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Insights into the fairness of cordon pricing based on origin–destination data
Journal of Transport Geography 49 (2015) 61–67
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Insights into the fairness of cordon pricing based on origin–destination data Ammar Abulibdeh, Jean Andrey ⁎, Matthew Melnik Department of Geography and Environmental Management, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada
a r t i c l e
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Article history: Received 5 September 2013 Received in revised form 3 October 2015 Accepted 23 October 2015 Available online xxxx Keywords: Congestion pricing Cordon zone Equity Travel pattern Socio-economic groups Canada
a b s t r a c t Origin–destination data are used to assess the vertical equity effects of a hypothetical road pricing zone in Canada's largest city. The assessment is based on the proportion of morning commuters affected by cordon pricing by virtue of residential location, trip destination, and travel mode. The overall findings for Toronto, Canada show that people with full-time employment and also those from higher income neighborhoods would be most affected by downtown road pricing; and this holds true when the population is broken out by gender, age group, household size and occupational class. The analysis also highlights that professionals, those who live in one- and two-person households, and those who are aged 65 and older would be disproportionately affected; those who work in manufacturing would be less affected. The equity effects of road pricing arise out of the commuting patterns of different sub-populations. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction Congestion pricing is the practice of surcharging users of a service or network during periods of high demand in order to bring demand into better alignment with system capacity. In the context of transportation, congestion pricing has been explored in various contexts, from airports (Hoffman et al., 2012) to parking (Pierce and Shoup, 2013); however, it is most often applied to urban roadways where peak-period demand results in congestive delays. Urban road pricing includes both corridor and areal systems. Corridor pricing can be applied to a single link or to a group of assets, and it ranges in complexity from a simple two-tiered schedule (i.e., congested versus uncongested) through to dynamic toll calculations associated with actual flows (Murray, 2012). The most common form of corridor pricing is high-occupancy toll (HOT) lanes, as implemented in a number of U.S. states. Areal systems focus on specific geographic regions, typically the economic core of large metropolitan areas. These systems can be further divided into cordon systems, whereby a fee is charged for crossing in or out of the bounded area (e.g., Stockholm, Sweden; Singapore), and zonal systems (also referred to as area licensing systems) that charge a daily fee for operating a vehicle within the charge area (e.g., London, England; Trondheim, Norway) (Ecola and Light, 2010; Vonk Noordegraff et al., 2014a, b). While there is clear evidence that congestion pricing leads to travel adjustments that contribute to congestive relief, the implementation of these systems is not without controversy. A primary source of controversy relates to fairness (Taylor and Kalauskas, 2010), which connects ⁎ Corresponding author. E-mail address: [email protected] (J. Andrey).
http://dx.doi.org/10.1016/j.jtrangeo.2015.10.014 0966-6923/© 2015 Elsevier Ltd. All rights reserved.
with how tolls are collected and how revenues are used. Recent reviews of the equity of urban road pricing systems (Santos and Rojey, 2004; Ecola and Light, 2009, 2010; Levinson, 2010; TRB, 2011) provide several observations that are the starting point for the current study. First, equity can be considered from a number of dimensions: vertical equity (also known as social equity, which refers to the extent to which members of different classes are treated similarly), horizontal equity (the extent to which individuals within a class are treated similarly), spatial equity (the extent to which costs and benefits are distributed equally over space), intergenerational equity (the ways in which costs and benefits are distributed over time), and market equity (the extent to which benefits received are proportional to the price paid). The various perspectives on equity provide varied insights into the fairness of congestion pricing. Second, even when focusing on only the vertical dimension of equity, the literature provides sometimes conflicting statements about whether road taxes in general, and urban congestion pricing in particular, disproportionately affect those who are more (or less) able to pay. Some studies provide evidence that road pricing disproportionately benefits wealthy people (Schweitzer and Taylor, 2008; Di Ciommo and Lucas, 2014), thus exaggerating inequalities; others show no clear pattern of burden according to income (Karlstrőm and Franklin, 2009); and some argue that road pricing can be less regressive than other ways of raising funds for transportation infrastructure or even progressive either because a disproportionate number of wealthy commuters are affected by tolls or because the wealthy do not benefit as much from the redistribution of revenues to improved transit services (Levinson, 2010). Third, geography matters. The locations of residences, employment opportunities, and other activity nodes affect origin–destination flows,
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and thus the ways and extent to which different trip makers are impacted by road pricing. Traveler exemptions and discounts are also sometimes geographically based, e.g., residents of the cordon charge zone may be charged differently than others. Geography is also important when considering modal shifts in travel patterns because, by virtue of residential and job locations, different socio-economic groups have differential access to public transit (Delbosc and Currie, 2011). All of these complexities suggest that different contexts and systems are likely to give rise to different fairness issues and outcomes. As argued by Santos and Rojey (2004, 21), “impacts are town specific and depend on where people live, where people work and what mode of transport they use to go to work”. Thus, detailed travel-based studies are required in order to provide informed commentary on the potential equity effects of congestion pricing in any particular jurisdiction. The current study uses data from an origin–destination survey to explore the equity implications of a hypothetical cordon system in the downtown of Canada's largest urban metropolitan area—the Greater Toronto Area. In the North American context, Toronto is both highly congested but also relatively transit-friendly with a vibrant urban core. Various initiatives have been taken or are being discussed in response to deteriorating traffic conditions in Toronto, for example the launch of the Smart Commute program in 2008 and the implementation of high-occupancy vehicle lanes on two feeder highways. While a downtown congestion charge, as well as other initiatives such as carbon pricing and a parking sales tax, have recently been described as being “too expensive, too punitive or too risky” (The Record, 2013), and congestion pricing has never been considered by the City (City of Toronto, 2013a), road pricing systems of various types have been discussed over the past decade (Abdulhai, 2013; Butts and Pennachetti, 2007). Also, despite decentralization of economic activity and population in the GTA, discussions frequently focus on downtown Toronto, which remains an important employment zone and the urban area with the most developed public transit system. This study demonstrates the value of existing data sets for such analyses, and draws conclusions about the relative degree to which congestion pricing would affect different groups in this Canadian city. 2. Study area Canada's largest metropolitan area, the Greater Toronto Area (GTA), includes the City of Toronto and the four regional municipalities that surround it—Halton, Peel, York and Durham. The region, with over 5.5 million residents, has experienced rapid population and business growth over the past half century. Even while policies and programs have been implemented with the intention of managing the growing demand for automobile travel (City of Toronto, 2010; Metrolinx, 2008), there is considerable evidence that the GTA has moved toward increased dependence on the automobile and increased congestion, in large part because of the decentralization of economic activity away from the central business district (Miller and Shalaby, 2003; Miller et al., 2004) and the suburbanization of residential development (in Fig. 1, note the growth in Halton, Peel, York and Durham relative to the Rest of Toronto. Planning District (PD) 1 coincides with Toronto's downtown core). Not surprising then, road congestion is an increasingly serious problem, with associated costs estimated to be at least $2.5 billion (Transport Canada, 2006) and up to $11 billion (Dachis, 2013) annually depending on whether the estimate is based only on time lost or is inclusive of other costs such as increased transportation costs for businesses, the need to pay workers higher wages to compensate for higher commuting costs, and the costs of residents not taking advantage of the benefits of living there because of congestion. Indeed, the average commute time is higher in the GTA than in any other Canadian city (Statistics Canada, 2013), and the ratio of peak-period travel time to free-flow travel time has been estimated to be 1.88, with projections of worsening traffic in the coming years (HDR Corporation, 2008).
Fig. 1. Residential location of GTA workers by year (based on analysis of Transportation Tomorrow Survey data, various years).
