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Madison Avenue Dual Bus Lanes The dual bus lanes were implemented on Madison Avenue in midtown Manhattan in 1981 as part of a Service and Methods Demonstration (SMD) project. The two right-side lanes on the five-lane street were reserved between 2:00 PM and 7:00 PM for buses only. Parking was prohibited along the 17-block (0.85-mile) segment during this time period, making three lanes available for GP mixed traf- fic. Taxis were allowed to make right turns at two intersections and to use a four block section of the lanes. These changes were made without adverse effects on Madison Avenue mixed traffic. Project survey results showed that removing the friction between buses and other vehicles improved mixed traffic speeds by 10 percent during the rush hour period. This improvement occurred despite a 10 percent increase in through volumes (Schwartz et al., 1982; Kuzmyak, 1984). Over 700 buses operated on Madison Avenue during the 2:00 PM to 7:00 PM time period, with up to 200 during the 5:00 PM to 6:00 PM peak hour. Average express bus travel times along the 17-block segment were reduced during the peak hour by 42 percent with the implementation of the reserved lanes, from 15 to 9 minutes in round numbers, a 6 minute savings. Travel times for local buses declined by 35 percent, from 16 to 11 minutes, a 5 minute savings. Afternoon peak-period bus reliability, using a variability measure expressed as the standard deviation divided by the mean travel time, improved from 40 percent to 27 percent for express buses and from 40 percent to 16 percent for local buses. Ridership on both local and express routes increased during the 17 months after the bus lanes were implemented. Ridership gains were higher on local service. Average weekday local service riders increased from 9,450 to 12,385, or 31 percent. Approximately 17 percent indicated they started to use service on Madison Avenue because of the lane. About half of these were riders changing from other transit services. Some 62 percent of local service riders reported that their trips were consistently faster because of the bus lanes. Ridership increases on express buses were more modest. Daily ridership increased from 14,614 to 15,524, or 6 percent, during the first 17 months of operation. Although express buses saved 6 min- utes as a result of the bus lanes, this figure represented a small amount of the total travel time for many express passengers. Nevertheless, some 75 percent of the express passengers felt their trip was consistently faster due to the bus lanes (Kuzmyak, 1984). Not only the small percentage travel time savings, but also the low viability of walking and taxis as alternative modes, may have dampened the relative effect on express bus ridership. UNDERLYING TRAVELER RESPONSE FACTORS Reduced travel times and more reliable trip times are key elements provided by many HOV facili- ties for encouraging choice of a high occupancy commuting mode over driving alone. Other factors influencing traveler response to HOV facilities include ambient travel patterns, underlying urban area characteristics, certain features of HOV facilities and their operation, and external incentives to HOV use such as the degree of transit service provided, park and ride lots, and Travel Demand Management (TDM). These and other factors are explored here; however, the primary park and ride coverage is in Chapter 3, "Park-and-Ride/Pool," while TDM is addressed in Chapter 19, "Employer and Institutional TDM Strategies." Choice of HOV Facilities Travel demand model and user survey research provide an overview perspective on the relative importance of, and interactions among, the various influences affecting the decision to use an HOV 2-54

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facility. The results of a major modeling effort and several user surveys are drawn upon here before examining individual factors. Insights from Travel Demand Modeling A late 1980s HOV research project developed a travel mode and carpool occupancy choice model based on detailed travel data. The data set was rich in surveyed travel choices made in the presence of a major HOV facility--Northern Virginia's Shirley Highway (I-395) into Washington, DC. The analysis indicated that trip makers perceive automobile and bus travel as very different choices, with carpooling and vanpooling viewed more as a subset of auto travel. The study also found that the least difference in perception and resistance to change was among various shared-ride occu- pancy levels, such as three-person versus four-person carpools. The decision to share a ride rather than driving alone was in between the extremes. It thus appeared that the greatest resistance to mode change was between transit and ridesharing, suggesting that these two primary modes do not closely compete for the same travelers, at least not when both are offered HOV travel time advantages. The modeling results also indicated that the in-vehicle travel time savings offered by an HOV facil- ity are more important to a potential carpooler or vanpooler in the mode choice decision than ordi- nary in-vehicle travel time savings. In the Shirley Highway corridor, carpoolers value the travel time savings from the HOV lane 2-1/2 times more than normal driving or riding time savings. This effect is believed to reflect perceived travel time savings on the HOV facility and perhaps also the reliability of the HOV travel time--not otherwise accounted for in the modeling effort. Characteristics of the workplace were also found to be strong determinants in the decision to rideshare. Working for the federal government or other large employer was estimated to be equiv- alent to 8 to 12 minutes of ordinary time savings, parking incentives for ridesharing were worth 8 minutes, and flextime was equivalent to 3 minutes (Comsis, 1989). Insights from User Surveys The finding that transit and ridesharing do not closely compete with each other matches results from HOV lane user surveys. Surveys from the 1970s showed that while buses on HOV facilities attracted some carpool passengers, a higher proportion of auto drivers changed to riding the bus. Similarly, some transit riders on HOV lanes were attracted from carpools, but proportionately more lower occupancy auto commuters were attracted (Pratt and Copple, 1981). This continues to be the case with more recent HOV lanes observations. (See "Related Information and Impacts"-- "Sources of HOV Users," Tables 2-26 and 2-27.) Surveys of HOV lane users also provide further information on the importance of the facility and the other factors that help influence changes in travel behavior. For example, the periodic surveys con- ducted in Houston indicate that between 54 and 76 percent of passengers riding buses on the Houston HOV lanes viewed the opening of the HOV facilities as very important in their decision to ride the bus. Further, between 22 and 39 percent of the survey respondents indicated that they would not be riding the bus without the presence of the HOV lane (Bullard, 1991; Turnbull, Turner and Lindquist, 1995). Surveys of bus riders on the Shirley Highway HOV lanes completed in 1971 and 1974 identified shorter bus travel times and reduced levels of congestion in the HOV lanes as important factors in 2-55

