1.1.2.1 Overview

This section covers two main aspects:

• The major part (Sections 1.1.2 - 1.1.4) applies an approach based on demand elasticity methods and estimates to assess the impacts of changes to PT services (principally in terms of fare levels and service levels) on the usage (patronage) of these services, having regard to the timescale over which these impacts will arise.
• The following part (Section 1.1.5) then addresses the cross-modal (modal switching) impacts that are expected to occur consequential on the changes in the PT service characteristics.

The section draws heavily on work undertaken by Douglas Economics and Ian Wallis Associates (2013) as part of the Australian Federal Government’s ATAP Guidelines for Public Transport.² While that work has an Australian focus, it also includes some New Zealand evidence. Based on these studies and our expert advice, in terms of market responses to PT system improvements, the relevant elasticities in the two countries are indistinguishable. Given this, while our estimates given in this section are largely based on Australian evidence, there may be applied with some confidence to the New Zealand transport market.

1.1.2.2 Direct elasticities of demand – primary components

Table 1.1 presents a set of short-run default elasticity estimates for public transport (with a focus on bus services) demand with respect to fares, service levels and in-vehicle time.

1.1.4.1 Valuation of vehicle quality and stop/station quality

Market surveys using a rating-based approach been undertaken to develop passenger valuations for both vehicle/on-board quality aspects and stop/station aspects. This market research involved three large-scale surveys undertaken in NZ (Auckland, Christchurch, Wellington), NSW and Victoria in 2012-14. In total, the vehicle quality surveys resulted in some 26,000 completed questionnaires and the stop/station surveys some 29,000 responses.

The three studies use the same hybrid approach involving stated preference and rating questionnaires and transforming their results into passenger willingness-to-pay (per trip and/or per km travelled) for higher quality relative to lower quality vehicle and stop features.

The vehicle quality surveys estimated user (perceived) benefits of up to about 4 IVT minutes per trip (eg for Wellington, for the new Matangi units relative to the old Ganz Mavag units). For rail stations, a major station upgrade would result in a perceived benefit of up to about 4 IVT per person trip relative to a station which had not been substantially refurbished for many years.

Mode Specific Constants (MSCs) measure the residual difference in modal quality after differences in travel convenience (notably access/egress time, in-vehicle time, service frequency, transfer, crowding, reliability and fare) have been allowed for. They are often used in multi-modal studies such as forecasting the patronage for new services.

1.1.7.1 New Zealand evidence

Table 1.5 summarises previous econometric studies on petrol consumption elasticities in New Zealand.

1.1.7.2 Australian evidence

• Sterner et al’s short-run estimate is supported by Harding’s survey (2001). His elasticity estimate of -0.05 is based on household consumption only, and may underestimate the total impact, but is still more robust than many time-series estimates

Table 1.6 summarises econometric studies of petrol consumption elasticities in Australia.

• One of the most robust and transparent studies is that by Sterner et al. (1992). This fitted a partial adjustment model to Australian data from 1960 to 1985 and employed valid tests for autocorrelation. It also fitted a number of alternative models which all produced long-run estimates in the -0.1 to -0.2 range.
• These long-run estimates are supported by research by Samimi (1995), who estimated a long-run elasticity of -0.13 from data for the period 1980-1993. (Samimi’s research covered both petrol and diesel, so the petrol price elasticity would be expected to be rather larger than this estimate.)
• Sterner et al’s short-run estimate is supported by Harding’s survey (2001). His elasticity estimate of -0.05 is based on household consumption only, and may underestimate the total impact, but is still more robust than many time-series estimates

1.1.7.3 International evidence

Sterner et al. (1992) produced estimates of short-run and long-run elasticities for 21 OECD countries. These estimates are useful for comparisons across countries because they were produced using the same model (a partial adjustment model) and with data covering the same period (1960-1985). Their estimates aggregated by countries or regions of particular interest are summarised in Table 1.7.