3. Methods The current study presents an analysis of commuting patterns as observed in the most recently available regional travel survey in order to explore the equity implications of cordon pricing. This section first discusses the general approach taken, followed by a description of the spatial units of analysis and the data sources and variables. Four key decisions provide the structure for the analysis: 1. While different interpretations of equity are possible, the focus in this study is on vertical or social equity, and is based on whether traveler groups of different economic means would be more or less affected by cordon pricing. Income and employment status are the two variables used to differentiate the various groups. 2. Insight into vertical equity is provided through the analysis of trip patterns. For each traveler group, the proportion of trips that would be affected by a hypothetical cordon pricing system is calculated, and these are compared. The analysis is conducted first in aggregate, and then based on gender, age, household size and occupational class. 3. Congestion charges are typically higher during peak-period travel, which coincides with the morning and afternoon commutes to and from work. Given this, and the format of available data, the decision was made to concentrate on home-based, morning peak-period (6 a.m. to 9 a.m.) trips made by those who are 15 years of age or older. Approximately one-half of the trips in and out of the downtown occur during this three-hour window. 4. Different pricing systems are possible—those that charge for entering and exiting the cordon area, regardless of place of residence; and those that charge non-residents only for entering the cordon zone. The general approach taken in this study is to identify all flows that would be affected by a cordon pricing system, assuming that both residents and non-residents of the cordon zone would be charged when crossing the charge-zone boundary; however, the aggregate flows are separated out so as to provide insight into the relative magnitude of the inbound and outbound flows during morning peak-period travel. Spatially, the GTA is divided into three areas for the purposes of this study (Fig. 2 displays both a regional map and a map of the various planning districts (PDs) that are used to define the three areas). The first is the proposed cordon pricing zone in the City of Toronto, which coincides with the boundaries of PD1 and is defined by Jarvis, Bathurst and Bloor Streets on the east, west and north, respectively, and Lake Ontario on the south. PD1, which covers an area of 18 km2, has the highest employment density of anywhere in Canada. It includes Toronto's financial district (the 12th most influential centre in the world; City of Toronto, 2013b), and is the setting of one of the country's most intense and longstanding condominium booms (RBC, 2012). Cordon counts of traffic
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Fig. 2. Left to right: Greater Toronto Area; and 2006 TTS Survey Area Planning Districts, City of Toronto. Sources (respectively): City of Toronto (2012), Data Management Group (2012).
in 2006, which were conducted by the City of Toronto, show that more than 180,000 vehicles cross the boundaries of PD1 each day. As well, a recent study of gridlock in the Greater Toronto Area highlights the extent to which downtown travel speeds are compromised by congestion (Sweet et al., 2015). The other two spatial units are much larger. The second area is the rest of the City of Toronto, i.e., PD2 to PD16, which together cover approximately 610 km2. The third area covers close to 6500 km2 and includes the municipalities of Halton, Peel, York and Durham—referred to here as the rest of the GTA. Table 1 provides summary data on each of these areas. The main data source is the 2006 regional travel survey, referred to as the Transportation Tomorrow Survey (TTS) (Data Management Group, 2012). TTS data are based on responses from approximately five percent of households, and these sample data are available in expanded form so as to represent the total population of the study area. While a more recent survey was conducted in 2011, these data are not yet available for research purposes. Since the travel data do not provide income information, neighborhood-level trip data from the municipal TTS were merged with household-level income data from the national census available through Statistics Canada. More specifically, average household-level income data were available at the neighborhood level (referred to by Statistics Canada as dissemination areas, each with a population between 400 and 700 people), and these were then combined in order to calculate a weighted average household income for each traffic zone (comprising two or more disseminations, covering a geographic area between 0.02 and 120.27 km2 each). Then each home-based morning trip in the TTS was assigned to the traffic zone of the trip maker's residence, and the trip maker was described in terms of the income level of his/her residential neighborhood as well as his/her individual employment status as indicated in the TTS data. Altogether, of the 11.062 million passenger trips taken each day in the GTA, 2.365 million are made from people's residences during the morning peak period, between 6 a.m. and 9 a.m.
In subsequent analysis, the income data were summarized in four categories: low-income neighborhood (average household income less than $60,000), lower-middle income neighborhood (average household income of $60,000 to $89,999), upper-middle income neighborhood (average household income of $90,000 to $119,999) and high-income neighborhood (average household income of $120,000 or higher). The decision to use $60,000 as the upper limit for low-income neighborhoods is based on Statistics Canada's (2009) low-income cut-off value for large families of seven or more people. The upper limits for the next two categories were based on increments of $30,000. It was intentional to have the lowest and highest income classes relatively small in size in order to focus on the least and most economically advantaged. This categorization singles out those residents who live in neighborhoods with the lowest 7.6% of incomes and the highest 17.6% of incomes; those who reside in the lower-middle and upper-middle income neighborhoods account for 46.6% and 30.8%, respectively. These income groups were then cross-tabulated with two employment status categories (employed full-time versus employed part-time or not employed) to create eight socio-economic groups as summarized in Table 2. 4. Equity analysis 4.1. Overall results Analyzing traffic flows provides a way of estimating the number of trips that would be affected by cordon pricing. As indicated in the bottom row of Table 3, of the 2.365 million home-based trips taken during the morning peak-period, approximately 14,700 (0.6%) are auto trips that exit PD1 en route to other parts of the metropolitan area; and approximately 87,000 (3.7%) are auto trips that have PD1 as a destination but originate from areas outside PD1. As such, a cordon pricing system in downtown Toronto would potentially affect 4.3% of
Table 1 Population and employment characteristics of the study area. Geographic area
Population (2006)
Employment (2006)
Unit of analysis
Planning district (PD) or municipality
People
% change since 1986
Density (per ha)
Jobs
% change since 1986
Density (per ha)
Proposed cordon zone Rest of the City of Toronto Rest of the GTA
PD1, City of Toronto PD2 to PD16 inclusive, City of Toronto Halton Peel York Durham
188,668 2,257,271 422,730 1,119,122 857,521 539,493
67% 23% 60% 94% 149% 70%
49.1 37.0 4.3 8.9 4.1 2.1
105,121 1,057,790 217,772 570,945 440,791 275,774
43% 2% 54% 75% 138% 67%
33.8 17.5 2.2 4.6 2.1 1.1
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Table 2 TTS data for Greater Toronto, 2006. Employment status of trip maker
Income of trip # persons # daily maker's trips residential neighborhood
Employed full-time (FT)
Low Lower-middle Upper-middle High All income levels Employed part-time Low or not employed Lower-middle (not FT) Upper-middle High All income levels Overall total
# home-based daily trips, 6 a.m. to 9 a.m.
135,192 979,081 695,625 390,617 2,065,323
323,755 2,608,598 2,066,243 1,131,143 6,129,739
84,239 631,547 463,201 258,449 1,437,436
3.6% 26.7% 19.6% 10.9% 60.8%
265,034 1,463,817 917,583 533,170 3,179,604
296,270 2,040,267 1,571,413 1,024,335 4,932,285
66,186 399,522 291,414 170,457 927,579
2.8% 16.9% 12.3% 7.2% 39.2%
5,244,927 11,062,024 2,365,015 100.0%
all morning trips in the GTA. This is a conservative estimate, as trips that pass through PD1 are not included. By examining the right-most column of Table 3, one gets a sense as to the socio-economic mix of commuters who would be affected by the toll. More than two-thirds of these commuters would be full-time workers; and nearly one in five would be from the top income category. As would be expected in a multi-nucleated North American city such as Toronto, most (63.5%) morning trips are made by automobile and do not enter or exit the downtown core. Trips by transit (22.6%) and non-motorized modes (9.6%) in combination account for nearly one in three morning trips. There are modal differences across the various socio-economic groups, however. Reliance on transit, cycling, or walking is highest for those from low-income neighborhoods, being 33.1% and 63.6% for full-time workers and others, respectively. For the lower-middle income group, the corresponding percentages are 26.0 and 52.0. The percentages from higher income neighborhoods are lower: 16.2 and 42.1 for upper-middle and 22.6 and 37.5 for high income. Fig. 3 provides a systematic comparison of the equity effects of a cordon pricing system in downtown Toronto on eight socio-economic groups. For those who are employed full-time, trips affected by cordon pricing rise from 3.1% for those in low-income neighborhoods to 5.4% and 5.2% for lower-middle and upper-middle income neighborhoods, to 9.6% for those from high-income areas. For those who are not employed full-time, the equivalent percentages are 1.5, 1.8, 1.3 and 2.5. As such, some groups would be disproportionately affected by cordon pricing in the downtown. However, the progression across the four income groups is not linear. Rather, the percentage of affected trips is similar for those from lower-middle and upper-middle income
Fig. 3. Percentage of morning peak-period trips affected by cordon pricing, by socioeconomic group.
