Introduction: One of the most persistent groups of impaired drivers that are seemingly unaffected by socialpressure, moral appeals, and the fear of arrest is that of the repeat impaired driver. This smaller group ac-counts for a disproportionate number of all impaired driving trips, often with high blood alcohol contents.New approaches are needed to identify and deal with the repeat impaired driver. Method: We propose amethod based on the discovery that almost 10% of all impaired driving calls for service involve repeat vehi-cles. Using the number of times a vehicle appears in our data, the average time to repeat, and the personalitycharacteristics of the repeat impaired driver, we are able to create a comprehensive and predictive descrip-tion of a Repeat Target Vehicle (RTV). Conclusions: Our method provides an opportunity to explore newand innovative crime reduction strategies that were never before possible.
There has been significant progress in the reduction of impaireddriving over the years, but this progress has slowed (Fell & Voas,2006; Williams, 2006). This slowing may be due to the “easy gains”achieved by the development of tactics affecting the population ofimpaired drivers that are more easily influenced by moral appeals,social pressure, and the fear of arrest (Beirness, Mayhew, & Simpson,1997; Dula, Dwyer, & LeVerne, 2007). The remaining group of impaireddrivers is a smaller butmore persistent group that accounts for a dispro-portionate number of all impaired driving trips (Beirness et al., 1997).More specifically, 3% of licensed drivers are responsible for 86% of allimpaired driving trips (Beirness, Simpson, & Desmond, 2003). Indeed,some estimate that prior to arrest, the typical impaired driver offendsbetween 80 and 2,000 times (Scott, Emerson, Antonacci, & Plant,2006). These drivers have learned from experience that the risk ofgetting caught is very low (Dula et al., 2007). Not only does the repeatimpaired driver account for somany incidents, but they typically offendwith much higher blood alcohol contents as compared to the moreoccasional impaired driver (Beirness et al., 1997; Simpson, Beirness,Robertson, Mayhew, & Hedlund, 2004). Finding a way to deal withthis group is challenging because many are not even known to police.In fact, most alcohol related crashes involve drivers that have neverbeen previously arrested (Scott et al., 2006). The repeat impaired driverrepresents one of the largest threats to public safety. Consequently, the
Council and Elsevier Ltd. All rights
literature has identified the need to develop strategies to deal with thisdangerous group (Beirness et al., 1997; Simpson et al., 2004).
There are slightly varying definitions for what constitutes a repeatimpaired driver. In Simpson et al. (2004), the authors summarizeseveral of these definitions and offer their own operational definitionas one that frequently drives with a blood alcohol content in excess of0.15%, and has been charged with two or more offenses. For our pur-poses, we adopt the more general definition given in Beirness et al.(1997) that describes the repeat impaired driver as onewho repeatedlydrives after drinking, often with a very high blood alcohol content orBAC (i.e., 0.15% or higher), may have been previously convicted of animpaired driving offense, and may display signs of alcohol abuse. Weprefer this definition because it recognizes the diversity present withinthe population of repeat impaired drivers and that many are not knownto police. Impaired drivers are a heterogeneous group (Jonah &Wilson,1986), and thus any strategies developed need to be createdwith this inmind.
Currently, there does not exist a reliable way to identify and dealwith repeat impaired drivers who are either unknown to police orhave not been convicted enough times to trigger more serious sanc-tions. The low risk of being caught may be the single largest reasonthat people drink and drive (Scott et al., 2006). What is proposed is amethod that analyzes calls for service data and highlights patterns ofrepeat activity. Using the patterns in the data, and the known character-istics of repeat impaired drivers, we are able to create a comprehensiveand predictive description of a Repeat Target Vehicle (RTV). This is aunique find that affords police agencies the opportunity to developnew and innovative crime reduction strategies specifically targetingthe repeat impaired driver. While this work is currently still under
40 J. Stewart / Journal of Safety Research 43 (2012) 39–47
evaluation as part of a larger case study, preliminary evidence suggeststhat it may be possible to identify many dangerous vehicles before theywould otherwise be known to police.
The rest of this paper is structured as follows: background infor-mation is given in section 2; our method and preliminary validationis presented in section 3; crime reduction strategies and their policyimplications are discussed in section 4; and, finally, conclusions anddirections for future work are given in section 5.
2 In Saint John, a file is generated for those calls for service where an offense has oc-
2. Background
Several in-depth studies exist on the repeat impaired driver. InBeirness et al. (1997), a useful model is provided showing the threedistinct stages of impaired driving and corresponding interventionpoints. This model is illustrated in Fig. 1 and defines the distinct stages:before, during, and after the offense. The authors also provide a summa-ry of the strategies traditionally applied at each intervention point.
At the initial stage, before the offense occurs, strategies typicallyinvolve the promotion of such initiatives as media campaigns, serverintervention programs, designated driver programs, and tighter alco-hol control policies. While these appear to influence the more sociallyresponsible individual, it is unclear how much of an effect they haveon the repeat impaired driver.
Throughout the second stage, during the offense, strategies typicallyfocus on enforcement activities. Spot checks and saturation patrols arecommon as are lookout operations that encourage the public to reportsuspected impaired drivers. Enhanced detection techniques like passivealcohol sensors can be used to help officers quickly detect the presenceof alcohol, thus providing the necessary grounds to continue with amore rigorous investigation. These techniques can be effective butmay not be enough to deter the repeat impaired driver. These drivershave learned that the chances of being pulled over by police are lowand, even if they find themselves in a spot check, for example, theirchances of making it through undetected are relatively high. That is,about 50% of all impaired drivers elude detection at spot checks (Fell,Lacey, & Voas, 2004).
The third stage of impaired driving occurs after the offense has takenplace. Interventions at this stage can be categorized as driver-basedsanctions (e.g., license suspension, incarceration), vehicle-based sanc-tions (e.g., impoundment, alcohol ignition interlocks), and remedialprograms (e.g., education and treatment). No matter how effective
1 Reprinted with permission from Beirness et al. (1997). Highlights: DWI RepeatOffenders: A Review and Synthesis of the Literature. Traffic Injury Research Foundation,Ottawa, Ontario.
strategies are at this stage, they are inherently limited simply becausemost repeat impaired drivers never reach this stage due to the lowprobability of being caught and convicted.