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their decision to use transit (McQueen et al., 1975). Bus riders on the San Bernardino Transitway in 1977 identified the ability to avoid congestion and travel time savings provided by the facility as the main reasons for riding the bus. Carpoolers identified similar factors influencing their use of HOV facilities (Crain & Associates, 1978). Bus and especially carpool and vanpool users of HOV lanes in Houston likewise put congestion and travel time savings at or close to the top of the list, but with time to relax, trip time reliability, and cost savings close behind, as shown in Table 2-20 (Christiansen and Morris, 1990). Travel Time Savings The economic and travel behavior impacts of HOV facilities depend largely on the amount of time saved. As time savings increase there are operating cost savings for transit operators and impacts on mode choice favoring transit and ridesharing. HOV Facilities Individual examples of travel time savings that HOV lanes provide to buses, vanpools, and car- pools relative to travel on the general-purpose (GP) lanes or adjacent facilities were included in the preceding "Traveler Response by Type of HOV Application" sections. Time savings realized by travelers in the HOV lanes depend on a number of factors. These include length of the facility, access treatments, traffic volumes in the HOV lane, and congestion levels in the GP lanes. Without the presence of mixed traffic congestion, no HOV facility can offer a significant time advantage for high occupancy vehicles except for exclusive ramps or separate roadways that provide more direct routes. Table 2-21 brings together examples of peak-hour travel time savings reported on various HOV facilities. Except where noted, the information is based on comparisons of the travel times between the HOV facility and the GP lanes for commuters traveling the full length. The reported time sav- ings presumably pertain to the peak hour, and may be averages, or normal upper limits. Note that circa 2000 AM and PM peak-period travel time savings data for 12 additional Los Angeles facilities opened in the 1990s were included within Table 2-9 of the "Traveler Response by Type of HOV Application" section. Table 2-20 Reasons Reported by Houston HOV Lane Users for "Transitway" Use Katy HOV Lane North HOV Lane Why Use Transitway Bus Car/Vanpoolers Bus Passengers Vanpoolers Passengers Freeway Too Congested 20% 19% 23% 20% Saves Time 16 20 20 20 Time to Relax 18 14 15 13 Reliable Trip Time 14 12 15 13 Cost Less 14 14 12 15 Dislike Driving 11 -- 10 -- Source: 1986 Texas Transportation Institute surveys as reported in Christiansen and Morris (1990). 2-56

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Time savings will vary from day to day, and may be much less in the shoulders of the peak than in the time span of peak congestion on the GP lanes. The travel time savings assembled in Tables 2-9 and 2-21 range from practically nothing to almost 40 minutes. It may be observed that: HOV lanes that function as queue bypasses at toll stations and other bottlenecks such as water crossings provide substantial savings--from about 6 up to 20 minutes per mile--on HOV facil- ities that are typically short. Longer HOV facilities along freeways save up to about 1.6 minutes per mile, averaging 0.7 min- utes per mile for the relatively new HOV facility segments in Los Angeles (Table 2-9) and 1.0 for other long freeway facilities nationwide (Table 2-21). HOV lanes on arterial streets typically save about 0.5 minutes per mile. These savings pertain to the full length of the HOV facility (or study segment in the case of Los Angeles) and relate only to the portion of the trip on the HOV facility. The impact of the HOV facil- ity on the total trip time of travelers may be different. Changes in travel behavior will be influenced by the total travel time, not just the HOV section. Travel time savings, as outlined in the preceding section, have been reported by HOV facility users as an important factor in their decision to change from driving alone. For example, time savings provided by Houston's Katy and Northwest HOV lanes were rated an important factor by 72 per- cent of the carpoolers using both facilities in a 1995 survey (Turnbull, Turner and Lindquist, 1995). Houston studies suggest a guideline of 7 to 8 minutes travel time savings on the overall facility as an indicator of success, or alternatively, 5 to 10 minutes (Christiansen and Morris, 1990 and 1991). Offering meaningful travel time savings is, quite possibly, the most important single function of HOV lanes in inducing HOV use. However, primary reliance must be placed on results of surveys and travel demand modeling at the individual trip level for assessing degree of importance (see "Choice of HOV Facilities"--"Insights from Travel Demand Modeling," above). Examined at the facility level, corridor characteristics cloud the results. For example, regression analysis of historical data from Texas HOV evaluations established a pos- itive relationship between HOV lane person movement (in the Texas context) and HOV lane peak- hour travel time savings. However, the scatter pattern of the data points suggests that time savings are overshadowed by other factors associated with individual facilities (Stockton et al., 1997). It seems likely that factors such as quantity of individual corridor population and employment and other corridor characteristics cause this result. HOV lane users in many areas appear to substantially overestimate the travel time savings they realize, and have been doing so fairly consistently from the outset of HOV operations (Pratt and Copple, 1981). In a 1995 survey, bus riders on the Katy HOV lane in Houston reported travel time savings of 23 minutes in their morning commute and carpoolers reported 25 minutes, while travel time surveys using the floating car technique indicated actual travel time savings of some 17 min- utes compared to the GP lanes. On the other hand, bus riders and carpoolers on the Northwest HOV lane reported AM peak-hour travel time savings of 17 minutes and 20 minutes respectively, compared to actual savings of approximately 22 minutes. Bus riders on the East R. L. Thornton HOV lanes in Dallas reported travel time savings of 13 minutes in the morning and carpoolers indi- cated 15 minutes, compared to 5 minutes in measured time savings (Turnbull, Turner and Lindquist, 1995; Stockton et al., 1997). Carpoolers using the interim I-394 HOV lane in Minneapolis reported travel time savings of 10 minutes in the morning when the actual travel time savings recorded in field surveys was 5.2 minutes (SRF, Inc., 1987). 2-57

