Tag Archives: national highway traffic safety administration

A New Safety Threat on Our Roads

We’ve been driving cars for 125 years. We have been talking on telephones for 100 years. We’ve only combined these two activities, to any great degree, in the last 10 to 15 years.

Motor vehicle crashes are the No. 1 cause of accidental death in the U.S. Crashes are the leading cause of all death, accidental or otherwise, for everyone between the ages of five and 35. Those between the ages of 15 and 20 are more likely to die in a car crash than the next three leading causes of death combined – homicide, suicide and cancer. According to the National Highway Traffic Safety Administration (NHTSA), the critical reason for 94% of crashes is driver error, as opposed to vehicle- or environment-related reasons. Recognition and decision errors, which include driver distraction, represent 74% of driver error.

Alarmingly, after decades of decline, total fatalities from vehicle crashes and fatalities per million miles driven have been increasing for the past two years. There is a new threat on our nation’s highways, and it’s distracted driving. Drivers have always been at risk of distraction, but today, because of the rapid adoption of mobile communications technology, drivers are now distracted in ways we never dreamed possible 20 years ago.

See also: Distracted Driving: a Job for Insurtech?  

An Important Issue for Employers and the Insurance Industry

Cell phone use while driving has become an important safety and liability issue for all employers. Those who expect employees to use cell phones while driving as part of their business must recognize that doing so exposes their employees to a preventable crash risk and employers to costly liability.

Consider a situation in which an employer knew a behavior in some area of its operations exposed employees to a much greater risk of injury. Would employers still expect, or even encourage, that behavior? That is precisely what happens when an employer permits or encourages employee cell phone use while driving. With the intense publicity surrounding cell phone distracted driving in recent years, it would be difficult for employers to argue that they’re not aware of the dangers.

Employers are responsible for ensuring employees adhere to applicable federal agency regulations and federal, state and municipal laws. However, what is often not understood is that these regulations and laws are a minimum standard and, in many cases, are not be enough to keep people safe.

Employers should establish policies about cell phone use and driving that exceed existing laws. Safety policies and systems in many companies are designed to reduce significant risks and protect employees. Companies whose leaders are committed to safety excellence know that their safety systems and policies often exceed OSHA requirements or applicable laws, because regulations and laws often prescribe minimum standards, not best-in-class safety. Designing safety policies that only comply with federal rules, regulations or state laws often leaves employees vulnerable to injury and companies exposed to liability and financial costs. Cell phone use while driving is, in this way, no different than many other occupational safety issues.

No Impact on Business Operations

Contrary to what one might think, companies that have implemented total bans on mobile device use while driving have overwhelmingly reported no negative impact on productivity, customer service or other business operations. In two studies conducted by the National Safety Council, 90% of companies with policies reported no impact on productivity. Of the 10% that reported a change, nine out of 10 claimed productivity actually increased. Only 1% thought productivity had decreased.

All Distractions Are Not the Same

Drivers who use their cell phones while driving expose themselves to a significant safety risk that affects both them and those with whom they share the road. Cell phone distraction involves all three types of driver distraction: visual, manual and cognitive.

Distracted driving crashes are the result of two factors; 1) the risk of the activity, and 2) the prevalence of that risk. Most people, including lawmakers and some researchers, only focus on risk and ignore risk exposure. In evaluating what causes crashes, both are equally important.

We typically have little concern for a risk to which we are seldom exposed, but we have great concern for a risk to which we are continuously exposed, as in the case of cell phone distracted driving. It is risk exposure that makes cell phone use while driving such a dangerous activity. NHTSA has stated (based on its annual NOPUS study) that more than 10% of all drivers are using their cell phones at any given time. No other distracting behavior or risk comes close to that level of exposure. It is risk exposure that makes cell phones the most dangerous distraction, by far, that drivers face on a continuing basis.

