Pedestrian Crossing Compliance Characteristics At-Grade Signalized Crosswalks: Case Study in a...

Pedestrian crossing compliance characteristics at-grade signalized crosswalks: case study in a downtown-university campus environment (07-0114)

Darcin Akin

Department of City and Regional Planning, Gebze Institute of Technology Istanbul Cad. No:101, Gebze, 41400 Kocaeli, TurkeyTel:+90-262-605-1642, fax: +90-262-653-8495e-mail: [email protected], alternate e-mail: [email protected]

Virginia P. Sisiopiku

Department of Civil, Construction and Environmental Engineering, The University of Alabama at Birmingham, 140 Hoehn Engineering Building, 1075 13th Street South, Birmingham AL, 35294-4440 USA Tel: +1-205-934-9912, fax: +1-205-934-9855e-mail: [email protected]

Date of Submission: 11/14/2006 11:16 PM

Number of Words in the text: 5250 words

Number of Tables & Figures: 4 Tables and 5 Figures (2250words)

Total Paper Length: 5250 + 2250 = 7500 (max.7500 words)

Submitted for presentation & publication at 86th Annual Meeting of Transportation Research Board, 21-25 January 2007, Washington, D.C.

Reviewed on 11/14/2006

TRB 2007 Annual Meeting CD-ROM Paper revised from original submittal.

2

ABSTRACT

This paper presents the findings of an investigation of pedestrian crossing compliance characteristics at signalized crossings in a downtown-university campus environment. The study was designed to obtain information on pedestrian crossing compliance with "pedestrian green” (signal compliance) and with “crosswalk location” (spatial compliance). Pedestrian movements were observed along a 1-km section of a downtown boulevard adjacent to the campus of Michigan State University, East Lansing, MI, USA. The facility included several types of crossings such as midblock, unsignalized and signalized crossings. The results from the analysis indicated that a strong correlation existed between the presence of a positive type of traffic control and pedestrian crossing compliance. Pedestrians recognized and used the signalized intersection crosswalks properly in regard to their locations. However, they did not seem to be complying with the WALK signal in the same degree that they complied with the location. The collected data were used to model pedestrian crossing compliance characteristics based on various definitions of the compliance: spatial, temporal, and overall.

Key words: Signalized crosswalks, pedestrian crossing compliance, temporal (signal) compliance, spatial (crosswalk) compliance, overall compliance.


Akin and Sisiopiku 3

1. INTRODUCTION

The purpose of establishing crosswalks at-grade crossings is to channel pedestrian traffic to designated crossing locations and separate pedestrian movements from vehicular traffic by using proper channelization and signal designs. Separation of pedestrians from vehicular traffic by the introduction of high quality service crosswalks and the provision of an acceptable level of service in crosswalks are expected to increase pedestrian safety as well as crossing compliance. More importantly, proper usage of crosswalks by pedestrians and motorists increases pedestrian safety (1, 2) and reduces potentials for pedestrian crash occurrences (3). Researchers (4, 5, 6, 7, 8, 9) have been interested in the behavior of pedestrians crossing at various types of environments in order to reduce the conflicts with motor vehicles by increasing spatial as well as temporal compliance of pedestrians (10, 11).

1.1. Pedestrian Crossing Compliance

Many pedestrians crossing at signalized intersections tend not to comply with the signal indication of pedestrian green especially at times when low vehicular traffic flows are observed an/or vehicles platoon along the street. If one is interested in the assessment of the attractiveness of signalized crosswalks relative to unsignalized intersection or midblock crosswalks, spatial crossing compliance (compliance with location) should be the criterion to measure the effectiveness of such crossings because that creates a base to compare the two crosswalk types (3, 6, 11). In this way, the effect of signalization on the crossing compliance is excluded and the efficiency of the crosswalks is determined in a more comparable way. By this type of comparison, the design problems with signal timing and signal phasing might not conceal the real attractiveness of signalized crosswalks as compared to others. In addition, to improve the temporal compliance (compliance with signal timing), required signal timing and phasing adjustments can be done very easily. Signal (temporal) compliance is related to signal design (signal timing and phasing plans) and vehicular flow characteristics (gap, arrival type, speed, etc.), and presumably to pedestrian attitudes. On the other hand, spatial crossing compliance is a very important measure of effectiveness (MOE), which indicates whether or not the location of a crosswalk is properly selected relative to pedestrian paths, trip origins and destinations, and whether or not the crosswalk is well marked, safe and convenient for pedestrian crossing.