In addition to the work above, there are several other significantstudies that focus on the repeat impaired driver. For example,Simpson and Robertson (2001), a first installment of a major study onimproving the effectiveness of dealing with repeat impaired drivers,examines policy and practice pertaining to enforcement and highlightsseveral areas in need of improvement. This is a significant contributionthat focuses on strengthening traditional methods versus exploringnew techniques.
In Century Council (2008), several strategies are reviewed withrespect to the identification, apprehension, and subsequent sanctionsimposed on repeat impaired drivers. One strategy specifically targetingthe repeat impaired driver involves the circulation of hot-sheets con-taining the names of drivers that have been suspended following fiveor more impaired driving arrests. This strategy has had some positiveoutcomes but also suffers inherent limitations since convicted driversrepresent only a fraction of the total population of impaired drivers(Jonah & Wilson, 1986). Another study that recognizes that morework needs to be done on the non-convicted impaired driver is givenin Williams, McCartt, and Ferguson (2006). Here the authors contendthat the largest proportion of repeat impaired drivers go undetected.Consequently, their work identifies the need to develop more effectivedeterrentmethods to increase both the perceived and real risk of appre-hension so that even impaired drivers that have not yet been arrestedmay be dealt with.
Finally, Williams (2006) illustrates the importance of focusing onall impaired drivers regardless of their level of impairment. The authorsassert that too much emphasis is placed on the heavy consumer (i.e., aBAC of 0.15% or higher), thus removing focus from the more commonbut still dangerous lighter consuming impaired driver (i.e., a BAC ofabout 0.08%). The authors also contend that repeat impaired driverscan indeed be influenced by deterrent methods. It would appear then,that the biggest challenge is to identify them in the first place.
As we will show, our work provides a way to identify and dealwith the repeat impaired driver at all three intervention points,often before they are known to police. In the next section we describeour methodology for selecting a Repeat Target Vehicle.
3. Method
As context, the Saint John Police Force consists of 167 sworn officersand 29 civilian employees serving approximately 70,000 people over anarea of 300+ square kilometers within Canada's oldest incorporatedcity. In Saint John, over the 5-year period between 2004 and 2008,there was nearly a four-to-one ratio between the total number ofimpaired driving calls for service received and the total number ofimpaired driving files generated.2 This difference is not unexpecteddue to the large number of vehicles that are “gone on arrival,” orreported with no direction of travel, or even stopped but determinedto be unimpaired (either correctly or incorrectly).
Table 1 shows the distribution of impaired driving calls for servicefrom 2004 through 2010.3 As one can see from the table, the numberof calls has increased markedly since the recorded low in 2005. In-deed, the number of calls for service received has increased by55.1% since 2004. This suggests that lookout-operations like MADD(Mothers Against Drunk Driving) Campaign 911 – a nation-wide ef-fort to encourage and empower the public to report suspected
curred, or when it is necessary to document important details of an incident.3 Note that 2009 and 2010 were included here to confirm a continuing trend. All oth-
er analysis is done on the five years of calls for service data occurring between 2004and 2008.
41J. Stewart / Journal of Safety Research 43 (2012) 39–47
impaired drivers (MADD Canada, 2007) – are resulting in significantlymore calls from the public.
Notice, however, that the corresponding number of files generatedsince 2004 has essentially remained unchanged. Of course, therecould be several explanations for this including, for example, thatperhaps the public are simply not as well trained to identify impaireddrivers. In our case, however, we found that the number of impaireddriving calls concluding with “No Report – Gone on Arrival” hasincreased by 226% from 2004 to 2010. This suggests that police agen-cies need to create systems that are capable of utilizing the increasedvolume of call data before they can truly benefit from such lookoutoperations.
Table 3Repeat Call Outcomes.
First Call for Service
3.1. The Repeat Effect
Impaired driving is a crime type that essentially requires police tocatch the offender in the act in order to charge them with an offense.Police agencies are conditioned, then, to forget about past impaireddriving calls. In the words of one officer, “you either get ’em, or youdon't.” An analysis of these past calls, however, yields some veryinteresting results.
Our method is based on the discovery that a high percentage ofimpaired driving calls for service involve vehicles that appear inour Computer Aided Dispatch (CAD) system two or more times,many of which have never been dealt with by police. Again fromTable 1, during the five-year period between January 1, 2004 andDecember 31, 2008, there were 3,125 calls for service for impaireddriving and 874 impaired driving files generated. 79.4% of thesecalls for service contain complete license plate information (i.e.,2480/3125). Another 1.1% of calls contain partial plate informa-tion (i.e., 33/3125). For those vehicles containing complete licenseplate information (i.e., 2480), we show the breakdown of repeatvehicles in Table 2. From the table, one can see that 163 vehiclesappear in our system two times, 39 vehicles appear three times,5 vehicles appear four times, all the way up to a single vehicleappearing eight times. Alarmingly, some vehicles appear in our sys-tem as many as five times for impaired driving without ever beingchecked by police.
To summarize, 20.6% of the impaired driving calls for servicethat contain complete license plate information pertain to vehiclesthat appear in our CAD system two or more times (i.e., 512/2480).Since the first occurrence is not considered a ‘repeat,’ we calculatethe actual number of repeat calls as 11.9% (i.e., 296/2480), or 9.5%of all impaired driving calls regardless of having complete licenseplate information or not (i.e., 296/3125). This is a very strong effectthat may in fact be under-representing the true number of repeatsdue to the 20.6% (i.e., 645/3125) of calls for service that do nothave complete license plate information but may possibly representadditional repeat incidents.
