Preliminary Evaluation of Operational Performance ...

1 Wu/Du/Jang/Chan/Boriboonsomsin 1

Preliminary Evaluation of Operational Performance between

2

Different Types of HOV Facilities in California: Continuous-Access

3

vs. Limited-Access

4 5 6 7 8 9 10

Guoyuan Wu, Ph. D. Candidate CE-CERT, University of California at Riverside 1084 Columbia Ave, Riverside, CA 92507, USA Tel: (951) 781-5630, Fax: (951) 781-5790 E-mail: [email protected]

11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Yaoqiong Du California PATH Program, University of California, Berkeley 1357 S. 46th Street, Richmond, CA 94804, USA Tel: (510)665-3659, Fax: (510)665-3757 E-mail: [email protected]

29 30 31 32 33 34 35 36 37 38 39 40 41

Kanok Boriboonsomsin, Ph. D., P. E. CE-CERT, University of California at Riverside 1084 Columbia Ave, Riverside, CA 92507, USA Tel: (951) 781-5792, Fax: (951) 781-5790 E-mail: [email protected]

Kitae Jang, Ph. D. Candidate Institute of Transportation Studies, University of California, Berkeley 416F McLaughlin Hall #1720, Berkeley, CA 94720, USA Tel:(510)642-4348, Fax:(510)643-9922 E-mail: [email protected] Ching-Yao Chan, Ph. D., P. E. California PATH Program, University of California, Berkeley 1357 S. 46th Street, Richmond, CA 94804, USA Tel: (510)665-3621, Fax: (510)665-3757 E-mail: [email protected]

[6 Tables and 4 Figures: 2,500 words] [Text 4923 words] Word count: 7423 words Paper for the 90th Annual Meeting of Transportation Research Board Washington D. C. January 2011

2 Wu/Du/Jang/Chan/Boriboonsomsin 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

ABSTRACT High-occupancy vehicle (HOV) facilities have been regarded as an effective and environmentally friendly approach to improve the mobility and productivity of freeway systems in metropolitan areas. Based on the traffic data from over 700 vehicle detector stations (VDS) during a six-month period, a comparative study of operational performance at the route level was conducted between continuous-access and limited-access HOV facilities in California. The evaluation results of both HOV lanes and adjacent general purpose (GP) lanes revealed several operating characteristics of these lanes, including: 1) the ingress/egress areas in limited-access HOV facilities may affect the formation of bottlenecks along HOV lanes; 2) the speed on HOV lanes and the speed differential between the HOV and adjacent GP lanes are statistically shown to be greater in continuous-access facilities than those in the limited-access facilities; and 3) the characteristics of speed-flow distribution of HOV lanes exhibit observable differences between the two types of HOV facilities, but those of adjacent GP lanes are similar regardless of the access type. Furthermore, statistical analyses show that some performance measures at the route level, including the space mean speed and vehicle-mile-traveled (VMT) share of the HOV lanes, are significantly different for HOV facilities with different access types. Keywords: High-occupancy vehicle (HOV) lane, adjacent general purpose (GP) lane, operational performance, lane configuration

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INTRODUCTION High-occupancy vehicle (HOV) facilities, which are reserved for vehicles with more than a pre-determined number of occupants, have been developed for decades as a part of the roadway network system to relieve pressures from ever-increasing travel demands. Recently, “the FHWA encourages the implementation of HOV lanes as an important part of an area-wide approach to help metropolitan areas address their requirements for improved mobility, safety and productivity, while also being sensitive to environmental and quality of life issues” (1). Recognizing the construction of HOV facilities as a multi-modal operational strategy to improve freeway performance, many states including California, are in the process of completing the HOV lane network. Of all HOV facilities currently operated in California, there are two major access types (see Figure 1): a) continuous-access, and b) limited-access. Traditionally, Northern California HOV lanes, such as those in the San Francisco Bay Area, have been operated with continuous-access and peak-hour only, while those in Southern California have mostly been operated with limited-access and 24-hour. Only a small portion of HOV facilities in Southern California have been operated either with limited-access and part-time only, (e.g. SR-14 in Los Angeles county) or with continuous-access and 24-hour, such as SR-22 and SR-55 in Orange county. HOV Lane Adjacent GP Lane

21 22 23

On-ramp

Off-ramp

(a) Continuous access.