neighborhoods. This occurs despite a comparatively greater reliance on transit and non-motorized travel by the lower-middle income group, and can be explained by the relatively strong attraction of people from lower-middle income neighborhoods to general office and sales jobs in the downtown core. 4.2. Analysis by socio-demographic variables The previous results suggest that a cordon pricing scheme would affect disproportionately more people who are employed full-time and/or from high-income neighborhoods, and disproportionately fewer people who are not employed full-time and/or are from low-income neighborhoods. This next section explores whether these patterns hold true when the trip data are organized by socio-economic variables including gender, age, household size and occupational class. Table 4 illustrates the trends for morning trip makers who are employed full-time. The results by gender and occupational group essentially mirror the original results: the percentage of trips that would be affected is relatively low for those who reside in low-income neighborhoods (b4.0%), higher for those who reside in middle-income groups, and highest (N 8.0%) for those who reside in high-income neighborhoods—with similar progression for females versus males and for those employed in general office, professional, sales or manufacturing. The patterns by household size show similar progression across the four income groups, except for one-person households, in which case, the percentage of trips affected by cordon pricing is between 4.0% and 5.0% for those from low and lower-middle income neighborhoods and then rises to 8.7% and 11.6% for those from upper-middle and high-income neighborhoods, respectively. The breakouts by the age of the traveler also show progression across the four income groups for
Table 3 Morning peak-period auto trips (as driver or passenger). Socio-economic status of trip maker
Flows out of PD1 to
Employment status of trip maker
Income of trip maker's residential neighborhood
Rest of the City of Toronto
Rest of the GTA
Rest of the City of Toronto
Rest of the GTA
Full-time (FT)
Low Lower-middle Upper-middle High Low Lower-middle Upper-middle High
4.4% 36.7% 19.2% 16.3% 2.0% 10.5% 5.8% 5.2% 100.0% 9200
5.3% 40.4% 23.3% 23.9% 0.3% 4.7% 1.8% 0.4% 100.0% 5514
3.1% 37.3% 15.4% 27.3% 1.5% 8.2% 2.4% 4.9% 100.0% 55,883
0.6% 24.2% 39.5% 21.8% 0.0% 4.5% 6.2% 3.2% 100.0% 30,910
Not full-time (not FT)
All groups Morning peak-period auto trip count
Flows into PD1 from
Total flows into and out of PD1
All auto trips in GTA
2.6% 33.5% 23.5% 24.5% 1.0% 7.1% 3.8% 4.2% 100.0% 101,507
3.5% 29.2% 24.2% 12.5% 1.5% 12.0% 10.5% 6.6% 100.0% 1,603,143
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Table 4 Percentage of morning peak-period trips potentially affected by cordon pricing for different sub-populations. Employment status
Persons employed full-time
Neighborhood income
Low
Lower middle
Upper middle
High
Low
Persons employed part-time or not employed Lower middle
Upper middle
High
Female Male Aged 16–19 Aged 20–34 Aged 35–49 Aged 50–64 Aged 65+ 1-person household 2-person household 3-person household 4-person household 5+ person household Occupation general office Occupation professional Occupation sales Occupation manufacturing Not employed
2.1 3.9 0.0 4.3 2.7 2.4 7.3 4.8 3.0 3.0 3.1 2.6 2.9 3.9 3.1 2.0
4.8 5.9 0.4 4.9 5.7 5.7 4.6 4.5 8.0 5.0 4.8 4.0 5.2 7.0 5.0 3.1
4.6 5.6 0.6 4.8 5.5 5.4 8.5 8.7 7.4 5.0 4.1 3.8 4.7 6.4 4.9 2.4
8.4 10.7 0.9 7.0 10.5 10.8 11.7 11.6 11.2 9.4 8.3 8.4 7.2 11.6 8.4 5.1
1.6 1.0 1.5 1.0 1.4 5.2 3.3 2.9 2.1 1.7 1.7 0.9 3.0 2.5 2.1 1.0 1.4
2.3 1.9 1.5 1.2 1.5 4.0 4.2 3.6 3.6 1.8 1.4 1.1 2.7 5.6 2.1 2.0 1.6
1.3 0.8 2.5 0.9 1.3 2.2 1.9 2.7 2.1 1.3 1.1 1.2 2.1 3.1 1.4 0.9 1.2
2.4 2.6 3.0 0.3 2.8 4.6 5.6 4.3 4.8 2.3 1.8 2.1 2.3 6.1 2.4 2.3 2.2
all ages, with the effects of a cordon pricing system being similar for those aged 20–35, 35–49, and 50–64; the percentages of affected trips are low for those from low-income neighborhoods (≤4.3%), higher for those from middle-income neighborhoods (4.8% to 5.7%), and highest for those from high-income neighborhoods (7.0% to 10.8%). The youngest age group does not exactly follow this pattern but constitutes only 1.5% of morning trips. The oldest age group of full-time workers, also accounting for only a small fraction of the total (0.9%), has a comparatively low percentage of trips that would be affected by a downtown cordon pricing system (b1.0%) for all income groups. Both the youngest and oldest age groups are primarily entering, rather than exiting, PD1. For all four income groups, males would be more affected by cordon pricing than females, which seems to be related to the mix of employment opportunities in downtown Toronto. Also, professionals would be more affected than other occupational groups. Amongst the four occupational classes, those employed in manufacturing are least likely to be affected. These patterns are explained by the concentration of financial and other professional services in Toronto's downtown core, and concentration of manufacturing jobs in outlying areas, especially Peel and York Regions. The data also suggest that commuters from one- and two-person households would be more affected overall and those who reside in households of three or more persons would be less affected. This may reflect differences in both locational preferences and modal split for people at different life stages. The patterns for morning travelers who are not employed full time (i.e., working part-time, retired or not employed) differ from those of full-time workers. First, the overall percentage of affected trips is much lower for those who are not employed full time. Those who are not working full time and are from upper-middle income neighborhoods would be less affected by cordon pricing than those from lower or higher income neighborhoods. This is largely because their morning trips tend to start and end in the municipalities outside of the City of Toronto. Those from both lower and higher income neighborhoods would be more affected, because of higher rates of travel into the core. This is true for both genders, for all age groups except for the very youngest group (aged 15 to 19), for all household sizes except for those with five or more people, and for all occupational groups except professionals. The findings about which sub-populations would be disproportionately affected are similar in terms of occupational class and household size. Professionals would be more affected than other occupational groups; and those living in one- and two-person households would be more affected than those in larger households. For those who are not employed full-time, females would be more affected than males—partly
reflecting the number of part-time retail and office positions, as well as non-work attractions, in downtown Toronto. Also, the patterns by age, even when focusing only on the three main groups (aged 20–34, 35– 49 and 50–64) are not the same for full-time and not full-time workers: in the latter case, those aged 50–64 would be disproportionately affected. 