1.1.8.1 New Zealand evidence

The Kennedy and Wallis (2007) study appears to be the first study in New Zealand to attempt estimates of traffic volume elasticities with respect to petrol prices: no previous studies have been identified. Most likely, the absence of previous studies into traffic volume elasticities reflects the lack of data available in earlier years.

1.1.8.2 Australian evidence

Relevant Australian evidence on traffic volume (or VKT) elasticities also appears to be very limited, and we have not been able to identify any studies more recent than Luk & Hepburn’s (1993) review. That review relied largely on the work of Hensher and colleagues in the early 1980s, which collected data from a four-wave panel of Sydney area households from 1981 onwards. Luk & Hepburn’s conclusions were that VKT elasticities were around -0.10 in the short-run and -0.26 in the long-run. However, to the extent that these were derived from a sample of Sydney households, we would caution to what extent they would be representative of Australia overall.

1.1.8.3 International evidence

A reasonably significant body of international evidence on traffic volume (VKT) price elasticities exists, although this is not as substantial as that for consumption elasticities. Table 1.8 summarises this evidence, as drawn from major review studies over the last 15 years. The mean elasticity values given here are remarkably consistent, being around -0.15 in the short-run and around -0.30 in the long-run. Unfortunately, without an extensive in-depth appraisal. it is not possible to assess the quality of the original studies making up these mean values, nor to examine differences between countries.

1.1.8.4 Comparisons and conclusions

We would have anticipated (along with most other researchers in this field) a systematic relationship between traffic volume elasticities and petrol consumption elasticities. In the short-run, traffic volume elasticities would be expected to be somewhat lower than consumption elasticities because behavioural adaptations other than reduced mileage are possible even in the short-run, through changes in driving styles and speed, use of smaller cars in multi-car households etc. In the longer run, further adaptations would be expected in terms of changes in vehicle size and energy efficiency. Thus long-run traffic volume elasticities would be expected to be substantially lower than consumption elasticities: over the longer run, defences between these two elasticity values would be expected to be partly the result of more fuel-efficient vehicles on the market (and being used), and partly the result of changes in driving styles in response to higher prices (in real terms).

These expected relationships appear to be present in the international data. Comparing the results from Table 1.7 and Table 1.8, the typical short-run VKT elasticity for US, Canada and Europe (around -0.15) is somewhat below the consumption elasticity (around –0.24); whereas the long-run VKT elasticities (around -0.30) are markedly lower than the corresponding averaged consumption elasticities (around -1.00). Goodwin’s reviews of 1992 and 2004 gave a broadly similar pattern of results.

Luk & Hepburn’s (1993) review of Australian elasticity evidence also produced similar results (although this may have been a lucky outcome given the few and disparate data sources used). Their best estimates of VKT elasticities (based heavily on Sydney data) were -0.10 in the short-run, -0.26 in the long-run; while their best estimates of consumption elasticities were -0.12 and -0.50.

The results from the work by Kennedy and Wallis (2007) do not exhibit this pattern. In the short-run (within 1 year), their best estimate traffic volume elasticity was around -0.20 to -0.22, while petrol consumption elasticity was around -0.15. Similarly, in the medium-run (up to 2 years), our traffic volume elasticity was around -0.30, while our petrol consumption elasticity was around -0.20.

The reasons behind these apparently anomalous results are not fully clear. The authors conjecture that this inconsistency may have been due to the different time periods used for their analysis. Originally, the consumption analyses covered only the period 1974-2006, while the traffic volume analyses covered the much shorter period 2002- 2006. However, the 12-month annual differences model using the counts from 1999-2006 also produced a short-run consumption elasticity of around -0.15.

