The US Context for Highway Congestion Pricing

M1288 - RICHARDSON TEXT.qxd 31/1/08 3:52 pm Page 327 Phil's G4 Phil's G4:Users:phil:Public: PH 17. The US context for highway congestion pricing Bumsoo Lee and Peter Gordon 1 INTRODUCTION If price does not ration, something else will. We also know that auto ownership and use respond to rising income and that congestion has become the default rationing mechanism on most of the world’s roads and highways. Economists and others have pointed out that this is increasingly wasteful and have argued that time-of-day pricing should be implemented (see, for example, the recent collection of essays edited by Roth, 2006). Modern monitoring and collection technologies suggest that this can now be done at low cost – although that assertion is challenged in a recent examination of the Stockholm road pricing trial, by Prud’homme and Kopp (2006). Policy makers in the US, however, have for the most part been reluctant to go along, fearing the prospect (or the appearance) of regressive impacts – even though they are thereby forgoing a new and considerable revenue source. In Chapter 19 of this volume, King et al. argues that improved revenue targeting and sharing schemes could develop greater political support. The world’s best-known experiments with road pricing have been the area-pricing programs in Singapore (since 1975)1 and London (since 2004). On a smaller scale, there have been scattered cases around various cities of the developed countries with moderately scaled pricing experiments on specific areas or on specific stretches of highways. Recently, some writers have suggested that the US is now near a tippingpoint, and that many more road pricing projects will soon be implemented (Poole and Orski, 2006). What do we know about the modern US urban transportation context? What does it suggest for further pricing projects in this country? This chapter is a survey of recently accumulated descriptive research findings and attempts to answer both questions. There are two key results that emerge. First, in most major US cities, trip origins and destinations are dispersed. This makes London- or Singapore-style area-pricing impractical. A HOV- to HOT-lane conversion 327 M1288 - RICHARDSON TEXT.qxd 328 31/1/08 3:52 pm Page 328 Phil's G4 Phil's G4:Users:phil:Public: PH The United States plan may be more appropriate. Second, the growth of non-work travel – many trips which are probably more responsive to dollar cost increases than commutes – at all times of the day makes the pricing alternative more attractive than had heretofore been thought. A look at the online TDM encyclopedia,2 which includes a summary of recently estimated transportation demand elasticities, suggests that more specific knowledge in this area would be helpful. Our study of non-work travel also depicts the growth of chained tours that combine work trips with non-work trips. These are not likely to be done via transit or carpools. This further strengthens the case for the HOT-lane approach. 2 DATA Cities have been decentralizing for many years. In the US, the group of 75 largest cities gained population share until about 1940, but have been losing their proportionate importance ever since. The suburbanization of origins and destinations has been used to explain relatively benign commuting times – in spite of the absence of road pricing (Gordon et al., 1989; Crane and Chatman, 2003). In fact, average travel speeds on US roads had been increasing to 1995. Less benign travel speed and time trends since 1995 have been explained by the prosperity of the late 1990s (more income, more cars, more errands by car, see below) coupled with a decline in road construction (Gordon et al., 2004). Our analysis begins where these well-known facts leave off, by considering trends for the smallest spatial units for which data are