A study of walking activities and the characteristics of pedestrians in Beijing, Chinadefined illegal crossing as “crossing a road during the red signal” (12). Basically the temporal compliance is used as an MOE. However, if one really would like to examine how efficient the signalized intersection crosswalks are, overall pedestrian compliance (compliance with both location and signal) should be the criterion to assess the efficiency of signalized crosswalks since compliance with both location and signal timing are two main characteristics to describe signalized crossings (3, 6, 11).

Compliance rates reported in the literature imply that pedestrian compliance variedfrom location to location, city to city, and neighborhood to neighborhood. For example, a study that observed the characteristics of Chinese pedestrians reported that 70 and 57 percent compliance rates were observed at two study locations in Beijing (12). Another study done in Ohio reported that pedestrian crossing compliance rates at signalized and unsignalized midblock crosswalks were 85.4, and 86.4 percent with pedestrian sign, respectively (84.2percent without pedestrian sign) (10). In addition, crosswalk features, pedestrian characteristics, user preferences and habits are expected to affect compliance rates. Therefore, the comparison of crossing compliance rates from one place to another may not be very meaningful without knowing local pedestrian characteristics and behaviors, traffic conditions,



and the location of crosswalks relative to major pedestrian paths leading to crosswalks. When possible, various crossing options should be evaluated in the same environment. This approach is preferred for more meaningful comparisons since the effects of pedestrian characteristics, traffic exposure, and geometric features of pedestrian paths and roadways can be isolated from the effect of crosswalk features. Moreover, pedestrian behavior and attitudes are very important determinants in crossing compliance. Thus, it is important to know of what type of facilities pedestrians favor and what the attitudes of pedestrians towards such facilities are at different crosswalk and environmental settings (3, 6, 11).

1.2. Pedestrian Behavior and Attitude

A study by Forsythe and Berger (13) presented the results of interviews with pedestrians crossing unsafely during DON’T WALK signal indication or pedestrian red interval. It was reported that the reason for unsafe crossing was mainly time-related. A need to hurry or a desire to keep moving was the main reason behind the lack of compliance with pedestrian signals. In the study of pedestrian compliance in Beijing (12), the impatience of pedestrians and the lack of effective enforcement were reported as the main reasons for the low compliance with respect to pedestrian signals. Pedestrians were also less enthusiastic about using overpasses and underpasses than signalized crosswalks. However, when the safety aspects and other related attributes were appraised, pedestrians did not favor any type of crossing in particular. It was concluded that pedestrians in Beijing would accept any type of crossing facility as long as they were appropriate and sufficient (12). However, one should be cautious when applying conclusions from the behavioral studies conducted abroad to U.S. conditions since the pedestrian behaviors as well as attitudes might be very different due to differences in socioeconomic characteristics and physical conditions of the study populations.For example, it is reported that Asian pedestrians walk at slower pace compared to their Western counterparts (14, 15).

1.3. Research Motive

Traffic engineers and planners have always been desirous of gaining insight into the compliance of pedestrian crossings at locations with various crosswalk designs and features. Information obtained from the analysis of pedestrians’ preferences, behaviors and attitudes can be used as a guide for future projects through the identification of treatments that are favored by pedestrians (5, 6, 7, 8, 9). The objective of this study is to evaluate the effectiveness of the design that was implemented through the analysis of spatial and temporal pedestrian compliance rates at signalized crossings in a downtown-university environment. The data for this analysis were collected along Grand River Avenue in downtown East Lansing, Michigan, U.S., a city of 46,525 residents in 2000 (16, 17). East Lansing is home of Michigan State University (MSU), with over 40,000 students, faculty and staff. Most of the university students live within the city, with 31.3% of all or 14.573 persons lived in group quarters (16). Sorority and fraternity houses located downtown host a great deal of students and the high student population, whose mobility mostly depends upon walking and bicycling, puts significant pressure on the pedestrian network of the campus as well as the city’s. In recognition of the importance of pedestrian and bicycle modes in urban transportation, the city of East Lansing with the collaboration of the state of Michigan initiated a project of redesigning downtown East Lansing. The renovations along Grand River Avenue from Abbott to Bogue Streets aimed at improving traffic flow through downtown, increasing safety of pedestrian movements along and across the boulevard, and enhancing aesthetics of the corridor.



