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Accident Analysis and Prevention 43 (2011) 2166– 2172

Contents lists available at ScienceDirect

Accident Analysis and Prevention

jo ur n al hom ep a ge: www.elsev ier .com/ locate /aap

he dark side of stimulus control—Associations between contradictory stimulusonfigurations and pedestrians’ and cyclists’ illegal street crossing behavior

lorian Lange ∗, Michael Haiduk, Anke Schwarze, Frank EggertU Braunschweig, Institute of Psychology, Department of Research Methods and Biopsychology, Braunschweig, Germany

r t i c l e i n f o

rticle history:eceived 20 January 2011eceived in revised form 11 May 2011ccepted 14 June 2011

a b s t r a c t

Since illegal pedestrian behavior represents a major source of accidents, research investigating possiblereasons and risk factors for crossing against the lights is pivotal for enhancing safety in traffic. The presentapproach regards behavior at signalized intersections as a result of multiple stimulus discrimination.Hence, it is expected that at crossings divided by a median refuge the excitatory potential of a “consecutive

eywords:timulus controlllegal crossing behaviorisk factorsedestrian

green light” or “oncoming pedestrians” (S+*) attenuates the inhibition of crossing behavior induced bythe relevant red light (S−). Standardized observations at critical intersections in Braunschweig, Germany,were conducted to investigate these hypotheses. Comparing outside traffic participants’ behavior in thepresence of different stimulus configurations identified the assumed S+* as substantial risk factors forillegal crossings. Moreover, the presented model of stimulus control integrates past risk factor research

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ignalized intersections and may help develop fut

. Introduction

.1. Illegal crossing behavior

Since the introduction of the first pedestrian traffic light symbolsn 1961 by German traffic psychologist Karl Peglau (Duckenfieldnd Calhoun, 1997), generations of Europeans have learned an ele-entary discrimination rule from childhood on: red man means do

ot cross, green man means cross. Despite its simplicity, this prin-iple is frequently violated. Previous research indicates that 20%in Brisbane, Australia; King et al., 2009) to 35% (in Dublin, Ireland;eegan and O’Mahony, 2003) of all pedestrians cross illegally (i.e.,gainst the lights or away from the lights) at signalized intersec-ions. According to King et al. (2009), such behavior is involvedn over 58% of police-reported crashes at intersections and, there-ore, increases the relative crash risk per crossing event by a factorf eight. Given this hazard, diverse research has been looking foreasons and risk factors. Several surveys examining pedestrians’elf-reported behavior indicate that time is the relevant factoredestrians tend to optimize while moving in traffic (Gårder, 1989;isiopiku and Akin, 2003). Evidently, waiting periods induced byraffic signals interfere with this tendency to reduce delays at inter-ections. Hence, long durations of red light phases increase the

robability of pedestrians’ non-compliance (Yang et al., 2006). Fur-hermore, inclement weather conditions (Li and Fernie, 2010) asell as low traffic volumes (Yagil, 2000) appear to be risk factors

∗ Corresponding author.E-mail address: florian.lange@tu-bs.de (F. Lange).

001-4575/$ – see front matter © 2011 Elsevier Ltd. All rights reserved.oi:10.1016/j.aap.2011.06.008

revention measures.© 2011 Elsevier Ltd. All rights reserved.

for crossing against the lights. Mullen et al. (1990) found smallbut significant increases in the frequency of jaywalking producedby disobedient models (i.e., other pedestrians who were observedcrossing the street illegally and, thus, acted as a model in termsof social learning theory). In order to improve traffic engineeringand planning, diverse research has focused on the impact of sitecharacteristics. Although a median refuge lowers the crash rateson multi-lane streets by two to four times (Zegeer et al., 2004),compliance with the signals decreases as well (Jacobs et al., 1968;Li and Fernie, 2010). Weeber (1980) showed that to a large extentthis effect results from the additional waiting phase for pedestri-ans. Cambon de Lavalette et al. (2009) argued that central trafficislands reduce the demands on human visual surveillance and thusallow for less dangerous crossing against the lights.