Therefore, alternative explanations for the apparent inconsistency were sought:

• The traffic volume analyses cover only state highway traffic, and thus seem likely to over-represent longer distance journeys (relative to traffic movements overall). Our hypothesis is that longer distance journeys are more price-elastic than the market as a whole because petrol costs comprise a larger proportion of total travel costs for longer trips, and many such trips may be of a discretionary nature. Hence our traffic volume elasticity estimates would be higher than the total market estimates.
• The traffic count analyses cover only vehicles up to 5.5 metres length. A number of vehicles that run on petrol may be in the 5.5 to 11 metres range and yet are not as responsive to petrol prices (being primarily commercial vehicles). Therefore, the analyses undertaken may over-estimate the total traffic volume elasticity with respect to petrol price.

Kennedy and Wallis’s (2007) judgement is that the first of these two explanations is likely to be the main cause of the unexpected traffic volume elasticity results. The second explanation appears unlikely to be a major cause, as the number of petrol vehicles over 5.5 metres length is very small relative to the number of petrol vehicles under 5.5 metres. Further investigation of this issue by analysing count data for local roads may be worthwhile, if a consistent time series of such data for a number of sites could be obtained.

1.1.9.1 Overview

Comparisons of public transport cross-elasticity values in different situations should be used with considerable caution. Compared with direct elasticities, cross-elasticities are generally more difficult to measure; are sensitive to the ‘base’ market shares of the two modes; and are not as readily transferable between different cities and situations (I. Wallis, 2004). Cross-elasticities tend to be higher in situations where the public transport mode share is low, as a given percentage change in car travel will represent a higher percentage change in public transport trips.

While the following sections (1.1.9.2 - 1.1.9.5) discuss cross-elasticity results for public transport demand with respect to fuel prices across different countries, cities and situations, the findings should be interpreted with caution given the reservations about the transferability of results.

1.1.9.2 New Zealand evidence

The previous New Zealand evidence on public transport cross-elasticities with respect to petrol prices is summarised in Table 1.9.

1.1.9.3 Australian evidence

The literature provides a wide array of differing cross-elasticity estimates for Australia, as shown in Table 1.10.

This evidence indicates that urban rail passenger patronage (both in Sydney and Melbourne) is more responsive to petrol prices than other modes of public transport, with cross-elasticities for rail services from +0.48 to +0.80. Several characteristics of rail transport make it more amenable to mode shift, such as:

• Rail transport is generally used for longer distance trips, while bus and tram have a higher proportion of short-distance trips. Commuters who travel long distances seem more likely to shift to public transport (ie rail) than commuters who need to travel short distances only, as the petrol costs are much more significant on longer trips.
• Rail transport may be more appealing to car users than are bus or tram; hence a shift from car to rail is more likely than a shift from car to bus or tram.

1.1.9.4 International evidence

The international evidence generally suggests that the New Zealand estimates are low to average by international standards. Goodwin (1992) reviewed three studies examining cross-price elasticities: Bland (1984), Doi & Allen (1986), and Wang & Skinner (1984). He concluded that the average effect of petrol prices on public transport use is represented by an elasticity of +0.34.

Since then, other research on cross-elasticities has become available, including the estimates shown in Table 1.11. More discussion of research on cross-elasticities is provided in Wallis (2004).

1.1.9.5 Comparisons and conclusions

The previous cross-elasticity estimates in New Zealand are reasonably similar, ranging from +0.07 to +0.40. In contrast, the cross-elasticity estimates from Australia are more variable, ranging from +0.01 to +0.8. However, the high end of this range can be attributed to rail patronage which, in New Zealand, is not as dominant.

Australia exhibits a similar range if the scope is limited to the more general patronage modes (of bus, tram); the range is then 0.0 to +0.2. However, two potential problems with the cross-elasticity estimates discussed above are as follows:

• The cross-elasticities produced using time series data (including the evidence provided in this report) could be affected by patronage trends in the data that are unique to each region or mode.
• To some extent, this problem can be circumvented with examination of estimates produced using cross-sectional or stated preference studies: these studies suggest cross-elasticities in the +0.07 to +0.20 range.