available to us and by considering employment as well as population locations. We relied on two datasets. One was the 2000 Census Transportation Planning Package (CTPP) data, drawn from the decennial census journeyto-work survey. The CTPP is one of the few sources of employment data by place of work for small geographical units such as census tracts or traffic analysis zones (TAZs). It provides tabulations of households, persons and workers by place of residence, by place of work, and for journeys to work. This information is all-important for grasping the extent of decentralization and dispersion of population and jobs in US metropolitan areas. We also worked with data from the 1990 and 1995 Nationwide Personal Transportation Surveys (NPTS) and the 2001 National Household Travel Survey (NHTS) for the study of work and non-work travel patterns. In the 11-year interval, the US population grew by 16 percent while the number of workers grew by 22 percent, household vehicles increased by 23 percent, person-trips by 34 percent and person-miles by 40 percent. Constant dollar per capita income over the 11 years grew by 21 percent. M1288 - RICHARDSON TEXT.qxd 31/1/08 3:52 pm Page 329 Phil's G4 Phil's G4:Users:phil:Public: PH The US context for highway congestion pricing 329 The surveys, initiated in 1969 by the US Department of Transportation (USDOT), provide detailed data on households, people, vehicles and travel for all purposes by all modes. Thus, the NPTS/NHTS data series are one of the best data sources for the analysis of nationwide travel trends. Nevertheless, there are some comparability issues from one survey to another because the survey techniques changed between survey years. In particular, a travel diary (replacing memory recall) and household rosters have been used only since the 1995 survey. These changes have significantly improved interview responses. Hu and Young (1999) provide a method to adjust 1990 data for comparison with 1995 and 2001 results by estimating the impact of the two new techniques that had been used in the 1990 survey. Another problem is that, given what is known of work-trip trends in the 1990s, the 1995 survey is believed to overestimate work trips. For these reasons, our trip-level analysis relies on the 1990 and 2001 data only. However, we did use 1995 and 2001 data for tour-level analyses because there was no information on trip-chaining behavior in the 1990 data. 3 CENTRALIZATION, DISPERSION AND DECENTRALIZATION London- or Singapore-style area pricing schemes are effective in cities with highly centralized employment or sizable central business districts (CBDs). For instance, the original congestion charging zone in central London3 – an eight-square mile area inside the inner ring road – contained about 1.1 million jobs, or 27 percent of total employment in Greater London as of 2005 (Santos and Fraser, 2006). Singapore’s Restricted Zone covered an area of about 2.8 square miles including the CBD and 315,000 jobs, or about 20 percent of the city state’s total employment were centralized in the zone as of 1990 (McCarthy and Tay, 1993). However, US metropolitan areas are much more decentralized and dispersed. We were able to define and identify major employment centers and subcenters. In general, urban employment centers are defined as clusters of zones that have higher employment density than the surrounding areas. Two types of procedure have been popularly used in identifying density peaks, a minimum density procedure (Giuliano and Small, 1991) and a nonparametric method (McMillen, 2001). In a previous study (Lee, 2007), we found that employment centers defined by the latter best reflect downtowns as we know them. We identified CBDs and subcenters in the 79 largest US metropolitan areas using a modified version of McMillen’s geographically weighted regression (GWR) procedure. Whereas he identified TAZs that have higher M1288 - RICHARDSON TEXT.qxd 330 31/1/08 3:52 pm Page 330 Phil's G4 Phil's G4:Users:phil:Public: PH The United States actual density than the estimated GWR surface, we compared two estimated employment density surfaces – one with a small window size (10 neighboring census tracts) and the other with a large window size (100 census tracts) (for detailed descriptions of the procedure, see Lee, 2007). Among the identified employment density peaks, we qualified only those with more than 10,000 jobs as employment centers. Table 17.1 shows CBD size and employment share by location type in the largest US metropolitan areas and average values in each metro size class. In every one of the major metro areas (those with population over 3 million), the largest share of employment was dispersed, outside of any identifiable center. Also in every one of the largest metro group, the CBD accounts for less than 10 percent of total metro employment. The table also reveals similar tendencies for the smaller metros in our sample. Agglomeration opportunities come in many shapes and sizes but there are now many more Silicon Valleys than Manhattans. The dispersion tendency, of course, takes some congestion pressures away from centers but it also decreases the possible usefulness of the area pricing approach. Table 17.2 presents (one-way) commute times in 2000, by workplace location type. These data are for drive-alone commuters only so that mode mix changes do not perturb the results. The table shows that the shortest commutes are for workers with destinations at dispersed locations. CBD workers spend significantly more time in commuting than other metro commuters, especially in largest metro areas. CBD workers’ commute time is almost twice as long as the metropolitan average in New York and they spend 40 to 53 percent extra commute time in Philadelphia, Chicago and Boston, which have relatively large CBDs. These older CBDs can still offer enough agglomeration economies to fund the wages that offset the longer commutes. We were also able to describe changes in employment decentralization and dispersion over the last one or two decades. But, unfortunately, census tract level employment data conversion between census years was possible only for six metro areas. Los Angeles and San Francisco are more polycentric than the other four metropolitan areas in relative terms. Employment decentralization occurred for the years shown in all six of these metro areas. The CBD’s employment share shrank in each metro. Second, subcenters’ employment share also fell in New York, Boston, Philadelphia and Portland while significant subcentering occurred in the two western polycentric metros. It is interesting to find that more centralized places experienced increased dispersion. Nevertheless, either version of these spatial evolutions makes area congestion charging schemes even less attractive. Metro name Averages by metropolitan area population size group 3 million ⫹ 1–3 million 0.5–1 million Source: modification from Lee (2006). Emp. (thousands) Area (sq.miles) 937.1 190.1 297.8 283.3 205.6 239.7 238.1 129.8 126.0 165.5 166.9 121.0 163.1 104.4 1.9 2.5 1.2 1.6 0.8 2.4 1.7 4.7 4.7 4.1 3.9 4.0 1.7 5.5 No. of subcenters Share of employment (%) CBD Subcenters Dispersed 33 53 17 16 22 6 12 22 10 14 6 6 7 9 9.9 2.8 7.0 7.4 5.9 8.6 8.0 5.2 4.9 8.0 8.0 7.5 9.3 7.1 11.2 28.8 11.9 11.8 24.2 4.5 8.0 22.2 15.8 20.8 10.7 15.0 11.9 12.9 78.8 68.4 81.1 80.8 70.0 86.9 84.0 72.6 79.3 71.2 81.3 77.5 78.8 79.9 17.0 2.6 0.9 7.1 10.8 12.2 15.0 7.0 5.2 77.9 82.2 82.6 Page 331 Phil's G4 Phil's G4:Users:phil:Public: PH 9,418 6,717 4,248 3,815 3,513 2,781 2,974 2,509 2,566 2,076 2,088 1,624 1,745 1,464 CBD 3:52 pm 21,200 16,370 9,158 7,608 7,039 6,188 5,829 5,456 5,222 4,670 4,112 3,876 3,555 3,252 Employment (thousands) 