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Table 2-21 Examples of Reported AM Peak-Hour Travel Time Savings Associated with HOV Facilities and Bus Lanes Travel Time Savings a Length Minutes Facility (miles) Yearb Total (minutes) per Mile Exclusive Freeway HOV Lanes Houston, Texas I-45N (North) 13.5 1996 14 1.0 I-45S (Gulf) 12.1 1996 4 0.3 I-10W (Katy) 13 1996 17 1.3 US 290 (Northwest) 13.5 1996 22 1.6 US 59 (Southwest) 12.2 1996 2 0.2 Los Angeles, California San Bernardino Transitway 12 1992 17 1.4 Minneapolis, Minnesota I-394 (exclusive & concurrent flow) 11 1992 5 0.5 Washington, DC I-95/I-395 (I-95 and Shirley Hwy.) 27 1997 39 1.4 I-66 (exclusive & concurrent flow) 27 1997 28 1.0 Concurrent Flow Freeway HOV Lanes California SR 55, Orange County 11 1986 18 1.6 SR 91, Los Angeles 8 1992 10 1.2 SR 101, San Francisco Bay Area 11 1989 5 0.5 SR 237, San Francisco Bay Area 4 1989 4 1.0 Bay Bridge, San Francisco Bay Area c 2 1998 20 10.0 Massachusetts I-93(N) Boston d 2.5 1999 10 (max) 4.0 (m ax) Maryland I-270 8 1997 5-6 (AM peak) 0.6-0.8 9-12 (PM peak) 1.1-1.5 Miami Ft. Lauderdale Palm Beach I-95 45 1998 6 (AM/northbound) 0.1 7 (PM/northbound) 0.2 16 (AM/southbound) 0.4 Because the time savings reported address only the trip segment on the HOV facility, not the con- nections to and from the lanes, the impact of an HOV project on the total travel time may be more or less. For example, picking up carpoolers may add time to a trip compared to driving alone. Conversely, HOV lane users may save additional time by missing congestion at upstream or downstream locations, by availing themselves of improved bus service frequencies on the HOV lanes, or by using preferential carpool parking at their destination. 2-58

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Table 2-21 Examples of Reported AM Peak-Hour Travel Time Savings Associated with HOV Facilities and Bus Lanes, continued Travel T i m e Savings a Length Minutes Facility (m iles) Yearb Total (minutes) per Mile Contraflow Freeway HOV Lanes East R. L. Thornton, Dallas 5.2 1996 6 1.2 Route 495, New York/New Jersey c 2.8 1991 18 6.4 Gowanus, New York c 0.9 1982 20 22.2 (max) (max) Arterial Street HOV Lanes San Thomas Expressway, San Jose 11 1989 5 0.5 Montague Expressway, San Jose 5 1989 3 0.6 Airport Road, 128th Street, Seattle 3.4 1993 1 0.3 Eglington Avenue, Toronto 7 1996 3 (AM) 2.5 (PM) 0.4 e Hastings Street, Vancouver 4.4 1996 3 (AM/westbound) 0.7 5 (PM/eastbound) 1.1 Arterial Street Bus Lanes Second Avenue Contraflow, New York f 0.09 n/a 10 111.1 49t h-50 th Bus/Taxi Street, New York 0.88 n/a 7 8.0 Madison Avenue Bus Lane, New York g 0.85 1981 6-8 (express buses) 7.0-9.4 5-7 (local buses) 5.9-8.2 a Notes: Comparison of travel time in the HOV lanes over the general-purpose lanes (in known cases, unless otherwise noted) for commuters traveling the full length of the HOV facility. b Year travel time savings documented. c Queue bypass on approach to toll plaza. d Queue bypass on approach to merge and lane drop. e Applies only to buses, negligible time savings for 3+ carpools. f Queue bypass on approach to congested bridge entrance (no longer exists). g Represents savings from before/after lanes implemented. Sources: Turnbull (1992b); Stockton et al. (1997); SRF, Inc. (1995); Henderson, Vandervalk and Cromartie (1998); Kuzmyak (1984); Ho (1996); New York City DOT (1983); Lisco (1999); Schwartz et al. (1982); Municipality of Metropolitan Toronto (1997). The overestimation of travel time savings by some users may be partially the result of reductions in total trip travel times, not just the portion associated with the HOV lane. It may also be the result of comparing the HOV travel time with the worst case travel time in the GP lanes, or of extrapo- lating from perceptions of a fast trip. The more successful HOV systems will tend to be those which combine on-facility time savings with increases in reliability and actions to make HOV door-to- door trip times competitive with low occupancy auto travel. Arterial Bus Lanes Several studies have documented the effectiveness of arterial bus lanes in reducing travel times, although no analyses have been encountered directly linking the resultant time savings to traveler 2-59

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response. Early capacity research cited increases in peak-hour bus speeds of about 1.5 to 2.0 miles per hour when bus lanes were installed (Rainville et al., 1961). Bus rapid transit studies have demonstrated how time savings vary inversely with the preexisting bus speed. CBD and arterial street bus lane applications have been shown to provide time savings ranging from about 8 minutes per mile of time savings at prior condition operating speeds of 3 to 5 miles per hour, to 1 to 3 minutes per mile of time savings at prior operating speeds of 6 to 12 miles per hour (Wilbur Smith and Associates, 1975).3 Reported time savings of bus lanes and bus streets in New York City are appended to Table 2-21. The benefits shown are greater than those experi- enced with conventional concurrent flow bus lanes where violations and right-turn conflicts are common. Trip Time Reliability It is not only the higher operating speeds and shorter travel times of HOV lanes that are important to users. Ongoing reliability of time savings, reflected in travel time consistency and bus on-time performance, is also important. Measuring travel time reliability requires historical speed and travel time data on both the HOV facility and the GP lanes. Bus on-time performance data also pro- vides an indication. Travel time reliability has been found in a number of cases to be significantly improved by HOV facilities. Most examinations of HOV facility travel time reliability have utilized periodic surveys using the floating car data collection technique or monitoring of bus on-time performance. A more detailed analysis has been conducted using data from the AVI traffic monitoring system in Houston. Eight months of these data were used to examine peak-period travel time reliability on the Katy HOV lane and the GP lanes. Trip reliability was assessed by comparing standard deviations of travel times for weekdays within each month. Figure 2-1 provides an example of the travel times for the Katy HOV lane (lower set of travel times in the graph) and the GP lanes (higher set of times in the graph) over the 8 month period. Both the travel time savings offered by the HOV lanes and the greater variability in travel times in the GP lanes are evident. Figure 2-2 illustrates the travel time reliability for the HOV lane and GP lanes in terms of the range of times within one standard devi- ation (Turner, Carlin and Henk, 1995; Turner, 1997). Among other evaluations of HOV facility travel time reliability is an assessment done of traffic incidents on the Gowanus Expressway in Brooklyn, when its HOV lane was operating in the configuration that pertained in 1998, until August. Reported traffic incidents were one per month on the HOV lane (which was moving 11,000 persons in the AM peak hour) and 18 per month, total, on the three GP lanes (moving 5,040 persons total, AM peak hour). The HOV lane had at least one incident requiring more than 15 minutes clearance time on 6 percent of all work days; the corresponding measure for the GP lane was 54 percent of all workdays (Sverdrup/ Urbitran, 1998). Another study, done in connection with the occupancy requirement change on the I-5 North HOV lanes in Seattle, found that reliability declined somewhat when the vehi- cle occupancy requirement was lowered from 3 to 2 (Ulberg et al., 1992). Partly as a result of this change, Washington State DOT developed guidelines based on minimum average speed and 3 Computation of transit operating speeds such as these, sometimes referred to as effective velocity or (pri- marily in Europe) commercial speed, includes time spent stopped to load and unload passengers as well as time incurred in traffic stops and delays along with acceleration and deceleration effects. 2-60