The cross-elasticities estimated for a particular city and mode cannot be transferred to a different situation, especially if the mode shares differ considerably.

1.1.10.1 What conclusions can be drawn

Table 1.12 draws together and summarises all the evidence presented previously, providing traffic volume and petrol consumption elasticities from our New Zealand analyses, previous New Zealand analyses, Australian analyses, and international analyses.

Focusing particularly on the short-run results, our findings are that:

• In terms of traffic volume elasticities, our New Zealand estimates (based on recent data, 2002-2006) are higher than typical Australian and international values. As noted earlier, these VKT elasticities appear to be inconsistent with consumption elasticities and may only be representative of the impact of petrol prices on state highway traffic.
• In terms of consumption elasticities, our New Zealand estimates (based on a long- term data series, 1974-2006) are on the high side of previous New Zealand and Australian studies, slightly lower than the US/Canadian estimates, and substantially lower than the European average (but above the UK estimates, at least in the short run).

1.1.10.2 What conclusions can be drawn

Drawing on all the results presented in this report, for future policy analysis purposes we would suggest that the following elasticity values are the most appropriate for

New Zealand:

• Fuel consumption elasticities:

Overall: short-run -0.15, long-run -0.30.

• VKT elasticities:

Overall: short-run (<1 year) -0.12, long-run (5+ years) -0.24.

These estimates are based particularly on results from Kennedy & Wallis (2007) plus previous New Zealand (and Australian) studies, but also attempt to reflect the prevailing international relationships between VKT and consumption elasticities, and between long-run and short-run estimates. As is evident from Table 0.1, these recommended values are similar to the prevailing estimates found in other New Zealand and Australian studies.

The justification for the fuel consumption elasticities is as follows:

• A short-run (c 1 year) elasticity of -0.15 is suggested on the following grounds:

  • This is the estimate produced by model A (adjusted for the ‘carless days’ policy) which we have judged to be the preferred model based on significance of coefficients and investigation of the residuals.

  • This estimate is consistent with previous New Zealand studies (Section 3.2.1) which generally found that the short-run elasticity is in the -0.03 to -0.20 range.

• A medium-run (c 2 years) elasticity of -0.20 is recommended because a range of models fitted for this research produced around this figure after 2 years.

• A long-run elasticity of -0.30 is suggested, based on the following two arguments:

  • The international literature generally finds that long-run effect is around 2.5 times the short-run effect. For example, Goodwin et al. (2004) found that the average ratio is 2.56. Graham & Glaister (2004) found that the average ratio is 3.1. We have assumed that the long-run effect is around 2 times the short-run effect because previous New Zealand studies (see Section 3.1.1) have generally found lower ratios; also, the research undertaken for this report failed to detect significant long-run effects beyond two years.

  • A long-run elasticity higher than -0.30 would seem unreasonable given that none of the New Zealand studies found a long-run elasticity above -0.19. Similarly, most Australian studies estimated a long-run elasticity no higher than -0.22 (with the sole exception of Donnelly (1984) who used older data and an unexamined econometric method).

The vehicle traffic elasticities produced by Kennedy & Wallis (2007) appear to provide statistically robust estimates of the impacts of petrol prices on state highway (largely longer distance) vehicle traffic for cars and vans. The model used to produce the elasticities seemed statistically robust and produced plausible estimates. Furthermore, similar estimates were produced using four separate datasets.

However, the inconsistency (between the vehicle traffic elasticities and the petrol consumption elasticities) suggest that the vehicle traffic elasticities represent the impact of petrol prices on state highway VKT but not on total national VKT.

The resolution to this inconsistency, as suggested in the absence of further information, is to discount the petrol consumption elasticities produced by the 2007 research, to produce the following estimates for total VKT elasticities:

• A short-run (c 1 year) VKT elasticity of -0.12;
• A medium-run (c 2 years) VKT elasticity of -0.15;
• A long-run (c 5 years) VKT elasticity of -0.20.