Ingress/Egress HOV Lane Adjacent GP Lane

24 25 26 27 28 29 30

On-ramp

Off-ramp

(b) Limited access. FIGURE 1 Illustration of different types of access control for HOV facilities in California. Although various studies (2-9) had been conducted on the evaluation of operational performance of HOV facilities, most of them focused on the data

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observed from a limited number of collection points along one or a few routes. The methodologies used in such studies may not be readily applied to a larger scope of research. Some of them are too time-consuming to process the data set of a considerable size. Others are only illustrative for short segments of HOV facilities, e.g. the spatial interval covered by one vehicle detector station (VDS). In addition, some conclusions drawn from a specific case may be limited to the associated conditions, such as the traffic demand and the segment configuration. It is difficult for these conclusions to be generalized to fit into other situations. Other studies (10-14) involved evaluation of operational performance for regional HOV lane systems. However, the collection of certain types of data in these studies, e.g. user surveys on HOV facilities, may take much effort for a long period. Other data for analysis, such as motion information from the probe vehicle, may be only available from a small sample pool, from which the analysis results may be biased. Furthermore, as mentioned in (15), many of these studies only investigated HOV facilities operating under conditions when the HOV demand is less than the lane capacity. Based on a large set of data obtained from VDS across California in 2005, (16) questioned the benefits from constructions of HOV lanes by conducting an empirical evaluation on effectiveness of statewide HOV facilities, including HOV lane utilization, HOV lane capacity loss, and travel time savings, etc. But the results were not differentiated by different access types. With access to 5-year collision data from the Traffic Accident Surveillance and Analysis System (TASAS), (17) evaluated the safety performance of both continuous-access and limited-access HOV facilities in California. An interesting finding was that the HOV facilities with limited-access did not appear to provide better safety performance than the ones with continuous-access in terms of the collision rate and collision severity. The study described in this paper can be viewed as an extension of (17) for evaluating the operational performance of HOV facilities with two different access types in California. The objective of this study is to obtain a better understanding of the nature of traffic operation in HOV facilities with different access types across California by conducting an empirical evaluation of operating characteristics of both HOV lanes and adjacent general purpose (GP) lanes, and comparing selected route-level performance measures between the two types of HOV facilities. The remainder of this paper is organized as follows. Section 2 presents an overview of study corridors and describes the data sources available. In Section 3, several corridors are selected as examples to illustrate the operational performances of HOV facilities with different access types in California. Further analysis at the route level is conducted across all study corridors in Section 4. The last section summarizes this paper with concluding remarks and future research steps. STUDY SITES AND DATA DESCRIPTION As mentioned above, this study aims at extensively evaluating the operational performance of HOV facilities with different access types in California. A total of twenty-three corridors with HOV facilities have been investigated in the current study

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(see Table 1). These corridors include eleven HOV facilities with continuous-access and twelve with limited-access. Seven out of eleven HOV facilities with continuous-access have been operated only during peak hours (part time) while the rest operated with 24-hour (full time). As for those twelve corridors with limited-access, only two of them have been operated during peak hours while others are in full-time operation.

8

TABLE 1 List of HOV Facilities within the Scope of Current Study HOV Type

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28

Corridor

District

County

Study Boundary*

Length**

Start

End

(mile)