The above repeat effect is so strong that we can actually detect anelevated level of risk for future incidents that decays smoothly overtime. Fig. 2 illustrates this risk decay. When an impaired driving callfor service is received, a repeat incident is most likely to occur withinthe next 1 to 30 days. That is, 24.3% of repeat incidents occur duringthis time period, followed by a relatively smooth decline in the levelof risk for future incidents. The 0 days category captures those situa-tions where a single incident is reported by more than one person,or when a vehicle is reported multiple times within a 24 hour period.In our data, it is rare to find a repeat occurrence that is caused bymore than one person reporting the same incident because subse-quent calls are typically linked to the active call already in progress.These subsequent calls normally do not contain vehicle informationand thus do not show up in our repeat totals. Most calls in this cate-gory, therefore, are due to additional sightings made several hoursafter the initial call. The 360+ category captures all other repeat inci-dents that occur more than one year after the previous incident.
The smooth risk decay depicted in Fig. 2 is especially impressiveconsidering that we are presumably analyzing a very small propor-tion of the actual repeat impaired driving population. This riskdecay suggests that the process of selecting a Repeat Target Vehicleneeds to consider not only how often but how recent a vehicle ap-pears in our data. Consequently, even those vehicles with only twocalls for service may be of interest to us. From an analytical perspec-tive, this is a unique find since most crime types require three of-fenses before a pattern emerges. Here we can detect an actionablepattern allowing us to make contact with potential repeat impaireddrivers after just two incidents. This manner of thinking may be ex-tendable to other crime types that are traditionally forgotten soonafter the call has been concluded.
In addition to considering the time between incidents, one canalso make several interesting observations regarding the outcomesof successive incidents. Table 3 summarizes the outcomes of all im-paired driving calls involving vehicles that appear in our data exactly
call contact file total %
Second Call for Service Call 58 12 20 90 56.6%Contact 11 3 5 19 11.9%File 36 6 8 50 31.4%Total 105 21 33 1594 100.0%% 66.0% 13.2% 20.8% 100.0%
4 The discrepancy in the number of repeat calls pertaining to vehicles appearing twice(i.e., 159 vs. 163 shown in Table 2) is due to two separate incidents that were bothcanceled by a follow-up call stating that the intoxicated person was not, in fact, driving.
42 J. Stewart / Journal of Safety Research 43 (2012) 39–47
two times. Each call for service is categorized as either a call (no po-lice contact made), a contact (police contact was made), or a file (afile was generated during police contact). The counts in each cell rep-resent the total number of times each first and second call outcomecombination occurred. For example, one can see that six files weregenerated during the second call for service when the first call for ser-vice ended with a police contact (with no file generated). This situa-tion, along with the reverse situation of having five contacts madeduring the second call when the first call resulted in a file, suggeststhat at least a subgroup of repeat impaired drivers may be able toelude police detection even when they are stopped as a result of animpaired driving complaint. This is further evidence to Fell et al.(2004) and shows how difficult it is to detect impairment evenwhen focused on a single driver outside the hurried context of a vehi-cle spot check.
Also from the table it would appear, at least anecdotally, that pa-trol members are slightly more likely to locate the suspect vehicle(shown in the table as either a contact or a file) during a second callthan they are during a first call (43.3% vs. 34.0%). Further, a higherproportion of these additional contacts seem to result in files beinggenerated (31.4% vs. 20.8%). One may also notice that 66% of secondcalls occur when the first call for service ended in no police contactbeing made. A causal relationship should not be inferred by the per-centage drop to 34% (the combined percentage for contacts andfiles) when police contact is made. This difference in magnitude issimply due to the fact that the most common outcome of an impaireddriving call for service is to have no police contact being made. Thisagain illustrates the need to develop better ways of identifying andmaking contact with reported impaired drivers.
Another subgroup that may be visible in Table 3 is represented bythe eight files that are generated during the second call for serviceeven though the first call also resulted in a file being generated. Thismay be the group of repeat impaired drivers that identify themselvesas having “nothing to lose” (Fetherston, Lenton, & Cercarelli, 2002). Afinal subgroup that may be visible here is represented by the threepolice contacts that were made during the second call even thoughthe first call also resulted in police contact. If someone is reportedmultiple times for impaired driving but deemed to be unimpairedeach time they are stopped, it should be possible to determine whytheir driving style is attracting attention. For example, for the repeatcalls in Table 3, about 10 police contacts were made as a result of el-derly drivers requiring a retest and drivers with medical conditions.All of the above subgroups should be kept in mind when developingreduction strategies so that the specific characteristics of each canbe addressed.
3.3. Characteristics of the Repeat Offender
In order to effectively deal with the repeat impaired driver it isuseful to know some of the characteristics that are common amongthese individuals. In Beirness et al. (1997), the authors offer severalsuch characteristics describing the repeat impaired driver as mostlymale (i.e., 90%), most often between 25 and 44 years of age, single,separated, or divorced, with large variations in aggressive personalitytraits and thrill-seeking behavior. The repeat impaired driver is alsomore likely to suffer from alcoholism and has likely been chargedwith other types of traffic violations. Education and income werenot found to be distinguishing characteristics of the repeat impaireddriver, although Jones and Lacey (2000) found this group less likelyto be university educated and more likely to be low income earners.
Additional characteristics of the repeat impaired driver are givenin Fetherston et al. (2002). These include various mental health issues(e.g., mania, depression), as well as the aforementioned sensation-seeking behaviors. The authors also suggest that the repeat impaireddriver is more likely to be unskilled or unemployed, and report themedian time to reconvict as 31 and 39 months, for males and females,
respectively. This work reports that 65% of repeat impaired driversare under the age of 25, which differs from the most common cohortreported above. The work given in Ouimet et al. (2007) also suggeststhat neurocognitive impairments are common with repeat impaireddrivers and may play a factor in their persistence and resistance tointerventions.
Jonah and Wilson (1986) describe the repeat impaired driver asbeing externally controlled, maladjusted, and resentful. They alsocharacterize this group as heavy consumers of alcohol that exhibitmore aggressive driving styles (i.e., higher speeds, hostility, and lesscautious). This work also found that most repeat impaired driverswere employed, which differs from the previously cited study. Anoth-er finding in contrast to other work shows that 41.9% of females self-report to have driven while being impaired versus the 7.5% that areactually convicted for the offense. This discrepancy may be explainedby McMillen, Pang, Wells-Parker, and Anderson (1991) in which theauthors compared the characteristics of impaired drivers detected atspot checks and those detected as a result of an accident or movingviolation. It was found that impaired drivers detected at spot checkswere very similar behaviorally to non-convicted and non-impaireddrivers. It is supposed that convicted impaired drivers may have ahigher likelihood of being selected by police simply because of theirrisky driving behaviors. The authors also state that the non-convicted repeat impaired driver consumes less but more frequentlywhereas the convicted repeat impaired driver consumesmore heavilyon each occurrence.