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Figure 2-1 Daily and monthly average peak-hour travel times on Houston's Katy Freeway Source: Turner (1997). Figure 2-2 Morning peak-hour travel time reliability for Houston's Katy Freeway Source: Turner (1997). speed reliability for use in determining when increases in vehicle occupancy levels should be considered. Documented improvements in bus on-time performance include the results of opening the Shirley Highway HOV lanes in 1969. In that case, the percentage of affected bus trips arriving early or on time in downtown Washington, DC, improved from 33 percent to 92 percent (McQueen et al., 1975). Improvements in bus reliability from 16 percent "on time" to 55 percent "on time" were observed with opening of the Oakland Bay Bridge approach HOV lanes. Lesser but positive bus reliability 2-61

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improvements have been recorded for other HOV lane openings on freeways, and a wide range of reductions in bus trip time variance has been reported for arterial street bus lanes. The average reported improvement is a halving of "late" bus arrivals for all types of facilities (Pratt and Copple, 1981). For the Madison Avenue dual bus lanes example of reliability improvement, refer back to "Response to Arterial Street Bus-Only Facilities" under "Traveler Response by Type of HOV Application." Bus Service, Urban Area, and Facility Characteristics To assist in examination of other factors potentially important in determining HOV facility usage, peak-hour HOV facility utilization information from Tables 2-2, 2-8, and 2-10 has been assembled in a consolidated and augmented tabulation. Available utilization information supports inclusion of 35 observations from HOV facilities along freeways in North America, roughly 40 percent of the total. (Toll roads, river crossings, and expressways are, for short, subsumed within the term "freeways.") The data augmentation consists of having added an HOV-persons total along with several descrip- tors of the operating environment and characteristics. The result is presented as Table 2-22; sorted in order of decreasing HOV person volume, it provides the sum of HOV facility bus passengers and van/carpool occupants. Scatter plots were prepared relating several of the HOV facility descriptors to the person volumes. Figure 2-3, discussed below, is an example. The information in Table 2-22 supports the finding presented earlier that travel time savings are a crucially important determinant of HOV facility usage. Of the 17 facilities for which travel time savings information is listed, five have an estimated saving of 20 minutes or more. Three of these facilities correspond to the top four in total HOV-person volume, and four correspond to the top six. There is insufficient data for a comparable assessment of trip time reliability. The findings from examining several different data-sorts of the information presented in Table 2-22, and the scatter plots prepared from it, have been combined with conclusions from other sources to assemble the discussion of bus service, urban area and facility characteristic factors presented next. Bus Service Levels Many HOV facilities, but almost entirely those oriented toward downtown CBDs, have relatively high bus volumes. These applications--facilities with substantial levels of bus service--have dra- matically higher total HOV person volumes than facilities with little or no bus service. Other facil- ities, especially those focusing on suburb to suburb travel patterns, fall in the little or no bus service category. This, in turn, tends to be an indicator of lower HOV facility person volumes. The HOV facility on New Jersey's I-287, suspended during 1998 in its eleventh month of operation, was in the latter category (see "Related Information and Impacts"--"Terminations of HOV Projects"). Figure 2-3 illustrates a scatter plot relating AM peak-hour total HOV person volumes to the bus vehicle volume on each facility during the same time period. The relationship, with bus vehicle volumes serving as a measure of transit service levels, is extremely strong. The total peak-hour per- son volume may be approximated on most facilities using the linear regression relationship: Total peak-hour HOV person volume = 1,864 46 (peak-hour bus vehicle volume) 2-62