31/1/08 331 New York Los Angeles Chicago Washington San Francisco Philadelphia Boston Detroit Dallas Houston Atlanta Miami Seattle Phoenix Population (thousands) M1288 - RICHARDSON TEXT.qxd Table 17.1 Employment share by location type and CBD size in the largest US metropolitan areas, 2000 M1288 - RICHARDSON TEXT.qxd 31/1/08 332 3:52 pm Page 332 Phil's G4 Phil's G4:Users:phil:Public: PH The United States Table 17.2 Commute time by drive alone mode by location type, 2000 Metro name New York Los Angeles Chicago Washington San Francisco Philadelphia Boston Detroit Dallas Houston Atlanta Miami Seattle Phoenix Population (thousands) Employment (thousands) 21,200 16,370 9,158 7,608 7,039 6,188 5,829 5,456 5,222 4,670 4,112 3,876 3,555 3,252 9,418 6,717 4,248 3,815 3,513 2,781 2,974 2,509 2,566 2,076 2,088 1,624 1,745 1,464 2000 commute time by drive alone mode (min) Metro 28.5 27.8 28.9 30.3 28.4 26.1 27.1 26.2 27.4 28.1 30.9 27.9 26.2 25.4 Averages by metropolitan area population size group 3 million⫹ 27.8 1–3 million 24.1 0.5–1 million 22.3 CBD Subcenters Dispersed 55.6 36.6 41.8 40.2 39.3 36.6 41.6 31.0 31.5 32.9 36.0 33.8 30.7 31.1 30.2 28.9 32.1 30.2 29.3 26.1 25.9 27.7 28.0 28.9 31.4 28.9 26.3 24.7 27.8 27.0 28.0 29.8 27.8 25.7 26.7 25.4 27.1 27.3 30.3 27.1 25.8 25.0 37.1 26.9 23.3 28.5 23.4 21.7 27.2 23.8 22.2 Source: Modification from Lee (2006). 4 NON-WORK TRIPS Much of the discussion of the ‘urban transportation problem’ focuses on commuting, and commuting is thought to be a peak-hour problem. Both thoughts require some re-examination. In recent research, we found that most travel by Americans does not involve commuting. In fact, a large majority of peak period travel is not work related. In a recent paper, we investigated work and non-work travel patterns in terms of temporal variation (Lee et al., 2006). All trips in 1990 and 2001 were grouped by 10 distinct periods of the week according to their departure time (Table 17.3). Non-work trips accounted for more than four-fifths of all trips in each year of the surveys, and were a sizable majority in every one of the 10 time-of-week periods including peak-hour periods (Table 17.4). They also grew more quickly between the 1990 and 2001 survey years M1288 - RICHARDSON TEXT.qxd 31/1/08 3:52 pm Page 333 Phil's G4 Phil's G4:Users:phil:Public: PH 333 The US context for highway congestion pricing Daily person-trip 0.900 90 Work 0.800 90 Non-work 0.700 01 Work 01 Non-work 0.600 0.500 0.400 0.300 0.200 Source: Sunday Saturday Friday night-time Friday pm peak Friday daytime Friday am peak Mon–Thu night-time Mon–Thu pm peak Mon–Thu daytime 0.000 Mon–Thu am peak 0.100 Trip start time Lee et al. (2006). Figure 17.1 Average daily person-trips per person by trip purpose and by time of week, 1990 to 2001 Table 17.3 Definitions of 10 periods of the week Time of day/week Week Departure time Mon.–Thu. am peak Mon.–Thu. day off-peak Mon.–Thu. pm peak Mon.–Thu. night off-peak Mon.–Thu. Mon.–Thu. Mon.–Thu. Mon.–Thu. 6:00am–8:59am 9:00am–3:59pm 4:00pm–6:59pm 7:00pm–5:59am Friday am peak Friday day off-peak Friday pm peak Friday night off-peak Friday Friday Friday Friday 6:00am–8:59am 9:00am–3:59pm 4:00pm–6:59pm 7:00pm–5:59am Saturday Sunday Saturday Sunday 0:00am–12:59pm 0:00am–12:59pm than work trips (by 30 percent as opposed to 23 percent, while the US population grew by 15.8 percent) The Monday–Thursday am peaks included the largest number and share of work trips, but these work trips were never the majority trip type, and their share even fell significantly between survey years. The Friday am peak M1288 - RICHARDSON TEXT.qxd 31/1/08 334 3:52 pm Page 334 Phil's G4 Phil's G4:Users:phil:Public: PH The United States Table 17.4 Annual person-trips by trip purpose and by time of week, 1990 to 2001 All Work (%) Non-work (%) (%) 1990 All (millions) Mon–Thu am peak Mon–Thu