The Human Brain Does Not Truly Multi-Task

The field of cognitive neuroscience has studied human attention for more than 80 years. These scientists will tell you there is no such thing as true “multi-tasking.” When we are reading a book or magazine article and the phones rings, we naturally stop reading, answer the phone and have a conversation. Most of us would never consider continuing to read as we talk on the phone. That is because the human brain does not multi-task, it toggle tasks. It switches back and forth between two tasks, never engaged in both at precisely the same time. We know that if we try to read and talk on the phone, we are not doing either task well, so we rarely try to do both at the same time. Yet, most of us think it is perfectly fine to talk on the phone and drive a vehicle. If we make a mistake reading a book, we can re-read a paragraph. If we make a mistake driving a vehicle, it can damage our lives or someone else’s.

Hands-Free is Not the Answer

As traffic safety professionals pursue a culture change around cell phone use while driving, It will be much easier to convince drivers to switch to hands-free rather than to stop using phones altogether while driving. Unfortunately, there is no evidence that hands-free phone use reduces distraction or crashes. More than 30 research studies have found that hands-free devices offer no safety benefit, because they do not reduce the cognitive distraction of the phone conversation. All major U.S. traffic safety organizations, including the National Safety Council (NSC) and the National Transportation Safety Board (NTSB), have made public statements, after reviewing research, that hands-free is not safer than hand-held phone use.

See also: Don’t Be Distracted by Driverless Cars  

NSC and NTSB

In January 2009, based on input from many of its 10,000 plus business members, NSC called for a total ban on cell phone driving. In December 2011, the NTSB issued the recommendation that all states enact complete bans of all portable electronic devices for all drivers — including banning the use of hands-free devices. This follows its total ban recommendation for commercial drivers in October 2011. NTSB recommendations are based on their investigations of serious and fatal crashes that found driver or operator cell phone use was a factor in the crashes.

Conclusion

The rapid advancement of mobile communications technology has enabled drivers to engage in all kinds activities while driving a vehicle that have nothing to do with driving. As long as crashes are killing and seriously injuring so many people, and as long as driver error is the overwhelming leading cause of crashes, does it make sense to allow, and even encourage, the driver to engage in phone calls, Facebook updates, voice based texting and other activities that have nothing to do with the already dangerous task of driving?

The auto and consumer electronics industries have claimed that “eyes on the road and hands on the wheel” are the only critical requirements for distraction free driving. They seem to believe the mind is not required to safely operate a vehicle. This contradicts years of science and, most importantly, common sense. It is time that we focus first and exclusively on the task of driving, for our safety and for the safety of everyone with whom we share the road. It is also time for the Insurance Industry to take the lead on this issue by implementing total ban policies for their employees and encouraging their insureds to drive cell phone free.

When Hackers Take the Wheel

Operator errors, driving under the influence, and product defects have long been blamed for catastrophic accidents in the transportation industry. However, recent headlines revealed how cyber risk has emerged as a new and disturbing threat to airlines, railways, auto manufacturers and ocean cargo carriers.

Those in the transportation sector have embraced the “Internet of Things” and transformed what were once far-reaching concepts into some of the most common components of the cars they manufacture and the planes they fly. They often rely on a secure internet connection to function safely and efficiently. Recent headlines, however, raised concern and started a debate: Can the transportation sector be hacked? If so, what are the consequences?

Automobiles

In July 2015, Fiat Chrysler announced a recall of 1.4 million vehicles after white hat hackers demonstrated that they could take control of a Jeep Cherokee’s braking systems, change vehicle speed and affect operation of the transmission, air conditioning and radio controls. Hackers gained remote access by exploiting a software vulnerability in the vehicle’s Uconnect entertainment system.

The stakes have been raised even higher with recent advances made in the development of driverless cars, as more vehicles will become completely reliant on secure technology. Safety concerns were raised after a series of crashes allegedly caused by the failures of Tesla’s Autopilot technology, resulting in the death of a passenger. This prompted Tesla to announce efforts to improve its Autopilot software, including “advanced processing of radar signals.”

See also: How to Measure ‘Vital Signs’ for Cyber Risk  

The Department of Transportation has also recognized the risks associated with technology. In January 2016, the department entered into an agreement with 17 major automakers to enhance driver safety, including information sharing to prevent cyberattacks on vehicles. According to the agreement, the National Highway Traffic Safety Administration will propose industry guidance for safe operation for fully autonomous vehicles.