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Table 2-22 Consolidated Freeway HOV Lane Utilization Data with Urban Area and Facility Descriptors Bus Bus Van/Car- Total Travel 1996 Combined Conges- Vehi- Pass- pool Oc- HOV Time Area Pop. Facility Facility tion Location and HOV Facility cles engers cupants Persons Savings Facility Type (000) Length Orientation Measure NJ Rte. 495 (to Lincoln Tunnel) 725 34,685 0 34,685 18 min. Contraflow 17,150 3 miles Radial 1.18 Alameda Co., CA I-80 Bay Br. 101 3,535 8,273 11,808 20 Concurrent Flow 3,890 2 Radial, Bridge 1.33 No. VA/DC I-95/I-395 Shirley 118 3,085 8,212 11,297 39 Exclusive Rev. 3,460 27 Radial 1.43 New York City, Gowanus Expy. 202 8,686 899 9,585 20 Contraflow 17,150 2 Radial 1.18 New York City, I-495 L.I. Expy. 165 7,838 394 8,232 n/a Contraflow 17,150 4 Radial 1.18 Los Angeles, I-10 San Bernardino 71 2,750 4,352 7,102 17 Exclusive 2-way 12,220 12 Radial 1.57 No. VA/DC I-66 16 484 6,486 6,970 28 Excl. Rev. & Conc. 3,460 17 Radial 1.43 Seattle, I-5 North 64 2,605 3,039 5,644 n/a Concurrent Flow 1,950 14 Radial 1.27 Montreal, Champlain Bridge 91 5,300 0 5,300 n/a Contraflow 1,016 4 Out.-Radial, Br. n/ a Minneapolis, I-394 (inner) 56 1,834 3,341 5,175 5 Excl. Rev. & Conc. 2,250 10 Radial 1.12 Houston, I-45 North Fwy. 53 2,100 2,725 4,825 14 Exclusive Rev. 3,060 14 Radial 1.11 Houston, US 59 Southwest 38 1,420 3,147 4,567 2 Exclusive Rev. 3,060 11 Radial/Circ. 1.11 Houston, US 290 Northwest 22 1,035 3,030 4,065 22 Exclusive Rev. 3,060 14 Radial 1.11 Seattle, SR 520 56 3,140 498 3,638 n/a Concurrent Flow 1,950 2 Out.-Radial, Br. 1.27 Dallas, I-30 R.L. Thornton 64 1,041 2,494 3,535 6 Contraflow 2,290 5 Radial 1.11 Marin Co., CA US 101 57 1,995 1,490 3,485 5 Concurrent Flow 3,890 13 Radial, Bridge 1.33 Houston, I-10 Katy Fwy. 40 1,355 2,091 3,446 17 Exclusive Rev. 3,060 13 Radial/Circ. 1.11 Houston, I-45 Gulf Fwy. 31 740 2,682 3,422 4 Exclusive Rev. 3,060 12 Radial 1.11 Boston, I-93 North 35 1,050 2,320 3,370 10 Concurrent Flow 3,010 2 Radial 1.09 Minneapolis, I-394 (outer) 29 1,031 1,797 2,828 5 Conc. & Excl. Rev. 2,250 10 Outer Radial 1.12 Pittsburgh, I-279/579 23 1,050 1,527 2,577 n/a Exclusive Rev. 1,930 4 Radial 0.85 Seattle, I-5 South 28 1,176 1,320 2,496 n/a Concurrent Flow 1,950 16 Radial 1.27 Santa Clara Co., CA SR 237 18 630 1,720 2,350 4 Concurrent Flow 1,595 6 Circumferential 1.11 Norfolk, I-64 0 0 2,130 2,130 n/a Exclusive Rev. 1,010 8 Circumferential 0.96 Dallas, I-35E Stemmons Fwy. 9 310 1,667 1,977 n/a Concurrent Flow 2,290 7 Radial 1.11 Seattle, I-90 34 1,250 660 1,910 n/a Conc. & Excl. Rev. 1,950 13 Out.-Radial, Br. 1.27 Dallas, I-635 LBJ Fwy. 1 10 1,812 1,822 n/a Concurrent Flow 2,290 7 Circumferential 1.11 Minneapolis, I-34W 15 469 1,318 1,787 n/a Concurrent Flow 2,250 5 Radial 1.12 Hartford, I-91 11 280 1,416 1,696 n/a Exclusive 2-way 635 9 Radial 0.93 Norfolk/Va. Beach, SR 44 0 0 1,520 1,520 n/a Concurrent Flow 1,010 4 Out.-Radial, Br. 0.96 Hartford I-84 12 288 1,193 1,481 n/a Exclusive 2-way 635 10 Radial 0.93 Vancouver, BC H-99 27 1,080 0 1,080 n/a Concurrent Flow 514 4 Outer Radi al n/ a Denver, US 36 Boulder Tpk. 28 1,000 0 1,000 n/a Concurrent Flow 1,770 4 Radial 1.12 Santa Clara Co., CA US 101 3 105 803 908 n/a Concurrent Flow 1,595 25 Radial/Circ. 1.11 New Jersey I-287 2 45 711 756 n/a Concurrent Flow 4,522 20 Circumferential 1.18 Sources: Developed with HOV characteristics and utilization data from Tables 2-1, 2-2, 2-7, 2-8 (see footnote "a"), 2-10, 2-11 and 2-21; 1996 population and congestion measure data from Texas Transportation Institute (1998c); New Jersey I-287 and Canadian population data from U.S. Census and Canadian Embassy sources, respectively; facility orientation determinations by Handbook authors.

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Figure 2-3 Total peak-hour peak-direction person volumes on 35 HOV facilities related to bus vehicle volumes 14,000 CA I-80 Bay Bridge 12,000 No. VA I-395 A.M. Peak Hour Peak Direction Total HOV Persons 10,000 NY Gowanus Expy. Person Volume = 1863.7 + 45.99(Bus Volume) NY I-495 L.I. Expy. R2= 0.92 8,000 No. VA I-66 6,000 40,000 35,000 NJ 495 30,000 25,000 4,000 20,000 15,000 10,000 2,000 5,000 0 0 200 400 600 800 0 0 50 100 150 200 250 A.M. Peak-Hour Peak-Direction Bus Vehicles Note: Person volumes include bus passengers plus carpool and vanpool occupants. See text for discussion of labeled data points. Source: Developed from AM peak-hour peak-direction bus vehicle volumes and total HOV facility person volumes data for 35 HOV facilities as consolidated in Table 2-22 from Tables 2-2, 2-8 (see footnote "a"), and 2-11.