4.3. Potential for transit as an alternative to auto trips One limitation of the analysis of origin–destination data is that it only identifies those trips and sub-populations that would be affected by cordon pricing, stopping short of considering behavioral adaptations that might ensue—and how these link with current transit services. Thus, in a follow-up analysis, we explored the adequacy of current transit routing for providing an alternative for auto trips affected by the cordon pricing. The analysis considers only those morning peak-period auto trips that originate within the City of Toronto, are destined for PD1, and are work-related—a data set constituting just over 35% of the total affected trips—but a large enough subset to provide insights into the potential for the current transit to provide an alternate travel mode and also the appropriateness of the proposed cordon boundaries. The starting point for this analysis is that most of the auto trips affected by cordon pricing—at least those from the City itself—should be possible via public transit, since mode switching is one of the objectives and likely outcomes of road pricing. In order to operationalize this, transit availability was explored in a micro-analysis at the traffic zone level using a geographic information system. The first consideration was the ‘transit coverage’ of each of the 625 traffic zones in the City of Toronto, i.e., the percentage of each traffic zone's area that is located within 400 m of a transit stop, by sidewalk, i.e., within an acceptable walking distance (c.f., Des Rosiers et al., 2010; Larsen et al., 2010). While the transit coverage is 100%, or nearly so, in the 45 downtown traffic zones that comprise PD1, the average across the City is only 72%. Accordingly, in some cases, long walking distances from the point of origin to the closest transit station would make mode switching unlikely. Information on transit service coverage from all the different origin zones was then combined for each destination zone in the downtown core. These aggregate data were then used to define various cordon zone boundaries based on different thresholds of transit coverage in the zones of origin. The idea was to identify those destination zones in the downtown that are accessible by public transit from the various zones of origin from which people currently drive. We identified thresholds for transit coverage, and then tallied the trips that originate from traffic zones that meet or exceed this threshold to individual destination
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zones in PD1, and then represented these tallies as percentages of the total auto trips to each destination. If the resulting percentage is high, then this indicates that a large proportion of the destination's auto trips could be made using public transit, suggesting that this traffic zone could be a good candidate for inclusion in the cordon zone. Different transit coverage levels (40%, 50%, 60%, 70% and 80%) were considered. While it is possible to also vary the convertible percentage threshold, here we illustrate the results using 75% as the percentage of convertible trips that would be desirable for the destination zone to be included in the priced area. As shown in Fig. 4, if the transit coverage is set relatively low (i.e., 40% or 50% of the land area in the zones of origin is within a 400 m walk of a transit station), all of the 45 traffic zones in PD1 are attracting trips that could, for the most part, be made by transit. If the transit service threshold is increased to 60%, then three of the 45 traffic zones in the southern part of PD1—all of which are along a major rail and highway corridor and attract a low number of trips—would be excluded. If the transit coverage threshold is increased even more, to 70% or 80%, then more of the destination zones on the edge of downtown would meet the threshold of being able to provide transit connections for at least 75% of present-day auto commuters. Accordingly, one could argue that there is a spatial element of inequity associated with a cordon zone that is too large—given Toronto's current commuting patterns. Still, on the whole, it appears that the choice of PD1 as a cordon zone for exploring equity is appropriate.
5. Conclusions and discussion Using the Greater Toronto Area as a case study, this paper reminds us how “lessons regarding road pricing equity can be learned from both implemented and non-implemented cases” (Vonk Noordegraff et al., 2014a, b). More specifically, the paper provides a detailed analysis of who would potentially be affected by a downtown cordon pricing scheme in Toronto by considering morning commuter flows using origin–destination data. The assessment is based on the proportion of morning commuters affected by cordon pricing by virtue of residential location, trip destination, and travel mode. The overall findings for Toronto, Canada show that people with full-time employment and also those from higher income neighborhoods would be most affected by downtown road pricing; and this holds true when the population is broken out by gender, age group, household size and occupational class. Accordingly, these results suggest that, in aggregate, cordon pricing in Toronto would be progressive in the sense that advantaged socio-economic groups would be more affected than others. The analysis also highlights that professionals, those who live in one- and two-person households, and those aged 65 or older would be disproportionately affected; and those in manufacturing would be less affected. An analysis of commuting flows into the downtown from the rest of the City of Toronto also shows that a large proportion of present-day auto commuters could reach downtown destinations using public transit.
Fig. 4. Downtown Toronto cordon zone possibilities based on transit service. Shaded area is included in cordon scenario with 75% auto conversion rate and 400 m transit coverage of (A) 40%, (B) 50%, (C) 60%, (D) 70%, or (E) 80%.
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It is also important to note that perceptions of equity are highly subjective, and the public is usually averse to the imposition of any new mandatory fee, notwithstanding claims or evidence of effectiveness. Still, as noted by Levinson (2010), “it is possible to identify which neighborhoods and groups will be most affected by road pricing” based on available data. The current study demonstrates one approach for doing this. The approach could also be used for more detailed analysis, which could then be the basis for working through alternatives with affected groups in collaborative ways. Acknowledgments The authors are grateful to University of Toronto's Data Management Group in the Department of Civil Engineering for their provision of data and related guidance. References Abdulhai, B., 2013. Congestion management in the GTHA: balancing the inverted pendulum. Residential and Civil Construction Alliance of Ontario http://www.rccao.com/news/ files/RCCAO_Apr2013_CongestionManagement_LOWRES.pdf. Butts, R., Pennachetti, J.P., 2007. Climate change, clean air and sustainable energy action plan: moving from framework to action. City of Toronto. http://www.toronto.ca/ legdocs/mmis/2007/pe/bgrd/backgroundfile-4982.pdf. City of Toronto, 2010. Toronto official plan. http://www.toronto.ca/planning/official_plan/ pdf_chapter1-5/chapters1_5_dec2010.pdf. City of Toronto, 2012. Toronto's location. http://www.toronto.ca/special_events/pme/ location.htm. City of Toronto, 2013a. Congestion pricing in Toronto. http://www.toronto.ca/311/ knowledgebase/61/101000040261.html. City of Toronto, 2013b. Financial district BIA. http://www1.toronto.ca/wps/portal/ contentonly?vgnextoid=2d52ad51a40ea310VgnVCM10000071d60f89RCRD&vgnext channel=8e3032d0b6d1e310VgnVCM10000071d60f89RCRD&vgnextfmt=default. Dachis, B., 2013. Cars, congestion and costs: a new approach to evaluating government infrastructure investment. Commentary No. 385. C.D. Howe Institute, Toronto http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2303900. Data Management Group, 2012. Transportation tomorrow survey. http://www.dmg. utoronto.ca. Delbosc, A., Currie, G., 2011. Using Lorenz curves to assess public transport equity. J. Transp. Geogr. 19, 1252–1259. http://dx.doi.org/10.1016/j.jtrangeo.2011.02.008. Des Rosiers, F., Thériault, M., Voisin, M., Dubé, J., 2010. Does an improved urban bus service affect house values? Int. J. Sustain. Transp. 4, 321–346. http://dx.doi.org/10. 1080/15568310903093362. Di Ciommo, F., Lucas, K., 2014. Evaluating the equity effects of road-pricing in the European urban context — the Madrid metropolitan area. Appl. Geogr. 54, 74–82. http://dx.doi. org/10.1016/j.apgeog.2014.07.015. Ecola, L., Light, T., 2009. Equity and congestion pricing: a review of the evidence. Technical Report 680. RAND Infrastructure, Safety, and Environment, RAND Corporation, Santa Monica, California http://www.rand.org/pubs/technical_reports/TR680.html. Ecola, L., Light, T., 2010. Making congestion pricing equitable. Transp. Res. Rec. 2187, 53–59. http://dx.doi.org/10.3141/2187-08. HDR Corporation, 2008. Costs of road congestion in the Greater Toronto and Hamilton Area; impact and cost benefit analysis of the Metrolinx draft regional transportation plan. Final Report http://www.metrolinx.com/en/regionalplanning/costsofcongestion/ISP_ 08-015_Cost_of_Congestion_report_1128081.pdf.