Planes

Boeing recently became the subject of a hacker demonstration when a security researcher accessed the entertainment systems of one of the company’s planes in mid-flight. Boeing was adamant that the hacker could not have gained access to the aircraft’s critical functions due to segregation of the two networks. However, the incident raised concerns throughout the airline industry, and an FBI investigation followed.

Railway Systems

German security researchers SCADA Strangelove demonstrated, without naming the rail systems in question, that they, too, are vulnerable. Their December 2015 report highlighted vulnerabilities related to outdated software, default passwords and lack of authentication. Moreover, entertainment and engineering systems were operating on the same network, leading to speculation that if one system is compromised hackers could gain access to the other. Because rail switches are automated and dependent on properly operating networks, the theory of a system compromise leading to a head-on collision with another train was explored in the report.

Marine Shipping

An investigation by Verizon Risk concluded that modern-day pirates are increasingly relying on network intrusions as a means to carry out crimes on the high seas. Verizon concluded that an unidentified shipping company’s networks were penetrated by hackers, giving them precise information on which ships were carrying the most valuable contents. Hackers then targeted their attacks on specific vessels, using bar codes to focus on individual shipping containers.

As of this writing, we have not seen any incidents of bodily injury or loss of life in the transportation sector directly attributed to a deliberate network compromise. Yet the findings of various researchers across multiple transportation sectors lead to some alarming conclusions. Law enforcement and transportation safety regulators have taken these findings seriously and conducted investigations of their own.

We can therefore expect with some degree of certainty that the transportation sector may be held to higher cybersecurity standards and will see increased regulatory scrutiny that has been witnessed in other industries, such as healthcare and financial services. When networks containing sensitive data may be compromised, regulators that oversee that industry often propose protection standards that ultimately become mandates. Failure to comply often leads to lawsuits, settlements, fines and significant reputational harm.

See also: Protecting Institutions From Cyber Risks  

Until then, the transportation sector can start by following the best practices as outlined in the National Highway Traffic Safety Administration’s “A Summary of Cybersecurity Best Practices,” published in October 2014 . Key observations and recommendations include:

  • Cybersecurity is a life-cycle process that includes elements of assessment, design, implementation and operations as well as an effective testing and certification program.
  • The aviation industry has many parallels to the automotive industry in the area of cybersecurity.
  • Strong leadership from the federal government could help the development of industry-specific cybersecurity standards, guidelines and best practices.
  • Sharing learning with other federal agencies is beneficial.
  • Use of the NIST cybersecurity standards as a baseline is a way to accelerate development of industry-specific cybersecurity guidelines.
  • International cybersecurity efforts are a key source of information.
  • Consider developing a cybersecurity simulator. It could facilitate identification of vulnerabilities and risk mitigation strategies and can be used for collaborative learning (government, academia, private sector, international).
  • Cybersecurity standards for the entire supply chain are important.
  • Foster industry cybersecurity groups for exchange of cybersecurity information.
  • Use professional capacity building to address and develop cybersecurity skill sets, system designers and engineers.
  • Connected vehicle security should be end-to-end; vehicles, infrastructure and V2X communication should all be secure.

The transportation sector is yet another industry that must learn to adapt to the systemic nature of cyber risk. Because of ever-increasing reliance on evolving technology, cyber risk will certainly begin to move toward the top of the list of transportation safety concerns. The captains of this industry can no longer claim ignorance to cybersecurity issues or completely delegate responsibility. They owe a duty to safeguard the flow of information that effectively keeps our planes airborne and our cars on the road. Failure to do so could be catastrophic.

Predictive Analytics, Text Mining, And Drug-Impaired Driving In Automobile Accidents

Drug-impaired driving is an increasingly difficult problem for property-casualty insurers, law enforcement officers, prosecutors, judges, and policy makers. From a nationally representative survey, 16% of weekend nighttime drivers tested positive for illicit drugs or medications.1 One in eight high school seniors responding to a 2011 survey reported driving after smoking marijuana in the two weeks preceding the survey. One in three drivers who died in fatal crashes and had known drug-test results tested positive for drugs (illicit substances as well as over-the-counter and prescription medications).2 Even as the total number of drivers killed in motor vehicle crashes declined 21% from 2005 to 2009, the involvement of drugs in fatal crashes increased by 5% over the same time period.3

Why the interest (especially on the part of property-casualty insurance stakeholders) in identifying drug- impaired driving for drivers involved in an automobile accident? Let’s begin with three reasons.