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The facilities least well represented by this formula, those whose plots are furthest from the linear regres- sion line in Figure 2-3, are "outliers" for good reasons. The Northern Virginia I-66 and I-395 facilities, and CA I-80 San Francisco Bay Bridge, are paralleled by rail rapid transit lines, tending to deflate bus relative to carpool volumes. The Long Island and Gowanus Expressway facilities in New York City, and three others below the linear regression line, do not (or did not when the data was collected) permit car- pools, limiting person volumes. NJ Route 495 (inset) is both paralleled by rail lines and bus-only, but lies in an exceedingly high volume corridor focused on Midtown Manhattan and its over 500,000 jobs. The relationship presented above not only reflects the relatively obvious cause and effect of bus vehicle volume on bus passenger volume, but also an approximate yet robust correlation between the ability to support substantial bus service and the ability to attract large numbers of carpools. The travel patterns and parameters that support one also support the other. When there are only carpools and vanpools in an HOV lane, lane productivity is often limited. In such cases, an HOV lane might carry more people than a GP lane only in very large urban areas. When there are less than 15 buses in the hour, total AM peak-hour peak-direction HOV person vol- umes generally do not exceed 2,200 on any existing facility. Los Angeles County--a very large multi-nucleated area of dense urban sprawl--does indeed prove to be an exception, achieving higher HOV volumes with insubstantial bus volumes. Morning peak-hour volumes in the 2,200 to 3,500 range are encountered there on 7 of 11 facilities that may be presumed to have fewer than 15 buses in the peak hour. The same 7 facilities also carry more persons in the peak hour peak direction than the adjacent GP lanes. (See "Traveler Response by Type of HOV Application"-- "Response to Concurrent Flow Freeway HOV Lanes"--"Los Angeles County Examples," noting that the bus counts in Table 2-9 are not peak hour peak direction, but daily in both directions). Urban Area Characteristics The importance to HOV facility usage of underlying travel patterns and parameters, such as pro- portion of travel headed to the CBD, downtown parking costs, degree of concentration or disper- sion of traffic, and indeed absolute quantity of travel activity, is fairly obvious. These underlying travel characteristics are in turn shaped by the size and nature of the urban area in question: Population. The number of passengers using HOV facilities tends to increase as urbanized popu- lation increases. Most HOV facilities along freeways are found in urban areas with more than one million people. Generally, in the larger urban areas, the city centers--and other activity centers-- are stronger, and there is more bus service. The patterns are not fully consistent, however, as indi- vidual facilities within single urban areas display wide variability. This variability results in part from differences in the development of individual corridors within regions. Employment. HOV facilities are heavily work trip oriented, thus the amount of employment and its distribution should be as important as population. Lack of nationwide consistency in the tabu- lation of employment by sectors such as CBDs hampers analysis, however. The best that can be said is that presence of a major employment center with 100,000 or more jobs within the immedi- ate destination service area of an HOV facility appears to be critical. Urban Form. Physical barriers such as water bodies or steep topography constrict development, travel, and traffic flow, creating the travel concentrations and traffic congestion that enhance HOV facility attractiveness and use. Most freeway-based HOV facilities are clustered along the East Coast, the West Coast, and in Texas, with only a few facilities in other Midwestern cities. Most of 2-65

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the East and West Coast installations are in cities with wide river barriers, if not greater physical constraints, but the Texas and other Midwestern systems are significant exceptions. Facility Characteristics Facility characteristics that affect HOV usage include both physical and operational characteristics of the HOV facility and the freeway or other roadway along which the HOV facility is installed. Facility Type. Among freeway HOV facilities, there is little relationship between type of HOV facility and usage. The three top passenger volume HOV facilities in Table 2-22 are a contraflow lane, a set of concurrent flow lanes, and a reversible exclusive facility. There is a tendency for con- traflow lanes to be heavy carriers of person volumes, but this is probably because of selection of the contraflow design in response to the constrained space and substantial bus service typical of highly developed areas and river barrier crossings. Facility Length. Where HOV facility time savings over travel in the GP lanes is uniform throughout the length of a facility, length is obviously important. Examination of the observations in Table 2-22 shows no pattern, however, relating distance to facility usage. This result reflects the mix of facilities across North America that gain their time advantage, if any, from operating alongside GP lanes of varying degrees of congestion, along with short queue bypass lanes that take HOVs around severe congestion at the approaches to toll barriers, lane drops and other sources of major delay. Facility Orientation. Most HOV facilities focus on the city center or, in some cases, other very major employment concentrations. In Table 2-22, it can be seen that the top 60 percent of facilities in the data set have an orientation that is, at least in part, radial to the center city CBD. These are the facil- ities in the peak-hour volume range of 2,500 to 35,000 persons. Peak volumes on all purely circum- ferential HOV facilities are generally in the 750 to 2,400 persons range.4 Eligibility Requirements. Either allowing carpools to use a bus-only lane or reducing HOV carpool occupancy requirements will result in an increase in HOV lane usage, measured either in terms of vehi- cle or person volumes, so long as the vehicular capacity of the priority lane is not exceeded (Christiansen and Morris, 1990 and 1991). For examples and analyses, refer back to "Traveler Response by Type of HOV Application"--"Response to Changes in Occupancy Requirements and Operating Hours." Years in Service. Available data covering individual HOV facilities exhibit patterns of strong growth over 3- to 20-year periods (Christiansen and Morris, 1990 and 1991). Clearly a number of facilities serve lesser volumes in whole or in part because of fewer years in service. Further information on this phenomenon is provided under "Related Information and Impacts"--"Time to Establish Ridership and Use." Supporting Facilities. HOV facility usage in general, and HOV facility bus ridership in particular, can be enhanced through provision of supporting facilities. Potential supporting features range from park-and-ride and park-and-pool lots to downtown bus lanes and even connecting busways, as in Seattle, which also has a connecting bus tunnel. For further coverage, refer back to "Traveler Response by Type of HOV Application"--"Response to Arterial Street Bus-Only Facilities" in this chapter, and to Chapter 3, "Park-and-Ride/Pool," and Chapter 4, "Busways, BRT and Express Bus." 4 Los Angeles County, covered separately in Table 2-9, is ever the exception. Circumferential facility peak-hour peak-direction person volumes there range from 1,500 (CA 118 Ronald Reagan Freeway, AM and PM) to 3,400 (I-405 San Diego Freeway, southwest segment, PM). 2-66