This paper presents the results from the analysis of pedestrian crossing compliance characteristics at signalized intersection crosswalks along the section of Grand River Avenue between Abbott and Bogue Streets. The collected data were used to model pedestrian crossing compliance in downtown East Lansing. The data and established relationships could be used as a basis for the development of pedestrian compliance models for the city of East Lansing and/or other large university campus cities in the U.S. with similar pedestrian characteristics.

2. SITE DESCRIPTION

Grand River Avenue is an east-west corridor at the northern boundary of Michigan State University (MSU) campus. The selected section was a 1 km (0.63 mi) long divided boulevard between Abbott and Bogue Streets (32,000 AADT in 2000). This section was renovated from 1994 through 1995 to include well-marked intersection and midblock crosswalks, pedestrian signs and shelters at the median, brick paving at crosswalk medians and curbs, and physical barriers, 2-foot high concrete walls placed at some locations to separate vehicle and pedestrian traffic. With new crosswalk layouts, traffic conflicts between pedestrians and left-turning vehicles from Grand River Avenue onto cross streets are aimed to be reduced by locating the crosswalks only at the east side of the intersections (Figure 1a, b).

To facilitate data collection the study site was divided into two sections, almost equal in length. The site had four signalized intersections, two of which are cross-intersections (Abbott and Collingwood), and the other two are T-intersections without the south leg (M.A.C. and Division, see Figure 1a, b). The Collingwood Street intersection was the only one that had crosswalks on both sides of the intersection. Crosswalk lengths varied from 27.8 to 37.9 m (91.2 to 124.3 ft). Median width also varied throughout the study site. To make the drawing of the study site’s scheme simpler, the median was drawn in a constant width. With respect to signal phasing design, the westbound traffic flow received the red signal a few seconds earlier than the eastbound traffic at the Abbott and Division intersections. The purpose of this arrangement was to allow the maneuver of eastbound left-turning vehicles onto the cross streets. During this signal interval, traffic signal remained red for pedestrians crossing the Grand River Avenue. However, pedestrians traveling southbound kept crossing the westbound portion of the roadway despite the steady DON’T WALK signal indication since westbound vehicles were stopped and pedestrians on the crosswalk observed no threat from the traffic.

3. METHODOLOGY

To study pedestrian crossing compliance characteristics, several definitions of pedestrian compliance rates are introduced. In general, crossing compliance rate (percent) is defined as the number of pedestrians crossing at a crosswalk location divided by the number of pedestrians in the “crosswalk influence area” in a period of time, usually an hour.

3.1. Crosswalk Influence Area (CIA)

The calculation of pedestrian crossing compliance rates (PCCRs) is based upon the assumption that each crosswalk has an influence area that attracts pedestrians crossing the street. In order to determine the crosswalk influence area (CIA) for each crosswalk, the distance between a pair of two consecutive crosswalks is divided into two by an imaginary…..



: barrier walls

FIGURE 1a Scheme of the Study Site: Section 1 (not to scale).

:

DIVISION ST

E

NS

tude

ntB

ook

Sto

re,

SB

SJa

cobs

on’s

sto

re

MSU

Student

Union

132.

6 m

(43

5’)

Abbott Signalized x-walk

38.4

m (

126’

)

MSU Student Union Midblock x-walk

ABBOTT ST

: shelter

M.A.C. AVE.

M.A.C. Ave Signalized x-walk61

.9m

(20

3’)

Jacobson’s Midblock x-walk

82 m

(26

9’)CHARLES ST

Charles St Unsignalized x-walk

113.

7m (

373’

)

Division St Signalized x-walk

signal

: stop sign

: signalized x-walks



FIGURE 1b Scheme of the Study Site: Section 2 (not to scale).

Collingwood St-East Signalized x-walk

E

N

Division St Signalized x-walk

MSU Berkeley Hall

MS

U F

eder

al

Cre

dit U

nion

B

ranc

h

DIVISION ST

Goo

d T

imes

P

izza

78.3

m (

257’

)

Berkeley Hall Midblock x-walk

: shelter

137.

8 m

(45

2’)BAILEY ST

Bailey St Midblock x-walk

70.7

m (

232’

)

Collingwood St-West Signalized x-walk

COLLINGWOOD ST

39.3

m (

129’

)10

1.8

m (

334’

)ORCHARD ST

Orchard St Unsignalized x-walk

signal

stop sign



line. Thus, each crosswalk is located between the two consecutive dividing lines serving as the boundaries of the so-called “crosswalk influence area.” Distances between three consecutive crosswalks (i-1, i, and i+1) are Li-1 and Li+1. Then, length of the CIA (Li

CIA) for crosswalk i is the sum of Li-1/2 and Li+1/2 (Figure 2).

