methylation (Methylation Chip array, n=140) context. All patients were treated in population based German study trials (GMALL, ALL-BFM, COALL).ResultsWithin this BCP-ALL cohort (n=225), we could assign 180 patients to established molecular driver subgroups: Ph-like (n=42), DUX4/ERG (n=38), aneuploid (n=28), Ph-positive (n=23), ZNF384 (n=18), KMT2A (n=14), TCF3-PBX1 (n=6), ETV6-RUNX1 (n=4), MEF2D (n=4), PAX5-ETV6 (n=3).Among samples that lacked characteristics of any of the above subgroups (n=45), we identified a novel cluster of samples (n=17, median age: 21 years, range 9 - 64 years) by a distinct gene expression profile: all of these patients harbored PAX5 sequence variants. These patients had either homozygous mutations (n=14) or mutations occurring at two different positions in one patient(n=3). The predominant PAX5 amino acid change was p.P80R (n=15), occurring exclusively in this patient cluster. The PAX5 paired domain was affected at least once in all patients of this cluster and predictions (SWIFT, Polyphen and Condel) consistently indicated a deleterious effect on protein integrity for these mutations.RAS-pathway activating (n=15) and CDKN2A inactivating (n=15) alterations were identified in almost all patients of this patient cluster (n=17, further on named PAX5-plus), creating an exclusive pattern of co-occurrence, which was not observed in any of the other subgroups. RAS-pathway alterations were either established hotspot sequence variants (NRAS n=8, KRAS n=5) or NF1 inactivating alterations (n=2). CDKN2A alterations were copy number losses (n=14) and a homozygous sequence variant. Sequence variants affecting other PAX5 protein domains without RAS or CDKN2A alterations were enriched in the Ph-like cluster (n=6 of 42 samples).Unsupervised clustering of top differentially expressed genes and top differentially methylated CpG sites clearly separated the PAX5-plus subgroup (n=17) from established driver subgroups (n=180) and from the remaining unclassified samples (n=28). PAX5-plus patients showed differential expression of 1836 target genes (up: 548 / down: 1288, FDR<0.05). Gene set enrichment analysis revealed genes activated by PAX5 in pro-B cells (Revialla-I-Domingo R, 2012) to be down-regulated (NES -2.05, FDR=0.005) and genes repressed after PAX5 restoration in a PAX5 knock-down BCP-ALL model (Liu GJ, 2014) to be up-regulated in PAX5-plus samples (NES 4.16, FDR<0.001). Moreover, 70% of genes differentially expressed in PAX5-plus patients had been identified as PAX5 target genes by ChIP-Seq in murine pro B cells (Revilla-I-Domingo R, 2012), together establishing PAX5 mutations as bona-fide drivers of transcriptional deregulation in these patients.ConclusionWe have identified a novel subgroup, PAX5-plus BCP-ALL, among patients lacking alterations of previously described driver subgroups. PAX5-plus BCP-ALL is characterized by triple-hits of homozygous PAX5 sequence mutations, RAS pathway activating and CDKN2A inactivating alterations, which is in contrast to the established BCP-ALL subgroups being defined by chromosomal translocations or ploidy disorders. Defined DNA methylation and gene expression signatures specify this subgroup and relate deregulated transcription to PAX5 loss of function. DisclosuresNo relevant conflicts of interest to declare.

Providing an optimized model to detect driver genes from heterogeneous cancer samples using restriction in subspace learning