I-80W/E

4

ALA

5.3

15.3

10.0

Part-time

US-101N/S

4

SCL

367.3

401.8

34.5

Continuous-access

I-680N/S

4

CC

31.4

43.3

11.9

I-880N

4

ALA

10.5

30.3

19.8

Full-time

SR-22W/E

12

ORA

1.5

13.5

12.0

Continuous-access

SR-55N/S

12

ORA

12.0

18.0

6.0

Part-time Limited-access

SR-14N/S

7

LA

0.0

18.5

18.5

I-105W/E

7

LA

1.2

16.9

15.7

I-201E

7

LA

24.8

39.9

15.1

Full-time

I-405S

7

LA

36.7

46.0

9.3

Limited-access

I-5N/S

12

ORA

79.2

101.2

22

I-405N/S

12

ORA

0.0

24.0

24

SR-55N

12

ORA

6.0

12.0

6

SR-57S

12

ORA

0.5

12.0

11.5

* With absolute post-mile ** For each corridor The data used in this study were obtained from the California Freeway Performance Measurement System or PeMS, and covered typical weekdays, i.e. Tuesdays, Wednesdays and Thursdays, in the 6-month period between May 2009 and October 2009. The data were selected from all study corridors (listed in Table 1) which cover about 390 miles of freeway segments with HOV facilities and over 700 VDS. For these study corridors, the traffic count and occupancy data are available lane by lane, aggregated over 5-minute intervals at detector stations whose average spacing may vary from 0.3 mile to 1.9 miles. To supplement the flow and occupancy data, speed is another useful measurement of choice in analyzing the operational performance of HOV facilities. However, this parameter is difficult to measure directly from the inductive loop detector, especially the single-loop detector in the field. The estimation of speed, in general, requires configuration information of each individual detection loop, certain assumptions and a specific algorithm, which is out of the scope of this paper. Readers interested in this topic may refer to PeMS online help (18). It should be also noted that the density used in the following analysis is computed from the flow and speed by using the fundamental relation, flow equals speed times density.

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In addition, due to different VDS layouts for HOV facilities in Northern California and Southern California, the lane by lane data have been pre-processed to extract the information on HOV lanes and associated adjacent GP lanes. Unless explicitly stated otherwise, all of the data used in this study are observed values without any “holes filling”.

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ANALYSIS OF LANE OPERAITNG CHARACTERISTICS In this section, a few HOV facilities of different access types were selected as “representatives” for detailed analysis by investigating: 1) percentile-based speed contour; 2) percentile-based speed difference (HOV lane – AGP lane) vs. densities of HOV lane and AGP lane; 3) speed-flow probability histogram. It should be noted that most observations from the exemplary routes are readily to be applied to the other routes with the same access type within this study scope.

15 16 17 18 19 20 21 22

Percentile-based Speed Contour As mentioned above, the speed data for each individual detector used in this study covered a six-month period. To explore these data over multiple days instead of a single day, percentile speed data were used to generate a “representative” speed contour for each study route (19). The p-th percentile speed over D days at the i-th loop detector station during the t-th discrete time interval (e.g. 5 minutes) is denoted by , and the probability of observing speed at detector i at time t on the d-th day, , lower than can be defined as:

23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43

where

represents the probability. In the following analysis, the 50th percentile (or median) of the speed sample set was selected for each individual detector during each time interval over multiple days. Such method is more robust than choosing an average speed, which may be biased by “outliers” that may arise due to abnormal traffic conditions such as incidents, special events, and road construction. Therefore, recurrent bottleneck(s) of a study route can be identified more easily by using the percentile-based speed contour. The lower the percentile is selected, the more bottlenecks with less recurrence can be identified. Figure 2 demonstrates the 50th percentile speed contours of HOV lane and adjacent GP lane along a segment of I-105E with limited-access. The yellow dots and dashed lines represent the locations of ingress/egress areas. It can be observed that most of the HOV lane bottlenecks in the afternoon peak hours occur on the upstream of the ingress/egress areas and they are highly related to the adjacent GP lane bottlenecks. Such results can also be expected in other study routes with limited-access. A hypothesis of this phenomenon is that the relatively more concentrated lane changing maneuvers around the ingress/egress areas may cause the upstream congestion along HOV lanes. In addition, the downstream congestion along adjacent GP lanes may deteriorate the upstream congestion along HOV lanes.

7 Wu/Du/Jang/Chan/Boriboonsomsin 50th Percentile Speed Contour (HOV Lane) 70 21:00

60

18:00 Time of Day

50 15:00 40

12:00

30

09:00

Traffic Direction 06:00

20

03:00 10 00:00

2

4

6

1

8 10 12 Absolute Postmile (mile)

14

16

(a) HOV lane.

