6 Personal Security and Public Safety
The advent of many of the innovative mobility services, and particularly app-based ride services, has been accompanied by considerable concerns about safety—both personal security (criminal behavior) and public safety (driver and vehicle safety). Critics have argued that because transportation network companies (TNCs) are subject to fewer safety regulations than other for-hire services, they may pose a greater risk to passengers and the public. These concerns have been amplified by a handful of high-profile safety incidents—both crashes and assaults—involving TNC drivers. In this respect, TNCs have encountered the same dynamic as long-time shared-ride services such as taxis: public demand for action based on high-profile cases, but without a systematic analysis of the risks, costs, and benefits of potential approaches to risk management.
This chapter focuses on questions of personal security and safety given that these issues are considerably more complex than many imagine, and because of the current lively debates over the security and safety regulation of TNCs compared with taxis. Security and safety issues have not arisen for other mobility services to the same degree, except for debate over helmet requirements in bikesharing programs, which the chapter does address.
Driver Security
Driving other people for money has historically been a dangerous job; drivers are sometimes an easy target for the criminally minded
or simply desperate. All for-hire drivers are essentially in the business of letting strangers into their vehicles, and this activity fundamentally involves some risk. (It should be noted that casual carpooling, which occurs in some United States cities, also involves strangers driving together.) Driving a taxi, in particular, is quite dangerous relative to other occupations. The Census of Fatal Occupational Injuries shows that drivers and chauffeurs have a much higher rate of violent death (15 to 20 fatalities per 100,000 workers, 56 to 80 percent of which were homicides, from 2006 to 2012) than workers in general (3 to 5 deaths per 100,000 workers) (Feeney 2015). Taxi drivers, in other words, are more likely to be murdered than the average worker is likely to die of any cause.1
Taxi driving has historically been a dangerous occupation because, while the drivers are usually vetted, the passengers are not, and the vehicles typically are not tracked. Particularly in the case of street-hail service, drivers pick up unidentified people off streets, and there may be no record (in the vehicle or with the dispatcher) of where that pickup occurred or where the passenger was dropped off. Taxis also often hold substantial amounts of cash, and many lack secure driver–passenger partitions. In most cities today, taxis are dispatched through computer-aided systems with GPS, making locations known at all times, but many unknowns still characterize a taxi trip, including passenger identity and destination, particularly with street hails.
The risk of violence against drivers can be reduced if the rider becomes less of a stranger (less anonymous) and if the rider has less means or motivation to attack the driver. Anonymity can be reduced by identifying passengers and tracking vehicles, while the means for committing crime can be reduced through physical changes to a vehicle, such as bulletproof partitions separating drivers and passengers. Finally, the motivation for crime can be diminished by reducing the amount of cash in the vehicle. There is some evidence that making street transactions cashless can reduce street crime (Wright 2014).
TNC technologies that match passengers and drivers offer some solutions to these problems by removing the anonymity associated
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1 The frequency of taxi and chauffeur homicides is high compared with the frequency for other transportation workers. In 2012, for example, there were 28 murders of taxi and chauffeur drivers compared with none of intercity and transit bus drivers (U.S. BLS 2012).
with taxi trips—both among drivers and, especially, passengers. When a passenger books a trip, there is a record of who that passenger is, his or her credit information, where the pickup took place, and the drop-off location and time. The driver and managers at the TNC know the passenger’s name, and the trip can be tracked via GPS. Perhaps most important, no cash changes hands—the entire transaction, including any tip, occurs digitally and is not negotiated in the vehicle. TNC vehicles, unlike some taxis, do not have partitions, bulletproof or otherwise, but the absence of both cash transactions and anonymity may make such barriers less necessary (Smith 2005).