Facing this evidence, it seems reasonable to construct signal-ized pedestrian crossings at wide intersections with a medianrefuge and a traffic light control enabling pedestrians to cross inone attempt. Several German cities including Braunschweig havealready realized such a traffic design in order to obviate additionaldelays due to the central traffic island. However, this produces anew risk factor, which may reduce the design’s purpose to absur-dity and inspired the present study: the presence of contradictorystimulus configurations (Lange et al., 2010).

1.2. Stimulus control and stimulus discrimination

During the procedure of stimulus discrimination learning, a spe-cific behavior becomes differentially reinforced depending on thepresence of an antecedent stimulus, which therefore, gains controlover this behavior by signaling its consequences. When contin-

and Prevention 43 (2011) 2166– 2172 2167

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ently associated with positive consequences (i.e., reinforcement)ollowing the execution of an instrumental behavior, a stimulusunctions as an excitatory discriminative stimulus (S+). As a result ofhe repeated pairing with negative consequences (i.e., extinction orunishment), however, a stimulus functions as an inhibitory stim-lus (S−) (Skinner, 1938; Spence, 1936). The extent to which thealue of a discriminative stimulus determines the occurrence of theehavior is described by the concept of stimulus control (Terrace,966). Apparently, the control provided by an S+ and the controlrovided by an S− interfere with each other and, hence, a hugemount of classical behavioral research has focused on the interac-ions between S+ and S− (Spence, 1936; Honig et al., 1963; Hearst,968). In experiments of Brown and Jenkins (1967) and Davis1971), pigeons’ responding to an S+ substantially decreases when

previously conditioned S− was presented simultaneously. Theuthors concluded that the inhibitory effects of an S− transferredo the response to an S+. This interference between excitatory andnhibitory discriminative stimuli will be crucial for the followingnalysis of pedestrian behavior at signalized intersections.

.3. The model

Most of the studies mentioned in Section 1.1 examined the rea-ons for disobedience to or non-compliance with traffic signals.ur behavioral approach, however, assumes that pedestrians are, in

act, obedient to signals. They do obey to signals, which have beenontingently associated with consequences during their lifetimeearning experiences, which, in turn, allow for far more accurateredictions concerning the optimization of movements in traffic.ence, pedestrians’ crossing behavior at signalized intersectionsan be regarded as a result of multiple stimulus discriminationrocesses (Mechner, 2008).

By observational and instructional learning as well as by ownxperiences, a green traffic light has become an excitatory stimulusS+) for the behavior “crossing” (i.e., signaling positive conse-uences following the execution of the behavior). On the otherand, a red signal has become an inhibitory stimulus (S−) forhe behavior “crossing” (i.e., signaling less positive consequencesollowing the execution of the behavior). During exploratorybservations at signalized intersections in Braunschweig, furtherotentially discriminative stimuli could be identified. Pedestriansrriving at different times of the traffic signal cycle are exposedo different stimulus patterns. First, the relevant signal (i.e., theignal on the median refuge) can be red while the consecutive sig-al (i.e., the signal behind the median refuge, which is supposedo be irrelevant for the crosser until (s)he arrives on the medianefuge) is green. It can thus be hypothesized that the consecutivereen traffic light being excitatory (S+) for the behavior “cross-ng” interferes with the inhibitory property of the red signal (S−).onsequently, the number of pedestrians crossing against the rel-vant red light should increase in presence of the S+ “consecutivereen light”. Moreover, pedestrians are regularly exposed to legallyrossing oncoming pedestrians during their own red light phase. Its evident that oncoming pedestrians have been associated withrotection provided by the signals. Hence, they have become an+ for the particular behavior “crossing”. Consequently, the num-er of pedestrians crossing against the relevant red light should

ncrease in the presence of the S+ “oncoming pedestrians”. Withoutn apparent reason to assume that pedestrians and cyclists differ inhis elementary learning process we extend our model to cyclists.