2 3

50th Percentile Speed Contour (AGP Lane) 70 21:00

60

18:00 Time of Day

50 15:00 40

12:00

30

09:00

Traffic Direction 06:00

20

03:00 10 00:00

4 5 6 7 8 9 10 11 12 13 14

2

4

6

8 10 Postmile (mile)

12

14

16

(b) Adjacent GP lane. FIGURE 2 Speed contour (50th percentile) along I-105E (full-time and limited-access in District 7). Percentile-based Speed Difference vs. Density Density is useful in identifying the states of traffic conditions along freeways. As for HOV facilities, densities of HOV lanes and adjacent GP lanes and the associated speed differences may also provide stimuli for drivers to change lanes across the access. Figure 3(a) presents the 50th percentile speed difference (HOV lane – AGP

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lane) vs. both HOV lane density and adjacent GP lane density for I-80E with continuous-access. Each data point represents the median speed difference with respect to each combination of vehicular densities along both lanes. It should be noted that the data points have been aggregated with a resolution of 2 veh/mile in density. 50th Percentile Speed Difference (HOVL-GPL) 180 60

NI = 161

HOVL Density (vpm)

150

I

120

N = 146997 II

II

40 20 0

90

N = 4474 III

60

III

30

-20 -40 -60

0

0

30

60 90 120 AGPL Density (vpm)

6 7 8

150

180

(a) I-80E (part-time and continuous-access in District 4). 50th Percentile Speed Difference (HOVL-AGPL) 180 60

NI = 16249

HOVL Density (vpm)

150

I

120

N = 416791 II

II

40 20 0

90

N = 16203 III

60

III

30

-20 -40 -60

0

9 10 11 12 13 14

0

30

60 90 120 AGPL Density (vpm)

150

180

(b) I-5S (full-time and limited-access in District 12). FIGURE 3 50 percentile speed differential (HOV lane – AGP lane) vs. HOV lane density and AGP lane density. The figure has been partitioned into three regions (with boundaries by black dashed lines). If the density of HOV lane is much higher than that of adjacent GP lane th

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(dHOVL – dAGPL > 30 vehicles per mile, or vpm), as shown in Region I, then the speed difference appears to drop drastically and is negative, i.e. HOV lane traffic is moving much slower than that along adjacent GP lane. If the densities of HOV lane and AGP lane are not remarkably different (Region II), then the speed difference falls within a medium range. This appears to be true for either congested or uncongested conditions. When adjacent GP lane is much more congested than HOV lane (dAGPL – dHOVL > 30 vpm), as depicted in Region III, the speed difference becomes significantly larger and is positive. Figure 3(b) presents the result for I-5S with limited-access. For further illustration, the number of data samples in each region has also been displayed in Figure 3 (e.g. NI = 161 in Figure 3(a)). Compared with that for I-80E (continuous-access), the occurrence of sample points is much more frequent in Region I but less in the upper right portion of Region II. This observation is also supported by the analysis of data from the other study routes. The difference between the patterns as exhibited in Figures 3(a) and 3(b) reveals that the access type may have contributed to the distribution of speed difference under various traffic conditions. It should be noted that the density across lanes, in general, could be evenly distributed through lane-changing maneuvers. However, the distinctive density patterns are more pronounced in limited-access HOV facilities. Hence, a hypothesis for this phenomenon is that lane-change maneuvers are more restrictive in limited-access HOV facilities than in continuous-access ones. This results in a more concentrated occurrence of congested states in HOV lane traffic even though the density of HOV lane is much higher than that of adjacent GP lane. In both figures, the sub-regions (enclosed by black solid lines) inside Region III represent a set of traffic states in which the density of HOV lane is less than 11 veh/mile (i.e. vpm) and the density of adjacent GP lane is greater than 45 veh/mile, or dAGPL > 45 vpm. As suggested in (20), such states mean that the level of service (LOS) of HOV lane is A (under free-flow condition) while LOS of adjacent GP lane is F (congested). To determine whether the HOV lane speed and speed difference are statistically different between different types of HOV facilities under these states, statistical tests were performed for the data samples from all study routes. It was assumed that the data samples from continuous-access HOV facilities and limited-access ones are independent. The Welch’s t-test (assuming unequal variance) was used to test whether the population means of two access types are different. The statistics is,