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Hoffman, K., Berardino, F., Hunter, G., 2012. Congestion pricing applications to manage high temporal demand for public services and their relevance to air space management. Transp. Policy 28, 28–41. http://dx.doi.org/10.1016/j.tranpol.2012.02.004. Karlstrőm, A., Franklin, J.P., 2009. Behavioral adjustments and equity effects of congestion pricing: analysis of morning commutes during the Stockholm trial. Transp. Res. A 43, 283–296. http://dx.doi.org/10.1016/j.tra.2008.09.008. Larsen, J., El-Geneidy, A., Yasmin, F., 2010. Beyond the quarter mile: re-examining travel distances by active transportation. Can. J. Urban Res. 19 (1), 70–88. Levinson, D., 2010. Equity effects of road pricing: a review. Transp. Rev. 30, 33–57. http://dx.doi.org/10.1080/01441640903189304. Metrolinx, 2008. The big move: transforming transportation in the Greater Toronto and Hamilton Area. http://www.metrolinx.com/thebigmove/Docs/big_move/TheBigMove_ 020109.pdf. Miller, E.J., Roorda, M.J., Haider, M., Mohammadian, A., 2004. Empirical analysis of travel and housing expenditures in the Greater Toronto, Canada, Area. Transp. Res. Rec. 1898, 191–201. http://dx.doi.org/10.3141/1898-23. Miller, E.J., Shalaby, A., 2003. Evolution of personal travel in Toronto area and policy implications. J. Urban Plann. Dev. 129, 1–26. http://dx.doi.org/10.1061/(ASCE)07339488(2003)129:1(1). Murray, P., 2012. Congestion pricing for roads: an overview of current best practice, and the economic and transport benefits for government. Public Infrastructure Bulletin 1(8), Article 8 http://epublications.bond.edu.au/pib/vol1/iss8/8/. Vonk Noordegraff, D., Annema, J.A., van Wee, B., 2014a. Policy implementation lessons from six road pricing cases. Transp. Res. A 59, 172–191. http://dx.doi.org/10.1016/j. tra.2013.11.003. Pierce, G., Shoup, D., 2013. Getting the prices right: an evaluation of pricing parking by demand in San Francisco. J. Am. Plan. Assoc. 79, 67–81. http://dx.doi.org/10.1080/ 01944363.2013.787307. RBC, 2012. Booming Toronto condo market does not imply a bubble. RBC Economic Research, Royal Bank of Canada. http://www.rbc.com/economics/market/pdf/ torontocondo.pdf Santos, G., Rojey, L., 2004. Distributional impacts of road pricing: the truth behind the myth. Transportation 31, 21–42. http://dx.doi.org/10.1023/B:PORT.0000007234. 98158.6b. Schweitzer, L., Taylor, B.D., 2008. Just pricing: the distributional effects of congestion pricing and sales tax. Transportation 35, 797–812. http://dx.doi.org/10.1007/ s11116-008-9165-9. Statistics Canada, 2009. Low income cut-offs for 2008 and low income measures for 2007. Income Research Paper Series. Catalogue no. 75F0002M, no. 002 http://www.statcan. gc.ca/pub/75f0002m/75f0002m2009002-eng.pdf. Statistics Canada, 2013. Commuting to work. http://www12.statcan.gc.ca/nhs-enm/2011/ as-sa/99-012-x/99-012-x2011003_1-eng.cfm. Sweet, M.N., Harrison, C.J., Kanaroglou, P.S., 2015. Gridlock in the Greater Toronto Area: its geography and intensity during key periods. Appl. Geogr. 58, 167–178. http://dx.doi. org/10.1016/j.apgeog.2015.01.011. Taylor, B.D., Kalauskas, R., 2010. Addressing equity in political debates over road pricing. Transp. Res. Rec. 2187, 44–52. http://dx.doi.org/10.3141/2187-07. The Record, 2013. 400 highway tolls, GO parking fees could fund Toronto-area transit growth. http://www.therecord.com/news/canada/article/911923-400-highwaytolls-go-parking-fees-could-fund-toronto-area-transit-growth. Transport Canada, 2006. The Cost of Urban Congestion in Canada. Transport Canada, Environmental Affairs, Ottawa (March 22). TRB, 2011. Equity of evolving transportation finance mechanisms. Special Report 303. Transportation Research Board, Washington, D.C http://onlinepubs.trb.org/ onlinepubs/sr/sr303.pdf. Vonk Noordegraff, D., Annema, J.A., van Wee, B., 2014b. Policy implementation lessons from six road pricing cases. Transp. Res. A 59, 172–191. http://dx.doi.org/10.1016/j. tra.2013.11.003.
Contents lists available at ScienceDirect
Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrg
Insights into the fairness of cordon pricing based on origin–destination data Ammar Abulibdeh, Jean Andrey ⁎, Matthew Melnik Department of Geography and Environmental Management, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada
a r t i c l e
i n f o
Article history: Received 5 September 2013 Received in revised form 3 October 2015 Accepted 23 October 2015 Available online xxxx Keywords: Congestion pricing Cordon zone Equity Travel pattern Socio-economic groups Canada
a b s t r a c t Origin–destination data are used to assess the vertical equity effects of a hypothetical road pricing zone in Canada's largest city. The assessment is based on the proportion of morning commuters affected by cordon pricing by virtue of residential location, trip destination, and travel mode. The overall findings for Toronto, Canada show that people with full-time employment and also those from higher income neighborhoods would be most affected by downtown road pricing; and this holds true when the population is broken out by gender, age group, household size and occupational class. The analysis also highlights that professionals, those who live in one- and two-person households, and those who are aged 65 and older would be disproportionately affected; those who work in manufacturing would be less affected. The equity effects of road pricing arise out of the commuting patterns of different sub-populations. © 2015 Elsevier Ltd. All rights reserved.
1. Introduction Congestion pricing is the practice of surcharging users of a service or network during periods of high demand in order to bring demand into better alignment with system capacity. In the context of transportation, congestion pricing has been explored in various contexts, from airports (Hoffman et al., 2012) to parking (Pierce and Shoup, 2013); however, it is most often applied to urban roadways where peak-period demand results in congestive delays. Urban road pricing includes both corridor and areal systems. Corridor pricing can be applied to a single link or to a group of assets, and it ranges in complexity from a simple two-tiered schedule (i.e., congested versus uncongested) through to dynamic toll calculations associated with actual flows (Murray, 2012). The most common form of corridor pricing is high-occupancy toll (HOT) lanes, as implemented in a number of U.S. states. Areal systems focus on specific geographic regions, typically the economic core of large metropolitan areas. These systems can be further divided into cordon systems, whereby a fee is charged for crossing in or out of the bounded area (e.g., Stockholm, Sweden; Singapore), and zonal systems (also referred to as area licensing systems) that charge a daily fee for operating a vehicle within the charge area (e.g., London, England; Trondheim, Norway) (Ecola and Light, 2010; Vonk Noordegraff et al., 2014a, b). While there is clear evidence that congestion pricing leads to travel adjustments that contribute to congestive relief, the implementation of these systems is not without controversy. A primary source of controversy relates to fairness (Taylor and Kalauskas, 2010), which connects ⁎ Corresponding author. E-mail address: [email protected] (J. Andrey).
http://dx.doi.org/10.1016/j.jtrangeo.2015.10.014 0966-6923/© 2015 Elsevier Ltd. All rights reserved.