Since the length of the green light phase has been shown to be a

elevant factor for illegal pedestrian crossings in general (Weeber,980) and especially in combination with misleading traffic lightsHaiduk et al., 2010), a systematic examination of the interactionetween the green-phase duration and contradictory stimulus con-

Fig. 1. The consecutive green light condition.

figurations could provide additional insight into the behavioralmechanisms accounting for illegal pedestrian crossings.

Hypotheses

(1) The relative frequency of pedestrians and cyclists crossingagainst a red light increases when crossers are exposed to a far(consecutive) green light compared to an unambiguous base-line condition.

(2) The relative frequency of pedestrians and cyclists crossingagainst a red light increases when crossers are exposed tooncoming pedestrians compared to an unambiguous baselinecondition.

(3) A shortened green-phase duration increases the risk of illegalcrossings provided by a far (consecutive) green light.

2. Method

2.1. Site selection and characteristics

Multiple complaints from various sources concerning uncom-mon traffic light designs motivated pilot observations at severalintersections in the city of Braunschweig. According to trafficplanners’ intention to obviate delays due to the median refuge,pedestrian crossings featuring the hypothetically risky signal con-figurations were found across all of the city’s arterial streets.Intersections were selected when they were representative, i.e., fre-quented by a non-selective sample of pedestrians and cyclists, andenabled non-participating observations. Accordingly, two differentsignalized pedestrian and cyclist crossings across the same 7-laneurban road, each divided by a median refuge, were selected. Thesequences of signal phases regularly produced both unambiguousand contradictory stimulus configurations, so crossers arriving atsignal A (see Figs. 1 and 2) were allocated to different categories(unambiguous vs. consecutive green light at intersection 1 andunambiguous vs. oncoming pedestrians at intersection 2, respec-tively).

In a follow-up observation concerning hypothesis (3), a thirdpedestrian crossing was investigated regarding the potential asso-ciation between the S+ “consecutive green light” and illegal crossingbehavior. The chosen site did not differ from the first intersectionin any characteristic but the length of the green light phase.

2.2. Observation design and data collection

Crossing behavior (crossing against the red light/legal cross-ing) was recorded depending on the variables gender, means oftransportation (pedestrian/cyclist) and stimulus configuration. Inorder to investigate the differential hypotheses, differential obser-

2168 F. Lange et al. / Accident Analysis and P

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ation forms tailored on the particular contradictory stimulusonfigurations were developed and refined through pilot observa-ions. During the examination of hypothesis (1), crossing behaviornder an unambiguous stimulus configuration was compared torossing behavior in the “consecutive green light” condition (i.e.,o-occurrence of the S− “red light” and the S+ “consecutive greenight”; as depicted in Fig. 1). Potential interfering variables suchs the supposed S+* “disobedient model” (i.e., a pedestrian whos observed crossing illegally) and “oncoming pedestrians” wereikewise recorded.

During the examination of hypothesis (2), crossing behaviornder an unambiguous stimulus configuration was compared torossing behavior in the “oncoming pedestrians” condition (i.e., co-ccurrence of the S− “red light” and the S+“oncoming pedestrians”;s depicted in Fig. 2). Potential interfering variables such as theupposed S− “approaching car” or the supposed S+* “disobedientodel” and “consecutive green light” were likewise recorded.Observations were made from an unobtrusive observation spot

nd immediately entered in observation forms by two indepen-ent coders. Preliminary investigations revealed a high inter-ratereliability (� = 0.94), thus ensuring a standardized observation andonsistent classification.

Each intersection was observed in 4 h periods across the day onour different business days in September and October, so that theotal observation period summed up to 16 h ranging from 6:30 a.m.o 10:30 p.m. Hence, effects of different times of the day could bexcluded. The time between 10:30 p.m. and 6:30 a.m. was excludedue to the low baseline of pedestrians crossing at night.

During the investigation of hypothesis (3), we systematicallybserved a third pedestrian crossing for further 8 h, proceeding asescribed for the examination of hypothesis (1). In addition, thisollow-up study was intended to contribute to the validation of ouresults.