Finally, in Baca, McMillan, and Lapham (2009), the authors showthe importance of considering drug use when determining an indivi-dual's traffic risk. It was found that those addicted to both alcohol anddrugs pose a greater risk to public safety than those addicted to alco-hol only. This finding is alarming given the emerging evidence thatdrug impaired driving may now be nearly as common as alcohol im-paired driving (Beirness & Beasley, 2010).
Knowing the characteristics of the repeat impaired driver and rec-ognizing the diversity present within the group should position us tobe more effective in identifying and dealing with these individuals.
3.4. The Repeat Target Vehicle (RTV)
The findings outlined in the previous three sections form the basisof our formal method for selecting a Repeat Target Vehicle. Thismethod has already evolved from the initial version as a result ofour improved understanding of our calls for service data and isgiven in Fig. 3. This most recent version takes into account that im-paired driving activity often appears under several call types otherthan impaired driving. For example, we have found useful impaireddriving information in such calls for service as hit and runs, drivingcomplaints, damage to property, and suspicious persons. Consequent-ly, the first step in our selection process is the automated extraction ofall vehicles that appear in our CAD system during the last 30 days re-gardless of call type. This also includes officer self-initiated vehiclestops which, as we will later show, should still be considered usefuleven though a vehicle may have been stopped several times withoutincident. Treating each vehicle stop as a one-off actually works infavor of anyone trying to elude detection. Instead, knowing that a ve-hicle is attracting ongoing attention may give officers the confidenceto make a more well-informed decision based on the more completepattern of behavior.
For a typical 30 day period in Saint John, this extraction step re-sults in a list of about 1,365 unique vehicles. Next, for each vehiclein the list, our automated query extracts the entire CAD history asso-ciated to the vehicle over the past 5 years. Again, for a typical month,this results in about 2,344 total calls for service. All calls are thengrouped by vehicle and all vehicles are sorted in descending orderby the number of calls associated to them. In our case, and consider-ing all call types, it is not uncommon to have some vehicles appearing
1. Extract a list of all vehicles appearing in CAD during thelast 30 days for all call types and vehicle stops.
2. Extract the 5 year history of each vehicle on the above list, group all calls by vehicle and sort the vehicles in descending order by the # of calls associated to them.
3. Highlight all impaired driving calls in red and all vehicle plates appearing within the last week in green.
4. Create a shortlist by applying heuristics to each vehicle, for example:a. How many calls are associated to the vehicle?b. How recent is the call history and what is the nature?c. What was the outcome of the most recent call?d. Has the vehicle been previously flagged as an RTV?e. How serious was the behaviour described?f. Are callers unique or appear to be the same person?g. Is the caller a bartender, drive-thru, motorist, etc.?h. Are the calls originating from a consistent area?
5. For each vehicle on the shortlist, query the registeredowner on motor vehicle, CPIC, and the in-house records management system.a. Has the owner been previously charged for anything?b. Is there evidence of violence? Drug use?c. Does the owner have other traffic offenses?d. Are there any other vehicles associated to the owner?
6. Create the Repeat Target Vehicle map containing all information that might be useful to locate the vehicle in question.
Fig. 3. RTV Selection Process.
43J. Stewart / Journal of Safety Research 43 (2012) 39–47
in the list more than a dozen times. To make the remaining steps inthe selection process more practical, our automated query highlightseach impaired driving call in red and each plate that appears withinthe last week in green. While the focus is on the impaired drivingcall type, even vehicles without an impaired driving call may be of in-terest depending on the number and the types of other calls associat-ed to them.
These first three steps are automated and take but a few minutesto perform. The next three steps, however, are manual in natureand use a heuristics-based approach to shortlist the vehicles of inter-est. A heuristics-based approach is used by design because it is at thislevel of understanding that we have the greatest flexibility to adapt tounanticipated situations allowing for both a valid and reliable process(Martin, 2009, p.26). Over time, some of these heuristics may betransformed into a repeatable algorithm that may then be automatedas well. It is important, however, that there continues to be a manualheuristics-based step to allow the method to evolve based on an im-proved understanding. Some example heuristics are shown in Fig. 3.
Once we have shortlisted our vehicles we query the registeredowner of each on motor vehicle, CPIC,5 and our in-house recordsmanagement system for both criminal and non-criminal patterns ofbehavior that may be consistent with the repeat impaired driver. Itis important to emphasize that we only look at the registered ownerafter having shortlisted our vehicles of interest based on the reportedbehaviors of each. This by definition is not profiling because we arebasing our selection on behavior and information communicated topolice (Ramirez, McDevitt, & Farrell, 2000). When querying the regis-tered owners one may find additional vehicles associated to them.These additional vehicles may also be checked in the CAD systemfor other possible patterns of behavior. Finally, based on the balanceof information gathered, we select our Repeat Target Vehicle(s).
This selection process can be repeated as often as desired with anupdated pool of vehicles based on the 30 day sliding window of
5 CPIC, or the Canadian Police Information Center, is a computerized informationsystem created in 1966 to provide Canadian law enforcement agencies access to crim-inal information.
activity. The total number of Repeat Target Vehicles selected at anygiven time will vary depending on how many high quality choicesare present and how many operational resources we have available.If we consider the impaired driving call type exclusively, we can ex-pect a frequency of five repeat impaired driving calls per month(i.e., 296 repeat calls / 60 months). The actual number may be higherbecause we now consider all call types during the selection process.However, many of these candidates may not be suitable or mayhave already been dealt with by police during the most recent inci-dent. Agencies should resist pressure to publish too many RTVs atone time. Publishing too large a list, especially before the organizationhas built the necessary program capacity, may result in a loss of focus.This may be a difficult proposition for those who believe that the onlyway to make a measurable difference is to make a large number of ar-rests. Beginning an initiative on a smaller but more efficient scale,however, is better for increasing the perceived risk of apprehension(Lobmann, 2002).