Extracting the drivers from genes with mutation, and segregation of driver and passenger genes are known as the most controversial issues in cancer studies. According to the heterogeneity of cancer, it is not possible to identify indicators under a group of associated drivers, in order to identify a group of patients with diseases related to these subgroups. Therefore, the precise identification of the related driver genes using artificial intelligence techniques is still considered as a challenge for researchers. In this research, a new method has been developed using the subspace learning method, unsupervised learning, and with more constraints. Accordingly, it has been attempted to extract the driver genes with more precision and accurate results. The obtained results show that the proposed method is more to predict the driver genes and subgroups of driver genes which have the highest degree of overlap due to p-value with known driver genes in valid databases. Driver genes are the benchmark of MsigDB which have more overlap compared to them as selected driver genes. In this article, in addition to including the driver genes defined in previous work, introduce newer driver genes. The minister will define newer groups of driver genes compared to other methods the p-value of the proposed method was 9.21e-7 better than previous methods for 200 genes. Due to the overlap and newer driver genes and driver gene group and subgroups. The results show that the p value of the proposed method is about 2.7 times less than the driver sub method due to overlap, indicating that the proposed method can identify driver genes in cancerous tumors with greater accuracy and reliability.

Is driving without a valid license associated with an increased risk of causing a road crash?

The aim of this study was to estimate the association between each cause of driving without a valid license (DWVL) and the risk of causing a road crash, considering driver, vehicle and environmental factors. A case-control study based on data from the Spanish Register of Road Accidents with Victims was carried out between 2014 and 2017. Cases included 28,620 drivers of moving private cars, vans and off-road vehicles involved in single crashes plus 50,100 drivers deemed responsible for clean collisions (i.e. those in which only one driver was labeled as responsible). In accordance with the quasi-induce exposure approach, drivers not responsible for clean collisions comprised the control group (N = 51,656). Logistic and multinomial regression models were used to estimate crude and adjusted Odds Ratios or Relative Risk Ratios between each reason for DWVL and the risk of being a case of all, single and multi-vehicle collisions. A significant association was found between all reasons for DWVL and the risk of causing a road crash. This association was particularly high for drivers with a suspended license and drivers who had never obtained a license. In these subgroups of drivers, the proportion of the relationship explained by high-risk driving behaviors is high. Our results support the need for applying continued strategies to identify and control these subgroups of drivers.

A non-parametric Bayesian change-point method for recurrent events

This paper proposes a non-parametric Bayesian approach to detect the change-points of intensity rates in the recurrent-event context and cluster subjects by the change-points. Recurrent events are commonly observed in medical and engineering research. The event counts are assumed to follow a non-homogeneous Poisson process with piecewise-constant intensity functions. We propose a Dirichlet process mixture model to accommodate heterogeneity in subject-specific change-points. The proposed approach provides an objective way of clustering subjects based on the change-points without the need of pre-specified number of latent clusters or model selection procedure. A simulation study shows that the proposed model outperforms the existing Bayesian finite mixture model in detecting the number of latent classes. The simulation study also suggests that the proposed method is robust to the violation of model assumptions. We apply the proposed methodology to the Naturalistic Teenage Driving Study data to assess the change in driving risk and detect subgroups of drivers.

Drivers of Supplier Participation in Ride-Hailing Platforms

ABSTRACT Ride-hailing platforms such as Uber offer service suppliers more flexible work conditions but lower security than permanent jobs. This study examines the preferences of ride-hailing drivers when they are offered contract and platform design options related to flexibility, financial security, and information features. The sample design and the discrete choice experiment approach we use permit the exploration of differences in the preferences of sub-groups of suppliers, including active and inactive drivers, drivers with and without other jobs, and those who multi-home. We find that the utility and willingness of drivers to work for ride-hailing platforms generally increase when the ride-hailing company provides a minimum wage guarantee, a company-sponsored benefit plan, and information features that protect drivers’ privacy and allow them to screen for undesired passengers. These attributes are important enough for drivers so that they are willing to sacrifice scheduling flexibility by committing to minimum working hours in exchange for those attributes. We identified significant heterogeneity in driver preferences. For drivers with a primary job or jobs other than ride-hailing, the perception of working conditions in the primary job affects their willingness to restart ride-hailing services. Preferences also vary depending on whether a driver participates in one or more platforms. These results suggest that offering a menu of contract options could provide novel tools for ride-hailing companies to improve the stability and predictability of supply. It may also alleviate some of the concerns about driver working conditions that have led to increasing calls for the regulation of ride-hailing services to protect drivers.