.3. Data analysis

Data were entered into Microsoft OfficeExcel spreadsheets.escriptive and inferential statistical analysis was supported byASW Statistics 18. In addition to the examination of relative fre-uencies, 2 × 2 contingency tables were constructed to analyzehether crossing behavior was statistically independent of the

ther recorded variables. In order to measure strengths of asso-iation and compare different risks provided by different stimulusonfigurations, odds ratios (Edwards, 1963) and their correspon-

ent 95% confidence intervals were calculated. Since observedases of potential interfering variables (i.e., discriminative stimuliecorded in addition to the hypothesized contradictory config-rations) were very rare, we excluded these cases to eliminate

revention 43 (2011) 2166– 2172

confounding variables. Data were further stratified into sub-groups (male pedestrians, female pedestrians, male cyclists, femalecyclists) to examine whether particular groups of traffic partici-pants were differentially susceptible to the investigated risk factors.

In a second analysis, we used a stratified sampling method con-cerning the second hypothesis. Since the S−“approaching car” washypothesized and proved to be a deterministic predictor for thenon-occurrence of crossing behavior (Das et al., 2005; Lange et al.,2010), the second hypothesis was retested to the exclusion of casesunder the influence of the S−“approaching cars” (i.e., in the subpop-ulation of pedestrians and cyclists having the opportunity to crossdue to the absence of crossing cars).

3. Results

As presented in Table 1, 7.17% of pedestrians and cyclists wereobserved crossing illegally during the first examination. Note thatthe listed percentage is not equivalent to the incidence of illegalcrossing because the sample (N = 1157) excludes crossers underother than tested contradictory configurations (i.e., crossers inthe presence of disobedient models or oncoming pedestrians). Itis, thus, the sum of crossers in the unambiguous condition andcrossers in the “consecutive green light” condition.

In order to investigate hypothesis (1), we compared the fre-quency of crossing against the light in the “consecutive green light”condition with the frequency of crossing against the light in theunambiguous condition. As can be seen in Table 1, the frequencyof crossing illegally escalated to 74.73% in the “consecutive greenlight” condition, which yielded an odds ratio of 207.15 (95% CI[103.37–415.15]). Neither the crossers’ gender nor their means oftransportation was significantly associated with the illegal cross-ing rate. Focusing on gender and transportation specific behavior,respectively, in the “consecutive green light” condition (Table 2a),male gender appeared to increase the odds of crossing illegally(OR = 2.85, 95% CI [1.06–7.63]). Stratifying the data into subgroups(Tables 2b and 2c) indicated that this increase is due to an increasedrisk of crossing illegally among male cyclists.

In order to investigate hypothesis (2), we compared the fre-quency of crossing against the light in the “oncoming pedestrians”condition with the frequency of crossing against the light inthe unambiguous condition. Results (Table 3) showed compara-tive distributions concerning gender and means of transportation.Whereas the presence of the S+ “oncoming pedestrians” increasedthe frequency of crossing against the lights to 17.69% (OR = 4.29,95% CI [2.60–7.07]), the S−“approaching car” reduced the cross-ing rate to zero. Analyzing the impact of oncoming pedestriansin a stratified sample to the exclusion of cases under the influ-ence of approaching cars resulted in an odds ratio of 9.70 (95% CI[5.73–16.425]). Tables 4a–4c indicate that crossing behavior in the“oncoming pedestrians” condition was independent of both genderand means of transportation.

Our third hypothesis was investigated by examining whethershortened green light phase duration at a third intersectionincreases the proportion of illegal crossings in the “consecutivegreen light” condition. Compared to the first observed signal con-trolled intersection (green light phase duration: 9 s), the frequencyof illegal crossing behavior at the third intersection with a consid-erable shorter green light phase (4 s) was significantly increasedto 12.81% which yielded an odds ratio of 1.90 (Table 5). Oncloser examination, this increase resulted from a remarkable risk

escalation in the “consecutive green light” condition. With theexception of one pedestrian, all subjects crossed against the lightunder this contradictory stimulus configuration (OR = 10,010, 95%CI [1025.90–97,670.65]). Table 5 also shows that males more fre-