Passenger Security
Passengers in TNC vehicles can be exposed to risk if drivers behave recklessly, are less skilled than professional drivers, are prone to distraction, or criminally assault passengers. Many critics of app-based ridesharing have raised concerns about the skill and potential criminality of TNC drivers. Some critics also claim that the background checks used to ferret out unsavory drivers are generally more extensive, and effective, for taxi drivers than for TNC drivers, while others counter that less extensive vetting is needed for the latter because passengers retain an electronic record of each trip and driver, including his or her photo. In 2014 and 2015, there were several media reports of (primarily Uber) drivers assaulting passengers (e.g., Annear and Pattani 2015; Huet 2014; Lafrance and Eveleth 2015); there were also several reports of taxi drivers assaulting passengers (Manning 2014; NBC Connecticut 2014; Pulkkinen 2014). The committee was unable to determine whether the rate at which such incidents occur is higher in for-hire transportation than among other types of worker–customer interactions, or whether these incident rates vary appreciably between taxis and TNCs. Therefore, while companies and governments need to take reasonable precautions to prevent such behavior, appropriate precautions are difficult to determine in the absence of good data. The current practice across the car-for-hire industry is to review the criminal background of driver applicants (Daus and Russo 2015). (The lack of a criminal record does not ensure that a driver will not commit an offense in the future, but presence of
a criminal record has been shown to be a strong indicator of likelihood to offend again [Kurlychek et al. 2006].)
Most crimes are prosecuted at the state level. Therefore, local and state governments most commonly check for state criminal records of taxi driver applicants. In some cases, these checks include fingerprints; in other cases, just the applicant’s name and date of birth are used. In the latter case, the background checks are subject to potential errors resulting from variance in the spelling of names, inclusion of middle names or initials, and so forth. In addition to state criminal record checks, local and state governments have increasingly checked the FBI criminal record database, which goes back to 1924, and also performed interstate checks to pick up criminal records in neighboring states, a process that can take up to 16 weeks (Lafrance and Eveleth 2015). It is important to note, however, that while the FBI database is national in scope, reporting to that database is voluntary, and thus it is less comprehensive than state records. In addition, local police often send the FBI the fingerprints of people who have been arrested for a felony, but may fail to follow up if that person is later acquitted of the crime or if the charges were dropped or reduced to a misdemeanor. Because almost one-third of felony arrests do not result in a conviction, the possibility of a fingerprint scan’s yielding a false positive may be high (Neighly and Emsellem 2013).
Little detailed information is available about the procedures used to assess any criminal records that are found and how many drivers are rejected as a result of background checks. Anecdotal information indicates that regulators make decisions on a case-by-case basis to protect the public while also being fair to applicants.
Uber and Lyft both use private companies to perform background checks on their applicants. The companies providing this service report an average turnaround time of less than 2 days for all applicants (Isaac 2014). The methods the companies use do not include fingerprinting but do include checks of government criminal records in drivers’ counties of residence. These checks are generally less comprehensive than FBI background checks in a geographic sense and have limitations in terms of name matches. Nonetheless, the companies argue that their checks pick up records that are lost in processing before they arrive at a state or federal agency. Uber also has
stated that up to 10 percent of driver applicants, including some current taxi drivers, fail the background check (Hui 2015; Lafrance and Eveleth 2015).
Driver background checks are an area in which anecdotes are many but reliable data are few. The International Association of Transport Regulators is developing a national criminal background check clearinghouse for regulators, which will allow the trade organization to collect data on licensee criminal convictions and share those data among states (IATR New Orleans 2014 Recap 2014). Further study of both background check procedures and the frequency and types of incidents that occur in the for-hire industry could be used to evaluate how well these regulatory efforts protect the public and what improvements may be necessary.
Currently, jurisdictions that have legislation specific to TNCs require that each TNC conduct a background check on each potential driver before accepting that driver on its service. Where regulation applies, the state requires completion of a criminal background check that includes a review of the national sex offender database, based on applicant name and social security number. TNCs began driver background checks prior to regulatory mandates, but these background checks are now codified in regulations currently affecting the companies. TNCs reject driver applicants who have had any convictions within 7 years for driving under the influence (DUI), fraud, sexual offenses, any felonies that include a motor vehicle, any crimes involving property damage or theft, or acts of violence or terror. Without fingerprints, however, TNCs cannot access the FBI’s national criminal database. TNC representatives claim that databases may not be up to date, and external audits have found that the TNCs’ background check methodologies offer some improvements over traditional taxi background checks (Isaac 2014). Nonetheless, drivers with driving or arrest records that should have raised red flags do occasionally pass the screening (e.g., New York Times Editorial Board 2014).
