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User perceptions and engineering definitions of highway level of service: an exploratory statistical comparison
Transportation Research Part A 38 (2004) 677–689 www.elsevier.com/locate/tra
User perceptions and engineering definitions of highway level of service: an exploratory statistical comparison Kasem Choocharukul a b
a,1
, Kumares C. Sinha
b,2
, Fred L. Mannering
b,*
Department of Civil Engineering, Chulalongkorn University, Bangkok 10330, Thailand School of Civil Engineering, Purdue University, West Lafayette, Indiana 47907, USA
Received 22 December 2003; received in revised form 19 July 2004; accepted 24 August 2004
Abstract The level of service (LOS) concept in the Highway Capacity Manual has been used as a qualitative measure representing freeway operational conditions for over 35 years. One key element that has not been adequately addressed is how road users perceive LOS. This exploratory research examines road-user perceptions of freeway LOS by presenting study participants with a series of video clips of various traffic conditions (taken from cameras on overpasses to allow a complete view of the traffic stream) and asking them their perceptions of LOS. A random effects ordered probability model is then used to statistically link participant-recorded perceptions of LOS with measurable traffic conditions (speed, density, flow, percentage of trucks, vehicle headways) and participant characteristics. The findings suggest that the Highway Capacity Manuals use of traffic density as a single performance measure for LOS does not accurately reflect road-user perceptions. The statistical analysis shows that a number of attributes besides traffic density determine public perceptions of LOS and that these perceptions vary depending on both traffic conditions and road-user characteristics. Ó 2004 Elsevier Ltd. All rights reserved.
*
Corresponding author. Tel.: +1 765 494 2159; fax: +1 765 494 0395. E-mail addresses: [email protected] (K. Choocharukul), [email protected] (K.C. Sinha), flm@ecn. purdue.edu (F.L. Mannering). 1 Tel.: +662 218 6578; fax: +662 251 7304. 2 Tel.: +1 765 494 2211; fax: +1 765 496 1105. 0965-8564/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.tra.2004.08.001
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1. Introduction Levels of traffic congestion are currently measured by the widely accepted concept of level of service (Mannering et al., 2005). The implementation of this concept to determine specific level of service (LOS) categorization of freeway facilities is primarily based on the judgment of transportation professionals (Transportation Research Board, 2000). How closely this professional judgment corresponds to road-user perceptions of levels of traffic congestion is an open question. However, it is an important question to answer because these perceptions can affect the planning, design, and operational aspects of transportation projects as well as the allocation of limited financial resources among competing transportation projects. The LOS concept was first introduced in the 1965 Highway Capacity Manual. The measure has evolved such that the current Highway Capacity Manual defines LOS as, ‘‘a qualitative measure describing operational conditions within a traffic stream, generally in terms of such service measures as speed and travel time, freedom to maneuver, traffic interruptions, and comfort and convenience’’ (Transportation Research Board, 2000). This definition is applied in a six-level scale (levels of service A–F) that are distinguished in the current Highway Capacity Manual by traffic density––the sole criterion used to distinguish between LOS A and B, LOS B and C and so on. In recent years, several alternatives have been suggested for improving the scaling and determination of LOS. For example, the studies of Baumgaertner (1996), Cameron (1996), Maitra et al. (1999) and Brilon (2000) all provided some insight into the limitations of the current LOS measure. Among the approaches suggested by these studies, expanding the six current LOS designations to nine or more in an attempt to better describe traffic conditions was a common theme. Another common theme in past research has been the issue of understanding the role that roaduser preferences should play in LOS determination. For example, Matsui and Fujita (1994) conducted a survey asking freeway drivers to express the speed at which they would classify the road as being congested. The results were analyzed and psychologically interpreted by applying Blochs Law, a psychological concept of the relationship between stimulation and its perception, to the definition of a drivers judgment of delay. Mizokami (1996) developed a model to distinguish delay and non-delay as perceived by drivers, using a set of attributes that included trip purpose, experience of trip, experience of delay, travel time without congestion, and actual travel time. In other work, Kita (2000) investigated merging behavior at an on-ramp section of an expressway to determine traffic performance based on a drivers perceptions of the driving environment and an LOS measure was derived from an aggregated driving utility function. Further, Nakamura et al. (2000) found that the traffic flow rate was the factor most strongly affecting the degree of drivers satisfaction and perceived LOS. Other significant parameters included the number of lane changes, the elapsed time while following other cars, and driving experience. In the United States, Laetz (1990) noted, through direct measurement and public-opinion polls, that perceptions of traffic congestion substantially influence what constitutes an acceptable LOS and what motorists are willing to tolerate. Perception of the severity of congestion was found to be dependent on not only experience with traffic delays but also economic productivity, environmental quality, and human stress levels. In subsequent work, Pfefer (1999) provided a framework to develop tools to reflect road user perceptions on service quality by defining the quality of service as a function of five performance measures––mobility, perception of the lack of safety, environment, comfort and convenience, and road user direct cost.
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Video laboratory studies were used by Pe´cheux et al. (2000) to study user perceptions of service quality at signalized intersections. Participants were asked to watch and rate video clips collected from various intersection approaches. Cluster analysis was applied to group intersection approaches that received similar ratings by the participants. Results showed that some adjustments in the Highway Capacity Manual LOS service ranges were warranted for a number of reasons, including the observation that road users did not really perceive LOS on six levels and that they were more tolerant of long delays than traffic engineers may have thought. Hall et al. (2001) determined motorists views on what aspects of freeway travel are important to them. Employing focus groups, four primary factors affecting perceived quality of service were identified: travel time, density/maneuverability, road safety, and traveler information. Travel time was identified as the most important aspect in the focus groups and distinctions in speed, density, or other indicators based on the existing LOS boundaries did not correspond well to the breakpoints that mattered to drivers. Without a doubt, past research provides overwhelming evidence that LOS is a difficult concept to quantify, and one that likely includes a complex interaction of many traffic parameters and road-user perceptions. The current Highway Capacity Manual procedure of using traffic density as the only criterion for determining LOS on freeways needs to be carefully assessed in light of recent studies that show road-user perceptions and other measurable traffic-stream characteristics affect LOS. The intent of this study is to provide a multivariate statistical analysis of the factors that influence road-user perceptions of freeway LOS and to compare these factors to the current Highway Capacity Manual LOS criterion.