F. Lange et al. / Accident Analysis and Prevention 43 (2011) 2166– 2172 2169

Table 1Illegal crossing rates at intersection 1.

n % Crossings at red Odds ratio 95% Confidence interval

Means of transportation Pedestrians 275 5.82 1.00 0.76–2.34Cyclists 882 7.60 1.33

Gender Male 727 7.57 1.18 0.73–1.88Female 430 6.51 1.00

Presence of S+ (consecutive green light) No 1066 1.41 1.00 103.37–415.15Yes 91 74.73 207.15

Total 1157 7.17

Table 2aDistribution of crossing against the light in the consecutive green light condition.

n % Crossings at red Odds ratio 95% Confidence interval

Means of transportation Pedestrians 20 60.00 1.00 0.86–7.20Cyclists 71 78.87 2.49

Gender Male 49 83.67 2.85 1.06–7.63Female 42 64.29 1.00

Table 2bStratified distribution of crossing against the light (consecutive green light condition).

n % Crossings at red Odds ratio 95% Confidence interval

Male Pedestrians 9 66.67 1.00 0.66–18.65Cyclists 40 87.50 3.50

Female Pedestrians 11 54.55 1.75 0.43–7.14Cyclists 31 67.74 1.00

Table 2cStratified distribution of crossing against the light (consecutive green light condition).

n % Crossings at red Odds ratio 95% Confidence interval

Pedestrians Male 9 66.67 1.67 0.27–10.33Female 11 54.55 1.00

Cyclists Male 40 87.50 3.33 1.002–11.09Female 31 67.74 1.00

Table 3Illegal crossing rates at intersection 2.

n % Crossing at red Odds ratio 95% Confidence interval

Means of transportation Pedestrians 429 8.39 1.27 0.78–2.08Cyclists 492 6.71 1.00

Gender Male 466 9.87 1.66 0.99–2.78Female 386 5.96 1.00

Presence of S+ oncomingpedestrians)

No 703 4.55 1.00 2.60–7.07Yes 218 16.97 4.29

Presence of S− (approaching car) No 820 8.41 n. def. n. def.Yes 101 0.00

(stratifieda) Presence of S+oncoming pedestrians)

No 703 4.55 1.00 5.73–16.425Yes 117 31.62 9.70

TD

Total 921

a Includes cases in the absence of the S− ‘approaching cars” only.

able 4aistribution of crossing against the light in the oncoming pedestrian condition.

n

Means of transportation Pedestrians 99

Cyclists 119

Gender Male 113

Female 105

7.49

% Crossing at red Odds ratio 95% Confidence interval

20.20 1.52 0.75–3.0914.29 1.00

21.24 1.91 0.92–3.9812.38 1.00

2170 F. Lange et al. / Accident Analysis and Prevention 43 (2011) 2166– 2172

Table 4bStratified distribution of crossing against the light (oncoming pedestrian condition).

n % Crossings at red Odds ratio 95% Confidence interval

Male Pedestrians 59 25.42 1.71 0.68–4.30Cyclists 54 16.67 1

Female Pedestrians 40 12.50 1.02 0.31–3.36Cyclists 65 12.31 1.00

Table 4cStratified distribution of crossing against the light (oncoming pedestrian condition).

Pedestrians Male 59 25.42 2.386 0.79–7.21Female 40 12.50 1

Cyclists Male 54 16.67 1.43 0.51–3.99

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uently crossed illegally at an intersection with a 4 s green lighthase.