  • First, claim triage. Knowing that a driver (whether the insured driver or the other driver) might have been under the influence of a medication, prescription, drug, or illegal narcotic will help the insurer assign a claim adjuster or other specialist who is able to efficiently determine whether a drug-impaired condition existed at the time of the accident. As described below, determining whether a driver was driving under the influence of a drug (DUID) is much more complicated than determining whether a driver was under the influence of alcohol. Furthermore, the triage assignment could be specific to whether the driver had been taking a medication (such as an over-the-counter product), prescription, drug, or illegal narcotic.
  • Second, assignment of liability, and especially subrogation opportunities. Finding that the other driver was DUID may be cause for a subrogation recovery against that driver, or provide enough additional evidence to increase the likelihood or size of the recovery.
  • And third, knowing a driver had been involved in an accident while on a medication, prescription, drug, or illegal narcotic may be a reason to non-renew or to renew at a higher rate.

In this article, we demonstrate how information not commonly captured in automobile insurer data systems but available in automobile accident descriptions can improve an insurer’s ability to predict accident severity. We extract from accident descriptions information not typically captured in insurer data systems to capture whether one of the drivers in the accident was on a medication, prescription, drug, or an illegal narcotic. We found that the additional information in accident descriptions improved the ability to predict the severity of an accident. Narrative data can feed predictive analytics, improve claim-triage and subrogation recovery opportunities, and power a more intelligent approach to renewals and rate-classification. With DUID representing a measurable (but largely unrecognized) source of increased accident severity, automobile insurers have an opportunity to extract value from text mining and better manage the risk posed by driving under the influence of drugs.

Detecting DUID Is Difficult
Detecting drivers who are under the influence of a drug is much more complicated than detecting drivers who are driving under the influence of alcohol (or driving while intoxicated, DWI). Alcohol passes through the body in a reasonably predictable manner and has a reasonably consistent impact on a driver’s ability to operate a vehicle safely. Field tests can be performed efficiently for DWI with acceptable accuracy. Furthermore, it does not matter whether the blood alcohol content (BAC) was due to the intake of beer, wine, or hard alcohol; the sex, age, or body mass of the individual; or the length of time since consumption. In most states, a BAC of 0.08 grams per deciliter or higher is considered a per se case of DWI.

By contrast, tests for medications and prescriptions are more difficult to perform. It may take days, weeks, or months to obtain results. For the impairment-impact of drugs on an individual’s ability to operate a motor vehicle, there is no corollary to a BAC standard. Detecting drug-impaired driving is a complex problem due to the large number of substances with the potential to impair driving and impose the risk of an accident, the variations in the ways that different drugs can impair driving, the lack of basic information concerning the drugs that impair driving, and the differences in the ways that the drugs can affect the body and behavior.4

Identifying The Presence Of Drugs In Auto Accidents
In recent years, there has been an increased effort to train law enforcement to recognize drivers that may be DUID. The typical case is for an officer to perform a standardized field sobriety test for a driver’s blood alcohol content. If the BAC is found to exceed the statutory limit, the officer is unlikely to test for drugs, and consequently the incidence of DUID may be understated. If the BAC does not exceed the statutory limit, the officer may seek evidence for a DUID charge. In most states, a Drug Evaluation and Classification (DEC) program has been made available to law enforcement personnel and many officers have been trained to be Drug Recognition Experts (DRE). Nevertheless, identifying drivers under the influence of a drug is much more complicated than the testing for alcohol with a breathalyzer or urine test. For drug impairment, the tests may require a broader range of specimens (e.g., blood, urine, oral fluid, sweat, hair) and present technology often requires lab tests that may take days, weeks, or months for findings.