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Congestion. Unless severe congestion exists in the GP lanes on a recurring basis, usage of HOV facilities will not be high. As previously discussed, provision of meaningful travel time savings is perhaps the most important single factor influencing HOV facility use (Christiansen and Morris, 1991). Without congestion, there is no way for HOV facilities to generate time savings, except in the rare case of exclusive ramps (and potentially other installations) that save distance. From a sta- tistical perspective, HOV person volumes increase with city size and attendant traffic congestion, although the patterns exhibited by available data show wide ranges. In large part, the variability is introduced by use of regional rather than facility-specific published indicators of congestion. Without congestion, there is little reason for HOV lanes. Carpool Composition and Longevity Conventional Carpooling Most carpools draw upon family and co-workers for participants, and carpool users of HOV lanes are no exception. Surveys of carpoolers on the Houston HOV lanes over the years indicate that between 56 and 65 percent are formed with family members, 25 to 32 percent are composed of co- workers, and 8 to 13 percent are with neighbors or other individuals (Bullard, 1991; Turnbull, Turner and Lindquist, 1995). Further, responses to a 1995 survey on the Katy and Northwest HOV lanes indicate that most carpools are formed by the members themselves, with little outside assis- tance. Only 1 to 5 percent of the respondents reported using an employer rideshare program to help find someone to carpool with, and 1 percent indicated using the METRO Rideshare Program (Turnbull, Turner and Lindquist, 1995). In connection with Houston's Katy Freeway HOV lane QuickRide value pricing demonstration, reg- istrants were surveyed in 1998 to identify the composition of their carpools. The results, repre- senting carpools prepared to pay $2.00 for entry onto the HOV lanes during periods of 3 occupancy requirement, are outside the range previously identified for Houston HOV lane users. Family members composed 49 percent of reported members, less than for regular HOV lane car- poolers. Of these, 37 percent were adults and 12 percent were children. Co-workers accounted for 41 percent, followed by neighbors at 6 percent, and other members at 4 percent (LKC Consulting Services and Texas Transportation Institute, 1998). The 1977 survey of 3 carpoolers on the San Bernardino Transitway (El Monte Busway) in Los Angeles indicated 14 percent were formed with family members, 63 percent with co-workers, 8 percent with neighbors, 4 percent with help from Commuter Computer, and 12 percent in com- binations of these (Crain & Associates, 1978). A 2001 survey of Los Angeles County HOV lane users including the El Monte Busway and 15 HOV 2 lanes, in contrast, found carpool partners to be 62 percent family, 42 percent co-workers, 6 percent neighbors, and 5 percent "self" or "other" with multiple survey responses allowed. Corresponding vanpool responses were 9, 94, 5, and 3 percent, respectively (Parsons Brinckerhoff et al., 2002a). Results of a 1995 survey of carpoolers on the East R. L. Thornton HOV lane in Dallas indicated that 65 percent were formed with family members, 31 percent were composed of co-workers or friends, and 4 percent were with other individuals. The DART rideshare program had been used by 2 percent of the respondents and 1 percent used an employer sponsored program (Turnbull, Turner and Lindquist, 1995). Overall declines in carpooling during the 1980s and 1990s have, at least on a regional or national (rather than facility) basis, been linked with decreasing percentages of carpool members from beyond the immediate family. Recent surveys reporting particularly high percentages of family 2-67

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among carpool members, in addition to the Houston and Dallas surveys noted above, include two by the Southern California Association of Governments. They found household members com- posed 49 percent of carpoolers in 1996 and 55 percent in 1999. Analysis of National Household Travel Survey data found that carpools made up entirely of members from the same family repre- sented 76 percent of all journey-to-work carpools nationwide in 1990 and 83 percent in 2001. It has been further inferred by some that HOV 2 carpools composed of family members riding together, dubbed "fampools," don't take cars off the road and would exist without inducements such as HOV lanes that are open to use by 2 carpools (Poole and Balaker, 2005). These various findings over time are indeed suggestive of a shift away from co-worker carpooling in the transition from the gasoline scarcities of the 1970s to the epoch of plentiful and cheap gaso- line in the 1990s. Gasoline pricing and availability are, however, probably not the only factor. For example, the increase of women in the workforce may have increased opportunities for family members to carpool to work. Many HOV lanes by their very nature emphasize service to persons going to and from work. Clearly this work trip orientation applies to any facility whose operation is restricted to peak hours or the peak direction of workday travel flow. The Houston carpool com- position data provided above pertains to such facilities. It is also likely that all of the various sur- veys drawn from above were to some degree peak traffic flow oriented. Judging from recent Los Angeles information and Houston data breakouts, traveling to work remains the dominant reason for being on an HOV lane in peak periods (see "Related Information and Impacts"--"HOV Facility User Groups"--"User Trip Purposes and Other Characteristics"). Whatever the broad effects of carpool composition, limited analysis of HOV facility users--focus- ing on Houston experience--indicates that HOV lanes have a positive influence on the duration or life of carpools. Comparison of survey results for carpoolers using HOV lanes with those on free- ways without HOV lanes indicates that the median age of carpools is two to three times higher in the case of freeway HOV lanes. Median length-of-time in operation for three separate years was 13, 12 and 9 months for HOV lane carpools as compared to 3, 6 and 4 months, respectively, for non- HOV freeway carpools (Christiansen and Morris, 1990). Casual Carpooling In a few locations with supportive conditions, "casual carpooling" has spontaneously developed. Casual carpooling utilizes "impromptu carpools formed among strangers" (Burris and Winn, 2006). The impromptu carpool formation separates casual carpooling from "dynamic ridesharing," which as currently defined, involves matching--by an independent organization--of passengers with drivers for individual trips (Victoria Transport Policy Institute, 2005). Casual carpools have no fixed composition or overarching organization. Instead, without evident pre-arrangement, motorists ("bodysnatchers") pick up willing riders ("slugs") at established loca- tions in advance of HOV lane entry points. They do this in order to meet the HOV occupancy requirement and achieve the time saving and reliability offered by the HOV facility. Despite the personal safety issues raised in theory by this modern urban variant of hitchhiking, casual car- poolers form a significant niche market for individual HOV lanes. Exact figures are elusive, but casual carpoolers appear to represent 5,200 of the AM peak-period carpool occupants on Northern Virginia's I-95/I-395 Shirley Highway HOV lanes, with another 3,500 to 4,000 in the PM; 8,000 of AM peak-period carpool occupants on the Bay Bridge HOV lanes in the San Francisco Bay Area; and (after extrapolation to include drivers) around 750 of AM peak-period carpoolers on the I-10W Katy Freeway and US 290 Northwest Freeway HOV lanes in Houston (Spielberg and Shapiro, 2000; Beroldo, 1990; Rides for Bay Area Commuters, 1999; Burris and Winn, 2006). 2-68