3.2. Full and Partial Jaywalkers

Pedestrians complying with crossing location are those that cross within approximately 3.0 m (10 ft) from both sides of the crosswalk. This area is defined as crosswalk area (CA) in Figure2. Jaywalkers are pedestrians who do not comply with crosswalk location; i.e., do not walk within CA while crossing the street. On the other hand, partial jaywalkers are those whose crossing paths are partially in CA (3, 6, 11).

3.3. Pedestrian Crossing Compliance Rates (PCCRs)

Pedestrian crossing compliance rate (PCCR) is defined in three forms; namely, spatial, temporal and overall crossing compliance rates.

3.3.1. Spatial Crossing Compliance Rate (SCCR)

Spatial compliance refers to the crossing compliance of pedestrians with respect to crossing location or “CA” and SCCR discloses information on the percent of pedestrians crossing the street at designated locations (i.e., crosswalks) in regard to CAs:

)1(CIA

i

Li

i P

PSCCR =

where SCCRi = spatial crossing compliance rate at crosswalk i (percent); PiL = number of

pedestrians crossing within crosswalk area, CA, (ped/hr); and PiCIA = total number of

pedestrians in the crosswalk influence area (CIA) of crosswalk i (ped/hr).

(Li-1 / 2)

Crosswalk Influence Area (CIAi)

FIGURE 2 Definition of Crosswalk Influence Area (CIA) and Crosswalk Area (CA) for the Calculation of PCCR.

(Li+1/2)

Crosswalk i+1Crosswalk iCrosswalk i-1

Li-1 Li+1

Crosswalk area (CAi)

W



3.3.2. Temporal Crossing Compliance Rate (TCCR)

At signalized crosswalks, pedestrian crossing compliance can also be linked to the compliance with pedestrian WALK signal indication. As shown in Equation 2, temporal or signal compliance, TCCRi, is defined as the ratio of the number of the pedestrians (per hour) in CA, who comply with the pedestrian WALK signal indication, Pi

T, over the total number of the pedestrians within CA, Pi

CA

)2(CA

i

Ti

i P

PTCCR =

The reason that the denominator in Equation 2 is different from the one in Equation 1is because it is not meaningful to reason temporal crossing compliance when pedestrians do not comply with the crosswalk area at all. In other words, spatial compliance is the required condition for temporal compliance. Therefore, in determining TCCR only the pedestrians crossing in CA are considered.

3.3.3. Overall Crossing Compliance Rate (OCCR)

To combine the SCCR and TCCR for signalized crosswalks, the concept of overall crossing compliance rate (OCCR) is introduced. As shown in Equation 3, OCCRi is defined as the ratio of the number of pedestrians (per hour) who comply with both CA and pedestrian WALK signal, Pi

LT, over the total number of the pedestrians (per hour) within CIA, PiCIA

)3(CIA

i

LTi

i P

POCCR =

4. DATA COLLECTION AND REDUCTION

Pedestrian crossing patterns were studied through the analysis of field data collected by direct observation of pedestrian activities using video cameras set up at locations on the sidewalks along the study site. Pedestrian movements were observed and recorded for 30 min at each camera location during the noon- (10:30 am-1:00 pm) and afternoon-peak (2:30-6:00 pm)periods. Signalized crosswalks in the study area were observed over a total of thirty-six 30-min periods, while crossing data at unsignalized crosswalks were recorded over fourteen 30-min periods. Data collection was performed under various weather conditions (sunny, cloudy, snow sprinklers, cold, and warm). Eight video cameras were used simultaneously to record pedestrian movements in the site. Video cameras were consecutively located on both sides of the Grand River Avenue on the sidewalks in order to cover all possible pedestrian movements within the entire study area. The recording areas of consecutive cameras overlapped a little bit to ensure that all pedestrian walking activities were fully captured.