Once we have selected our Repeat Target Vehicle(s), we create amap showing the location of all calls for service relating to the select-ed vehicle along with any information that might be useful for en-forcement purposes. Fig. 4 shows the very first Repeat TargetVehicle Map that was created in April of 2010 based on an earlier ver-sion of the above method. This particular rendition of the map actual-ly serves two purposes. First, the hotspots displayed on the map are ofa predictive flavor and highlight the highest concentrations of all im-paired driving calls for service received during the previous 30 days,blended with all impaired driving calls for service received duringthe ‘next’ 30 days, using last year's data for this time period. The sec-ond and primary purpose of this map is to present all information re-lating to the selected Repeat Target Vehicle. This particular RTVexample is discussed in the following section along with other valida-tion results.
3.5. Method Validation
We are presently in the early stages of operationalizing the abovemethod within the context of a larger crime reduction strategy (dis-cussed in Section 4). While it is too early to report on the status ofthis initiative and the value of our method as a crime reduction tool,there are strong indications that our work has considerable merit. Re-call the RTV map presented in Fig. 4. The vehicle identified on thismap appears four times in our calls for service data for impaired driv-ing. All calls were received within the previous year, with the mostrecent call being one week prior to the map being published. Duringthe most recent incident, an officer actually made contact with thedriver and concluded that he was not impaired. Due to the nature ofthe previous three calls, however, and the known difficulties withdetecting impaired drivers even when face-to-face (Fell et al.,2004), we proceeded to take a closer look at this candidate.
A check of our in-house records management system revealedthat the registered owner of this vehicle was charged in 2006 for fail-ing to remain at a motor vehicle accident (a common escape for animpaired driver). No other charges were listed, for impaired drivingor otherwise. A CPIC check, however, revealed that the individualhad been flagged with a caution code for violent behavior (consistentwith the characteristics of a repeat impaired driver). After carefulconsideration of all available information, it was decided that this ve-hicle may well pose a threat to public safety. All available informationthat could be used to successfully target this vehicle was added to theRTV map.
This map was presented to unit managers during our biweeklyCrime Control Meeting6 and received strong positive feedback. Itwas suggested that the pattern illustrated by this map was
6 Crime Control Meetings are information sharing sessions modeled after the Comp-Stat meetings introduced by the New York Police Department in 1994.
44 J. Stewart / Journal of Safety Research 43 (2012) 39–47
compelling enough that we as a police agency may actually have a re-sponsibility to act upon it. Anyone experienced with implementingnew initiatives in a police organization, however, will understandthat sometimes the right context is needed before organizational ca-pacity can build to handle such a change. This context came threemonths later when the individual identified by our method wasarrested and charged with his very first impaired driving offense.According to the file narrative, the vehicle in question was found ina residential area with children nearby. It was parked diagonally inthe middle of a roadway and had clearly struck several trees as itwas covered in pine needles. The driver of the vehicle blew 0.24%, ex-actly three times the legal limit. This outcome was unfortunate butsignificant as it validated that our method was able to accurately pre-dict the incident. Luckily for us, no one was seriously injured, orworse. This example affirms that we as an agency do indeed have aresponsibility to further develop this method and related strategies.
Following this incident we went on to select six additional Re-peat Target Vehicles over the course of several months. We refer tothis time period as our ‘pilot project’ because it is characterized ashaving no formal reduction strategies or supporting processes inplace. In spite of not having the adequate processes in place to suc-cessfully target these vehicles, several additional examples came tolight illustrating the value of our method. Of the six additional vehi-cles flagged, three of them were involved in later accidents. Theseaccidents ranged from a minor collision, an abandoned vehiclefound in a ditch, and an officer being injured by an RTV while effect-ing the arrest of an impaired driver who was reported to be drivingerratically at high speeds. While our reduction strategies have notyet been evaluated, having four out of seven Repeat Target Vehiclesinvolved in later traffic accidents provides strong evidence that ourmethod may allow us to focus on a population that is target richwith dangerous drivers.
Another example illustrating the utility of our method occurredwhen a motorist reported a previously flagged Repeat Target Vehiclefor erratic driving. Officers were able to locate and perform a check ofthis vehicle and determined that the driver was not impaired. Giventhe additional context surrounding the vehicle, however, the officerswere able to successfully charge the driver for driving with unduecare and attention. This example illustrates that it may be possibleto apply our method to other risky driving behaviors in addition toimpaired driving.
As another level of validation, we reviewed 10+ years of alcoholrelated motor-vehicle accidents that resulted in either injury ordeath since January of 2000. Of the 64 incidents reviewed (including9 fatalities), we found that in 26 cases the suspect vehicle appeared inour CAD system prior to the incident. Most of these vehicles, however,appeared only once or twice, while six vehicles appeared more fre-quently (i.e., 4, 5, 5, 6, 7, and 8 times). Upon review of these incidentswe believe it likely that the current version of our method would haveflagged four of these vehicles as Repeat Target Vehicles before theirinvolvement in subsequent accidents. While this is only 6.25% of thetotal number of accidents with injury, this exercise has revealed apossible significant improvement to the selection process. That is, itmay be possible to increase the sensitivity of our method usingtext-mining software in order to highlight even those vehicles thatappear only once in our data. For example, one of the vehiclesreviewed had a single call for service entered two months before itsinvolvement in the fatality of a teenager. This call was recorded as asuspicious person and contained the keywords ‘teen,’ ‘drinking,’ and‘car’ in the call narrative. Having the ability to highlight such vehiclesthat appear less frequently in our call data would allow us to take ad-vantage of the relatively high percentage of high-risk vehicles thathave previous call histories. We are currently exploring this possibleimprovement and plan on validating a modified version of our
45J. Stewart / Journal of Safety Research 43 (2012) 39–47
method on all alcohol related accidents regardless of injury. Thiswould give us a larger dataset to work with and may yield resultsthat are more in line with our pilot study – besides, often the only dif-ference between an accident with injury and one without is a little bitof luck.