2. Data collection The approach of this study was to select a diverse sample of road users and have these individuals evaluate video clips of actual traffic on Indiana interstates to gauge their perceptions of LOS. A total of 195 individuals from five occupational groups participated in the study: 84 university graduate and undergraduate students, 32 transportation professionals from the Indiana Department of Transportation and the Northwestern Indiana Regional Planning Commission, 14 environmental management professionals from the Northwestern Indiana Regional Planning Commission, 35 truckers from the Indiana Motor Truck Association, and 30 clerical and support staff from state agencies. For the 195 participants, information was gathered on sociodemographic attributes including gender, age, occupation, education, and frequency of freeway use. For video-clip evaluation and LOS perceptions, video data were gathered from two locations along the Borman Expressway (Interstates 80/94) near Chicago and five locations in and around Indianapolis (Interstates-865, 69, 70, 65, 465). The locations were selected so that both video and inductive loop detector data could be gathered simultaneously because the loop detector data supplements the video data by providing detailed traffic flow information. A video camera with a timer was used to tape the traffic scene. Cameras were located on overpasses that allowed a clear view of freeway traffic as it traversed the loop detectors. The orientation was selected so that only the traffic on the focusing direction could be observed and the inductive loop on each traffic lane could be seen from the video. Inductive loop detector data were collected during the same period
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and a time synchronization was carried out prior to data collection for both video cameras and loop detectors so that data from both sources could be matched. From all of the video data collected, two sets of video clips were chosen for evaluation by the road-user sample. The video clips in both of the these bundles were each 45 s long and chosen such that two video clips where in each of the six LOS designations as currently defined by the Highway Capacity Manual, bringing the total to 12 in each bundle. The pairs of video clips within each of the six Highway Capacity Manual-defined LOS categories included one video clip that provided conditions on the low end of the LOS category (as defined by the traffic density ranges of the category) and one on the high end. One 12-clip bundle included videos only from the Borman Expressway and this bundle was shown to university students, transportation professionals, and environmental management professional groups. The other 12-clip bundle included 10 video clips from Indianapolis freeways and two from the Borman Expressway, and this bundle was presented to the trucker and clerical/support staff groups (see Table 1 for speed and density ranges of the two video-clip bundles). After providing participants with a written description of the conditions that determine each of the six Highway Capacity Manual levels of service (see
Table 1 Ranges of flows, speeds and densities on the video clips used in the collection of participants perceptions of congestion Level of service category
Video clip bundlea
Flow (pc/h)
Speed (mi/h)
Density (pc/mi/ln)
A
Borman only
320 680 297 441 828 926 774 1088 1206 1119 1183 1147 1550 1106 1548 1509 1422 1626 1953 1640 1151 568 1624 705
65 59 68 68 67 66 63 70 65 56 62 59 60 38 758 52 38 39 59 39 18 5 24 9
4.9 11.0 4.8 6.9 12.6 15.3 12.6 16.6 20.2 22 19.9 21.9 29.1 33.0 27.3 32.5 39.3 44.4 35.2 43.7 74.2 117.7 69.9 98.3
Borman/Indianapolis B
Borman only Borman/Indianapolis
C
Borman only Borman/Indianapolis
D
Borman only Borman/Indianapolis
E
Borman only Borman/Indianapolis
F
Borman only Borman/Indianapolis a
For the Borman-Expressway-only and Borman-Expressway-plus-Indianapolis-Freeway video-clip bundles, there are two video clips for each level-of-service category, one at the lower range of the category and one at the upper range of the category. In this paper: pc = passenger cars; h = hour; mi = mile(s); ln = lane.
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Table 2 Definitions of level of service categories (Transportation Research Board, 2000) provided to study participants Level of service category
Definition
A
Free-flow operations. Vehicles are almost completely unimpeded in their ability to maneuver within the traffic stream Reasonably free flow. Free-flow speeds are maintained. The ability to maneuver within the traffic stream is only slightly restricted, and the general level of physical and psychological comfort provided to drivers is still high Flow with speeds at or near the free-flow speed of the freeway. Freedom to maneuver within the traffic stream is noticeably restricted, and lane changes require more care and vigilance on the part of the driver Speeds begin to decline slightly with increasing flows and density begins to increase somewhat more quickly. Freedom to maneuver within the traffic stream is more noticeably limited, and the driver experiences reduced physical and psychological comfort levels Operation at capacity. Vehicles are closely spaced. Maneuverability within the traffic stream is extremely limited, and the level of physical and psychological comfort afforded the driver is poor Breakdowns in vehicular flow
B
C
D
E F
Table 2), individuals were shown the 12 video clips and asked their perceptions of LOS (A–F). For each of the 12 video clips, study participants were asked to indicate what level of service was appropriate for traffic condition shown in this video clip (they chose from the six discrete categories of: LOS A, B, C, D, E or F). These data were then used in the model development.
3. Methodological approach To determine how users perceive LOS, a statistical approach was needed to predict the probability of discrete data (LOS A, B, C, D, E, and F). Standard multinomial discrete-outcome modeling methods such as the multinomial logit model are a possibility, but such models do not take into account the ordered nature of the data (A is better than B, B is better than C and so on) and a loss in parameter efficiency would result (see Amemiya, 1985). To account for both the discrete and ordered nature of the data, an ordered probability approach is an appropriate modeling choice (see Washington et al., 2003, for a complete description). Ordered probability models are derived by defining an unobserved variable, z, that is used as the basis for modeling the ordinal ranking of data, in this case LOS rankings. To do this, an unobserved variable is defined that determines the perceptions of LOS as a linear function for each observation n (defined as each study participants evaluation of the 12 video clips, so the 193 participants would generate 2316 observations), such that zn ¼ bX n þ en ;
ð1Þ
where Xn is a vector of variables (such as traffic conditions and user characteristics) that determine perceived LOS, b is a vector of estimable parameters, and en is a random disturbance. Using this equation, observed LOS, yn, for each observation is written as (with LOS A, B, C, D, E and F corresponding to y = 1, 2, 3, 4, 5, and 6 respectively),
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yn ¼ 1
if zn 6 l1
yn ¼ 2
if l1 < zn 6 l2
yn ¼ 3
if l2 < zn 6 l3
yn ¼ 4
if l3 < zn 6 l4
yn ¼ 5
if l4 < zn 6 l5
yn ¼ 6
if zn P l5 ;
ð2Þ
where ls are estimable parameters (referred to as thresholds) that define yn. The ls are parameters that are estimated jointly with the parameter vector b. The estimation problem then becomes one of determining the probability of a participant selecting a particular LOS. To do this, let en be assumed to be normally distributed across observations with mean = 0 and variance = 1. It can be shown that l1 can be set equal to zero without loss of generality (Washington et al., 2003). With these assumptions, an ordered probit model results with selection probabilities, P ðy n ¼ 1Þ ¼ UðbX n Þ P ðy n ¼ 2Þ ¼ Uðl2 bX n Þ UðbX n Þ P ðy n ¼ 3Þ ¼ Uðl3 bX n Þ Uðl2 bX n Þ P ðy n ¼ 4Þ ¼ Uðl4 bX n Þ Uðl3 bX n Þ
ð3Þ
P ðy n ¼ 5Þ ¼ Uðl5 bX n Þ Uðl4 bX n Þ P ðy n ¼ 6Þ ¼ 1 Uðl5 bX n Þ where U(Æ) is the cumulative normal distribution, Z u 1 1 2 UðuÞ ¼ pffiffiffiffiffiffi e2w dw 2p 1
ð4Þ
This model can be estimated by standard maximum likelihood procedures. However, a complication arises because each of the 193 participants generates 12 observations since each participant evaluates 12 video clips for perceived level of service. This creates a problem because the 12 responses given by a specific participant will share unobserved effects. If this is not accounted for, and the model is estimated as though the 12 observations from each survey participant came from 12 independent participants, the standard errors of the models estimated parameters will be biased downward, resulting in inflated t-statistics, potentially erroneous inferences as well as potential biases in parameter estimates. This problem can be handled with a standard random effects approach by rewriting Eq. (1) as, zic ¼ bX ic þ eic þ ui
ð5Þ
where subscripting i denotes individual participants (i = 1, . . . , 193), subscripting c indexes the video clips (c = 1, . . . , 12), ui is the individual random effect term (assumed to be normally distributed with mean 0 and variance r2) and all other terms are as previously defined. Estimation of this random effects model results in an estimate of r, the significance of which determines the significance of the random effects formulation relative to the standard ordered probit model (see Greene, 2000).