Special efforts were made to avoid double counting of pedestrians that started to cross within a view of one camera and completed their crossing within the view of subsequent camera(s). Though this type of data reduction process was a tedious and labor-intensive task, it was very beneficial as it allowed detecting every pedestrian movement in the entire study site. Due to this data reduction approach, not only the information on pedestrian volume and crossing locations became available but also additional data about conditions during crossing (such as pedestrian signal indication, the presence of other pedestrians and motor vehicles,



etc.) were obtained through a careful processing of the videotapes in the laboratory environment. Following data were recorded for the analysis of pedestrian movements: a) number of pedestrians who started crossing the street during pedestrian WALK signal (regular users); b) partial jaywalkers; c) jaywalkers; d) number of pedestrians in the crosswalk area (CA) during “DON’T WALK” signal (sneakers); e) number of pedestrians who crossed the first portion of a crosswalk during “DON’T WALK” signal and then continued crossing during the “WALK” signal (partial sneakers); f) number of pedestrians who crossed from a curb to the median during the “flashing DON’T WALK” signal (late starters); and g) the total number of pedestrians within the CA and CIA. Data summary forms were developed to report summarized data and calculate PCCRs. An example of such data reduction forms for signalized crosswalks is given in reference (9). 5. DATA ANALYSIS AND RESULTS

Spatial (SCCR), temporal (TCCR) and overall crossing compliance rates (OCCR) for the five signalized crosswalks (SICWs) in the study area were calculated and tabulated for each data collection session. Results are presented in the following sections.

5.1. SCCRs at the SICWs

The SCCRs in the study site varied from 68.4% to 98.2% with an average value of 83.1%. Descriptive statistics of the SCCRs are summarized in Table 1. Mean values for Sections 1 and 2 are 82.78% and 83.40%, respectively. Overall, a little more than four-fifths of all pedestrians crossing in the CIAs complied with crosswalk location. The null hypothesis that the pedestrians do not comply differently with the location of the signalized intersection crosswalks throughout the study site is not rejected at the 5% level of significance (t= -1.666 and p=0.117>0.05). This means that the signalized crosswalks are equally attractive throughout the study site since they appeared to be well marked and equally recognized as proper crossing locations by pedestrians in the study site.

TABLE 1 Descriptive Statistics of Spatial Crossing Compliance Rates (SCCRs) at Signalized Intersection Crosswalks (SICWs)

SCCR (%)Study

SectionCrosswalks Ave.

PCIA N† Min Max Mean85th

percentile Std. Dev.Mean (85th

percentile)(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Abbott 150 8 78.4 90.3 85.64 89.74 3.9141 M.A.C. 153 8 69.9 89.1 79.93 87.60 6.497

82.78 (88.88)

Division 351 8 78.2 93.6 84.41 92.87 6.213

Collingwood-West 53 6 70.0 98.2 88.38 98.04 10.3802

Collingwood-East 66 6 68.4 91.2 77.07 91.90 10.228

83.40 (93.50)

Total 773 36 Overall mean = 83.13

Conclusion: t-value = -1.666 and p = 0.117 > 0.05. The difference between the two means is NOT statistically significant at the 0.05 level; thus, Ho is NOT rejected.

†: N refers to the number of 30-min. observation periods.



5.1.1. Modeling of SCCRs at the SICWs

The SCCRs are modeled using regression analysis. In this analysis, pedestrian volume (PV), pedestrian volume over the length of CIA (PV/LCIA), 2-way vehicular volume (VVtwo-way) and the length of crosswalk influence area (LCIA) are considered as potential independent variables. Two models were developed via the regression analysis using a forward stepwise procedure. Equation 4 shows the first model and the model fit to the observed data is shown in Figure 3.

)4()ln(*10*836.5379.4ln 2 IModelL

PVSCCR

CIA

−+=

The goodness-of-fit (R2) of model I in Equation 4 is 0.176. The model is statistically significant at the 0.05 level (F=7.068 and p=0.012<0.05). Both the constant and ln(PV/LCIA)term were also found to be statistically significant at the 0.05 level (t=207.963, 2.659, and p=0.000, 0.012). Overall, the model is statistically significant and its prediction power is fair (17.6%). It is believed that with higher sample size better regression coefficients can be estimated.

4.2

4.25

4.3

4.35

4.4

4.45

4.5

4.55

4.6

4.65

-1.50 -1.00 -0.50 0.00 0.50 1.00 1.50 2.00 2.50

ln (PV/LCIA) (ped/hr/m)

ln (

SC

CR

) (%

)

lnSCCR = 4.379+0.05836*ln(PV/CIA)

R2 = 0.176, F = 7.068, p = 0.012

FIGURE 3 Modeling Results of the SCCR: ln(SCCR) vs. ln(PV/LCIA).

The second model in Equation 5 fits the data better as examined visually in Figure 4. The goodness-of-fit (R2) of model II is 0.484. The model is statistically significant at the 0.05 level (F=20.665 and p=0.002<0.05). Comparing the models I and II, model II visually fits the data better and has higher prediction power (48.4%). However, prediction powers of the two models cannot be compared through the goodness-of-fit (R2) since the constant in model II is forced to be zero.