Another useful discovery is that 34.4% of previous calls for servicerelating to these vehicles were officer self-initiated vehicle stops con-taining no narrative to document the nature or conclusion of the stop.Many of these vehicle stops may have been related to possible im-paired drivers but without reasonable and probable grounds to takeaction the call type remains unchanged and typically no narrative isentered. However, even without narratives these vehicle stopstaken together can help to identify a valuable pattern of behavior.For example, one of the vehicles reviewed appeared in CAD fourtimes in the same year with all calls being officer self-initiated vehiclestops. Even though this vehicle was stopped four times in the sameyear without incident, it was involved in an alcohol related crashwith injury shortly after the most recent vehicle stop. Still another ve-hicle reviewed was stopped without incident just six hours before itsinvolvement in a fatal crash. We can only speculate that somethingabout these vehicles caught the attention of keen-eyed officers, butwithout sufficient articulable grounds no further action could betaken. As officers learn the value of our method, and the potentialfor saving lives by providing just a small amount of additional infor-mation (which until now would have been time wasted), evenmore data should be made available for analysis, thus strengtheningthe entire process.
4. Discussion
While the RTV map is the end product of our selection process, itmarks just the beginning of our crime reduction efforts. Armed withthis map we are able to create and apply new and innovative crimereduction strategies that strengthen each intervention point dis-cussed in Section 2. To document our efforts, each Repeat Target Ve-hicle selected is recorded as a ‘vehicle of interest’ in the fieldintelligence (FI) module of our records management system. This FIentry is central to the supporting process that is required to create or-ganizational focus as we implement the strategies discussed below.
At the first intervention point, before the offense occurs, we gen-erate notification letters to be sent to the registered owners of identi-fied Repeat Target Vehicles. These owners are informed that, “as aresult of our crime reduction initiatives, their vehicle has been flaggedfor possible unsafe operation, erratic driving, or possible impaireddriving, and that patrol members have been made aware and may,at their discretion, perform vehicle checks in accordance with theMotor Vehicle Act of New Brunswick.” These letters are non-accusatory and do not require any action from the registered ownerexcept to ensure that their vehicle is being operated in a safe andlegal manner. Each letter is served in person by a uniformed officerand documented as a supplement to the FI entry created above.Should a driver be later charged with impaired driving, we referencethe FI entry in the incident details. Every owner of a flagged vehiclereceives a letter, but the service of that letter may be purposefullydelayed so as to not unwittingly inform other would-be offendersthat they are not currently being focused on. For example, if letterswere served immediately one would know that they are not on policeradar by virtue of having not received a letter. We wish to create anenvironment where all repeat impaired drivers have to worry aboutgetting caught. We also recognize that the registered owner of the ve-hicle may not be the driver that is attracting attention to the vehicle.The registered owner could be, for example, a parent or a businessowner who is hopefully unaware of the behavior and will take neces-sary measures to correct it.
This type of strategy represents a very different way of thinkingfor police. In fact, several officers initially expressed concern that
sending such letters would prevent them from actually catching theoffender in the act. This position is understandable as even some lit-erature advocates that nothing short of swift arrest and prosecutionwill deter the repeat impaired driver (Dula et al., 2007). Decidedly,there are situations where arresting an offender is absolutely appro-priate and necessary. We know, however, that arresting offenders isonly a temporary fix. Consequently, we need to work towards a polic-ing culture that embraces crime reduction strategies that focus moreon influencing behaviors than simply arresting people. This particularstrategy of putting vehicle owners on notice is about influencingthese behaviors. Making direct contact with vehicle owners exposesthem to an element of enforcement, which is arguably the most im-portant factor for increasing the perception of risk (Beck, Fell, &Yan, 2009).
At the second intervention point, during the offense, our RTVmaps provide a useful tool for targeting the vehicles of interest. Vari-ous enforcement strategies are currently underway including, for ex-ample, members of the traffic unit conducting spot checks near thelocations identified on the map, and specially assigned patrol mem-bers being tasked with driving through specific locations periodicallythroughout their shifts. For this to be effective, it may be necessary toconduct spot checks in low traffic areas versus the more traditionalhigher volume choices. According to Vanlaar (2008), this is actuallybeneficial since both low and high volume traffic spot checks playan important role in reducing impaired driving. This aspect of ourstrategy is a rallying point for patrol because it encompasses a strongenforcement component. This is a significant benefit over many crimereduction initiatives that suffer criticism from officers as being “softpolicing.” This strong enforcement component is also essential toshow how effective our method is. One cannot increase the percep-tion of risk without having increased the actual risk.
Many officers will intuitively point out that the number of vehiclesbeing targeted is too small in comparison to the total population ofimpaired drivers to ever make a difference. While it is true that weare targeting a relatively small number of impaired drivers, this re-duction strategy is about influencing behaviors. Enforcement is akey factor to our strategy, but it is not necessary to arrest all offendersin order to influence the behavior of the larger population. Again,from Lobmann (2002), we know that the perception of risk is greatestfor a more focused but efficient initiative. Focusing on a smaller groupbut being more efficient (i.e., ensuring that those identified will mostcertainly be dealt with) puts all repeat impaired drivers in the posi-tion of having to worry about being included in this group. Shouldone find themselves in this group, the likelihood of apprehension in-creases dramatically. Increasing the risk of arrest has been shown tobe more of a factor in deterring offenders than the severity of punish-ment (Greene, 2003).
At the third intervention point, after the offense, our RTVmapmaybe utilized to better inform decision makers during the sentencingprocess. Traditionally, since the probability of getting caught is solow, and the chances of conviction are even lower, the informationavailable during sentencing is but a small snapshot of an offender'soverall activity (Simpson et al., 2004). Once an offender has been con-victed through the usual court process, our map may be used to pro-vide a more complete picture of their pattern of behavior. Thisinformation may impact the severity of the imposed sanctions fromjudges wishing to exercise a certain amount of latitude during sen-tencing; especially if the driver was previously warned that their ve-hicle had been flagged. Since our approach increases the risk of beingcaught, it stands to reason that even the imposed sanctions should bemore effective. Suspensions, for example, are often violated by repeatimpaired drivers because the risk of being caught is so low(Fetherston et al., 2002; Lenton, Fetherston, & Cercarelli, 2010).