These suggested values are acknowledged to be somewhat subjective, and are drawn from petrol consumption elasticities and hypotheses of household responses over time to petrol price changes.

The suggested short-run and medium-run values are based on the petrol consumption elasticities, which have been judged to be relatively good estimates:

• The short-run VKT elasticity of -0.12 is slightly less than the petrol consumption elasticity of -0.14, representing a presumption that the main response to petrol prices is reduced VKT by households.
• The medium-run VKT elasticity of -0.15 is less than the petrol consumption elasticity of

-0.20, with a greater difference between the VKT response and the consumption response because households may respond in the medium run with more efficient driving patterns and purchase of more fuel-efficient vehicles

The suggested long-run values are based on an assumed ratio between short-run and long-run VKT elasticities of around +1.7. In addition, the difference between the VKT response and the consumption response becomes greater in the long run, consistent with the purchase of more fuel-efficient vehicles in the long run.

1.1.10.3 ‘Best estimates’ for public transport cross-elasticities

The weight of evidence (from previous NZ studies and various international studies) indicates that:

• Typical New Zealand values, largely based on Wellington evidence, average around 0.1 to 0.2.
• The limited evidence (from New Zealand and elsewhere) is that peak cross-elasticities are in the order of 2 to 3 times off-peak elasticities. In our view, this difference is likely to reflect: (i) the longer trip distances (on average) or urban PT trips than off peak trips; and (ii) the relatively low proportion that petrol represents of the total GC for most off-peak trips, especially when the greater ‘convenience’ of car use is taken into account.
• The evidence (from Australian and international sources) suggests that elasticities are significantly higher than average for longer distance urban trips, especially by rail, and lower than average for shorter distance, largely bus, trips
• We investigated the cross-elasticities further in NZTA research report 714.

1.3.1.1 Overview

This section of the report focuses on the impacts of area-wide parking pricing policies on travel behaviour. However, such area-wide schemes are not commonly implemented and hence the directly relevant evidence is limited: most parking pricing schemes apply only to particular parking sub-sectors or sometimes individual sites, rather than comprehensively over an area. Where only certain sub-sectors or sites are affected, a common response is to park elsewhere in the area rather than to change travel mode. However, in recent years several cities in Australia and Europe have implemented area-wide parking schemes, in some cases focused on workplace parking for employees, in other cases relating to all publicly available spaces within the CBD or similar areas. We provide brief comments on such schemes in this section of the report (although this is not the focus).

In interpreting the results from the range of studies of parking pricing impacts, the following factors should be borne in mind:

• Parking policies are normally instituted as part of a package of traffic management/restraint measures rather than in isolation. This can make it very difficult to determine the separate effects of the different components of the parking (and associated) policies.
• Results are likely to be case-specific, depending on the range and quality of alternatives to the car parks which have their prices changed.
• Effects are often measured in different ways at different locations, making comparison of results very difficult. Typical monitoring outputs include:
• Changes in use at particular sites
• Change in the number of solo drivers
• Changes in the total number of car trips.

Moreover, the effects of parking charges on travel demand will be limited in circumstances where:

• A low proportion of car users pay for parking;
• Car travel is largely through-travel, with most car travellers having non-city/town centre destinations; and/or
• Employers subsidise or reimburse the parking costs of their employees.

1.3.1.2 Introductory

Much of the literature on the effects of parking charges on car travel demand relates to CBD commuters, with very limited evidence on non-commuter travel demand. The following provides details of the international evidence available, which can generally be divided into two main groups:

• North American literature, which focuses on total car travel demand (or solo driver demand) by commuters; and
• European (including UK) and Australasian (rather limited) data, which mostly relates to parking demand at a site or area.

Much of the material in this section is taken from the VTPI (Todd Litman) report ‘Understanding Transport Demands and Elasticities’ (2021b).