. Discussion

.1. Impact of a consecutive green light on illegal crossingehavior

The co-occurrence of the S− “red light” and the S+ “consecu-ive green light” produces significantly higher illegal crossing rateshan unambiguous stimulus configurations. Features of stimulusontrol present an accurate and scientifically economic expla-ation for the impact of the S+ on the inhibitory potential ofhe relevant red light. As a far (consecutive) green light signalshe non-occurrence of negative consequences when crossing theoad, this excitatory stimulus attenuates the inhibition of crossingehavior provided by the S− “red light” when presented simul-aneously. Interestingly, the observed phenomenon addresses alassical question in behavioral psychology: do individuals respondo particular stimulus features or to the configuration of a com-ound stimulus (Pearce, 1994)? Note that, in addition to theonflict of excitatory an inhibitory stimuli described above, theompound of these two stimuli could have become an excita-ory stimulus itself. Within the present observational setting, itemains unclear whether traffic participants tend to cross againsthe light more frequently in the presence of a consecutive greenight because of the conflict between S+ and S− or because the

ompound of a relevant red light and a consecutive green light sig-als continuing signal protection (i.e., continuing red light phase

or crossing cars) and, therefore, the absence of negative con-equences when crossing the road. Future experimental designs

able 5omparison of crossing rates between intersection 1 and 3.

n % Crossing at red

Green-light period 9 s 1157

4 s 531

In 9 s: presence of S+ (consecutivegreen light)

No 1066

Yes 91

In 4 s: presence of S+ (consecutivegreen light)

No 465

Yes 66

In 4 s: means of transportation Pedestrians 215

Cyclists 316

In 4 s: gender Male 295

Female 236

12.31 1.00

are required and planned to distinguish between these two dif-ferent perspectives on stimulus discrimination in this particularsituation.

Furthermore, it is worth mentioning that male gender does notpose a risk factor for illegal crossing behavior until outside trafficparticipants crossed in the “consecutive green light” condition.

4.1.1. The role of green light phase durationConsistent with Weeber, 1980, illegal crossing rates differ at

intersections, which exclusively vary in the duration of the pedes-trian green light phase. Evidently, the frequency of crossing againstthe lights did not increase in unambiguous conditions but onlyin the presence of the S+ “consecutive green light”. Presumably,with a shortened period of signal protection provided by the rele-vant green light, the (assumed) signal protection provided by thecompound of S+ and S− gains relative influence on the crosser’sbehavior.

4.2. Impact of oncoming pedestrians on illegal crossing behavior

The co-occurrence of the S− “red light” and the S+ “oncomingpedestrians” produces significant higher illegal crossing rates thanunambiguous stimulus configurations, as well. Although parts ofthis observation may be explained by alternative mechanisms suchas imitation reflexes, behavioral analysis favors, along the lines ofthe “consecutive green light” condition, a stimulus control model.

Oncoming pedestrians during a red light phase pose a considerablerisk factor for illegal crossings; however, the excitatory potential ofthis S+ appears not to be as high as the potential provided by theconsecutive green light. This difference might be due to different

Odds ratio 95% Confidence interval

7.17 1.00 1.36–2.6712.81 1.90

1.41 1.00 103.37–415.1574.73 207.15

0.65 1.00 1025.90–97670.6598.48 10010.00

9.77 1.00 0.93–2.7914.87 1.61

15.93 1.94 1.12–3.358.90 1.00

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ontingencies involved in previous learning experiences concern-ng the crossing situation.

.2.1. The role of approaching carsBecause the S− “approaching car” signalizes the consequences

f crossing more directly and deterministically, its inhibitoryotential overlaps the oncoming pedestrians’ excitatory potential.

hierarchy of discriminative stimuli is conceivable and would mostikely be headed by this S−.

.3. Base rates and incidences

In contrast to other studies (King et al., 2009; Mullen et al.,990), the examined data do not allow to estimate the relative

ncidence of crossing against the lights in a specific environment.n order to identify specific risk factors, we compared the ille-al crossing rates in a specific contradictory condition with thease rate of crossing against the light in unambiguous situations.hese considerably low base rates describe the mere tendency toross the road against the red light in the absence of all iden-ified S+*. Variations between the base rates of crossing againsthe lights at the observed intersections should consequently resultrom unidentified or undocumented discriminative stimuli. Dif-erences in traffic flow (i.e., a different appearance pattern of the−“approaching car”) are a manifest factor for these site-relatedariations.