In addition to addressing each intervention point above with a spe-cific reduction strategy, a thoughtful publicity campaign is necessary tohighlight enforcement activities. Consistent with the recommendations
46 J. Stewart / Journal of Safety Research 43 (2012) 39–47
in Barthe (2006), the message conveyed should complement enforce-ment activities by informing repeat impaired drivers of their increasedrisk of apprehension. This strong message is essential to the success ofour method as applied in the context of a larger crime reductionstrategy.
Finally, in order to facilitate later evaluation of the effectiveness ofour method and strategies, several measures shall be monitored on anongoing basis. Impaired driving is a crime type that we wish to main-tain, at least initially, the volume of calls for service but reduce thenumber of repeat calls. The number of alcohol related accidents, inju-ries, and fatalities are also tangible indicators of success. Other mea-sures being monitored include: the number of identified RepeatTarget Vehicles and their associated activity (i.e., additional calls, ac-cidents, and charges), the number of impaired files generated, theratio of calls to files, the differences in sentencing severity, and thenumber of complaints received. We are also monitoring the activityof all shortlisted RTV candidate vehicles in case there is an opportuni-ty to fine-tune our selection process.
4.1. Legal
Moving forward, we fully expect that our method, map, and re-duction strategies may create some controversy. The potential forcourt challenges should not discourage police agencies from explor-ing and expanding upon this method to fit within the context oflocal laws. The process of being challenged will only provide uswith additional opportunities to evolve and strengthen this method.Challengers will have difficulty denying that agencies have an obliga-tion to ensure that identified Repeat Target Vehicles are being operat-ed in a safe and legal manner. To reiterate, our method always beginswith the identification of a vehicle and only then moves on to high-light the registered owner based on a pattern of behavior unique tothat vehicle.
To ensure vehicle owners are not subjected to unjustified policeattention we are only using the RTV map as grounds to focus on a ve-hicle. Once located, officers are still required to formulate their morecomplete grounds for any subsequent vehicle checks. Presently, weare using the RTV map to strengthen these grounds, but not as thesole source of them. Should a vehicle stop take place as a result ofour RTV focus, a supplement containing the officer's observations isadded to the previously created FI entry. We are cognizant that falsereports may occur in order to draw undeserved attention to somepeople. The process given above, however, should minimize this pos-sibility. Further, any person submitting a false report does so at therisk of being investigated and charged just as they would for anyother type of false reporting. Finally, since we expect the Repeat Tar-get Vehicle map to play a part in the court process, matters of disclo-sure should be kept in mind when creating the map so as to protectany information that could be used by an offender to identify and po-tentially seek reprisal on callers.
4.2. Limitations
Although the above results are encouraging, this work should beconsidered preliminary as it is still under evaluation as part of an on-going case study. This method also needs to be validated against otheragency data to confirm the existence of a repeat effect in other juris-dictions. Finally, no method should be considered a silver bullet andwe cannot neglect other significant groups that contribute to the im-paired driving problem as a whole, such as youth, social drinkers, andalcoholics (Chamberlain & Solomon, 2001). Social drinkers, for exam-ple, are equally problematic because they are a larger group of peoplethat on occasion drink and drive (Williams, 2006). Each of thesegroups should be addressed as part of a larger crime reduction strat-egy, especially since the number of people who drive after consuming
any amount of alcohol appears to be on the rise (Vanlaar & Robertson,2010).
5. Conclusion
This research creates opportunities for developing new and inno-vative crime reduction strategies that were never before possible.These strategies can be created to address all three interventionpoints by increasing the perception of risk, increasing the actualrisk, and better informing the sentencing process. Furthermore,our method is predictive in nature and enables us to take actionagainst a dangerous group of drivers even before they are knownto police, regardless of being impaired by drugs or alcohol. This isan exciting development that takes us beyond what was previouslypossible when even expert opinion quite properly declared that wehave gone as far as we can with influencing the behaviors of im-paired drivers (Branswell & Meaney, 2010).
Future work will report on the evolution of our method, the statusof our crime reduction strategies, and key lessons learned. This workopens the door to exploring other more formalized predictive ap-proaches. We hope that other police agencies will build upon thismethod and initial strategies and develop even better ways to pres-sure the repeat impaired driver. Removing the ability for offendersto live in comfort thinking that they have eluded police increasesboth the real and perceived risk of being caught. As the single largestfactor in the decision to drink and drive, this should result in reducedlevels of impaired driving.
Acknowledgments
This work was supported by the Saint John Police Force. The opin-ions and findings presented in this paper are those of the author anddo not necessarily represent those of the Saint John Police Force.
References
Baca, J. C., McMillan, G. P., & Lapham, S. C. (2009). Repeat DUI Offenders Who Have Hada Drug Diagnosis: Are They More Prone to Traffic Crashes and Violations? TrafficInjury Prevention, 10(2), 134–140.
Barthe, E. (2006). Crime Prevention Publicity Campaigns. Problem-Oriented ResponseGuide, 5. Washington, DC: Office of Community Oriented Policing Services(COPS), U.S. Dept. of Justice.
Beck, K. H., Fell, J. C., & Yan, A. F. (2009). A Comparison of Drivers with High Versus LowPerceived Risk of Being Caught and Arrested for Driving Under the Influence ofAlcohol. Traffic Injury Prevention, 10(4), 312–319.
Beirness, D., & Beasley, E. (2010). A Roadside Survey of Alcohol and Drug Use amongDrivers in British Columbia. Traffic Injury Prevention, 11(3), 215–221.
Beirness, D., Mayhew, D., & Simpson, H. (1997).Highlights: DWI Repeat Offenders: A Reviewand Synthesis of the Literature. Ottawa, Ontario: Traffic Injury Research Foundation.