off-peak day Mon–Thu pm peak Mon–Thu off-peak night Friday am peak Friday off-peak day Friday pm peak Friday off-peak night Saturday all day Sunday all day 284,551 27,272 66,526 42,259 32,709 5,068 14,890 9,094 8,723 39,108 38,902 100 100 100 100 100 100 100 100 100 100 100 49,327 12,227 7,906 10,495 6,152 2,536 1,655 2,032 1,233 2,982 2,109 17.3 44.8 11.9 24.8 18.8 50.0 11.1 22.3 14.1 7.6 5.4 235,224 15,045 58,620 31,764 26,557 2,532 13,235 7,062 7,489 36,127 36,793 82.7 55.2 88.1 75.2 81.2 50.0 88.9 77.7 85.9 92.4 94.6 2001 All (millions) Mon–Thu am peak Mon–Thu off-peak day Mon–Thu pm peak Mon–Thu off-peak night Friday am peak Friday off-peak day Friday pm peak Friday off-peak night Saturday all day Sunday all day 366,458 36,121 89,124 48,367 33,750 9,136 24,927 13,240 10,180 54,218 47,395 100 100 100 100 100 100 100 100 100 100 100 60,651 13,683 10,724 11,712 7,818 3,270 2,712 2,679 1,815 3,786 2,452 16.6 37.9 12.0 24.2 23.2 35.8 10.9 20.2 17.8 7.0 5.2 305,807 22,438 78,400 36,655 25,932 5,866 22,215 10,561 8,365 50,431 44,943 83.4 62.1 88.0 75.8 76.8 64.2 89.1 79.8 82.2 93.0 94.8 Growth 1990–2001 (%) Mon–Thu am peak Mon–Thu off-peak day Mon–Thu pm peak Mon–Thu off-peak night Friday am peak Friday off-peak day Friday pm peak Friday off-peak night Saturday all day Sunday all day 28.8 32.4 34.0 14.5 3.2 80.2 67.4 45.6 16.7 38.6 21.8 23.0 11.9 35.6 11.6 27.1 28.9 63.9 31.8 47.1 27.0 16.3 30.0 49.1 33.7 15.4 ⫺2.4 131.7 67.9 49.5 11.7 39.6 22.2 Notes: 1. 1990 data are adjusted to be comparable with 2001 data because new survey techniques such as travel diary and household rostering have been used since 1995 NPTS (Hu and Young, 1999). 2. Persons of age 0 to 4 are excluded from 2001 data because they were not surveyed in the 1990 survey. M1288 - RICHARDSON TEXT.qxd 31/1/08 3:52 pm Page 335 Phil's G4 Phil's G4:Users:phil:Public: PH The US context for highway congestion pricing Family/personal (%) School/church (%) 335 Social/recreation (%) 130,770 6,700 40,296 19,240 11,897 1,198 9,268 4,199 2,957 19,646 15,368 46.0 24.6 60.6 45.5 36.4 23.6 62.2 46.2 33.9 50.2 39.5 27,848 6,968 7,189 2,153 1,853 1,113 1,235 191 184 752 6,211 9.8 25.5 10.8 5.1 5.7 22.0 8.3 2.1 2.1 1.9 16.0 76,605 1,377 11,135 10,371 12,807 221 2,731 2,672 4,349 15,728 15,214 26.9 5.0 16.7 24.5 39.2 4.4 18.3 29.4 49.9 40.2 39.1 168,438 11,177 53,182 19,648 10,806 3,043 15,333 5,745 3,192 27,420 18,891 46.0 30.9 59.7 40.6 32.0 33.3 61.5 43.4 31.4 50.6 39.9 37,659 8,328 8,589 3,573 2,204 2,028 1,898 625 331 1,686 8,397 10.3 23.1 9.6 7.4 6.5 22.2 7.6 4.7 3.3 3.1 17.7 99,711 2,934 16,629 13,434 12,923 794 4,984 4,191 4,842 21,325 17,655 27.2 8.1 18.7 27.8 38.3 8.7 20.0 31.7 47.6 39.3 37.3 28.8 66.8 32.0 2.1 ⫺9.2 154.1 65.4 36.8 8.0 39.6 22.9 3. 4. 35.2 19.5 19.5 66.0 18.9 82.2 53.7 227.2 80.3 124.3 35.2 30.2 113.1 49.3 29.5 0.9 258.9 82.5 56.9 11.3 35.6 16.0 Trips for which day of week or time of day are unknown are excluded. The column of all trips does not equal to total person-trips because it excludes trips for such purposes as work related, pleasure driving and vacation. Source: Lee et al. (2006). Commute (%) Non-commute Direct Chain (%) Direct Chain Other All 12,813 2,660 3,021 3,094 709 619 803 656 183 677 391 205,870 14,313 51,006 23,338 19,700 3,778 14,192 6,778 6,620 33,976 32,168 (75.5) (49.4) (78.9) (64.7) (73.8) (52.3) (80.4) (70.4) (80.4) (88.2) (91.6) 168,193 12,001 39,671 19,434 17,370 3,094 10,960 5,569 5,766 27,208 27,121 37,677 2,312 11,335 3,905 2,330 684 3,232 1,209 854 6,768 5,048 8,248 785 2,975 1,234 805 144 770 296 176 672 393 272,799 28,979 64,646 36,091 26,704 7,226 17,646 9,626 8,229 38,523 35,128 2001 All (millions) Mon–Thu am peak Mon–Thu off-peak day Mon–Thu pm peak Mon–Thu off-peak night Friday am peak Friday off peak day Friday pm peak Friday off-peak night Saturday all day Sunday all day 56,903 13,519 10,793 