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4. Level of service and traffic density To gain an initial understanding of how the sample of individuals in this study perceived LOS relative to the current Highway Capacity Manual procedures (using traffic density as the sole criterion), random effects ordered probability models were estimated with traffic density as the only independent variable. Model estimation results for each of the five categories of respondents (university students, transportation professionals, environmental management professionals, truckers, and clerical/support staff) and all respondents combined are presented in Table 3. Table 3 shows that the parameter estimates for density are highly significant, reflecting the importance of density in LOS perception. The positive density parameter indicates that higher values of traffic density make it more likely that worse levels of LOS are perceived. The constant term and all threshold estimates are also highly significant as indicted by the t-statistics. The standard deviation of the random effects was also highly significant, indicating the importance of considering random effects (had this been statistically insignificant, the random effects would not be justified and a standard ordered probit model would be sufficient). The table shows that goodness-of-fit, as measured index, q2, is also reasonable. Given these model estimates, a comparison to the Highway Capacity Manual density ranges can be made. The thresholds defining levels of service from the ordered probit models can be calculated as (lk b0)/b1 where k designates the five threshold values (l1 = 0 and the four threshold values are shown in Table 3). Given these five thresholds, cut-off values were computed for the six levels of service. 3 These estimated values, along with corresponding Highway Capacity Manual LOS designations, are presented in Table 4 and graphically in Fig. 1. It is apparent that perceived levels of service do not closely follow the Highway Capacity Manual criteria in many areas. All participating groups, except the transportation professionals, have a lower tolerance for LOS A where the average cut-off value was found to be 7 pc/mi/ln, compared to 11 pc/mi/ln in the Highway Capacity Manual. In contrast, participants tended to have a higher tolerance for worse LOS. Density values for LOS D in the Highway Capacity Manual fall mostly into perceived LOS C and those for LOS E in the Highway Capacity Manual are associated with perceived LOS D. In addition, participants appeared to be willing to withstand LOS E up to an average density value of 82 pc/mi/ln, implying that the current Highway Capacity Manual cutoff for LOS F (traffic breakdown) condition seems to be underestimated relative to participant perceptions. 4 The findings in Table 4 suggest that, while density strongly influences the perceived LOS on freeway traffic quality, the currently utilized threshold values in the Highway Capacity Manual do not seem to correspond to what participants perceive. It also appears, based on our findings,
3
Interestingly, using cluster analysis (which classifies data into relatively homogeneous groups on the basis of the similarity of group characteristics) it was found that study participants seemed to differentiate only three levels of freeway traffic conditions, as opposed to the six levels prescribed in the Highway Capacity Manual. However, we use six categories for the sake of comparison with the current six-scale LOS used in the Highway Capacity Manual. 4 LOS F can extend into heavily queued conditions and covers a much larger range of densities relative to the other LOS categories. To study the possible effects this may have on the model estimation results, we estimated the models using only data from video clips in the LOS A–E range. We found that estimated model parameters changed only slightly when this was done and that the same inferences could be drawn.
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Independent variable
Model 1 STU
Model 2 TPP
Model 3 ENV
Model 4 TRK
Model 5 CLK
Model 6 ALL
Constant
0.535 (3.84)
1.156 (3.05)
1.218 (1.87)
0.715 (1.73)
0.504 (1.93)
0.658 (7.09)
Traffic characteristic variable Traffic density in pc/miles/ln
0.107 (30.46)
0.107 (9.12)
0.122 (14.13)
0.103 (19.60)
0.080 (18.39)
0.099 (52.48)
Estimated thresholds Threshold l2 Threshold l3 Threshold l4 Threshold l5
1.445 3.061 4.704 8.333
1.250 2.884 4.389 6.854
1.663 3.069 4.505 7.363
1.386 2.467 4.077 8.255
1.328 2.424 3.573 6.546
1.366 2.741 4.222 7.440
(11.53) (23.21) (27.26) (26.75)
(6.41) (9.31) (9.48) (6.60)
(3.08) (6.76) (8.08) (8.26)
(5.76) (9.16) (14.25) (18.94)
(8.08) (13.15) (17.03) (17.91)
(21.55) (38.39) (47.36) (47.08)
Standard deviation of random effects r 0.573 (9.88)
0.636 (4.16)
0.815 (1.21)
0.540 (3.84)
0.507 (4.16)
0.580 (12.99)
Number of observations Log likelihood at zero Log likelihood at convergence q2
384 641.59 377.90 0.41
168 293.71 151.86 0.48
420 712.01 410.92 0.42
360 622.71 406.20 0.35
2340 4029.21 2343.50 0.42
a
1008 1728.08 938.88 0.46
In this table, the three letter acronyms refer to subgroups of the sample: STU = university students, TPP = transportation professionals, ENV = environmental management professionals, TRK = truckers, CLK = clerical/support staff, and ALL = all subgroups.
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Table 3 Parameter estimation results for density threshold values (t-statistics in parentheses)a
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Table 4 Comparison of level of service (LOS) criteriaa LOS
A B C D E F
Perceived density range (pc/mi/ln) Model 1 STU
Model 2 TPP
Model 3 ENV
Model 4 TRK
Model 5 CLK
Model 6 ALL
Highway Capacity Manual
0–5 >5–19 >19–34 >34–49 >49–83 >83
0–11 >11–23 >23–38 >38–52 >52–75 >75
0–10 >10–24 >24–35 >35–47 >47–70 >70
0–7 >7–20 >20–31 >31–47 >47–87 >87
0–6 >6–23 >23–37 >37–51 >51–89 >89
0–7 >7–21 >21–34 >34–49 >49–82 >82
0–11 >11–18 >18–26 >26–35 >35–45 >45
a
In this table, the three letter acronyms refer to subgroups of the sample: STU = university students, TPP = transportation professionals, ENV = environmental management professionals, TRK = truckers, CLK = clerical/ support staff, and ALL = all subgroups.
Density (pc/mi/ln)
100 90 80 70 60 50 40 30 20 10 0 STU LOS A
TPP LOS B
ENV LOS C
TRK LOS D
CLK LOS E
ALL
HCM LOS F
Fig. 1. Graphical comparison of level of service (LOS) criteria. In this figure, HCM refers to the Highway Capacity Manual level-of-service definitions and the other three letter acronyms refer to subgroups of the sample: STU = university students, TPP = transportation professionals, ENV = environmental management professionals, TRK = truckers, CLK = clerical/support staff, ALL = all subgroups.
that the Highway Capacity Manual LOS F measure is conservative (relative to user perceptions) because participants do not discern LOS F until density reaches 82 pc/mi/ln on average. However, as the extant literature suggests, factors other than density are likely to influence participants LOS perceptions and we explore this below.