As of this writing, Uber is using the background screener Hirease to review 7 years of county and federal courthouse records, a multi-state criminal database, the National Sex Offender Registry, a social security trace, and motor vehicle records. The company’s policy
is to reject any potential driver with a history of any of the following within the past 7 years: (1) any DUI or drug-related violation or severe infraction, (2) hit-and-run, (3) fatal accident, (4) reckless driving history, (5) violent crime, (6) sexual offense, (7) gun-related violation, (8) resisting or evading arrest, or (9) driving without insurance or with a suspended license (Uber.com 2014).
Similarly, as of this writing, Lyft is using the service Sterling (Isaac 2014) to check that a driver applicant (1) is at least 21 years old, (2) has had an active U.S. driver’s license for at least 1 year, (3) has had no more than three moving violations in the past 3 years, (4) has had no major traffic violations in the past 3 years, (5) has had no DUIs or other drug-related driving violations in the past 7 years, and (6) has had no extreme infractions (e.g., hit-and-run, felony involving a vehicle) in the last 7 years. For a criminal background check, Lyft checks as well for violent crimes, sexual offenses, theft, property damage, felonies, and drug-related offenses within the past 7 years. Lyft also states that the service does “not allow individuals to drive who are registered on the National Sex Offender Registry and DOJ 50-State Sex Offender Registry at the time our background check is conducted, regardless of how long ago the individual was put on that registry” (Lyft.com 2015).
In addition to background checks, neither Uber nor Lyft allows drivers to carry weapons in the vehicle. Uber initially required drivers to adhere to local and state laws on transporting firearms (Shavin 2015), but in June 2015, the company amended its policy to prohibit “firearms of any kind” for both passengers and drivers (MacMillan and Palazzolo 2015). Lyft has a strict no-weapons policy, and if a driver is reported to have a weapon in the vehicle, he or she is removed from the platform. Lyft retains the right to determine “what constitutes a ‘weapon’” (Shavin 2015). Rules about weapons in taxis vary by jurisdiction. New York City’s Taxi and Limousine Commission, for example, does not permit drivers to carry firearms (Smith 2015), but it is legal for taxi drivers in Washington, D.C., to carry a firearm if they have a concealed carry permit (Conneen 2014).
TNCs provide a passenger with information about his or her driver (name, vehicle type, license plate, and anonymized contact information) as soon as the match between rider and driver is made. This information is also included on the emailed receipt so the passenger
can access the information after the trip is complete. Most taxi regulations require that the name, photo, and license number of the driver be posted in the cab, but such postings are sometimes difficult to read and quickly forgotten unless the passenger writes down or photographs them, which itself could be perceived as a hostile act by a passenger. In contrast, the sharing of information about driver and passenger occurs automatically with TNCs once a transaction has been agreed upon, and this information is available to the TNC, the passenger, and ultimately law enforcement if required.
Although both the established taxi industry and the new TNCs provide detail about their background check methodologies, the committee was unable to find any careful empirical studies on the effectiveness of any of these methodologies with respect to passenger safety. Current practice, which strikes many as reasonable and prudent, is not evidence of best possible practice.
Vehicle Safety Inspections
In addition to driver safety records, which would be uncovered as part of the background checks described above, public regulators regularly impose vehicle safety requirements on taxis and limousines, while TNCs conduct safety checks on drivers’ vehicles before approving them for service. Taxi regulators typically conduct vehicle inspections that are more detailed or more frequent than state inspection requirements for private passenger vehicles. Nearly all agencies mandate periodic inspections of taxis, and nearly all agencies that regulate limousines impose mandatory inspections as well.2 Inspections typically are conducted annually, although a few jurisdictions have semiannual inspections, and New York City cabs are inspected three times a year. The inspections vary by jurisdiction but can be highly detailed; San Francisco’s inspections, for example, entail approximately 90 criteria on which vehi-
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2 Based on all agencies responding to the survey conducted for the paper presented in Appendix B and a 2012 International Association of Taxicab Regulators (IATR) survey of taxi regulators (IATR 2012).
cles are checked, from brakes and steering to interior cleanliness (Scribd 2014).