This built-in delay — and the variety of potential results — may pose a challenge when it comes to accurately tracking DUID instances, since the pertinent information may not be available at the time of the accident or when the police reports are prepared.

The Presence Of Medications, Prescriptions, Drugs, And Illegal Narcotics In Automobile Accidents
We have developed methods to efficiently read, organize, and analyze large volumes of narrative data captured in accident descriptions, adjuster notes, and other reports and documents where narrative information is recorded in an unstructured text format. Within a single narrative report and across reports, the same concept (such as taking his medications) can be expressed in numerous ways.

We have developed methods to organize the different-but-similar expressions into a format that can be categorized and then included in statistical analyses. A federal agency database on automobile accidents provided us with an opportunity to showcase these methods. The National Highway Traffic Safety Administration (NHTSA) compiled information on a broad representation of approximately 7,000 passenger automobile accidents. The accidents included single- and multiple-vehicle incidents, drivers of various ages, and accidents occurring in a variety of environmental conditions. The NHTSA compiled a narrative description of each accident, as well as the usual information on number of vehicles, road conditions, weather conditions, and time of day. The narrative described the environmental conditions, vehicular movements, and driver behavior at the time of the accident. On average, the narrative provided approximately 440 (and as many as almost 1,300) words describing the accident.

Processing The Accident Descriptions
Narrative descriptions for the 7,000 NHTSA accidents were broken into phrases, and similar phrases were grouped together using analytical models. After removing prepositions and uninformative prepositional phrases, the result was a data file with more than 13 million phrases (see Figure 1).

Incidence rates in automobile accidents and percent of accidents with injury for various conditions

Accident #1: “The driver of V1 sustained serious injuries during the crash…. The driver of V1 was taking several medications for various health problems….” (380 words)

Next, we used four different themes for identifying the presence of a medication, prescription, drug, or illegal narcotic. First, we identified phrases with a “taking medications” theme. We joined phrases with the word “medications” that indicated a driver may have been taking medications. For example, we joined “on many” and “taking pain” to form “on many medications” and “taking pain medications,” respectively.

Accident #2: [The driver] was transported, treated, and released at a local hospital for a head injury… An associated factor coded to this driver was the use of prescription medications … general health medication with possible side effects… (471 words)

The second theme followed the same process, replacing “medications” with “prescriptions,” which gave us phrases such as “on many prescriptions” and “taking pain prescriptions.” These two themes produced approximately 1,100 phrases.5

The third theme joined an action and a drug name. The result from these joins was a long list of phrases with “had taken [drug name],” “was on [drug name],” and so on, replacing [drug name] with the names of drugs. For the present analysis, we worked with 3,590 phrases with a drug name. The fourth theme was a list of 52 references to illegal narcotics that we considered red flags when seen on an accident description. This list included “cocaine,” “heroin,” and “marijuana.”

Accident #3: The driver was admitted … for a fractured femur and other injuries… a urine sample three hours after the collision tested positive for amphetamines and marijuana… (584 words)

In sum, the first two themes were general references to medications and prescriptions, the third theme captured references to drug names, and the fourth theme was a list of illegal narcotics that we considered “red flags” for a driver being under the influence of a drug. For each theme, a binary (0/1) variable was created to capture whether the presence of medications, prescriptions, a drug name, or an illegal narcotic was mentioned in the accident description.

An injury was reported to have occurred in 73% of the 6,949 accidents in the NHTSA database (see Figure 2).6 We found a reference to taking or being on a medication in approximately 16% of the accidents, and an injury occurred in 82% of these accidents. Similarly, we found a reference to taking or being on a prescription or a drug in approximately 6.5% of the accidents and an 80% injury occurrence for these subsets of accidents. Finally, we found reference to an illegal narcotic in 2.4% of the accidents and that an injury occurred in 89% of these accidents.

Presence of a medication, perscription, drug name, or illegal narcotic

Predictive Analytics
The variables created from the narrative data were combined with information from the structured data (such as day of the week, time of day, weather conditions at the time of the accident, and the nature of the accident) and used in a multivariate analysis designed to identify the factors associated with whether an injury occurred. The purpose for including the structured data was to take into consideration the information commonly available on auto accidents. If the variables from the narrative data did not improve the statistical results from the predictive analytics then the time and effort used to extract the information may not be worthwhile.