5. Complete model of level of service and user perceptions To obtain a more complete understanding of road user perceptions of LOS, a model was estimated to include participant characteristics and additional traffic characteristics (in addition to density). Because the group of truckers were asked slightly different questions (including the number of miles they drive in a typical year), two models were estimated, one that included all
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Table 5 Level of service (LOS) model estimation results non-truckers Independent variable Constant Socio-demographic variables High school graduate (1 if high school graduate, 0 otherwise) Participant between 21 and 50 years old (1 if yes, 0 if no) Participant between 51 and 65 years old (1 if yes, 0 if no) Using freeway everyday (1 if yes, 0 if no) Driving to work (1 if yes, 0 if no)
Estimated parameter 0.134
t-Statistic 0.210
0.887 0.652 0.384 0.399 0.396
3.18 3.01 1.62 2.52 2.32
0.781
3.96
0.688
2.79
0.068 0.041 0.078 0.002 2.518 0.027
12.11 6.06 3.80 14.18 1.73 3.97
Estimated thresholds Threshold l2 Threshold l3 Threshold l4 Threshold l5
1.969 3.860 5.475 7.485
20.86 33.87 37.22 34.13
Standard deviation of random effects r
0.634
10.23
Traffic characteristic variables Four-lane freeway (total both directions) (1 if 4-lane freeway, 0 if 6-lane or more) Freeway visibility (1 if good visibility, 0 if video clip was taken after sunset or in rainy/foggy conditions) Traffic density, in pc/mi/ln Average speed, in mi/h Standard deviation of speed, in mi/h Average flow, in veh/h/ln Standard deviation of headway, in mi Percentage of trucks in the traffic stream
Number of observations Log-likelihood at zero Log-likelihood at convergence q2
1752 3055.59 1519.82 0.50
subject groups except truckers and the other that included just the truckers. Model estimation results for these two models are presented in Tables 5 and 6. 5 Interpretation of the values in Tables 5 and 6 is such that a positive parameter means increases in the variable give a higher probability that a worse level of service will be perceived and a negative parameter means increases in the variable give a higher probability that a better level of service will be perceived. Note that for this statement to be correct, the marginal effects of the parameter estimates should be checked. This 5
Due to missing socio-demographic information on some respondents, the number of observations for the nontrucker model was 1752 instead of 1920 (14 of the 161 non-trucker participants had missing data) and for the trucker model was 384 instead of 420 (3 of the 32 truckers had missing data).
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Table 6 Level of service model estimation results for truckers Independent variable
Estimated parameter
t-Statistic
Constant
0.716
1.15
Socio-demographic variables Drive less than 40 000 miles in a year (1 if yes, 0 otherwise)
0.618
1.71
1.504
4.30
Traffic characteristic variables Four-lane freeway (total both directions) (1 if 4-lane freeway, 0 if 6-lane or more) Freeway visibility (1 if good visibility, 0 if video clip was taken after sunset or in rainy/foggy conditions) Traffic density, in pc/mi/ln Standard deviation of speed, in mi/h Standard deviation of headway, in mi Percentage of trucks in the traffic stream
0.738
3.20
0.096 0.072 15.712 0.028
10.85 1.89 6.98 1.67
Estimated thresholds Threshold l2 Threshold l3 Threshold l4 Threshold l5
2.341 3.789 5.637 8.478
6.62 9.33 12.38 14.56
Standard deviation of random effects r
0.712
4.19
Number of observations Log-likelihood at zero Log-likelihood at convergence q2
384 670.27 317.00 0.53
was done and the signs of these effects are in agreement with this statement. For more information on the need for considering marginal effects, see Washington et al. (2003). For participants that are not truckers, Table 5 shows that participants who have at least a high school education, use freeways every day, and drive to work are less likely to choose worse levels of service, holding other factors constant. Participants that are between 21 and 65 are more likely to choose worse levels of service, possibly because their value of time is higher than the very young (20 years old and less) and the old (65 years or greater). Good freeway visibility and high average speeds resulted in a lower probability of choosing worse levels of service. Freeways that were only 4-lanes (total both directions) and increases in traffic density, standard deviation of traffic speed, traffic flow, standard deviation of headways, and percent of trucks in the traffic stream all made it more likely that worse levels of service were perceived. All of these findings are consistent with expectations. However, the significance of so many variables in the model underscores the apparent inadequacy of using traffic density alone as a measure of LOS. 6 6 Please note that indicator variables for group types (for example, university students, transportation professionals, etc.) were found to be statistically insignificant.
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Table 6 shows that the results for truckers were similar but with some key differences. For truckers, whether the trucker drove less than 40 000 miles was the only socio-demographic variable that was significant, and it showed that truckers that drove less were more likely to perceive worse levels of service, all else being equal. Good freeway visibility and high average speeds resulted in a lower probability of choosing worse levels of service as was the case for non-truckers. Interestingly, in contrast to the non-trucker finding, higher percentages of trucks decreased the likelihood that a worse level of service would be perceived, which could be because of truckers perceiving the platooning of trucks to be a less onerous state. As was the case with non-truckers, freeways that were only 4-lanes (total both directions) and increases in traffic density and standard deviation of traffic speed all made it more likely that worse levels of service were perceived.
6. Summary and conclusions The LOS concept has been the primary criterion in the Highway Capacity Manual for several decades. It has served the transportation community well by providing a means to explain traffic operational quality to the general public. However, little is understood about how the currently defined LOS categories correspond to participant perceptions of traffic operational quality. The analysis presented in this paper provides evidence that perceptions of LOS are dependent on a number of traffic and road-user characteristics. Incorporating these findings in a new LOS measurement technique would be complex because of the dependence of LOS on the characteristics of the user population. However, if there is interest in linking LOS measurements with user perceptions, it is clear that our model estimates indicate that traffic characteristics other than density should be used. Also, our models provide some evidence that the current six-level LOS scale does not map well with user-perceived LOS. In particular, the Highway Capacity Manuals current range of LOS F conditions does not appear to be perceived by road users as the ‘‘breakdown in vehicle flow’’ that this LOS is intended to represent. As corroborated by a number of recent studies, this finding suggests that a potential change in the number of LOS levels may be warranted with a particular emphasis on better understanding what is currently defined as LOS F. To be sure, while this research provides some suggestive evidence on how road users perceive freeway traffic quality, there are a number of limitations. First, due to the small, regional nature of the sample, our findings must be viewed as exploratory in nature. Clearly, an expanded sample of road users would provide much more information. Second, the experimental approach of having road users making LOS judgments by simply viewing video clips has obvious limitations. Although much more difficult to execute, a better approach would be to have road users express their opinions of LOS while actually driving in the traffic stream with known traffic parameters (density, average speed, headways, and so on). Finally, it is potentially important to consider road users perceived LOS in light of their complete road trip as opposed to the point on a freeway segment used in this study. It is possible that perceptions may differ when all freeway segments traveled by road users are considered because expectations may change depending on immediate past exposure to traffic. It is hoped that the results and statistical approach presented in this study can serve as a basis for expanding the transportation fields understanding of the complex relationship between traffic conditions and perceptions of those conditions. An understanding of this relationship will allow
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transportation engineers and planners to better accommodate demand on transportation facilities and more efficiently allocate scarce transportation resources.