11,198 5,913 3,266 2,749 2,476 1,298 3,443 2,247 (20.7) (45.1) (16.7) (31.8) (22.8) (43.0) (15.6) (26.4) (16.3) (8.6) (6.1) 43,162 10,440 7,613 8,059 5,097 2,544 1,927 1,761 1,077 2,813 1,831 13,740 3,079 3,180 3,139 816 722 822 715 221 630 416 213,827 15,835 53,041 23,470 18,905 4,218 14,744 6,725 6,317 36,016 34,558 (77.7) (52.9) (82.1) (66.6) (72.8) (55.5) (83.5) (71.7) (79.3) (90.5) (93.3) 174,461 13,337 41,250 19,672 16,599 3,580 11,304 5,504 5,595 28,563 29,059 39,366 2,498 11,792 3,798 2,305 639 3,440 1,221 722 7,453 5,498 4,497 590 762 594 1,157 110 171 175 353 356 229 275,226 29,943 64,596 35,262 25,974 7,595 17,664 9,375 7,968 39,815 37,034 Source: Lee et al. (2006). Page 336 Phil's G4 Phil's G4:Users:phil:Public: PH 45,868 11,221 7,644 8,425 5,490 2,686 1,881 1,897 1,251 3,198 2,176 3:52 pm (21.5) (47.9) (16.5) (31.9) (23.2) (45.7) (15.2) (26.5) (17.4) (10.1) (7.3) 31/1/08 58,681 13,882 10,665 11,519 6,199 3,305 2,684 2,553 1,434 3,875 2,567 The United States 1995 All (millions) Mon–Thu am peak Mon–Thu off-peak day Mon–Thu pm peak Mon–Thu off-peak night Friday am peak Friday off-peak day Friday pm peak Friday off-peak night Saturday all day Sunday all day M1288 - RICHARDSON TEXT.qxd Direct and chained tours by period of the week, 1995 and 2001 336 Table 17.5 M1288 - RICHARDSON TEXT.qxd 31/1/08 3:52 pm Page 337 Phil's G4 Phil's G4:Users:phil:Public: PH The US context for highway congestion pricing 337 showed a larger and increasing proportion of non-work trips. The only period showing a large increase in the proportion of work trips was the Monday–Thursday night off-peak period. We found a stark contrast among growth patterns for work and nonwork trips in terms of their temporal distribution across weekly periods. Whereas work trips became more spread out, extending to off-peaks, nonwork trips grew faster in the morning peak. The spreading of work trips may be attributed to increasingly flexible work schedules while the growth in morning-peak non-work trips reflects the increased frequency of nonwork trip-chaining into commute tours (see below). Both tendencies, increasing flexibility in work schedules and the prevalence of non-work trips in peak hours, make peak-hour pricing more attractive. Trips for family or personal business (including shopping and doctor visits) accounted for the majority of non-work trips. Yet, there was also considerable growth in the school/church trips and the social/recreation trips categories. Non-work trip frequencies grew most in the Friday am peak period, perhaps the result of a trend towards early weekends. This point is sharpened when we introduce a tour-level analysis which highlights the non-work trips that are a part of many commutes (Table 17.5). The tour analysis is the more interesting because it accounts for some of the growth in non-work travel. It makes sense that a growing labor force participation rate, especially among women, causes more errands to be included in tours to and from work. The Federal Highway Administration (FHWA) defines a trip chain as ‘a sequence of trips bounded by stops of 30 minutes or less’ (McGuckin and Nakamoto, 2004, p. 1). Any stop of more than 30 minutes becomes either the origin or the destination of a tour. Thus, a tour denotes a single trip or chained trips bounded by two anchor destinations (of more than 30-minute dwell time). Unlike in previous research, the FHWA definition includes places other than home and workplace as anchor destinations that constitute either end of a tour. Thus, trip chain datasets for 1995 NPTS and 2001 NHTS classify all tours into nine tour types according to origin and destination place types: (i) home-to-home, (ii) home-to-other, (iii) home-to-work, (iv) other-to-home, (v) other-to-other, (vi) other-to-work, (vii) work-to-home, (viii) work-to-home, and (ix) work-to-work. The home-to-work