with how tolls are collected and how revenues are used. Recent reviews of the equity of urban road pricing systems (Santos and Rojey, 2004; Ecola and Light, 2009, 2010; Levinson, 2010; TRB, 2011) provide several observations that are the starting point for the current study. First, equity can be considered from a number of dimensions: vertical equity (also known as social equity, which refers to the extent to which members of different classes are treated similarly), horizontal equity (the extent to which individuals within a class are treated similarly), spatial equity (the extent to which costs and benefits are distributed equally over space), intergenerational equity (the ways in which costs and benefits are distributed over time), and market equity (the extent to which benefits received are proportional to the price paid). The various perspectives on equity provide varied insights into the fairness of congestion pricing. Second, even when focusing on only the vertical dimension of equity, the literature provides sometimes conflicting statements about whether road taxes in general, and urban congestion pricing in particular, disproportionately affect those who are more (or less) able to pay. Some studies provide evidence that road pricing disproportionately benefits wealthy people (Schweitzer and Taylor, 2008; Di Ciommo and Lucas, 2014), thus exaggerating inequalities; others show no clear pattern of burden according to income (Karlstrőm and Franklin, 2009); and some argue that road pricing can be less regressive than other ways of raising funds for transportation infrastructure or even progressive either because a disproportionate number of wealthy commuters are affected by tolls or because the wealthy do not benefit as much from the redistribution of revenues to improved transit services (Levinson, 2010). Third, geography matters. The locations of residences, employment opportunities, and other activity nodes affect origin–destination flows,
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and thus the ways and extent to which different trip makers are impacted by road pricing. Traveler exemptions and discounts are also sometimes geographically based, e.g., residents of the cordon charge zone may be charged differently than others. Geography is also important when considering modal shifts in travel patterns because, by virtue of residential and job locations, different socio-economic groups have differential access to public transit (Delbosc and Currie, 2011). All of these complexities suggest that different contexts and systems are likely to give rise to different fairness issues and outcomes. As argued by Santos and Rojey (2004, 21), “impacts are town specific and depend on where people live, where people work and what mode of transport they use to go to work”. Thus, detailed travel-based studies are required in order to provide informed commentary on the potential equity effects of congestion pricing in any particular jurisdiction. The current study uses data from an origin–destination survey to explore the equity implications of a hypothetical cordon system in the downtown of Canada's largest urban metropolitan area—the Greater Toronto Area. In the North American context, Toronto is both highly congested but also relatively transit-friendly with a vibrant urban core. Various initiatives have been taken or are being discussed in response to deteriorating traffic conditions in Toronto, for example the launch of the Smart Commute program in 2008 and the implementation of high-occupancy vehicle lanes on two feeder highways. While a downtown congestion charge, as well as other initiatives such as carbon pricing and a parking sales tax, have recently been described as being “too expensive, too punitive or too risky” (The Record, 2013), and congestion pricing has never been considered by the City (City of Toronto, 2013a), road pricing systems of various types have been discussed over the past decade (Abdulhai, 2013; Butts and Pennachetti, 2007). Also, despite decentralization of economic activity and population in the GTA, discussions frequently focus on downtown Toronto, which remains an important employment zone and the urban area with the most developed public transit system. This study demonstrates the value of existing data sets for such analyses, and draws conclusions about the relative degree to which congestion pricing would affect different groups in this Canadian city. 2. Study area Canada's largest metropolitan area, the Greater Toronto Area (GTA), includes the City of Toronto and the four regional municipalities that surround it—Halton, Peel, York and Durham. The region, with over 5.5 million residents, has experienced rapid population and business growth over the past half century. Even while policies and programs have been implemented with the intention of managing the growing demand for automobile travel (City of Toronto, 2010; Metrolinx, 2008), there is considerable evidence that the GTA has moved toward increased dependence on the automobile and increased congestion, in large part because of the decentralization of economic activity away from the central business district (Miller and Shalaby, 2003; Miller et al., 2004) and the suburbanization of residential development (in Fig. 1, note the growth in Halton, Peel, York and Durham relative to the Rest of Toronto. Planning District (PD) 1 coincides with Toronto's downtown core). Not surprising then, road congestion is an increasingly serious problem, with associated costs estimated to be at least $2.5 billion (Transport Canada, 2006) and up to $11 billion (Dachis, 2013) annually depending on whether the estimate is based only on time lost or is inclusive of other costs such as increased transportation costs for businesses, the need to pay workers higher wages to compensate for higher commuting costs, and the costs of residents not taking advantage of the benefits of living there because of congestion. Indeed, the average commute time is higher in the GTA than in any other Canadian city (Statistics Canada, 2013), and the ratio of peak-period travel time to free-flow travel time has been estimated to be 1.88, with projections of worsening traffic in the coming years (HDR Corporation, 2008).
Fig. 1. Residential location of GTA workers by year (based on analysis of Transportation Tomorrow Survey data, various years).
3. Methods The current study presents an analysis of commuting patterns as observed in the most recently available regional travel survey in order to explore the equity implications of cordon pricing. This section first discusses the general approach taken, followed by a description of the spatial units of analysis and the data sources and variables. Four key decisions provide the structure for the analysis: 1. While different interpretations of equity are possible, the focus in this study is on vertical or social equity, and is based on whether traveler groups of different economic means would be more or less affected by cordon pricing. Income and employment status are the two variables used to differentiate the various groups. 2. Insight into vertical equity is provided through the analysis of trip patterns. For each traveler group, the proportion of trips that would be affected by a hypothetical cordon pricing system is calculated, and these are compared. The analysis is conducted first in aggregate, and then based on gender, age, household size and occupational class. 3. Congestion charges are typically higher during peak-period travel, which coincides with the morning and afternoon commutes to and from work. Given this, and the format of available data, the decision was made to concentrate on home-based, morning peak-period (6 a.m. to 9 a.m.) trips made by those who are 15 years of age or older. Approximately one-half of the trips in and out of the downtown occur during this three-hour window. 4. Different pricing systems are possible—those that charge for entering and exiting the cordon area, regardless of place of residence; and those that charge non-residents only for entering the cordon zone. The general approach taken in this study is to identify all flows that would be affected by a cordon pricing system, assuming that both residents and non-residents of the cordon zone would be charged when crossing the charge-zone boundary; however, the aggregate flows are separated out so as to provide insight into the relative magnitude of the inbound and outbound flows during morning peak-period travel. Spatially, the GTA is divided into three areas for the purposes of this study (Fig. 2 displays both a regional map and a map of the various planning districts (PDs) that are used to define the three areas). The first is the proposed cordon pricing zone in the City of Toronto, which coincides with the boundaries of PD1 and is defined by Jarvis, Bathurst and Bloor Streets on the east, west and north, respectively, and Lake Ontario on the south. PD1, which covers an area of 18 km2, has the highest employment density of anywhere in Canada. It includes Toronto's financial district (the 12th most influential centre in the world; City of Toronto, 2013b), and is the setting of one of the country's most intense and longstanding condominium booms (RBC, 2012). Cordon counts of traffic
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Fig. 2. Left to right: Greater Toronto Area; and 2006 TTS Survey Area Planning Districts, City of Toronto. Sources (respectively): City of Toronto (2012), Data Management Group (2012).
in 2006, which were conducted by the City of Toronto, show that more than 180,000 vehicles cross the boundaries of PD1 each day. As well, a recent study of gridlock in the Greater Toronto Area highlights the extent to which downtown travel speeds are compromised by congestion (Sweet et al., 2015). The other two spatial units are much larger. The second area is the rest of the City of Toronto, i.e., PD2 to PD16, which together cover approximately 610 km2. The third area covers close to 6500 km2 and includes the municipalities of Halton, Peel, York and Durham—referred to here as the rest of the GTA. Table 1 provides summary data on each of these areas. The main data source is the 2006 regional travel survey, referred to as the Transportation Tomorrow Survey (TTS) (Data Management Group, 2012). TTS data are based on responses from approximately five percent of households, and these sample data are available in expanded form so as to represent the total population of the study area. While a more recent survey was conducted in 2011, these data are not yet available for research purposes. Since the travel data do not provide income information, neighborhood-level trip data from the municipal TTS were merged with household-level income data from the national census available through Statistics Canada. More specifically, average household-level income data were available at the neighborhood level (referred to by Statistics Canada as dissemination areas, each with a population between 400 and 700 people), and these were then combined in order to calculate a weighted average household income for each traffic zone (comprising two or more disseminations, covering a geographic area between 0.02 and 120.27 km2 each). Then each home-based morning trip in the TTS was assigned to the traffic zone of the trip maker's residence, and the trip maker was described in terms of the income level of his/her residential neighborhood as well as his/her individual employment status as indicated in the TTS data. Altogether, of the 11.062 million passenger trips taken each day in the GTA, 2.365 million are made from people's residences during the morning peak period, between 6 a.m. and 9 a.m.