References Amemiya, T., 1985. Advanced Econometrics. Harvard University Press, Cambridge, MA. Baumgaertner, W.E., 1996. Level of service––getting ready for the 21st century. ITE Journal 66 (1), 36–39. Brilon, W., 2000. Traffic flow analysis beyond traditional methods. In: Transportation Research Circular E-C018: 4th International Symposium on Highway Capacity, Transportation Research Board, National Research Council, Washington, DC, pp. 26–41. Cameron, R., 1996. G3F7––An expanded LOS gradation system. ITE Journal 66 (1), 40–41. Greene, W., 2000. Econometric Analysis, fourth ed. Prentice Hall, Saddle River, NJ. Hall, F.L., Wakefield, S., Al-Kaisy, A.F., 2001. Freeway quality of service: What really matters to drivers and passengers?. Transportation Research Record 1776, 17–23. Kita, H., 2000. Level-of-service measure of road traffic based on the drivers perception. In: Transportation Research Circular E-C018: 4th International Symposium on Highway Capacity, Transportation Research Board, National Research Council, Washington, DC, pp. 53–62. Laetz, T.J., 1990. Predictions and perceptions: defining the traffic congestion problem. Technological Forecasting and Social Change 38, 287–292. Mannering, F., Kilareski, W., Washburn, S., 2005. Principles of Highway Engineering and Traffic Analysis. John Wiley & Sons, New York, NY. Maitra, B., Sikdar, P.K., Dhingra, S.L., 1999. Modeling congestion on urban roads and assessing level of service. Journal of Transportation Engineering 125 (6), 508–514. Matsui, H., Fujita, M., 1994. Definition of congestion based on drivers consciousness. In: 1994 Vehicle Navigation & Information Systems Conference Proceedings. IEEE, pp. 339–344. Mizokami, S., 1996. An analytical study on the relation between traffic congestion and drivers perception. In: Third Annual World Congress on Intelligent Transport Systems, October 14–18. Nakamura, H., Suzuki, K., Ryu, S., 2000. Analysis of the interrelationship among traffic flow conditions, driving behavior, and degree of drivers satisfaction on rural motorways. In: Transportation Research Circular E-C018: 4th International Symposium on Highway Capacity, Transportation Research Board, National Research Council, Washington, DC, pp. 42–52. Pe´cheux, K.K., Pietrucha, M.T., Jovanis, P.P., 2000. User perception of level of service at signalized intersections: methodological issues. In: Transportation Research Circular E-C018: 4th International Symposium on Highway Capacity, Transportation Research Board, National Research Council, Washington, DC, pp. 322–335. Pfefer, R.C., 1999. Toward reflecting public perception of quality of service in planning, designing, and operating highway facilities. Transportation Research Record 1685, 81–89. Transportation Research Board, 2000. Special Report 209: Highway Capacity Manual. Transportation Research Board, National Research Council, Washington, DC. Washington, S., Karlaftis, M., Mannering, F., 2003. Statistical and Econometric Methods for Transportation Data Analysis. Chapman & Hall/CRC, Boca Raton, FL.
User perceptions and engineering definitions of highway level of service: an exploratory statistical comparison Kasem Choocharukul a b
a,1
, Kumares C. Sinha
b,2
, Fred L. Mannering
b,*
Department of Civil Engineering, Chulalongkorn University, Bangkok 10330, Thailand School of Civil Engineering, Purdue University, West Lafayette, Indiana 47907, USA
Received 22 December 2003; received in revised form 19 July 2004; accepted 24 August 2004
Abstract The level of service (LOS) concept in the Highway Capacity Manual has been used as a qualitative measure representing freeway operational conditions for over 35 years. One key element that has not been adequately addressed is how road users perceive LOS. This exploratory research examines road-user perceptions of freeway LOS by presenting study participants with a series of video clips of various traffic conditions (taken from cameras on overpasses to allow a complete view of the traffic stream) and asking them their perceptions of LOS. A random effects ordered probability model is then used to statistically link participant-recorded perceptions of LOS with measurable traffic conditions (speed, density, flow, percentage of trucks, vehicle headways) and participant characteristics. The findings suggest that the Highway Capacity Manuals use of traffic density as a single performance measure for LOS does not accurately reflect road-user perceptions. The statistical analysis shows that a number of attributes besides traffic density determine public perceptions of LOS and that these perceptions vary depending on both traffic conditions and road-user characteristics. Ó 2004 Elsevier Ltd. All rights reserved.
*
Corresponding author. Tel.: +1 765 494 2159; fax: +1 765 494 0395. E-mail addresses: [email protected] (K. Choocharukul), [email protected] (K.C. Sinha), flm@ecn. purdue.edu (F.L. Mannering). 1 Tel.: +662 218 6578; fax: +662 251 7304. 2 Tel.: +1 765 494 2211; fax: +1 765 496 1105. 0965-8564/$ - see front matter Ó 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.tra.2004.08.001
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1. Introduction Levels of traffic congestion are currently measured by the widely accepted concept of level of service (Mannering et al., 2005). The implementation of this concept to determine specific level of service (LOS) categorization of freeway facilities is primarily based on the judgment of transportation professionals (Transportation Research Board, 2000). How closely this professional judgment corresponds to road-user perceptions of levels of traffic congestion is an open question. However, it is an important question to answer because these perceptions can affect the planning, design, and operational aspects of transportation projects as well as the allocation of limited financial resources among competing transportation projects. The LOS concept was first introduced in the 1965 Highway Capacity Manual. The measure has evolved such that the current Highway Capacity Manual defines LOS as, ‘‘a qualitative measure describing operational conditions within a traffic stream, generally in terms of such service measures as speed and travel time, freedom to maneuver, traffic interruptions, and comfort and convenience’’ (Transportation Research Board, 2000). This definition is applied in a six-level scale (levels of service A–F) that are distinguished in the current Highway Capacity Manual by traffic density––the sole criterion used to distinguish between LOS A and B, LOS B and C and so on. In recent years, several alternatives have been suggested for improving the scaling and determination of LOS. For example, the studies of Baumgaertner (1996), Cameron (1996), Maitra et al. (1999) and Brilon (2000) all provided some insight into the limitations of the current LOS measure. Among the approaches suggested by these studies, expanding the six current LOS designations to nine or more in an attempt to better describe traffic conditions was a common theme. Another common theme in past research has been the issue of understanding the role that roaduser preferences should play in LOS determination. For example, Matsui and Fujita (1994) conducted a survey asking freeway drivers to express the speed at which they would classify the road as being congested. The results were analyzed and psychologically interpreted by applying Blochs Law, a psychological concept of the relationship between stimulation and its perception, to the definition of a drivers judgment of delay. Mizokami (1996) developed a model to distinguish delay and non-delay as perceived by drivers, using a set of attributes that included trip purpose, experience of trip, experience of delay, travel time without congestion, and actual travel time. In other work, Kita (2000) investigated merging behavior at an on-ramp section of an expressway to determine traffic performance based on a drivers perceptions of the driving environment and an LOS measure was derived from an aggregated driving utility function. Further, Nakamura et al. (2000) found that the traffic flow rate was the factor most strongly affecting the degree of drivers satisfaction and perceived LOS. Other significant parameters included the number of lane changes, the elapsed time while following other cars, and driving experience. In the United States, Laetz (1990) noted, through direct measurement and public-opinion polls, that perceptions of traffic congestion substantially influence what constitutes an acceptable LOS and what motorists are willing to tolerate. Perception of the severity of congestion was found to be dependent on not only experience with traffic delays but also economic productivity, environmental quality, and human stress levels. In subsequent work, Pfefer (1999) provided a framework to develop tools to reflect road user perceptions on service quality by defining the quality of service as a function of five performance measures––mobility, perception of the lack of safety, environment, comfort and convenience, and road user direct cost.
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Video laboratory studies were used by Pe´cheux et al. (2000) to study user perceptions of service quality at signalized intersections. Participants were asked to watch and rate video clips collected from various intersection approaches. Cluster analysis was applied to group intersection approaches that received similar ratings by the participants. Results showed that some adjustments in the Highway Capacity Manual LOS service ranges were warranted for a number of reasons, including the observation that road users did not really perceive LOS on six levels and that they were more tolerant of long delays than traffic engineers may have thought. Hall et al. (2001) determined motorists views on what aspects of freeway travel are important to them. Employing focus groups, four primary factors affecting perceived quality of service were identified: travel time, density/maneuverability, road safety, and traveler information. Travel time was identified as the most important aspect in the focus groups and distinctions in speed, density, or other indicators based on the existing LOS boundaries did not correspond well to the breakpoints that mattered to drivers. Without a doubt, past research provides overwhelming evidence that LOS is a difficult concept to quantify, and one that likely includes a complex interaction of many traffic parameters and road-user perceptions. The current Highway Capacity Manual procedure of using traffic density as the only criterion for determining LOS on freeways needs to be carefully assessed in light of recent studies that show road-user perceptions and other measurable traffic-stream characteristics affect LOS. The intent of this study is to provide a multivariate statistical analysis of the factors that influence road-user perceptions of freeway LOS and to compare these factors to the current Highway Capacity Manual LOS criterion.