36 37 38 39 40

where , S, and n represent the sample mean, sample standard deviation (SD) and sample size. The subscripts “C” and “L” denote the sample groups with continuous-access and limited-access, respectively. The null hypothesis, H0 is , or equivalently, , at 5% level of significance, while the alternative hypothesis, HA is . The results of statistical tests were

10 Wu/Du/Jang/Chan/Boriboonsomsin 1 2 3 4 5

6 7 8 9 10 11 12 13 14 15 16 17 18 19

examined for the HOV lane speed and speed difference (HOV lane – AGP lane) between continuous-access and limited-access HOV facilities as summarized in Table 2. TABLE 2 Results of Statistical Tests (dHOVL

11 vpm and dAGPL > 45 vpm)

Test

Access type

Mean (mph)

Sample SD

t statistics

HOV lane speed

Continuous

68.4

8.1

83.7

rejected

Limited

63.1

6.0

Speed differential

Continuous

43.2

12.8

49.3

rejected

(HOV lane – AGP lane)

Limited

37.6

11.5

All the null hypotheses were rejected at 5% level of significance, which means that when HOV lane is in LOS A but AGP lane is in LOS F, both HOV lane speed and speed difference along continuous-access HOV facilities (within this study scope) are (statistically) significantly greater than those along limited-access HOV facilities at the 95% level of confidence, based on the data from the study routes. Speed-Flow Probability Histogram To evaluate the operational performance of different access types of HOV facilities when the traffic demand is high, the peak period data samples (a.m. or p.m.) were selected to yield the speed-flow joint probability histograms for both HOV lane and adjacent GP lane of each individual route. This type of probability histogram has been used in (16) to evaluate the utilization and capacities of HOV lanes. Probability Histogram (HOV Lane, 3 p.m. to 7 p.m.)

x 10

Speed (mph)

90 80

16

70

14

60

12

50

10

40

8

30

6

20

4

10

2

0

20 21 22

Test result

0

50

100 150 Flow (veh/5-min)

(a) HOV lane.

200

250

-3

11 Wu/Du/Jang/Chan/Boriboonsomsin Probability Histogram (AGP Lane, 3 p.m. to 7 p.m.) 90 0.02

80 70

Speed (mph)

60 50 40

0.01

30 20 10 0

0

50

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

100 150 Flow (veh/5-min)

250

(b) Adjacent GP lane. FIGURE 4 Speed-flow probability histogram along I-880N (part-time and continuous-access in District 4). Figure 4 illustrates the result for I-880N segment with continuous-access and part-time operation. As displayed in Figure 4(a), the mode of the distribution (circled by the blue line), which represents the highest probability, occurs at a speed of 69 mph and a flow of 96 veh/5-min. The 90th percentiles of the speed samples and flow samples are 73 mph and 134 veh/5-min (denoted by black solid lines), respectively. Figure 4(b) shows the distribution for adjacent GP lane, whose mode occurs at 66 mph and 176 veh/5-min. The 90th percentiles of the speed samples and flow samples are 70 mph and 184 veh/5-min (denoted by black solid lines) for AGP lane, respectively. TABLE 3 Speed and Flow Statistics of HOV Lane and AGP Lane for All Study Routes during the Peak Period (Continuous Access vs. Limited Access) Lane

Statistics

Type HOV Lane

AGP Lane

Continuous Access

Limited Access

Mean

STD

Mean

STD

Mode_Speed

66.5

16.8

54.5

18.3

Mode_Flow

59.3

21.9

78.0

31.8

Speed of 90 Percentile

74.9

2.1

66.4

4.7

th

Flow of 90 Percentile

113.0

23.4

134.8

11.4

Mode_Speed

63.8

19.3

59.5

21.6

Mode_Flow

150.2

45.9

152.0

18.8

Speed of 90 Percentile

74.2

3.1

75.6

4.6

th

181.0

22.3

182.0

13.2

th

th

Flow of 90 Percentile

18 19

200

Table 3 lists the speed and flow statistics for all study routes with different

12 Wu/Du/Jang/Chan/Boriboonsomsin 1 2 3 4 5 6 7

access types. An interesting finding is that the means of these statistics for HOV lanes of continuous-access HOV facilities are quite different from those of limited-access ones, but both types of HOV facilities have similar characteristics for adjacent GP lanes. In addition, compared with limited-access HOV facilities within the study scope, higher speeds but lower flows may be expected for HOV lanes with continuous-access.