As of this writing, nearly all regulations by jurisdictions that permit TNC operations require vehicles to undergo an inspection covering all their major components before joining the service and annually thereafter. The inspections of Uber and SideCar vehicles must be performed by certified third-party mechanics. However, Lyft permits the vehicles to be inspected by other drivers, using a checklist provided by the company (Scribd 2015). Uber has expressed interest in allowing “peer mentors” to conduct inspections as well instead of requiring the use of licensed mechanics (Tyrrell 2015).
Vehicle-sharing services typically are not subject to public agency–imposed inspection requirements. Except for peer-to-peer services, carshare and bikeshare vehicles are maintained by the fleet operators and may be subject to safety-related requirements imposed by their insurance policies. Peer-to-peer carsharing companies typically have an age and mileage limit on eligible vehicles to help ensure quality.
Driver Distraction
An open question is whether the smartphone technologies upon which TNCs rely increase the risk of distracting drivers. While the proven risks of using a phone while driving, even in hands-free mode, have been widely publicized in recent years (e.g., Fitch et al. 2013; Klauer et al. 2014), drivers for TNCs must be alert to their phones continually and respond quickly to requests in order to accept paid rides and earn an income. Uber, for example, alerts an available driver of a service call by beeping, and the driver has 15 seconds to accept the fare by tapping the phone. If the driver does not respond within the 15-second window, regardless of the driving situation, the ride possibility is retracted and provided to another nearby driver. Flywheel, an app dedicated to taxi drivers, also requires very quick responses from a driver; when a customer requests a taxi, the message is sent to several nearby taxis, and the first driver to accept the request gets the fare (Richtel 2014). Uber has been sued by the family of a girl killed by an Uber driver in San Francisco. The suit alleges that the company’s
use of the app runs afoul of California’s distracted driving laws (Williams and Alexander 2014).3 At this point in time, no research is available on the risks of driver distraction from app-enabled services, but given the known risks of cell phone use while driving, the committee believes this is an issue worthy of examination.
TNCs and Drunk Driving
Driving while intoxicated is a significant cause of both motor vehicle crashes and vehicle-related mortality, and imposes large costs in terms of not only lost lives but also broader economic damage (Blincoe et al. 2014; Subramanian 2012). An analysis released jointly by Uber and Mothers Against Drunk Driving (using public data on drunk driving–related crashes and Uber’s own proprietary data) suggests that Uber’s entry into different cities may have resulted in less drunk driving. The analysis shows that demand for Uber spikes at times when bars close, that crashes declined in months after Uber began operating, and that crashes did not decline in markets Uber did not enter. The analysis further shows, albeit using only one city (Austin, Texas), that taxi availability declines after midnight, when Uber supply rises, in part because of surge pricing that brings Uber drivers out at late hours (Uber and MADD 2015). One possible interpretation of this fact is that without TNCs, many drinkers would not have rides home. Another analysis indicates that the entrance of UberX into a metropolitan area results in a 3.6 to 5.6 percent decrease in the rate of motor vehicle homicides per quarter in California, a potential savings of up to 500 lives annually (Greenwood and Wattal 2015). These results are specific to California and, while encouraging, still leave a great deal of the variation in death rates unexplained.
While these results are promising, they are far from conclusive. The studies conducted to date measured drunk driving in only a few cities, and many factors affect drunk driving, including socioeconomic conditions, population changes, enforcement, and cul-
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3 Conceivably the driver is not moving when he or she is pinged, since TNC drivers cannot pick up street hails.
tural factors. Crash data sets typically lag other traffic-related data by 1–2 years; because TNCs are still fairly new, sufficient data are as yet unavailable with which to complete analytical before-and-after studies measuring the changes in DUI-related crashes after the introduction of TNC services.
Other Issues
Many carsharing services charge a user based on the time that the vehicle is used, with some services charging by the minute and others by half-hour increments. This may lead to more dangerous driving (e.g., speeding) in order to reduce the length of the trip and therefore the monetary cost to the driver. In addition, driving an unfamiliar vehicle, as carshare drivers do, could potentially pose a safety risk, although such risk would be reduced over time as drivers used the service and the same cars frequently. The committee is unaware of any quantitative research on this topic.