2. Data collection The approach of this study was to select a diverse sample of road users and have these individuals evaluate video clips of actual traffic on Indiana interstates to gauge their perceptions of LOS. A total of 195 individuals from five occupational groups participated in the study: 84 university graduate and undergraduate students, 32 transportation professionals from the Indiana Department of Transportation and the Northwestern Indiana Regional Planning Commission, 14 environmental management professionals from the Northwestern Indiana Regional Planning Commission, 35 truckers from the Indiana Motor Truck Association, and 30 clerical and support staff from state agencies. For the 195 participants, information was gathered on sociodemographic attributes including gender, age, occupation, education, and frequency of freeway use. For video-clip evaluation and LOS perceptions, video data were gathered from two locations along the Borman Expressway (Interstates 80/94) near Chicago and five locations in and around Indianapolis (Interstates-865, 69, 70, 65, 465). The locations were selected so that both video and inductive loop detector data could be gathered simultaneously because the loop detector data supplements the video data by providing detailed traffic flow information. A video camera with a timer was used to tape the traffic scene. Cameras were located on overpasses that allowed a clear view of freeway traffic as it traversed the loop detectors. The orientation was selected so that only the traffic on the focusing direction could be observed and the inductive loop on each traffic lane could be seen from the video. Inductive loop detector data were collected during the same period
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and a time synchronization was carried out prior to data collection for both video cameras and loop detectors so that data from both sources could be matched. From all of the video data collected, two sets of video clips were chosen for evaluation by the road-user sample. The video clips in both of the these bundles were each 45 s long and chosen such that two video clips where in each of the six LOS designations as currently defined by the Highway Capacity Manual, bringing the total to 12 in each bundle. The pairs of video clips within each of the six Highway Capacity Manual-defined LOS categories included one video clip that provided conditions on the low end of the LOS category (as defined by the traffic density ranges of the category) and one on the high end. One 12-clip bundle included videos only from the Borman Expressway and this bundle was shown to university students, transportation professionals, and environmental management professional groups. The other 12-clip bundle included 10 video clips from Indianapolis freeways and two from the Borman Expressway, and this bundle was presented to the trucker and clerical/support staff groups (see Table 1 for speed and density ranges of the two video-clip bundles). After providing participants with a written description of the conditions that determine each of the six Highway Capacity Manual levels of service (see
Table 1 Ranges of flows, speeds and densities on the video clips used in the collection of participants perceptions of congestion Level of service category
Video clip bundlea
Flow (pc/h)
Speed (mi/h)
Density (pc/mi/ln)
A
Borman only
320 680 297 441 828 926 774 1088 1206 1119 1183 1147 1550 1106 1548 1509 1422 1626 1953 1640 1151 568 1624 705
65 59 68 68 67 66 63 70 65 56 62 59 60 38 758 52 38 39 59 39 18 5 24 9
4.9 11.0 4.8 6.9 12.6 15.3 12.6 16.6 20.2 22 19.9 21.9 29.1 33.0 27.3 32.5 39.3 44.4 35.2 43.7 74.2 117.7 69.9 98.3
Borman/Indianapolis B
Borman only Borman/Indianapolis
C
Borman only Borman/Indianapolis
D
Borman only Borman/Indianapolis
E
Borman only Borman/Indianapolis
F
Borman only Borman/Indianapolis a
For the Borman-Expressway-only and Borman-Expressway-plus-Indianapolis-Freeway video-clip bundles, there are two video clips for each level-of-service category, one at the lower range of the category and one at the upper range of the category. In this paper: pc = passenger cars; h = hour; mi = mile(s); ln = lane.
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Table 2 Definitions of level of service categories (Transportation Research Board, 2000) provided to study participants Level of service category
Definition
A
Free-flow operations. Vehicles are almost completely unimpeded in their ability to maneuver within the traffic stream Reasonably free flow. Free-flow speeds are maintained. The ability to maneuver within the traffic stream is only slightly restricted, and the general level of physical and psychological comfort provided to drivers is still high Flow with speeds at or near the free-flow speed of the freeway. Freedom to maneuver within the traffic stream is noticeably restricted, and lane changes require more care and vigilance on the part of the driver Speeds begin to decline slightly with increasing flows and density begins to increase somewhat more quickly. Freedom to maneuver within the traffic stream is more noticeably limited, and the driver experiences reduced physical and psychological comfort levels Operation at capacity. Vehicles are closely spaced. Maneuverability within the traffic stream is extremely limited, and the level of physical and psychological comfort afforded the driver is poor Breakdowns in vehicular flow
B
C
D
E F
Table 2), individuals were shown the 12 video clips and asked their perceptions of LOS (A–F). For each of the 12 video clips, study participants were asked to indicate what level of service was appropriate for traffic condition shown in this video clip (they chose from the six discrete categories of: LOS A, B, C, D, E or F). These data were then used in the model development.
3. Methodological approach To determine how users perceive LOS, a statistical approach was needed to predict the probability of discrete data (LOS A, B, C, D, E, and F). Standard multinomial discrete-outcome modeling methods such as the multinomial logit model are a possibility, but such models do not take into account the ordered nature of the data (A is better than B, B is better than C and so on) and a loss in parameter efficiency would result (see Amemiya, 1985). To account for both the discrete and ordered nature of the data, an ordered probability approach is an appropriate modeling choice (see Washington et al., 2003, for a complete description). Ordered probability models are derived by defining an unobserved variable, z, that is used as the basis for modeling the ordinal ranking of data, in this case LOS rankings. To do this, an unobserved variable is defined that determines the perceptions of LOS as a linear function for each observation n (defined as each study participants evaluation of the 12 video clips, so the 193 participants would generate 2316 observations), such that zn ¼ bX n þ en ;
ð1Þ
where Xn is a vector of variables (such as traffic conditions and user characteristics) that determine perceived LOS, b is a vector of estimable parameters, and en is a random disturbance. Using this equation, observed LOS, yn, for each observation is written as (with LOS A, B, C, D, E and F corresponding to y = 1, 2, 3, 4, 5, and 6 respectively),
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yn ¼ 1
if zn 6 l1
yn ¼ 2
if l1 < zn 6 l2
yn ¼ 3
if l2 < zn 6 l3
yn ¼ 4
if l3 < zn 6 l4
yn ¼ 5
if l4 < zn 6 l5
yn ¼ 6
if zn P l5 ;
ð2Þ
where ls are estimable parameters (referred to as thresholds) that define yn. The ls are parameters that are estimated jointly with the parameter vector b. The estimation problem then becomes one of determining the probability of a participant selecting a particular LOS. To do this, let en be assumed to be normally distributed across observations with mean = 0 and variance = 1. It can be shown that l1 can be set equal to zero without loss of generality (Washington et al., 2003). With these assumptions, an ordered probit model results with selection probabilities, P ðy n ¼ 1Þ ¼ UðbX n Þ P ðy n ¼ 2Þ ¼ Uðl2 bX n Þ UðbX n Þ P ðy n ¼ 3Þ ¼ Uðl3 bX n Þ Uðl2 bX n Þ P ðy n ¼ 4Þ ¼ Uðl4 bX n Þ Uðl3 bX n Þ
ð3Þ
P ðy n ¼ 5Þ ¼ Uðl5 bX n Þ Uðl4 bX n Þ P ðy n ¼ 6Þ ¼ 1 Uðl5 bX n Þ where U(Æ) is the cumulative normal distribution, Z u 1 1 2 UðuÞ ¼ pffiffiffiffiffiffi e2w dw 2p 1
ð4Þ
This model can be estimated by standard maximum likelihood procedures. However, a complication arises because each of the 193 participants generates 12 observations since each participant evaluates 12 video clips for perceived level of service. This creates a problem because the 12 responses given by a specific participant will share unobserved effects. If this is not accounted for, and the model is estimated as though the 12 observations from each survey participant came from 12 independent participants, the standard errors of the models estimated parameters will be biased downward, resulting in inflated t-statistics, potentially erroneous inferences as well as potential biases in parameter estimates. This problem can be handled with a standard random effects approach by rewriting Eq. (1) as, zic ¼ bX ic þ eic þ ui
ð5Þ
where subscripting i denotes individual participants (i = 1, . . . , 193), subscripting c indexes the video clips (c = 1, . . . , 12), ui is the individual random effect term (assumed to be normally distributed with mean 0 and variance r2) and all other terms are as previously defined. Estimation of this random effects model results in an estimate of r, the significance of which determines the significance of the random effects formulation relative to the standard ordered probit model (see Greene, 2000).