and workto-home tours are apparently commute tours, whether direct or chained. However, a commute tour in the general sense can be much more complex, possibly involving intervening stops of more than 30 minutes, such as a visit to a fitness center. To distinguish these kinds of commutes, we identified commutes with a stop of more than 30 minutes by connecting two pairs of continuing FHWA-defined tours in the categories hometo-other and other-to-work; and in the categories work-to-other and M1288 - RICHARDSON TEXT.qxd 31/1/08 338 3:52 pm Page 338 Phil's G4 Phil's G4:Users:phil:Public: PH The United States other-to-home. If, however, there are two or more intervening stops of more than 30 minutes en route to or from the workplace, we do not count the tour as a commute. Our new definitions add to our point about the dominance of non-work travel because they emphasize the fact that not only are non-work trips the vast majority in each peak period but, once we re-define commute tours, we find that many of them (23 percent in the Monday–Thursday am peak and 28 percent in the Monday–Thursday pm peak) also involve non-work trips. The proportion of chained commutes during both am and pm peaks reflect a significantly increased trip-chaining tendency between survey years. This increase of chained tours in the morning peak may be an important factor behind the increased road congestion found for the late 1990s (Gordon et al., 2004). Trip-chaining is an individual level strategy to economize on travel times by combining multiple trips on various purposes into a tour and can be done most easily by car (Hensher and Reyes, 2000; Lee et al., 2006). Therefore, the increasing tendency toward trip-chaining further strengthens the case for the HOT-lane approach over other approaches for coping with road congestion. 5 DISCUSSION Unpriced access to busy roads and highways has long served as a textbook example of a market failure. Actually, as new technologies make toll collection and road monitoring costs cheaper, the widespread lack of road pricing can be seen as a policy failure. The existence of growing networks of HOV lanes on the freeways of major US metro areas provides an interesting opportunity for policy makers to implement pricing without major disruption because most HOVs are presently underutilized. Where they exist, they occupy 25 percent of the road space (one of four lanes) but can accommodate just 7 percent of the vehicles (those estimated to carry two or more passengers). The availability of HOVs was supposed to increase carpooling but this has not happened. California law was recently changed to allow hybrid vehicles onto the state’s underutilized HOVs, no matter what the vehicle occupancy. The HOT-lane proposal (to convert HOV lanes to HOT lanes) is summarized in a recent paper by Poole and Orski (2006, pp. 453–4). Note that they describe it as a transit as well as a highway policy. By changing the access requirement from vehicle occupancy to willingness to pay a market price (for cars) but allowing super high-occupancy vehicles (buses M1288 - RICHARDSON TEXT.qxd 31/1/08 3:52 pm Page 339 Phil's G4 Phil's G4:Users:phil:Public: PH 339 The US context for highway congestion pricing Table 17.6 Comparative throughput of HOV lanes and HOT network Typical HOV-2 Typical HOV-3 0 788 150 10 2 950 2275 0 0 350 20 3 373 1365 SOVs (average 1.1 person/veh.) HOV-2s (average 2.1 person/veh.) HOV-3s (average 3.2 person/veh.) Vanpool (average 7.0 person/veh.) Express bus (average 35 persons/veh.) Vehicles/hour Persons/hour Ideal HOT HOV-3 Network 0 0 1200 20 40 1260 5380 1100 300 200 60 40 1700 4300 Sources: Table 19.2 in Poole and Orski (2006). and vanpools) to use the lanes at no charge, we can accomplish three important goals: 1. Generate sufficient new revenue to building out today’s fragmented HOV lanes into a seamless