8 9 10 11 12

ANALYSIS OF SELECTED ROUTE-LEVEL PERFORMANCE MEASURES In this section, the following two performance measures are selected for statistical analysis to obtain a better understanding of the operational performance of statewide HOV facilities: 1) Space Mean Speed, i.e.

13 14 15 16

In the freeway operation analysis, Q is usually viewed as the ratio of the output, VMT, to the input of the freeway system, VHT. The higher this quantity is, the better the freeway segment performs (18). The Q values for both HOV lane and adjacent GP lane can be described as,

17

and,

18

2) Share of VMT for HOV lane, i.e.

19 20 21 22 23 24

To examine the carpooling incentive, a hypothesis test was conducted on the relationship between the share of VMT for HOV lane, SHOVL, and AGP lane speed, vAGPL in (16). In this study, SHOVL, as one of indirect measurements of carpooling effectiveness, is used for comparison across different types of HOV facilities, when the traffic demand is high.

25 26 27 28 29 30 31 32 33

Sampling Procedure and Nested ANOVA As mentioned in Section II, the original data used for this study include lane by lane flow, occupancy and speed information, aggregated over 5-minute intervals. They cover typical weekdays between May 2009 and October 2009 (78 days) for each individual VDS of twenty-three study routes, of which eleven routes are with continuous-access and the rest are operating with limited-access. Vehicle-miles traveled (VMT) and vehicle-hours traveled (VHT) for the segment at the i-th lane over a predetermined period, e.g. the peak period, can be computed as

13 Wu/Du/Jang/Chan/Boriboonsomsin 1

and,

2

where, xj is the location (in post-mile) of the j-th VDS within the segment.

3

and

are the total flow and average speed on lane i at xj during time interval

4 5 6 7 8 9 10 11 12 13 14 15

t. Lj is the effective length of the j-th VDS (16). To generate the samples for statistical analysis, all the study routes were divided into two groups: continuous-access and limited-access. The 5-minute data samples were aggregated at the level of peak periods. For part-time operation HOV facilities, peak periods were selected based on the operation hours in the morning or afternoon (depending on which one is more congested). For full-time operation HOV facilities, the morning peak hours were assumed to be from 6 a.m. to 9 a.m. while the afternoon peak hours to be between 3 p.m. and 6 p.m.. Therefore, the sample size is 78 for each individual route (subgroup). Due to the nested structure of test samples, the following statistical tests will be conducted by using the nested analysis of variance (Nested-ANOVA). The model can be described as (21):

16 17 18

where yi,j,k is the k-th observation of the j-th subgroup within group i; is the overall mean; represents the “effect” for the i-th group; denotes the “effect” for subgroup j within the i-th group, and is the random error.

19 20 21 22 23 24 25 26 27 28 29 30

Test on Q Table 4 and Table 5 summarize the Nested ANOVA for QHOVL and QHOVL – QAGPL along all study routes with different access types. As shown in Table 4, a significant difference exists in QHOVL between HOV facilities of two access types although there are also considerable variations for the routes within each type. By calculation, the sample mean of QHOVL is 59.9 mph for continuous-access while it is 47.5 mph for limited-access. Similarly, there is a significant difference in QHOVL – QAGPL between different types of HOV facilities (see Table 5), whose sample means are 6.4 mph and -1.4 mph for continuous-access and limited-access, respectively. TABLE 4 Nested-ANOVA for QHOVL Sum of

Degrees of

Mean

F

Squares

Freedom

Square

statistics

Between Types

69359.6

1

69359.6

11.3

0.0030

39.6

Within Types

128829.5

21

6134.7

209.3

0*

43.9

Within Routes

51917.4

1771

29.3

250106.5

1793

Total

31 32

* less than 10

-5

P-value

Variance Component (%)