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4. Level of service and traffic density To gain an initial understanding of how the sample of individuals in this study perceived LOS relative to the current Highway Capacity Manual procedures (using traffic density as the sole criterion), random effects ordered probability models were estimated with traffic density as the only independent variable. Model estimation results for each of the five categories of respondents (university students, transportation professionals, environmental management professionals, truckers, and clerical/support staff) and all respondents combined are presented in Table 3. Table 3 shows that the parameter estimates for density are highly significant, reflecting the importance of density in LOS perception. The positive density parameter indicates that higher values of traffic density make it more likely that worse levels of LOS are perceived. The constant term and all threshold estimates are also highly significant as indicted by the t-statistics. The standard deviation of the random effects was also highly significant, indicating the importance of considering random effects (had this been statistically insignificant, the random effects would not be justified and a standard ordered probit model would be sufficient). The table shows that goodness-of-fit, as measured index, q2, is also reasonable. Given these model estimates, a comparison to the Highway Capacity Manual density ranges can be made. The thresholds defining levels of service from the ordered probit models can be calculated as (lk b0)/b1 where k designates the five threshold values (l1 = 0 and the four threshold values are shown in Table 3). Given these five thresholds, cut-off values were computed for the six levels of service. 3 These estimated values, along with corresponding Highway Capacity Manual LOS designations, are presented in Table 4 and graphically in Fig. 1. It is apparent that perceived levels of service do not closely follow the Highway Capacity Manual criteria in many areas. All participating groups, except the transportation professionals, have a lower tolerance for LOS A where the average cut-off value was found to be 7 pc/mi/ln, compared to 11 pc/mi/ln in the Highway Capacity Manual. In contrast, participants tended to have a higher tolerance for worse LOS. Density values for LOS D in the Highway Capacity Manual fall mostly into perceived LOS C and those for LOS E in the Highway Capacity Manual are associated with perceived LOS D. In addition, participants appeared to be willing to withstand LOS E up to an average density value of 82 pc/mi/ln, implying that the current Highway Capacity Manual cutoff for LOS F (traffic breakdown) condition seems to be underestimated relative to participant perceptions. 4 The findings in Table 4 suggest that, while density strongly influences the perceived LOS on freeway traffic quality, the currently utilized threshold values in the Highway Capacity Manual do not seem to correspond to what participants perceive. It also appears, based on our findings,
3
Interestingly, using cluster analysis (which classifies data into relatively homogeneous groups on the basis of the similarity of group characteristics) it was found that study participants seemed to differentiate only three levels of freeway traffic conditions, as opposed to the six levels prescribed in the Highway Capacity Manual. However, we use six categories for the sake of comparison with the current six-scale LOS used in the Highway Capacity Manual. 4 LOS F can extend into heavily queued conditions and covers a much larger range of densities relative to the other LOS categories. To study the possible effects this may have on the model estimation results, we estimated the models using only data from video clips in the LOS A–E range. We found that estimated model parameters changed only slightly when this was done and that the same inferences could be drawn.
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Independent variable
Model 1 STU
Model 2 TPP
Model 3 ENV
Model 4 TRK
Model 5 CLK
Model 6 ALL
Constant
0.535 (3.84)
1.156 (3.05)
1.218 (1.87)
0.715 (1.73)
0.504 (1.93)
0.658 (7.09)
Traffic characteristic variable Traffic density in pc/miles/ln
0.107 (30.46)
0.107 (9.12)
0.122 (14.13)
0.103 (19.60)
0.080 (18.39)
0.099 (52.48)
Estimated thresholds Threshold l2 Threshold l3 Threshold l4 Threshold l5
1.445 3.061 4.704 8.333
1.250 2.884 4.389 6.854
1.663 3.069 4.505 7.363
1.386 2.467 4.077 8.255
1.328 2.424 3.573 6.546
1.366 2.741 4.222 7.440
(11.53) (23.21) (27.26) (26.75)
(6.41) (9.31) (9.48) (6.60)
(3.08) (6.76) (8.08) (8.26)
(5.76) (9.16) (14.25) (18.94)
(8.08) (13.15) (17.03) (17.91)
(21.55) (38.39) (47.36) (47.08)
Standard deviation of random effects r 0.573 (9.88)
0.636 (4.16)
0.815 (1.21)
0.540 (3.84)
0.507 (4.16)
0.580 (12.99)
Number of observations Log likelihood at zero Log likelihood at convergence q2
384 641.59 377.90 0.41
168 293.71 151.86 0.48
420 712.01 410.92 0.42
360 622.71 406.20 0.35
2340 4029.21 2343.50 0.42
a
1008 1728.08 938.88 0.46
In this table, the three letter acronyms refer to subgroups of the sample: STU = university students, TPP = transportation professionals, ENV = environmental management professionals, TRK = truckers, CLK = clerical/support staff, and ALL = all subgroups.
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Table 3 Parameter estimation results for density threshold values (t-statistics in parentheses)a
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Table 4 Comparison of level of service (LOS) criteriaa LOS
A B C D E F
Perceived density range (pc/mi/ln) Model 1 STU
Model 2 TPP
Model 3 ENV
Model 4 TRK
Model 5 CLK
Model 6 ALL
Highway Capacity Manual
0–5 >5–19 >19–34 >34–49 >49–83 >83
0–11 >11–23 >23–38 >38–52 >52–75 >75
0–10 >10–24 >24–35 >35–47 >47–70 >70
0–7 >7–20 >20–31 >31–47 >47–87 >87
0–6 >6–23 >23–37 >37–51 >51–89 >89
0–7 >7–21 >21–34 >34–49 >49–82 >82
0–11 >11–18 >18–26 >26–35 >35–45 >45
a
In this table, the three letter acronyms refer to subgroups of the sample: STU = university students, TPP = transportation professionals, ENV = environmental management professionals, TRK = truckers, CLK = clerical/ support staff, and ALL = all subgroups.
Density (pc/mi/ln)
100 90 80 70 60 50 40 30 20 10 0 STU LOS A
TPP LOS B
ENV LOS C
TRK LOS D
CLK LOS E
ALL
HCM LOS F
Fig. 1. Graphical comparison of level of service (LOS) criteria. In this figure, HCM refers to the Highway Capacity Manual level-of-service definitions and the other three letter acronyms refer to subgroups of the sample: STU = university students, TPP = transportation professionals, ENV = environmental management professionals, TRK = truckers, CLK = clerical/support staff, ALL = all subgroups.
that the Highway Capacity Manual LOS F measure is conservative (relative to user perceptions) because participants do not discern LOS F until density reaches 82 pc/mi/ln on average. However, as the extant literature suggests, factors other than density are likely to influence participants LOS perceptions and we explore this below.