Does longevity impact the severity of traffic crashes? A comparative study of young-older and old-older drivers

Introduction: This article analyzes the effect of driver’s age in crash severity with a particular focus on those over the age of 65. The greater frequency and longevity of older drivers around the world suggests the need to introduce a possible segmentation within this group at risk, thus eliminating the generic interval of 65 and over as applied today in road safety data and in the automobile insurance sector. Method: We investigate differences in the severity of traffic crashes among two subgroups of older drivers –young-older (65–75) and old-older (75+), and findings are compared with the age interval of drivers under 65. Here, we draw on data for 2016 provided by Spanish Traffic Authority. Parametric and semi-parametric regression models are applied. Results: We identified the factors related to the crash, vehicle, and driver that have a significant impact on the probability of the crash being slight, serious, or fatal for the different age groups. Conclusions: We found that crash severity and the expected costs of crashes significantly increase when the driver is over the age of 75. Practical Applications: Our results have obvious implications for regulators responsible for road safety policies – most specifically as they consider there should be specific driver licensing requirements and driving training for elderly – and for the automobile insurance industry, which to date has not examined the impact that the longevity of drivers is likely to have on their balance sheets.

How many collateral casualties are associated with road users responsible for road crashes?

ObjectiveTo estimate the number of collateral casualties associated with road users considered responsible for a road crash. MethodWe analyzed the case series comprising all 790,435 road users involved in road crashes with victims in Spain from 2009 to 2013, recorded in a nationwide police-based registry. For each road user assumed to be responsible for a crash, we collected information relative to health outcomes in other people involved in it, and obtained the total number of collateral casualties per 100 road users considered responsible for the crash. We then estimated the strength of associations between sex, age and the number of collateral casualties generated by car drivers considered responsible for the crash, and calculated rate ratios and corresponding 95% confidence intervals. ResultsPedestrians responsible for crashes were associated with the lowest number of collateral casualties (13.1/100), whereas the highest number (153/100) was observed for bus drivers responsible for crashes. Car drivers were associated with 104.4/100 collateral casualties. The youngest and the oldest car drivers responsible for crashes were associated with 33% and 41% more deaths, respectively, than the 25–34 year old group. Male drivers were associated with 22% more collateral casualties than female drivers. ConclusionsRegardless of the type of road user who was responsible for a road crash, their active contribution to the crash led to an additional number of collateral casualties in other, non-responsible users. The number and severity of collateral casualties were related to the type of vehicle and the number of people involved. These results are potentially useful to support the need to promote safer driver practices among subgroups of high-risk drivers.

Relationships between risk of causing a motorway crash in Austria and attributes of drivers

ObjectiveThe emphasis of the study presented in this paper was to identify the attributes of drivers who are prone to cause a motorway crash. It was induced byambitions to enhance the effectiveness of traffic safety campaigns by identifying subgroups of drivers more precisely.MethodFor this purpose we conducted an accident data analysis of all injury crashes 2012–2014 on Austrian motorways. Since 2012 it is possible for the police to label the “mainly responsible person” for the crash in the Austrian electronic accident recording system. It turned out that the quality of the data had some limitations, which required considerable pre-processing. The analysis by comparing the proportions of “mainly responsible” and “not responsible” drivers was done using descriptive statistics and logistic regression models. The binary characteristic, if a person was, according to the police, mainly responsible for the crash served as response variable. It was regressed on various personal and vehicle characteristics in order to identify their influence on the probability of causing a crash.Results/ConclusionsThe results yield apparent tendencies, some of which support well-known pattern, e.g., the disproportionally high risk of motorcyclists and - to a lesser extent - young and elderly car drivers to cause an accident. Other results were less expected, e.g., the higher risk of foreign car drivers (compared to Austrian drivers) as well as drivers of light trucks, whereas drivers of heavy trucks revealed a below-average risk of causing a crash. The differences between male and female car drivers were small from young to middle age and fully disappeared in the high age group. There is some evidence that the frequency and experience of driving on a motorway have more influence on the risk of causing a crash than the driver’s gender. From this we conclude that motorway safety campaigns should on the one hand focus on the abovementioned groups of high risk drivers and on the other hand, in terms of car drivers, equally address both male and female drivers.