In subsequent analysis, the income data were summarized in four categories: low-income neighborhood (average household income less than $60,000), lower-middle income neighborhood (average household income of $60,000 to $89,999), upper-middle income neighborhood (average household income of $90,000 to $119,999) and high-income neighborhood (average household income of $120,000 or higher). The decision to use $60,000 as the upper limit for low-income neighborhoods is based on Statistics Canada's (2009) low-income cut-off value for large families of seven or more people. The upper limits for the next two categories were based on increments of $30,000. It was intentional to have the lowest and highest income classes relatively small in size in order to focus on the least and most economically advantaged. This categorization singles out those residents who live in neighborhoods with the lowest 7.6% of incomes and the highest 17.6% of incomes; those who reside in the lower-middle and upper-middle income neighborhoods account for 46.6% and 30.8%, respectively. These income groups were then cross-tabulated with two employment status categories (employed full-time versus employed part-time or not employed) to create eight socio-economic groups as summarized in Table 2. 4. Equity analysis 4.1. Overall results Analyzing traffic flows provides a way of estimating the number of trips that would be affected by cordon pricing. As indicated in the bottom row of Table 3, of the 2.365 million home-based trips taken during the morning peak-period, approximately 14,700 (0.6%) are auto trips that exit PD1 en route to other parts of the metropolitan area; and approximately 87,000 (3.7%) are auto trips that have PD1 as a destination but originate from areas outside PD1. As such, a cordon pricing system in downtown Toronto would potentially affect 4.3% of
Table 1 Population and employment characteristics of the study area. Geographic area
Population (2006)
Employment (2006)
Unit of analysis
Planning district (PD) or municipality
People
% change since 1986
Density (per ha)
Jobs
% change since 1986
Density (per ha)
Proposed cordon zone Rest of the City of Toronto Rest of the GTA
PD1, City of Toronto PD2 to PD16 inclusive, City of Toronto Halton Peel York Durham
188,668 2,257,271 422,730 1,119,122 857,521 539,493
67% 23% 60% 94% 149% 70%
49.1 37.0 4.3 8.9 4.1 2.1
105,121 1,057,790 217,772 570,945 440,791 275,774
43% 2% 54% 75% 138% 67%
33.8 17.5 2.2 4.6 2.1 1.1
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Table 2 TTS data for Greater Toronto, 2006. Employment status of trip maker
Income of trip # persons # daily maker's trips residential neighborhood
Employed full-time (FT)
Low Lower-middle Upper-middle High All income levels Employed part-time Low or not employed Lower-middle (not FT) Upper-middle High All income levels Overall total
# home-based daily trips, 6 a.m. to 9 a.m.
135,192 979,081 695,625 390,617 2,065,323
323,755 2,608,598 2,066,243 1,131,143 6,129,739
84,239 631,547 463,201 258,449 1,437,436
3.6% 26.7% 19.6% 10.9% 60.8%
265,034 1,463,817 917,583 533,170 3,179,604
296,270 2,040,267 1,571,413 1,024,335 4,932,285
66,186 399,522 291,414 170,457 927,579
2.8% 16.9% 12.3% 7.2% 39.2%
5,244,927 11,062,024 2,365,015 100.0%
all morning trips in the GTA. This is a conservative estimate, as trips that pass through PD1 are not included. By examining the right-most column of Table 3, one gets a sense as to the socio-economic mix of commuters who would be affected by the toll. More than two-thirds of these commuters would be full-time workers; and nearly one in five would be from the top income category. As would be expected in a multi-nucleated North American city such as Toronto, most (63.5%) morning trips are made by automobile and do not enter or exit the downtown core. Trips by transit (22.6%) and non-motorized modes (9.6%) in combination account for nearly one in three morning trips. There are modal differences across the various socio-economic groups, however. Reliance on transit, cycling, or walking is highest for those from low-income neighborhoods, being 33.1% and 63.6% for full-time workers and others, respectively. For the lower-middle income group, the corresponding percentages are 26.0 and 52.0. The percentages from higher income neighborhoods are lower: 16.2 and 42.1 for upper-middle and 22.6 and 37.5 for high income. Fig. 3 provides a systematic comparison of the equity effects of a cordon pricing system in downtown Toronto on eight socio-economic groups. For those who are employed full-time, trips affected by cordon pricing rise from 3.1% for those in low-income neighborhoods to 5.4% and 5.2% for lower-middle and upper-middle income neighborhoods, to 9.6% for those from high-income areas. For those who are not employed full-time, the equivalent percentages are 1.5, 1.8, 1.3 and 2.5. As such, some groups would be disproportionately affected by cordon pricing in the downtown. However, the progression across the four income groups is not linear. Rather, the percentage of affected trips is similar for those from lower-middle and upper-middle income
Fig. 3. Percentage of morning peak-period trips affected by cordon pricing, by socioeconomic group.
neighborhoods. This occurs despite a comparatively greater reliance on transit and non-motorized travel by the lower-middle income group, and can be explained by the relatively strong attraction of people from lower-middle income neighborhoods to general office and sales jobs in the downtown core. 4.2. Analysis by socio-demographic variables The previous results suggest that a cordon pricing scheme would affect disproportionately more people who are employed full-time and/or from high-income neighborhoods, and disproportionately fewer people who are not employed full-time and/or are from low-income neighborhoods. This next section explores whether these patterns hold true when the trip data are organized by socio-economic variables including gender, age, household size and occupational class. Table 4 illustrates the trends for morning trip makers who are employed full-time. The results by gender and occupational group essentially mirror the original results: the percentage of trips that would be affected is relatively low for those who reside in low-income neighborhoods (b4.0%), higher for those who reside in middle-income groups, and highest (N 8.0%) for those who reside in high-income neighborhoods—with similar progression for females versus males and for those employed in general office, professional, sales or manufacturing. The patterns by household size show similar progression across the four income groups, except for one-person households, in which case, the percentage of trips affected by cordon pricing is between 4.0% and 5.0% for those from low and lower-middle income neighborhoods and then rises to 8.7% and 11.6% for those from upper-middle and high-income neighborhoods, respectively. The breakouts by the age of the traveler also show progression across the four income groups for
Table 3 Morning peak-period auto trips (as driver or passenger). Socio-economic status of trip maker
Flows out of PD1 to
Employment status of trip maker
Income of trip maker's residential neighborhood
Rest of the City of Toronto
Rest of the GTA
Rest of the City of Toronto
Rest of the GTA
Full-time (FT)
Low Lower-middle Upper-middle High Low Lower-middle Upper-middle High
4.4% 36.7% 19.2% 16.3% 2.0% 10.5% 5.8% 5.2% 100.0% 9200
5.3% 40.4% 23.3% 23.9% 0.3% 4.7% 1.8% 0.4% 100.0% 5514
3.1% 37.3% 15.4% 27.3% 1.5% 8.2% 2.4% 4.9% 100.0% 55,883
0.6% 24.2% 39.5% 21.8% 0.0% 4.5% 6.2% 3.2% 100.0% 30,910
Not full-time (not FT)
All groups Morning peak-period auto trip count
Flows into PD1 from
Total flows into and out of PD1
All auto trips in GTA
2.6% 33.5% 23.5% 24.5% 1.0% 7.1% 3.8% 4.2% 100.0% 101,507
3.5% 29.2% 24.2% 12.5% 1.5% 12.0% 10.5% 6.6% 100.0% 1,603,143
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Table 4 Percentage of morning peak-period trips potentially affected by cordon pricing for different sub-populations. Employment status
Persons employed full-time
Neighborhood income
Low
Lower middle
Upper middle
High
Low
Persons employed part-time or not employed Lower middle
Upper middle
High
Female Male Aged 16–19 Aged 20–34 Aged 35–49 Aged 50–64 Aged 65+ 1-person household 2-person household 3-person household 4-person household 5+ person household Occupation general office Occupation professional Occupation sales Occupation manufacturing Not employed
2.1 3.9 0.0 4.3 2.7 2.4 7.3 4.8 3.0 3.0 3.1 2.6 2.9 3.9 3.1 2.0
4.8 5.9 0.4 4.9 5.7 5.7 4.6 4.5 8.0 5.0 4.8 4.0 5.2 7.0 5.0 3.1
4.6 5.6 0.6 4.8 5.5 5.4 8.5 8.7 7.4 5.0 4.1 3.8 4.7 6.4 4.9 2.4
8.4 10.7 0.9 7.0 10.5 10.8 11.7 11.6 11.2 9.4 8.3 8.4 7.2 11.6 8.4 5.1
1.6 1.0 1.5 1.0 1.4 5.2 3.3 2.9 2.1 1.7 1.7 0.9 3.0 2.5 2.1 1.0 1.4
2.3 1.9 1.5 1.2 1.5 4.0 4.2 3.6 3.6 1.8 1.4 1.1 2.7 5.6 2.1 2.0 1.6
1.3 0.8 2.5 0.9 1.3 2.2 1.9 2.7 2.1 1.3 1.1 1.2 2.1 3.1 1.4 0.9 1.2
2.4 2.6 3.0 0.3 2.8 4.6 5.6 4.3 4.8 2.3 1.8 2.1 2.3 6.1 2.4 2.3 2.2