Beirness, D., Simpson, H., & Desmond, K. (2003). The Road Safety Monitor 2003: Drinkingand Driving. Ottawa, Ontario: Traffic Injury Research Foundation.
Branswell, J., & Meaney, K. (2010). Ending Drunk-Driving Deaths is in Sight, Experts Say.Retrieved June 1, 2011, from the National Post Website: http://nationalpost.com
Century Council (2008). Sourcebook: The National Hardcore Drunk Driver Project (3 rd ed).Washington, DC: Author.
Chamberlain, E., & Solomon, R. (2001). The Toothfairy, Santa Claus, and the Hard CoreDrinking Driver. Retrieved June 1, 2011 from the MADD Canada Website: http://www.madd.ca
Dula, C. S., Dwyer, W. O., & LeVerne, G. (2007). Policing the Drunk Driver: MeasuringLaw Enforcement Involvement in Reducing Alcohol-Impaired Driving. Journal ofSafety Research, 38, 267–272.
Fell, J. C., Lacey, J. H., & Voas, R. B. (2004). Sobriety Checkpoints: Evidence of Effectiveness isStrong, but Use Is Limited. Traffic Injury Prevention, 5(3), 220–227.
Fell, J. C., & Voas, R. B. (2006). Mothers Against Drunk Driving (MADD): The First 25Years. Traffic Injury Prevention, 7(3), 195–212.
Fetherston, J., Lenton, S., & Cercarelli, R. (2002). The Repeat DrinkDrivers Study [HE 5620 .D7F43 2002]. Perth, Australia: The National Drug Research Institute.
Greene, J. W. (2003). Battling DUI: A Comparative Analysis of Checkpoints and SaturationPatrols. FBI Law Enforcement Bulletin, 72, 1–6.
Jonah, B. A., & Wilson, R. J. (1986). Impaired Drivers Who Have Never Been Caught: AreThey Different From Convicted Impaired Drivers? Alcohol Accidents, and Injuries,Society of Automotive Engineers, Warrendale, PA, Technical Report P- 173. Journalof Safety Research, 17(4), 186.
47J. Stewart / Journal of Safety Research 43 (2012) 39–47
Jones, R. K., & Lacey, J. H. (2000). Final Report: State of Knowledge of Alcohol-ImpairedDriving: Research on Repeat DWI Offenders. Washington, DC: U.S. Dept. of Transpor-tation, National Highway Traffic Safety Administration.
Lenton, S., Fetherston, J., & Cercarelli, R. (2010). Recidivist Drunk Drivers’ Self-ReportedReasons for Driving Whilst Unlicensed – A Qualitative Analysis. Accident; Analysisand Prevention, 42, 637–644.
Lobmann, R. (2002). Drunk Driving: Probability of Detection and its Perception. Centerfor Traffic Sciences, Wurzburg, Germany. Policing: An International Journal of PoliceStrategies & Management, 25(4), 770–788.
MADD Canada (2007). MADD's Campaign 911. Retrieved June 1, 2011 from the MADDCanada Website: http://www.madd.ca
Martin, R. (2009). Design of Business: Why Design Thinking is the Next CompetitiveAdvantage. Cambridge, MA: Harvard Business Press.
McMillen, D. L., Pang, M. G., Wells-Parker, E., & Anderson, B. J. (1991). Behavior andPersonality Traits Among DUI Arrestees, Nonarrested Impaired Drivers, and Non-impaired Drivers. Substance Use & Misuse, 26(2), 227–235.
Ouimet, M. C., Browna, T. G., Nadeau, L., Lepage, M., Pelletier, M., Couture, S., Tremblay, J.,Legault, L., Dongier, M., Gianoulakis, C., & Ng Ying Kin, N.M. K. (2007). NeurocognitiveCharacteristics of DUI Recidivists. Accident; Analysis and Prevention, 39(4), 743–750.
Ramirez, D., McDevitt, J., & Farrell, A. (2000). A Resource Guide on Racial Profiling DataCollection Systems. Promising Practices and Lessons Learned. US Department ofJustice. Retrieved May 18, 2011 from the DoJ Website: http://www.ncjrs.gov/pdffiles1/bja/184768.pdf
Scott, M. S., Emerson, N. J., Antonacci, L. B., & Plant, J. B. (2006). Drunk Driving. Problem-Oriented Policing Guide, 36, . Washington, DC: Office of Community Oriented Polic-ing Services (COPS), U.S. Department of Justice.
Simpson, H. M., Beirness, D. J., Robertson, R. D., Mayhew, D. R., & Hedlund, J. H. (2004).Hard Core Drinking Drivers. Traffic Injury Prevention, 5(3), 261–269.
Simpson, H. M., & Robertson, R. D. (2001). DWI System Improvements for Dealing withHard Core Drinking Drivers: Enforcement. Ottawa, Ontario: Traffic Injury ResearchFoundation.
Vanlaar, W. (2008). Less is More: The Influence of Traffic Count on Drinking and Driv-ing Behaviour. Accident; Analysis and Prevention, 40, 1018–1022.
Vanlaar, W., & Robertson, R. (2010). The Road Safety Monitor 2010: Drinking and Drivingin Canada. Ottawa, Ontario: Traffic Injury Research Foundation.
Williams, A. F. (2006). Alcohol-Impaired Driving and its Consequences in the UnitedStates: The Past 25 years. Journal of Safety Research, 37, 123–138.
Williams, A. F., McCartt, A. T., & Ferguson, S. A. (2006). Hardcore Drinking Drivers andOther Contributors to the Alcohol-Impaired Driving Problem: Need for a Compre-hensive Approach. Traffic Injury Prevention, 8(1), 1–10.
James Stewart is a crime analyst and member of the management team of the SaintJohn Police Force. He received his Master of Computer Science degree from the Uni-versity of New Brunswick in 1999 and has since split his time between working inthe software industry, teaching at the university, and pursuing his Ph.D. (which isnearing completion). As a crime analyst, he has worked on several successful crimereduction strategies and is especially interested in action research. That is, expandingand applying innovative research ideas to make them actionable for police. His cur-rent research interests include repeat victimization, predictive hotspot mapping,and repeat impaired driving.