Whole Genome Profiling of Adult B-Other Acute Lymphoblastic Leukaemia on the UKALL14 Trial

B-other ALL represents a working definition for patients with B cell precursor (BCP) ALL without a known primary chromosomal abnormality. In this study we use whole genome sequencing (WGS) to characterize adult B-other cases (age ≥ 25yrs) from the UKALL14 trial (NCT01085617). Of 652 patients aged 25-65yrs enrolled onto UKALL14, 333 (51%) had B-other ALL. Sufficient material was available to screen 156/333 B-other cases for recurrent Ph-like fusion events (CLRF2, JAK2, ABL1, ABL2PDGFRB) using FISH and MLPA (kit P335). This identified 28 (18%) Ph-like fusion events (21 CRLF2, 5 ABL-class fusions and 2 JAK). Of the remaining 128 B-other cases 57 had available samples for tumor normal paired WGS (read depth 60x and 30x respectively). Bioinformatic analysis was performed to determine small somatic mutations (SSMs); single nucleotide variants (SNVs) and insertion/deletions (INDELs) as well as copy number aberrations (CNA) and structural variants (SV). We also undertook de novo motif analysis to identify RAG mediated deletions. We present data for 30/57 cases (median age of diagnosis 44, range 25-65), the remainder are undergoing sequencing. Within this cohort we identified 784 SVs, 49,244 SNVs and 2,881 INDELs, with each case having a median representation of 23 SVs, 1,674 SNVs and 86 INDELs. The Median SSM burden was 0.55 per megabase (range 0.31-0.81), which is in the upper third of previous ALL estimates (median 0.26 range 0.03-2.9) but low compared to most other cancer types (Alexandrov et al. 2013 Nature). Fusion gene analysis identified 11/30 (37%) cases with recurrent rearrangements (2 ZNF384r, 2 PAX5r, 5 DUX4r and FGFR1r); this identified a novel 5' partner for ZNF384r (AKAP8) and MYO18A-FGFR1 a fusion previously seen in a single case of 8p11 myeloproliferative syndrome. A further 7 clonal coding drivers were identified, 3 PAX5alt, 3 Ph-like candidates and a single ETV6-RUNX1-like candidate (ETV6 R399C, a dominant negative mutation). We classified our cohort into 6 driver subgroups as shown in table 1 using Gu et al. as a framework (Gu et al. 2019 Nat Gen). We found no evidence of driver subgroups clustering with age, although the Ph-like candidates were all identified in patients 39yrs or older. There was evidence that known driver subgroups have differing mutation burdens but these were not significant in this preliminary cohort; the single MEF2Dr case had a high SV burden compared to all other known subgroups (137 vs. cohort median 22); Favorable outcome subgroups (ZNF384r and DUX4r n=7) had a lower SNV coding mutation burden (median 14 vs. 20; U = 60, p = 0.057; Mann-Whitney U test); PAX5alt (n=5) cases had a higher INDEL burden (median 174 vs. 82; U = 16.5, p =0.057). As expected we found recurrent CNA in CDKN2A/B (63%; 19/30), PAX5 (33%; 10/30), IKZF1 (27%; 8/30), ETV6 (11%; 3/30) and BTG1 (7%; 2/30) across the cohort. IKZF1 deletions were enriched in the Ph-like candidate (n=4) subgroup compared to all other known groups (75% vs. 12%; p = 0.028; Fisher's Exact). RAG mediated deletions have been established as an oncogenic driver mechanism in childhood ALL, so we sought to ascertain its role in adults. We identified 54% (128/236) of the breakpoint resolved deletions had a RAG heptamer site. There was a significant difference (U = 167.5, p = 0.021) in RAG deletion burden between patients over 40yrs and under 40yrs at diagnosis (median 1.5 vs 3). Within the unknown driver subgroup; three cases carried ZEB2 H1038R mutations, two with concurrent IGH-CEBPA/B fusions, identifying them as likely belonging to the G12 cluster identified by Li et al. (Li et al. 2018 PNAS); one case had a high translocation burden (37 cohort median 2) and a single case had a high RAG deletion burden (45, cohort median 2); of the remaining 7 cases, four had either a NRAS or FLT3 subclonal hotspot mutation. Here we present the WGS analyses of 30 cases classified as B-other on the basis of no cytogenetic findings by standard clinical assays. The landscape of adult B-other ALL is highly heterogeneous but with WGS we were able to find at least one disease defining event in 60% of our cohort. These events often encompass novel fusion partners of established genomic subtypes or a cytogenetically cryptic lesion such as IGH-DUX4. Taken together our findings demonstrate the clinical utility of WGS as a diagnostic assay to inform and improve the management of adult B-other ALL patients in the future. Disclosures Fielding: Pfizer: Consultancy; Incyte: Consultancy; Amgen: Consultancy; Novartis: Consultancy. Papaemmanuil:Celgene: Research Funding.