It is worth noting that public safety regulations protecting passengers appear to be missing an important opportunity. One of the most cost-effective passenger safety regulations is the requirement for seatbelt use. Lack of seatbelt use is particularly pronounced in taxis. In New York, for example, the state’s seatbelt law does not apply to taxis and livery vehicles (Hu 2015), and in a rider survey conducted by the city’s Taxi and Limousine Commission, only 38 percent of passengers reported regularly wearing seatbelts (NYC TLC 2014), even though the state’s overall rate of seatbelt use is 90 percent (U.S. BTS 2013).
Data limitations make it difficult to draw conclusions about the frequency of bikesharing-related crash injuries (Appendix C contains a summary of research on bikeshare safety). Cycling advocates view requirements to wear a helmet as an impediment to cycling, and many dispute whether helmets reduce injury risk for cyclists. Regarding the latter debate, a paucity of data on cycling crashes and injuries makes it difficult to conduct convincing epidemiological studies. Some recent studies, based on limited crash data, suggest
that helmets may not reduce injury risk (Rivara et al. 2015), whereas other recent studies indicate that they are beneficial (Bonander et al. 2014; Wang et al. 2015). Biomechanical analyses suggest that helmets should be highly effective in reducing the risk of head injury in head impacts (Cripton et al. 2014).
The available evidence indicates that between 18 and 40 percent of bikeshare users in selected North American cities wear a helmet regularly, and 15 to 54 percent never wear one (Shaheen et al. 2012, 2013, 2014; see Appendix C). The main explanations given by those who do not wear a helmet are lack of helmet availability for spontaneous trips and the inconvenience of carrying a helmet even when trips are planned (Shaheen et al. 2014).
Personal security and public safety are major concerns for many of the new mobility services, as they are for the conventional for-hire industry, but little research has been done to guide public policy in these areas. Incidents involving abuses of passengers by TNC drivers have drawn considerable media attention. Conversely, little attention has been given to security issues faced by for-hire drivers, who are at higher risk of fatal assault and injury than other workers in general. TNC technologies (and similar apps being adopted by the taxi industry) may mitigate risks to both passengers and drivers by documenting the details of trips and removing anonymity, as may the cashless transactions made possible through TNC billing systems. The lack of similar documentation for routine taxi street hails makes it more difficult to establish abuse by or of taxi drivers. A more thorough evaluation of the benefits of TNC technology for driver and passenger security would help in determining how to approach regulation in this area.
Appropriately, public entities—local authorities and municipal, regional, and state governments—are implementing public safety regulations for TNCs and other shared mobility services. Much has been made of the inconsistencies between the background checks applied to taxi and TNC drivers and of different vehicle inspection requirements for the two types of services. The committee under-
stands the calls for consistent safety regulations across competing providers, but it is also struck by how little information is available on the efficacy of the existing background check requirements. Likewise, vehicle safety inspection requirements vary in their rigor across the for-hire industry. Cities such as San Francisco and New York have stringent inspection requirements for taxis that exceed those for TNCs. That said, the committee could find no evidence on the efficacy of the existing requirements for either taxis or TNCs. Research on the efficacy and relative costs of the different background check and vehicle inspection requirements would be informative for policy makers.
Other public safety issues are worthy of deeper investigation as well. Some evidence suggests that the availability of TNC services reduces drunk driving, although that evidence is preliminary and in need of verification. Taxis also may have this effect, but the technologies and policies used by TNCs, including surge pricing that expands supply, may encourage better service in late-night hours. Of concern, however, is whether TNC drivers’ heavy reliance on smartphones increases the risk of distracted driving; the committee can only speculate on this issue, but it is clearly an area in which further research would be helpful to policy makers.
References
Abbreviations
| IATR | International Association of Taxicab Regulators |
| MADD | Mothers Against Drunk Driving |
| NYC TLC | New York City Taxi and Limousine Commission |
| U.S. BLS | United States Bureau of Labor Statistics |
| U.S. BTS | United States Bureau of Transportation Statistics |
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