5. Complete model of level of service and user perceptions To obtain a more complete understanding of road user perceptions of LOS, a model was estimated to include participant characteristics and additional traffic characteristics (in addition to density). Because the group of truckers were asked slightly different questions (including the number of miles they drive in a typical year), two models were estimated, one that included all
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Table 5 Level of service (LOS) model estimation results non-truckers Independent variable Constant Socio-demographic variables High school graduate (1 if high school graduate, 0 otherwise) Participant between 21 and 50 years old (1 if yes, 0 if no) Participant between 51 and 65 years old (1 if yes, 0 if no) Using freeway everyday (1 if yes, 0 if no) Driving to work (1 if yes, 0 if no)
Estimated parameter 0.134
t-Statistic 0.210
0.887 0.652 0.384 0.399 0.396
3.18 3.01 1.62 2.52 2.32
0.781
3.96
0.688
2.79
0.068 0.041 0.078 0.002 2.518 0.027
12.11 6.06 3.80 14.18 1.73 3.97
Estimated thresholds Threshold l2 Threshold l3 Threshold l4 Threshold l5
1.969 3.860 5.475 7.485
20.86 33.87 37.22 34.13
Standard deviation of random effects r
0.634
10.23
Traffic characteristic variables Four-lane freeway (total both directions) (1 if 4-lane freeway, 0 if 6-lane or more) Freeway visibility (1 if good visibility, 0 if video clip was taken after sunset or in rainy/foggy conditions) Traffic density, in pc/mi/ln Average speed, in mi/h Standard deviation of speed, in mi/h Average flow, in veh/h/ln Standard deviation of headway, in mi Percentage of trucks in the traffic stream
Number of observations Log-likelihood at zero Log-likelihood at convergence q2
1752 3055.59 1519.82 0.50
subject groups except truckers and the other that included just the truckers. Model estimation results for these two models are presented in Tables 5 and 6. 5 Interpretation of the values in Tables 5 and 6 is such that a positive parameter means increases in the variable give a higher probability that a worse level of service will be perceived and a negative parameter means increases in the variable give a higher probability that a better level of service will be perceived. Note that for this statement to be correct, the marginal effects of the parameter estimates should be checked. This 5
Due to missing socio-demographic information on some respondents, the number of observations for the nontrucker model was 1752 instead of 1920 (14 of the 161 non-trucker participants had missing data) and for the trucker model was 384 instead of 420 (3 of the 32 truckers had missing data).
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Table 6 Level of service model estimation results for truckers Independent variable
Estimated parameter
t-Statistic
Constant
0.716
1.15
Socio-demographic variables Drive less than 40 000 miles in a year (1 if yes, 0 otherwise)
0.618
1.71
1.504
4.30
Traffic characteristic variables Four-lane freeway (total both directions) (1 if 4-lane freeway, 0 if 6-lane or more) Freeway visibility (1 if good visibility, 0 if video clip was taken after sunset or in rainy/foggy conditions) Traffic density, in pc/mi/ln Standard deviation of speed, in mi/h Standard deviation of headway, in mi Percentage of trucks in the traffic stream
0.738
3.20
0.096 0.072 15.712 0.028
10.85 1.89 6.98 1.67
Estimated thresholds Threshold l2 Threshold l3 Threshold l4 Threshold l5
2.341 3.789 5.637 8.478
6.62 9.33 12.38 14.56
Standard deviation of random effects r
0.712
4.19
Number of observations Log-likelihood at zero Log-likelihood at convergence q2
384 670.27 317.00 0.53
was done and the signs of these effects are in agreement with this statement. For more information on the need for considering marginal effects, see Washington et al. (2003). For participants that are not truckers, Table 5 shows that participants who have at least a high school education, use freeways every day, and drive to work are less likely to choose worse levels of service, holding other factors constant. Participants that are between 21 and 65 are more likely to choose worse levels of service, possibly because their value of time is higher than the very young (20 years old and less) and the old (65 years or greater). Good freeway visibility and high average speeds resulted in a lower probability of choosing worse levels of service. Freeways that were only 4-lanes (total both directions) and increases in traffic density, standard deviation of traffic speed, traffic flow, standard deviation of headways, and percent of trucks in the traffic stream all made it more likely that worse levels of service were perceived. All of these findings are consistent with expectations. However, the significance of so many variables in the model underscores the apparent inadequacy of using traffic density alone as a measure of LOS. 6 6 Please note that indicator variables for group types (for example, university students, transportation professionals, etc.) were found to be statistically insignificant.
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Table 6 shows that the results for truckers were similar but with some key differences. For truckers, whether the trucker drove less than 40 000 miles was the only socio-demographic variable that was significant, and it showed that truckers that drove less were more likely to perceive worse levels of service, all else being equal. Good freeway visibility and high average speeds resulted in a lower probability of choosing worse levels of service as was the case for non-truckers. Interestingly, in contrast to the non-trucker finding, higher percentages of trucks decreased the likelihood that a worse level of service would be perceived, which could be because of truckers perceiving the platooning of trucks to be a less onerous state. As was the case with non-truckers, freeways that were only 4-lanes (total both directions) and increases in traffic density and standard deviation of traffic speed all made it more likely that worse levels of service were perceived.
6. Summary and conclusions The LOS concept has been the primary criterion in the Highway Capacity Manual for several decades. It has served the transportation community well by providing a means to explain traffic operational quality to the general public. However, little is understood about how the currently defined LOS categories correspond to participant perceptions of traffic operational quality. The analysis presented in this paper provides evidence that perceptions of LOS are dependent on a number of traffic and road-user characteristics. Incorporating these findings in a new LOS measurement technique would be complex because of the dependence of LOS on the characteristics of the user population. However, if there is interest in linking LOS measurements with user perceptions, it is clear that our model estimates indicate that traffic characteristics other than density should be used. Also, our models provide some evidence that the current six-level LOS scale does not map well with user-perceived LOS. In particular, the Highway Capacity Manuals current range of LOS F conditions does not appear to be perceived by road users as the ‘‘breakdown in vehicle flow’’ that this LOS is intended to represent. As corroborated by a number of recent studies, this finding suggests that a potential change in the number of LOS levels may be warranted with a particular emphasis on better understanding what is currently defined as LOS F. To be sure, while this research provides some suggestive evidence on how road users perceive freeway traffic quality, there are a number of limitations. First, due to the small, regional nature of the sample, our findings must be viewed as exploratory in nature. Clearly, an expanded sample of road users would provide much more information. Second, the experimental approach of having road users making LOS judgments by simply viewing video clips has obvious limitations. Although much more difficult to execute, a better approach would be to have road users express their opinions of LOS while actually driving in the traffic stream with known traffic parameters (density, average speed, headways, and so on). Finally, it is potentially important to consider road users perceived LOS in light of their complete road trip as opposed to the point on a freeway segment used in this study. It is possible that perceptions may differ when all freeway segments traveled by road users are considered because expectations may change depending on immediate past exposure to traffic. It is hoped that the results and statistical approach presented in this study can serve as a basis for expanding the transportation fields understanding of the complex relationship between traffic conditions and perceptions of those conditions. An understanding of this relationship will allow
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transportation engineers and planners to better accommodate demand on transportation facilities and more efficiently allocate scarce transportation resources.
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