Trip-Based Measures of Naturalistic Driving: Considerations and Connections With Cognitive Status in Older Adult Drivers

Older adult drivers can experience age-related health declines, particularly cognitive health, that can negatively impact driving performance and lead to driver license revocation. The measurement of naturalistic in-car driving behavior can be used to evaluate trip complexity, including diversity, length, and frequency of trips and associated destinations. This paper examined measurement methods for trip complexity and driving destinations using GPS data for older adult drivers with differing health statuses, focusing primarily on cognitive health status. Older driver subgroups included four groups with relatively stable health: better overall health, better cognitive health, worse cognitive health, and worse overall health and one group with declining cognitive health. Older drivers with better health status (overall and cognitive) had higher measured trip complexity compared to those with worse health status. Two variables, mean trip distance and percent of trips driven during the workweek evening rush-hour, declined significantly $(p \le 0.049)$ in-line with cognitive declines but did not meaningfully discriminate cognitively declining older drivers from cognitively stable drivers (sensitivity: 18.8%–43.8%; specificity: 58.0%–93.3%). This contrast between significant group differences and nonpredictive declining group changes may suggest that self-referential naturalistic driving measures are needed to identify meaningful changes in driving behavior. In addition to the development of self-referential driving measurement systems, careful consideration of big data analysis as they apply to naturalistic driving is warranted. These include but are not limited to issues of validation, anonymity, measure-based definitions, and occurrence of outlier-type driving events like city-to-city travel.

Clustering Analysis of Drivers Based on Behavioral Characteristics Regarding Road Safety

The present study was aimed at identifying driving behaviors and driving skills (i.e., ordinary violations, lack of experience errors, positive behaviors, lack of attention errors, dangerous errors, aggressive violations, perceptual-motor skills, and safety skills) in a large sample of young, middle-aged, and elderly tourists as specific subgroups of drivers based on combinations. Additionally, drivers’ risk-driving behaviors and skill deficits in terms of driving violations and errors, and traffic rule violations and accident involvements as measured by the Driver Behavior Questionnaire and the Driver Skill Inventory were evaluated. To this end, the questionnaires were completed by 681 participant drivers with three age subgroups (age 40), who had traveled to Baneh city in Iran. Based on a cluster analysis of driving behaviors and driving skills variables, five easily understandable drivers’ subgroups were investigated as safe drivers with low self-confidence, unsafe, and offensive drivers, safe, and skillful drivers, unsafe, and relatively unskilled drivers, and unskilled and relatively unsafe drivers. The drivers differed in terms of self-reported accident involvement, attitudes toward traffic safety and risk perception, and driving violations and errors. Finally, based on annual intentional and unintentional accidents and fines, the clusters were ranked. Then, the clusters were evaluated and analyzed statistically. The results indicated that unsafe and offensive cluster is the first unsafe subgroup among clusters. Safe and skillful cluster is classified as the safest subgroup among clusters.