.4. An integrative model of crossing behavior

Whereas previous approaches simply stated site-related dif-erences in crossing behavior (King et al., 2009) or examined theystematic congruence between the type of site and distributionf violations of the pedestrian signals (Cambon de Lavalette et al.,009), the presented model of stimulus control provides a par-imonious approach to the actual associations between situationnd behavior. The situational and behavioral approach of the lat-er study is rather a cognitive decision-making model affectedy site characteristics. In contrast, we assume that different sitesegularly produce different situations (i.e., configurations of stim-li) influencing pedestrians’ behavior (i.e., responding to stimuli).ccording to Cambon de Lavalette et al. (2009), environmentalonstraints affect a decision-making process and encourage or dis-ourage crossing behavior. However, this model is implicitly alsoased on stimulus control, thus providing the same explanatoryalue while requiring more latent constructs.

Note that each external risk factor examined so far can ade-uately be modeled as a discriminative stimulus. The critical gapodel by Das et al. (2005) presented a first attempt to explain

he risk provided by high traffic volume (Yang et al., 2006) onhe situational–behavioral level. According to this approach, traf-c flow produces a sequence of time gaps between vehicles and

waiting pedestrian rejects these gaps (i.e., avoids crossing) untilhe first gap whose duration exceeds his critical gap, the minimalap that the pedestrian is willing to accept. In terms of stimulusontrol, small gaps (i.e. gaps which are smaller than the criticalap) signalize negative consequences in case of crossing and, thus,unction as an S− while large gaps (which exceed the critical gap)unction as an S+. Similarly, a disobedient model (i.e. a pedestrianho is observed crossing illegally; Mullen et al., 1990) signalizes

bsence of negative consequences and therefore functions as an S+or the crossing behavior. Finally, the existence of a median refugeuggests increased safety (decreased probability of negative con-

equences) and can be considered as an S+ as well. Even inclementeather conditions manipulate the perceived consequences of ille-

al crossing by increasing the aversive value of waiting at a redight.

revention 43 (2011) 2166– 2172 2171

In order to enable future risk factor research to obtain con-trastable results, an integrative theoretical framework is inevitable.Considering outside traffic participants’ crossing behavior as beingcontrolled by multiple discriminative stimuli proved to be fruitfulto identify further risk factors and to educe prevention measures.In addition, a hierarchy of discriminative stimuli (cf. Section 4.2.1)may be constructed to allow further conclusions about humanbehavior under stimulus control in traffic.

4.5. Implications for pedestrian crash prevention

Since illegal crossing behavior appears to result from a conflictbetween excitatory and inhibitory potentials provided by discrim-inative stimuli, adjusting the stimulus configuration might elicitthe favored response. Police at intersections, for instance, signalizenegative consequences in case of crossing. Enlarged relevant trafficlights on the median refuge are likely to have increased an impacton crossers’ behavior. Moreover, size, position and color saturationof the consecutive traffic light signal can be varied to reduce the sig-nals’ influence on human perception and thus on crossing behaviorat signal A. Possibly, even posters or stickers conveying informa-tion about negative consequences of illegal crossing or appealingto pedestrians’ function as a role model (i.e., someone who informsothers about consequences) could change the stimulus configura-tion enough to evoke less jaywalking.

4.6. Limitations

4.6.1. RepresentativenessFocusing on only three intersections featuring the examined

signal configurations during the observation may limit the rep-resentativeness of the present study. However, as mentioned inSection 4.3, the examined crossing rates do not claim externalvalidity. The present observation design aimed for identifyingdifferences between situations within an intersection, so theinvestigated associations are representative for all stimulus con-figurations involving the same contingencies. All external variableswere controlled for by context stabilization; if the pavement’s char-acteristic influenced crossers’ behavior, it did so in all observedconditions. Nevertheless, biases caused by selective sampling can-not be excluded. In order to solve this problem, validation studiesare underway, which indicate an only slight variation in strengthsof association due to site-specific factors, namely traffic density.