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Necessary conditions for significant casual carpooling include (Beroldo, 1990; Spielberg and Shapiro, 2000): Significant travel time reduction and reliability gain for the driver through use of the HOV facility--enough to be worthwhile even subtracting out passenger pick-up and drop-off times. Need for additional riders to meet HOV access requirements (enhanced by a 3 or greater occupancy requirement). Well-known pickup locations having easy driver and rider access and offering good transit ser- vice available as backup for prospective riders. Very substantial employment concentration(s) as the focus for the morning commute, allow- ing quick and efficient passenger drop-off and dispersal to ultimate destinations. Nature of Casual Carpooling. The characteristics and extent of casual carpooling are further illus- trated here by drawing on observations from the three major casual carpooling locales: the Northern Virginia suburbs of Washington, the San Francisco East Bay area, and Houston. A key similarity among these areas is the existence of 3 carpool occupancy requirements, giving spe- cial impetus to the search for additional occupants (Burris and Winn, 2006). Observations at the Pentagon and elsewhere in Northern Virginia's I-95/I-395 corridor suggest that casual carpooling is "a highly egalitarian activity." Passengers and riders appear to make no differentiation on the basis of gender, race, military versus civilian, or military rank. More males than females were observed overall at six pickup locations, with females constituting 32 percent of persons in arriving vehicles and 40 percent of persons taking rides. Percentages varied among locations, for no readily discernable cause, with 60 percent females accepting rides at two of the more outlying locations. It has been theorized that rider comfort level is increased by the ability to pair up in accepting rides, an approach enhanced by 3 or greater HOV occupancy requirements. Of persons accepting rides, 88 percent did so in groups of two or three. Group gender composition (number of observed groups or persons) was 40 to 41 percent male-only, 21 percent female only, and 38 percent mixed. Slugs were observed to line up in destination-specific queues (Spielberg and Shapiro, 2000). Casual carpooling in the I-95/I-395 corridor, to and from the Pentagon and the District of Columbia core, started in the early 1970s. Drivers going the full distance in the 2004 AM peak period saved 37 minutes of I-95/I-395 travel time in exchange for forming casual carpools, better than halving their line haul travel time (Spielberg and Shapiro, 2000; Metropolitan Washington COG, 2005). A 1998 assessment of I-95/I-395 "slugging activity" concluded that all persons who slugged in the afternoon probably also did so in the morning. It also presumed that where morning bus ridership was less than afternoon ridership, the differential represented persons slugging in the morning. Based on these assumptions, the assessment produced an estimate of 900 persons slugging in the morning and returning by public transit, and 2,200 persons slugging in both directions, for a total of 3,100 persons accepting rides in the AM peak period. This estimate, thought to be understated given that smaller pickup locations were not surveyed, indicates that almost 11 percent of 28,000 carpoolers and bus riders on the HOV facility between 6:00 AM and 9:00 AM were slugs. Casual car- pool occupancy was observed to be 1.25 before slug pickup and 3.07 after slug pickup, with a filled- carpool makeup of 32.6 percent drivers, 8.2 percent non-slug passengers, and 59.2 percent slugs (Spielberg and Shapiro, 2000). The corresponding number of casual carpoolers would be 1.69 per 2-69

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slug, implying that some 5,200 persons in the 1998 AM peak period on the I-95/I-395 HOV facil- ity were casual carpooling. This represents nearly 19 percent of total carpool and bus AM peak- period facility usage. Another casual carpooling activity dating from the 1970s is focused on morning passage west- bound across the San Francisco-Oakland Bay Bridge. Morning pickup points are at AC Transit bus stops and BART stations. When extensively surveyed in the late 1980s, drivers filling their cars to 3 occupancy could achieve a 10 to 20 minute time savings and save a $1.00 toll. Following a period of sharp growth in casual carpooling activity it was concluded, on the basis of three sepa- rate methodological approaches, that about 8,000 people were involved in casual carpooling in 1989. This represented over 45 percent of carpoolers using the bridge. Casual carpooling was shortly thereafter disrupted by a 1-month earthquake-related Bay Bridge closure, but subsequently slowly recovered (Beroldo, 1990). As of 1998 Bay Bridge casual carpooling was again at a level where 8,000 persons was once more thought to be a valid approximation of weekday inbound activity. In the 2 years prior to the 1998 survey, the mode had been enhanced by a doubling of Bay Bridge tolls to $2.00, BART fare increases, and opening of I-80 HOV lanes through the northerly East Bay communities. Spurred by the I-80 HOV lanes and Environmental Defense Fund publicity, almost 10 percent of inbound casual carpoolers were returning home as casual carpoolers by 1998. Rail or bus transit was the mode used to get home by 84 percent. One-third had casual carpooled for less than a year, while 15 percent had been doing so for a decade or more. Many access modes are used by passengers to reach East Bay pickup points. The walk access per- centage has declined from 42 to 32 percent between 1987 and 1998, while the drive-alone-and-park percentage has increased from 29 to 41 percent over the same period. Passenger mode of access percentages in 1998 across eight pickup locations ranged from 3 to 68 percent walk, 17 to 61 per- cent drive/park, 6 to 25 percent dropped off, and 9 to 17 percent other including transit. For casual carpool drivers, average distance from home to pickup point has decreased, with access drives over 5 miles dropping from 28 percent in 1987 to 15 percent in 1998. In 1998, 67 percent of survey respondents indicated they were normally passengers, 22 percent said they were normally drivers, and 11 percent reported being sometimes one and sometimes the other. Some 84 percent casual carpool 4 to 5 days a week, 10 percent do it 2 to 3 days a week, and 6 percent carpool less frequently (Rides for Bay Area Commuters, 1999). The San Francisco Bay Area's Commute Profile survey has identified casual carpools areawide as making up from 4 or 5 percent (2004 and 2002 results, respectively) to 8 percent (2003) of all car- pools throughout the 9-county region. The reported variation is likely an outcome, at least in part, of small sample size (Rides for Bay Area Commuters, 2002 and 2004). In any case, the casual car- pooling percentage implied for the Bay Bridge HOV facility alone would be much higher, as it is the primary attraction for casual carpooling but serves only the inner portions of three out of a much larger total of major corridors throughout the Bay Area. Casual carpooling proceeds in accord with an established passenger pickup and ridesharing eti- quette, carefully observed and documented in both Northern Virginia and Houston. Rarely is money exchanged and it is understood that riders may turn down a ride they don't feel comfort- able with. During a 4-hour morning observation by a newspaper reporter of the pickup process at Houston's Addicks park-and-ride lot, it normally took only a minute or two for drivers and riders to match up. The maximum wait was 7 minutes and the maximum queue was 10 persons. If the line of riders grew too long, people opted for the bus. When drivers were waiting, people heading for the bus would accept a ride (Wall, 2002). Formal measurements at the same location in 2003 2-70