The analytic procedure was a logit analysis where the outcome measure was whether an injury occurred with the accident.7 The purpose of the analysis was to test whether the inclusion of the information from the accident descriptions improves the ability to predict accident severity. The database did not have information on the economic loss of the accident, and consequently we used whether an injury occurred as a proxy for accident severity; that is, we presumed that accidents with injuries are more expensive and serious than accidents without them.

In the logit analysis, we used information from the structured data to develop variables that would otherwise be included in an accident-severity analysis. The structured-data variables were whether the accident occurred at night, on a weekend, with poor weather, on a wet road surface, involved multiple vehicles, a rear-end or a head-on collision, or turning into the path of another vehicle, and if alcohol was present. We tested for the influence of four variables from the narrative data: reference to the presence of (1) a medication, (2) a prescription, (3) a drug, and (4) an illegal narcotic. For each of the four variables, we found that automobile accidents where the accident description indicated the presence of a medication, prescription, drug name, or illegal narcotic for at least one of the drivers increased the likelihood an injury occurred with the accident. The higher probabilities were statistically significant.

For each logit analysis, the chart in Figure 3 presents the probabilities for the reference group and with the inclusion of the narrative-data variable. For example, in the medications analysis, the probability of an injury was 0.57 for an accident that occurred in the daytime, on a weekday, in good weather, on a dry road surface, as a single-vehicle accident that was not a rear-end, head-on, or turning-into-the-path accident, and where alcohol was not present. For the same conditions, the probability of an injury increased to 0.75 if the accident description mentioned that a driver was taking or was on a medication — an increase of 18 percentage points or 30% over the no-medications probability. There was a similar finding for the presence of a prescription, drug, or illegal narcotic. Starting with the reference group’s 0.57 probability of an injury and holding all other variables constant, we found that the probability of an injury increased to 0.72 when one of the drivers was on a prescription, 0.72 when a drug name was mentioned, and 0.85 when there was mention of a specific illegal narcotic. Each of these findings was statistically significant at the 95% level of confidence.

The probability of an injury was higher when there was a presence of a medication, perscription drug, or illegal narcotic for one or more drivers

Concluding Comment
This analysis demonstrates that narrative data can be used to help insurers improve the results for predictive analytics. Information captured in claim adjuster notes and other text data can be used for improved claim-triage assignments, quicker identification of subrogation recovery opportunities, and to gather information on policyholders for renewal and rate-classifying purposes. As DUID becomes a more prevalent problem, the ability to identify these cases and respond accordingly will become an increasingly important aspect of an automobile insurer’s ability to efficiently administer and control claim costs.

1 Richard Compton and Amy Berning. Results of the 2007 National Roadside Survey of Alcohol and Drug Use by Drivers. Washington, DC: National Highway Traffic Safety Administration, Report No. DOT HS 811 175.

2 Office of National Drug Control Policy (October 2011). Drug Testing and Drug-Involved Driving of Fatally Injured Drivers in the United States: 2005-2009.

3 Office of National Drug Control Policy (November 2010). Working to Get Drugged Drivers Off the Road. Fact Sheet.

4 National Highway Traffic Safety Administration. Drug-Impaired Driving: Understanding the Problem and Ways to Reduce It: A Report to Congress. Report No. DOT HS 811 268, page 2.

5 For the present analyses, “aspirin,” “birth control,” and “vitamins” were excluded from the references for medications and prescriptions.

6 Although the NHTSA database provided case weights for the accidents, we did not apply the weights in our analyses. We take the perspective that the analyses are from the perspective of a property-casualty insurer, which is not likely to apply any weights when performing predictive analytics. The application of the case weights would have been more appropriate if the analysis was intended to extrapolate to all accidents or for public policy conclusions.

7 Given that the database was limited to accidents, an analysis on the impact of drugs on accident frequency was beyond the scope of the analysis.