)5(*4985.54 085742.0 IIModelPVSCCR =

5.2. TCCRs at the SICWs

The TCCRs varied from 24.1% to 73.5% with an average of 50.6% in the study site. Descriptive statistics of the TCCRs are summarized in Table 2. The mean values in Sections 1 and 2 are 45.14% and 55.02%, respectively. Overall, a half of the pedestrians crossing in CAs complied with pedestrian “WALK” signal. As calculated from Table 2 the average TCCR in Section 2 is 21.9% higher than that in Section 1. The null hypothesis that pedestrians do not comply with the WALK signal differently between the two study sections is rejected since the difference between the two compliance data sets is statistically significant at the 5% level of significance (t=-22.63 and p=0.039<0.05).

50

60

70

80

90

100

0 100 200 300 400 500 600 700 800 900

PV (ped/hr)

SC

RR

(%)…

SCCR = 54.4985 * PV0.085742

R2 = 0.484F = 20.665p = 0.002

FIGURE 4 Modeling Results of the SCCR: SCCR vs. PV.

Examining the average TCCR (50.6%) for the entire study site, it is believed that the rate is reasonable under the existing green time allocated for crossings because the cross streets (Abbott, M.A.C, Division and Collingwood) were allocated only approximately 36% of the whole green time of the intersections at the Grand River Avenue. If the arrival pattern of the pedestrians is assumed to be random with respect to the duration of the pedestrian WALK interval, only about 36% of the pedestrians are expected to cross the street during the WALK signal if they wish not to wait for the WALK signal upon their arrivals at the street. This brings the minimum expected TCCR to 36%. However, some pedestrians are forced to wait for the WALK signal because there are vehicles moving on the street when they arrive ……..



TABLE 2 Descriptive Statistics of Temporal Crossing Compliance Rates (TCCRs) at Signalized Intersection Crosswalks (SICWs)

TCCR (%)Study


PCA N† Min Max Mean85th

percentile Std.Dev.Mean (85th

percentile)(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Abbott 138 8 24.1 51.5 43.37 50.80 9.0201 M.A.C. 134 8 32.8 65.6 46.80 60.00 9.432

45.14 (50.42)

Division 321 8 48.9 73.5 57.64 70.05 7.999

Collingwood-W 51 6 37.5 60.7 52.05 60.62 8.2142

Collingwood-E 52 6 34.4 69.2 54.51 69.18 15.338

55.02 (67.95)

Total 696 36 Overall mean = 50.63Conclusion: t-value = -22.63 and p = 0.039 < 0.05. The difference between the two means is statistically significant at the 0.05 level; thus, Ho is rejected.

†: N refers to the number of 30-min. observation periods. the curb. Thus, the expected TCCR could be higher than the minimum expected values of TCCR. In conclusion, the percentage of signal obedient pedestrians as expressed in the observed average TCCR (50.6%) is found to be good enough under the existing signal design of the Grand River Avenue.

5.2.1. Modeling of TCCRs at the SICWs

Observation of the TCCR data in Table 2 indicates that pedestrian users do not comply equally with WALK signal indications at the two study subsections. The difference among the rates could occur due to several reasons, but one factor that can significantly affect the temporal compliance is the magnitude of vehicular traffic flow on the street. The expectation that pedestrians are forced to comply with signals when vehicular flow is high is examined and it is found that the volume of vehicular traffic (one-way), VVone-way, on the major street is significantly correlated with TCCRs at the 0.05 level (2-tailed Pearson correlation coefficient=0.380 and p=0.025<0.05). As also seen in Figure 5, regression analysis yieldedthe model in Equation 6.

)6(*10*212.2680.26 2wayoneVVTCCR −

−+=

R2, F-statistics and p values of the model in Equation 6 are 0.144, 5.553 and 0.025, respectively. Both the constant and VVone-way term were found to be statistically significant at the 5% level of significance (t=2.590, 2.356, and p=0.014, 0.025). Overall the model is statistically significant and its prediction power is similar to the one in Equation 4.