16.5 100.0

14 Wu/Du/Jang/Chan/Boriboonsomsin 1

TABLE 5 Nested-ANOVA for QHOVL – QAGPL Sum of

Degrees of

Mean

F

P-value

Variance

Squares

Freedom

Square

statistics

Between Types

26572.5

1

26572.5

20.9

0.0001

46.9

Within Types

26679.0

21

1270.4

79.9

0

26.7

Within Routes

28172.3

1771

15.9

Total

81423.8

1793

Component (%)

26.4 100.0

2 3 4 5 6 7 8 9 10 11 12 13 14 15

Test on SHOVL The Nested-ANOVA for SHOVL of all study routes is listed in Table 6. It can be observed that the difference of the HOV lane share is statistically significant between continuous-access and limited-access HOV facilities. The sample mean of SHOVL is 36.5% for continuous-access and 42.3% for limited-access. It needs to be pointed out that the average VMT shares for HOV lanes of both types of HOV facilities are lower than those for adjacent GP lanes, which indicates that fewer vehicles are served by HOV lanes than adjacent GP lanes. However, the occupancy requirement is 2+ for most HOV lanes operated in California, which implies that the average shares of person-mile-traveled (PMT) for HOV lanes of both access types are much higher than 50% during the peak period. It should be also noted that this quantity varies significantly among routes within each access type.

16

TABLE 6 Nested-ANOVA for SHOVL Sum of

Degrees of

Mean

F

P-value

Variance

Squares

Freedom

Square

statistics

Between Types

1.523

1

1.523

7.035

0.015

31.9

Within Types

4.545

21

0.216

624.879

0

60.5

Within Routes

0.613

1771

0.0003

Total

1.523

1

1.523

Component (%)

7.6 7.035

0.015

31.9

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DISSCUSSION AND FUTURE WORK Based on the six-month long PeMS data for twenty-three routes with HOV facilities, this study evaluated the operating characteristics and compared the operational performance between different HOV access types in California: continuous-access and limited-access. The results, which are based on the current scope of data analysis, can be summarized as follows: 1) For limited-access HOV facilities, the ingress/egress areas may affect the formation of bottlenecks along HOV lanes; 2) When speed differences are examined versus densities on HOV lane and AGP lane, distinct patterns are observed between HOV facilities with continuous-access and limited-access. In addition, the speeds on HOV lanes and speed differentials with associated adjacent GP lanes may not necessarily be higher in limited access HOV facilities than those of continuous-access when adjacent GP lanes are congested while HOV lanes are operating under free-flow conditions.

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3) As shown in the speed-flow joint probability histograms, HOV lanes of different types of facilities exhibit distinctly different patterns during peak periods, but adjacent GP lanes have similar characteristics. 4) Based on the Nested-ANOVA tables, the differences of several performance measures at the route level, including QHOVL, QHOVL – QAGPL and SHOVL are statistically significant for HOV facilities with different access types. Compared with continuous-access HOV facilities, the sample means of QHOVL and QHVOL – QAGPL are smaller for limited-access HOV facilities while the sample mean of SHOVL is larger. Due to the limitation of data samples, other performance measures including the average vehicle occupancy (AVO), HOV violation rate and etc., for different types of HOV facilities are not included in the current statistical analysis. Furthermore, this study focuses on the comparison between HOV facilities with different access types at the route level, without giving consideration to other geometric characteristics (such as the number of lanes, lane widths, locations of on-/off- ramps, etc.) as well as vehicle demand along the routes. A more elaborate analysis on the operational performance of HOV facilities that takes into account more controlled variables remains one topic of future research.

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ACKNOWDGEMENT This work was performed by the California PATH Program at the University of California at Berkeley, in cooperation with the California Department of Transportation (Caltrans), University of California at Irvine and CE-CERT at the University of California at Riverside. The authors are grateful to all members of the Panel from Caltrans Headquarter, District 4, District 7, District 8 and District 12 for their constructive comments. The authors would also like to thank researchers from UC Irvine for their continuing supports. The contents of this paper reflect the views of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the State of California. This paper does not constitute a standard, specification or regulation.

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