Moreover, our results are limited to crossings at intersectionsin the daytime of working days. Hence, future studies aiming toidentify discriminative stimuli for mid-block crossing behavior, atnight or at weekends, respectively, would account for both, moreexternally valid inferences and prevention measures tailored onspecific environmental characteristics.

4.6.2. Possible confounding variablesDuring our observations, we examined the influence of situa-

tional factors on individual crossing behavior. Thus, we did notdistinguish between individuals crossing singly and individualscrossing in a cohort of crossers, unless a particular crosser couldbe identified as a disobedient model (Mullen et al., 1990). Two ormore individuals who entered the street simultaneously during redlight were recorded as two distinct cases of illegal crossing. Sincesuch illegal crossing groups were infrequent at the intersectionsobserved in this study, they were not analyzed separately. Nev-ertheless, it remains possible that cohorts of crossers may be animportant unit of analysis that should be examined in the future.

Although we aimed to control for effects of observation time,we needed to be selective in choosing observation periods. Hence,the factors time of day and day of a week remain confounded, sincewe investigated different periods of the day on different days of

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he week. Even though the general learning mechanisms of stimu-us discrimination are unlikely to occur only at a particular time, aystematic comparison of the risks of illegal crossings at differentimes of the day or days of the week would be desirable, especiallyegarding the assignment of policemen at critical intersections.

Since the occurrence of the investigated stimulus configura-ions could not be actively manipulated, the impact of a particulartimulus configuration could not be examined in isolation from itsorrespondent position in the traffic light cycle. Thus, the increaseddds of crossing against the lights when exposed to contradictoryonfigurations might also result from differences in the (appar-nt) waiting time for pedestrians and cyclists. Such potential effectequires experimental investigation and would be most likely com-lemental to the discriminative mechanisms mentioned so far, ashe examined S+* signal both, the non-occurrence of negative con-equences when crossing illegally and a relatively long waiting timeor legal crossing.

.6.3. Observational imprecisionDespite the choice of an unobtrusive observation spot, it cannot

e ruled out that observers were perceived by crossers. However,f the observers’ presence influenced crossers’ behavior, it did so inll observed conditions. At worst, the direct observation led to annderestimation of the examined differences.

It is certainly beyond question, that all categorizations of situa-ional or behavioral characteristics require operational definitionshich draw artificial lines. Although observation forms wereesigned as explicit as possible (cf. Section 2.2), misclassificationsemain conceivable. Nevertheless, ensured inter-rater reliabilitynd the appointment of independently working observers dimin-shed this source of error.

.6.4. Practical significanceIn view of the fact that only one police-reported crash occurs

or every 173,000 illegal crossings (King et al., 2009), it is question-ble whether the identification of risk factors for illegal crossingignificantly assists in the enhancement of traffic safety. Further-ore, it is imaginable that, at a specific intersection, not a single

dditional crash occurs in the presence of contradictory stimuliecause the S+* actually provide signal protection. Nevertheless,he experience of positive consequences when crossing againsthe red light will reinforce this illegal behavior. Generalizing thiseinforced association to other intersections where contradictorytimulus configurations do not provide signal protection will sub-equently increase the crash risk in an indirect way.

Additionally, contradictory stimulus configurations not onlyrovoke risky but also disorderly behavior, which is punishabley law according to road traffic regulations. Development andnforcement of a system that regularly produces violations of itsegulations is inconsistent and thus improper.

. Conclusion

The present study examined risk factors for illegal crossingehavior at signalized intersections employing systematic obser-ational and behavior analytic methods. The postulated stimulusontrol model not only serves to explain the presented results but

lso provides a framework integrating past and promising futureesearch. Moreover, it enables traffic planners to enhance pedes-rians’ and cyclists’ safety at intersections by modifying particulartimulus configurations. Prevention measures may be educed from

revention 43 (2011) 2166– 2172

theory, thus avoiding expensive trial-and-error methods based onintuition.

Acknowledgement

The authors acknowledge and sincerely thank Anne Goralzik forpatiently supporting us with hundreds of helpful comments.

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