Another possible explanation for lower TCCR in study section 1 can be inappropriate signal timing that might introduce long delays and in turn encourage pedestrians to cross when an acceptable gap occurs in vehicular traffic. For this reason, average gap length (GAP) for a pedestrian to cross was compared between the two study sections (GAP1 = 8.96 sec and GAP2 = 9.99 sec) and TCCR was modeled using the independent variable of GAP observed at the signalized crossings (GAPABBOTT = 8.10 sec, GAPM.A.C. = 9.49 sec, GAPDIVISION = 10.42 sec, and GAPCOLLINGWOOD = 7.63 sec) as seen in Equation 7 in a quadratic form:

)7(GAP*6.451GAP*114.237-548.229 2+=TCCR



The model trial with average gap length (GAP) as seen in Equation 7 came up with better results. Absolutely the new model has higher prediction power with R2=0.217 than the model in Equation 6 and is significant at the 0.05 level (F=4.561, p=0.018<0.05). A cubic form of the regression model with the same independent variable yielded similar results (R2=0.213, F=4.458, p=0.019). Another alternative is to model TCCR with the number of adequate gaps per hour. However, we did not have a chance to try a model with this parameter since we did not collect data on that.

0

10

20

30

40

50

60

70

80

600 800 1000 1200 1400 1600

VVone-way (veh/hr)

TC

CR

(%

) ..

TCCR = 26.680 + 0.02212 * VVone-way

R2 = 0.144, F = 5.553, p = 0.025

FIGURE 5 Results of the Model for TCCR: TCCR vs. VVone-way.

5.3. OCCRs at the SICWs

The OCCRs at the study site varied from 20.5% to 60.7%, with an average of 42.98%. Table 3 presents the descriptive statistics of the OCCRs. The average OCCR at Section 2 is 21.7% higher than that obtained from Section 1. The difference between the average OCCRs of the two sections was found to be statistically significant at the 5% level of significance (t=-2.993, p=0.009<0.05). This means that overall pedestrians complied with the signalized intersection crosswalks differently between the two sections of the study site due to the variances in the temporal compliance between the sections.

5.3.1. Modeling of OCCRs at the SICWs

The OCCRs are modeled using the SCCRs and TCCRs as independent variables due to the definition of the OCCR. A regression analysis yielded the model shown in Equation 6.

)8(ln*992.0ln*977.0449.4ln TCCRSCCROCCR ++−=



TABLE 3 Descriptive Statistics of Overall Crossing Compliance Rates (OCCRs) at Signalized Intersection Crosswalks (SICWs)

OCCR (%)Study


PCIA N† Min Max Mean85th

percentile Std.Dev.Mean (85th

percentile)(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Abbott 150 8 21.9 47.9 38.50 47.31 9.3241 M.A.C. 153 8 20.5 48.7 38.21 47.23 8.462

38.36 (46.97)

Division 351 8 43.2 59.8 49.56 58.86 6.322

Collingwood-W 53 6 36.0 60.7 47.90 60.38 9.1162

Collingwood-E 66 6 30.6 50.0 41.62 49.88 7.813

46.68 (56.68)

Total 773 36 Overall mean = 42.98Conclusion: t-value = -2.993 and p = 0.009 < 0.05. The difference between the two means is statistically significant at the 0.05 level; thus, Ho is rejected.


R2, F-statistics and p values of the model in Equation 8 are 0.956, 350.878 and 0.000, respectively. The constant, lnSCCR and lnTCCR terms were found to be statistically significant at the 0.05 level (t=-9.894, 25.425, 10.691, and p=0.000 for all variables). Overall the model is statistically significant and its prediction power is quite high (R2=95.6%).

5.4. Effect of Pedestrian Signal on Crossing Compliance

The effect of the availability of pedestrian signal on pedestrian crossing compliance was analyzed to determine whether such features have any effect on crossing compliance. The null hypothesis used in the statistical analysis is that there is no difference between the SCCRs at crosswalks with and without a pedestrian signal. The signalized crosswalks included in this examination are Abbott, M.A.C., Division and Collingwood crosswalks. On the other hand, the unsignalized ones are Charles and Orchard St. crosswalks while the former is in Section 1 and the latter is in Section 2. The descriptive analysis of the SCCRs for both types of crosswalks is given in Table 4. The difference between the average values of the compliance rates for the two types of crosswalks was found to be statistically significant at the 5% level of significance (t=8.744 and p=0.000<0.05). Therefore, the null hypothesis that the existence of a pedestrian signal had no significant effect on the SCCRs was rejected.

TABLE 4 Comparison of SCCRs at Signalized and Unsignalized Intersection Crosswalks

SCCR (%) Crosswalks

N†Min Max Mean 85th percentile Std. Dev.