network; 2. Provide a congestion-free alternative for motorists on every congested freeway in the same metro area; and 3. Provide a congestion-free guideway for bus Rapid Transit service that can make this form of transit significantly more competitive with driving. The authors show that converting HOV-2 lanes (those that allow twoperson carpools) to HOT lanes would approximately double vehicleper-hour and person-per-hour throughput (Table 17.6). They also estimate the costs of implementing their system in eight of the major metro areas of the US. The estimates range from $2.7 billion (Miami) to $10.8 billion (Los Angeles) but dedicated revenue bonds would cover two-thirds of these costs. In the light of the high proportion of costs that can be met in this way, private capital and private management become plausible. This is an added attraction of the proposal. Interestingly, there are two HOT lanes currently in operation in Southern California. They have each been in operation for over 10 years and are described by Sullivan (2006): The two Southern California projects are applications of ‘value pricing,’ described in a U.S. DOT report to Congress as ‘a market-based approach to traffic management which involves charging higher prices for travel on roadways during periods of peak demand. Also known as congestion pricing or road pricing, value pricing is designed to make better use of existing highway capacity by encouraging some travelers to shift to alternative times, routes, or modes of transportation.’ . . . The Interstate 15 project uses dynamic value pricing where the toll can change in real time to adapt to unusual changes in demand. However, a schedule of typical daily tolls is also published. The State Route 91 M1288 - RICHARDSON TEXT.qxd 31/1/08 340 3:52 pm Page 340 Phil's G4 Phil's G4:Users:phil:Public: PH The United States project sticks to a published toll schedule, based on established patterns of daily demand. These projects have enjoyed substantial public acceptance, in part because they have been marketed as a kinder and gentler form of congestion pricing, in which innovative pricing is used to create a new product – a congestion-free travel option in an otherwise congested commute corridor. Travelers are free to use or avoid the value priced facilities as they see fit, since the original congested travel options remain available. This approach stands in sharp contrast to mandatory pricing of all private vehicle trips at targeted locations and times, which some regard as ideal congestion pricing. (pp. 189–90) The impact studies have shown that the value-priced toll facilities, where travelers can bypass congestion for a price, are associated with significant and systematic responses in travel behavior. This suggests that demand-dependent pricing can be a powerful tool for managing highway traffic and providing more choices to the traveling public in similar corridors elsewhere. (p. 214) The success of these facilities is not surprising because they exist and operate in the context described in our analysis of the US data. Origins and destinations are dispersed and travel patterns are most amenable to the use of singly-operated motor vehicles. 6 CONCLUSIONS Our findings complement and elaborate the recommendations of Poole and Orski (2006). Converting HOV lanes to HOT lanes and redirecting current planning away from more HOV lane development (as well as from conventional transit planning) towards their suggested plan is the way to go in the light of what we know of US settlement and travel trends. Dispersed origins and destinations are unlikely to be well served by conventional transit or by carpooling. And the increasing tendency to combine work trips with non-work trips reflects this and also favors the HOT-lanes policy. NOTES 1. 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