Canadian wildlife-vehicle collisions: An examination of knowledge and behavior for collision prevention

Objectives: This study examines drivers' responses to wildlife on Canadian roads. The objective of this paper is to demonstrate that knowledge of what to do when encountering wildlife on the road does not always translate into the appropriate behavior to avoid a collision. Methods: Data from the Traffic Injury Research Foundation's (TIRF) 2016 Road Safety Monitor (RSM) and data from TIRF's National Fatality Database from 2000 to 2014 were analyzed to test hypotheses based on the theory of planned behavior. Logistic regression and piecewise linear regression were used. Results: Analyses of the data showed that the prevalence of fatal WVCs has remained relatively consistent, and that the majority of persons killed in WVCs died in crashes that involved large mammals. The majority of fatalities occurred in the summer (182 or 38.4%) and fall (163 or 34.4%). The RSM data revealed that 60.9% [50.5, 70.4] of respondents who previously hit an animal indicated that drivers should slow down and steer straight when confronted with wildlife, while 47.3% [37.1, 57.6] of respondents indicated this was the action they took when they hit wildlife. Comparatively, 59.5% [56.6, 62.4] of respondent who have not hit an animal indicated this was an appropriate response. Additionally, 33.2% [24, 44] of respondents who previously hit an animal indicated that drivers should swerve to avoid a collision with wildlife, while 37.5% [28.2, 47.8] of respondents indicated this was the action they took when they hit wildlife. Conclusions: Many drivers are unaware of what the safest method of WVC prevention is. Further, while a subgroup of drivers may have the knowledge and intention to slow down and steer straight even if the animal is directly in the path, i.e., the safest possible behavior, they are not necessarily adopting this behavior. Practical applications: Recommendations are formulated to address this discrepancy, as well as practical applications.

Influence of drivers’ psychological risk profiles on the effectiveness of traffic calming measures

Road traffic injuries represent a serious public health problem and are one of leading cause of death, injury and disability around the world. Road accidents are often caused by an accumulation of factors; however, drivers appear to be by far the most decisive one. The driver’s behaviour is complex and depends on reflex (or involuntary) and voluntary driving actions. The first class of actions (reflex actions) are typical human reactions that remain inaccessible to awareness and refer to the direct interaction between the road user and the characteristics of the road and its surrounding environment. Conversely, voluntary actions are conscious behaviours adopted on the basis of planned decisions. Both types of driving actions act simultaneously and the interaction between them and their relative effects on road safety are an aspect not yet well examined. The main objective of this study was to provide, by means of a driving simulation experiment, an insight on this interaction by evaluating the influence of some psychological characteristics on the effectiveness of different types of traffic calming measures at pedestrian crossings, designed according to the Human Factors principles. Fifty-eight participants drove a virtual urban route while data on their performance, as they approached five configurations of pedestrian crossings equipped with different physical and perceptual treatments, were collected. The participants were preliminarily characterized by means of two psychological questionnaires, which allowed the identification of three distinct groups of drivers belonging to three risk profiles (careful, worried, and at risk). The three groups of drivers reacted differently to the proposed engineering treatments, confirming the clustering identified by the preliminary analysis. The results showed that the proposed traffic calming measures are effective on all psychological sub-groups of drivers, with different effectiveness. These first results support that, in the considered driving environment (pedestrian crossings), the Human Factors approach, with which traffic calming measures can be arranged, could be effective, even if different psychological sub-groups are differently affected.