(1) (2) (3) (4) (5) (6) (7) Signalized intersections 36 68.4 98.2 83.13 91.45 8.047Unsignalized intersections

14 55.6 78.0 67.45 76.56 6.318

Conclusion: t-value = 8.744 and p = 0.000 < 0.05. The difference between the two means is statisticallysignificant at the 0.05 level; thus, Ho is rejected.




6. SUMMARY AND CONCLUSIONS

In this study, pedestrian crossing compliance characteristics at signalized crosswalks were examined through a study of pedestrians crossing activities along a major urban corridor in East Lansing, MI, USA. Comparisons of the compliance rates for the signalized crosswalks showed that the spatial compliance (compliance with crosswalk area, CA) was very high (83.13%) while the temporal compliance (compliance with WALK signal) was low (50.63%). Overall crossing compliance was also low with an average value of 42.98%. In other words, signalized intersection crosswalks in the study site appeared to attract pedestrians as crossing points as they are highly visible and strategically located at intersections to where major pedestrian paths lead. However, signal timing and/or phasing schemes failed to convince the majority of pedestrians to cross during the pedestrian WALK interval. Another possible explanation for the low temporal compliance might be related to low vehicular volumes during some periods of the day, and long gaps between individual vehicles or platoons (observed gaps are between 3 and 29 sec with an average of 9.5 sec). These conditions provided pedestrians a motive and an opportunity to cross safely during the pedestrian DON’T WALK interval. The non-compliance of pedestrians with the WALK signal indication may also be linked to improper signal timing and/or signal phasing design. An in depth analysis of these issues is beyond the scope of the subject study as a variety of signal timing and phasing data are not available for consideration.

Main conclusions drawn from the analyses and modeling of the pedestrian compliance data are as follows:

• Signalized crosswalks are equally attractive throughout the study site since they appeared to be well marked and equally recognized as proper crossing locations bythe pedestrians in the study site.

• Signal compliance rates (TCCR) between the two study sections are found to be significantly different due to the variances in the vehicular traffic flows observed between the sections. The model developed to estimate the TCCRs supports this conclusion, too.

Approximately 4 out of 5 pedestrians (mean SCCR= 83.1%) complied with thecrossing location, whereas only a half of the pedestrians in the study site complied with the pedestrian traffic signals (mean TCCR=50.6%). This is likely due to improper signal timings that result in long pedestrian delays at the signal and/or low vehicular traffic flow and long gaps between the vehicle platoons that encourage pedestrians to cross against the signal when perceived safe crossing conditions exist. As a result, the mean overall compliance rate at signalized crosswalks was also low (mean OCCR= 42.98%). Lack of enforcement may be another reason why pedestrians at this study site often disrespect the signal indication when crossing.

• The availability of a positive type of traffic control (traffic signal) resulted in high spatial compliance. The effect of the availability of traffic signals was found to be statistically significant (t=8.744 and p=0.000<0.05) though the data were limited to only two unsignalized intersections. That means pedestrians were more likely to cross within the crosswalk area when there was a signal present.

• The study site includes some unique physical barriers to channel pedestrian flows to crosswalks. It therefore cannot be determined with certainty whether the crosswalks or the presence of barriers were more significant in creating the high spatial crossing compliance observed in this study.



7. RECOMMENDATIONS FOR FUTURE RESEARCH

In this paper, the analyses related to studying pedestrian crossing compliance characteristics were described and the results obtained were discussed. The collected data were limited in terms of different pedestrian signal timing and phasing schemes. Thus, the effect of different signal timings and phasing plans on pedestrian compliance was not studied. A future research should address this issue, together with the effect of platoon progression and signal coordination on pedestrian compliance. Through necessary signal adjustments, pedestrians may be encouraged to comply with the signals in order to increase temporal compliance as well as safety. High temporal compliance could also be achieved by installing pedestrian “push-buttons” at the signalized intersections. However, installing push buttons may not necessarily increase the temporal compliance because pedestrians still have to wait for the WALK signal and some may get impatient and cross the road as non-compliant. A future study ought to research the effects of signal design which favor pedestrians and the installation of pedestrian push-buttons on signal compliance.

ACKNOWLEDGEMENTS

The authors would like to thank the Michigan Department of Transportation for financially supporting the research study in this paper. They also acknowledge students in the transportation program of the Department of Civil and Environmental Engineering at Michigan State University for their help in data collection and reduction. The corresponding author also acknowledges the financial support from İSBAK A.Ş. for traveling to 86th Annual Meeting of TRB, 21-25 January 2007, in Washington, D.C., USA.

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