all ages, with the effects of a cordon pricing system being similar for those aged 20–35, 35–49, and 50–64; the percentages of affected trips are low for those from low-income neighborhoods (≤4.3%), higher for those from middle-income neighborhoods (4.8% to 5.7%), and highest for those from high-income neighborhoods (7.0% to 10.8%). The youngest age group does not exactly follow this pattern but constitutes only 1.5% of morning trips. The oldest age group of full-time workers, also accounting for only a small fraction of the total (0.9%), has a comparatively low percentage of trips that would be affected by a downtown cordon pricing system (b1.0%) for all income groups. Both the youngest and oldest age groups are primarily entering, rather than exiting, PD1. For all four income groups, males would be more affected by cordon pricing than females, which seems to be related to the mix of employment opportunities in downtown Toronto. Also, professionals would be more affected than other occupational groups. Amongst the four occupational classes, those employed in manufacturing are least likely to be affected. These patterns are explained by the concentration of financial and other professional services in Toronto's downtown core, and concentration of manufacturing jobs in outlying areas, especially Peel and York Regions. The data also suggest that commuters from one- and two-person households would be more affected overall and those who reside in households of three or more persons would be less affected. This may reflect differences in both locational preferences and modal split for people at different life stages. The patterns for morning travelers who are not employed full time (i.e., working part-time, retired or not employed) differ from those of full-time workers. First, the overall percentage of affected trips is much lower for those who are not employed full time. Those who are not working full time and are from upper-middle income neighborhoods would be less affected by cordon pricing than those from lower or higher income neighborhoods. This is largely because their morning trips tend to start and end in the municipalities outside of the City of Toronto. Those from both lower and higher income neighborhoods would be more affected, because of higher rates of travel into the core. This is true for both genders, for all age groups except for the very youngest group (aged 15 to 19), for all household sizes except for those with five or more people, and for all occupational groups except professionals. The findings about which sub-populations would be disproportionately affected are similar in terms of occupational class and household size. Professionals would be more affected than other occupational groups; and those living in one- and two-person households would be more affected than those in larger households. For those who are not employed full-time, females would be more affected than males—partly
reflecting the number of part-time retail and office positions, as well as non-work attractions, in downtown Toronto. Also, the patterns by age, even when focusing only on the three main groups (aged 20–34, 35– 49 and 50–64) are not the same for full-time and not full-time workers: in the latter case, those aged 50–64 would be disproportionately affected. 4.3. Potential for transit as an alternative to auto trips One limitation of the analysis of origin–destination data is that it only identifies those trips and sub-populations that would be affected by cordon pricing, stopping short of considering behavioral adaptations that might ensue—and how these link with current transit services. Thus, in a follow-up analysis, we explored the adequacy of current transit routing for providing an alternative for auto trips affected by the cordon pricing. The analysis considers only those morning peak-period auto trips that originate within the City of Toronto, are destined for PD1, and are work-related—a data set constituting just over 35% of the total affected trips—but a large enough subset to provide insights into the potential for the current transit to provide an alternate travel mode and also the appropriateness of the proposed cordon boundaries. The starting point for this analysis is that most of the auto trips affected by cordon pricing—at least those from the City itself—should be possible via public transit, since mode switching is one of the objectives and likely outcomes of road pricing. In order to operationalize this, transit availability was explored in a micro-analysis at the traffic zone level using a geographic information system. The first consideration was the ‘transit coverage’ of each of the 625 traffic zones in the City of Toronto, i.e., the percentage of each traffic zone's area that is located within 400 m of a transit stop, by sidewalk, i.e., within an acceptable walking distance (c.f., Des Rosiers et al., 2010; Larsen et al., 2010). While the transit coverage is 100%, or nearly so, in the 45 downtown traffic zones that comprise PD1, the average across the City is only 72%. Accordingly, in some cases, long walking distances from the point of origin to the closest transit station would make mode switching unlikely. Information on transit service coverage from all the different origin zones was then combined for each destination zone in the downtown core. These aggregate data were then used to define various cordon zone boundaries based on different thresholds of transit coverage in the zones of origin. The idea was to identify those destination zones in the downtown that are accessible by public transit from the various zones of origin from which people currently drive. We identified thresholds for transit coverage, and then tallied the trips that originate from traffic zones that meet or exceed this threshold to individual destination
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zones in PD1, and then represented these tallies as percentages of the total auto trips to each destination. If the resulting percentage is high, then this indicates that a large proportion of the destination's auto trips could be made using public transit, suggesting that this traffic zone could be a good candidate for inclusion in the cordon zone. Different transit coverage levels (40%, 50%, 60%, 70% and 80%) were considered. While it is possible to also vary the convertible percentage threshold, here we illustrate the results using 75% as the percentage of convertible trips that would be desirable for the destination zone to be included in the priced area. As shown in Fig. 4, if the transit coverage is set relatively low (i.e., 40% or 50% of the land area in the zones of origin is within a 400 m walk of a transit station), all of the 45 traffic zones in PD1 are attracting trips that could, for the most part, be made by transit. If the transit service threshold is increased to 60%, then three of the 45 traffic zones in the southern part of PD1—all of which are along a major rail and highway corridor and attract a low number of trips—would be excluded. If the transit coverage threshold is increased even more, to 70% or 80%, then more of the destination zones on the edge of downtown would meet the threshold of being able to provide transit connections for at least 75% of present-day auto commuters. Accordingly, one could argue that there is a spatial element of inequity associated with a cordon zone that is too large—given Toronto's current commuting patterns. Still, on the whole, it appears that the choice of PD1 as a cordon zone for exploring equity is appropriate.
5. Conclusions and discussion Using the Greater Toronto Area as a case study, this paper reminds us how “lessons regarding road pricing equity can be learned from both implemented and non-implemented cases” (Vonk Noordegraff et al., 2014a, b). More specifically, the paper provides a detailed analysis of who would potentially be affected by a downtown cordon pricing scheme in Toronto by considering morning commuter flows using origin–destination data. The assessment is based on the proportion of morning commuters affected by cordon pricing by virtue of residential location, trip destination, and travel mode. The overall findings for Toronto, Canada show that people with full-time employment and also those from higher income neighborhoods would be most affected by downtown road pricing; and this holds true when the population is broken out by gender, age group, household size and occupational class. Accordingly, these results suggest that, in aggregate, cordon pricing in Toronto would be progressive in the sense that advantaged socio-economic groups would be more affected than others. The analysis also highlights that professionals, those who live in one- and two-person households, and those aged 65 or older would be disproportionately affected; and those in manufacturing would be less affected. An analysis of commuting flows into the downtown from the rest of the City of Toronto also shows that a large proportion of present-day auto commuters could reach downtown destinations using public transit.
Fig. 4. Downtown Toronto cordon zone possibilities based on transit service. Shaded area is included in cordon scenario with 75% auto conversion rate and 400 m transit coverage of (A) 40%, (B) 50%, (C) 60%, (D) 70%, or (E) 80%.
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