1.3.1.3 North American evidence

The North American literature is most relevant to this review since it focuses on total car travel demand. The NZ/Australian literature on car user responses to parking charges is very limited.

Motorists tend to be particularly sensitive to parking price because it is such a direct charge. Compared with other out-of-pocket expenses, parking fees are found to have a greater effect on vehicle trips, typically by a factor of 1.5 to 2.0 (USEPA, 1998). For example, a $1.00 per trip parking charge is likely to cause the same reduction in vehicle travel as a fuel price increase averaging $1.50 to $2.00 per trip.

Concas and Nayak (2012); Lehner and Peer (2019), Spears, Boarnet and Handy(2010), and Vaca and Kuzmyak (2003) summarize various studies of parking price impacts on travel behavior, taking into account demographic factors and travel conditions, and type of trip; including changes in the magnitude and structure of prices, elimination of employee parking subsidies, rideshare vehicle parking discounts and park-and-ride facility pricing.

Kuzmyak, Weinberger and Levinson (2007) describe how parking supply affects parking and travel demand, but this may actually reflect price impacts (reduced supply increases prices). These studies indicate that the elasticity of vehicle trips with regard to parking prices is typically –0.1 to –0.3, with significant variation depending on demographic, geographic, travel choice and trip characteristics. A study of downtown parking meter price increases,

Clinch and Kelly (2009) find that the elasticity of parking frequency is smaller (–0.11) than the elasticity of vehicle duration (-0.20), indicating that some motorists respond to higher fees by reducing how long they stay.

Frank, et al. (2011) evaluated the impacts of urban design factors on vehicle travel and carbon emissions. They found that increasing parking fees from approximately $0.28 to $1.19 per hour (50th to 75th percentile) reduced VMT 11.5% and emissions 9.9%.

Barla, et al. (2012) measure the impact of travel time, financial costs and attitudes on commute mode share in Laval University. They find that elasticities with respect to time and cost parameters are relatively low, but their impacts are synergist, so combining several policy interventions is most effective at reducing automobile trips.

Farrell, O’Mahony and Caulfield (2006) survey university employees to determine how they would respond to parking pricing and cash out. They found that most employees would reduce their automobile trips in response to a €5 daily fee, and one third would reduce their trips in response to parking cash out.

Washbrook, Haider and Jaccard (2006) surveyed Vancouver, British Columbia region commuters to determine how they would respond to various incentives. Table 24 shows how various road and parking fees would affect their drive alone rates. For example, with unpriced roads and parking, 83% of commuters drive alone, but this declines to 75% if there is a CA$1.00 ($0.64 US) parking charge and a CA$1.00 daily road toll. A $9.00 ($5.72 US) parking fee and a $9.00 toll together reduce automobile commute mode share to 17%, which equals a total reduction in drive alone demand of 80%.

1.3.1.4 European and Australasian evidence

Hensher and King (2001) model the price elasticity of CBD parking and predict how an increase in parking prices in one location will shift cars to park at other locations and drivers to public transit (Table 1.18). Harvey (1994) finds that airport parking prices range from -0.1 for less than a day to -2.0 for greater than 8 days.

Table 1.18 shows elasticities and cross-elasticities for changes in parking prices at various Central Business District (CBD) locations. For example, a 10% increase in prices at preferred CBD parking locations will cause a 5.41% reduction in demand there, a 3.63% increase in Park & Ride trips, a 2.91% increase in Public Transit trips and a 4.69% reduction in total CBD trips.

1.3.1.5 Results from a meta-analysis of parking price elasticities

Error! Reference source not found. provides extracts from the most recent and most extensive meta-analysis of parking price elasticities that appears to exist worldwide. It focuses on regression modelling applied to some 50 previous parking elasticity studies (Lehner & Peer, 2019).

The 50 studies analysed date back as far as 1974, but with the great majority being undertaken since year 2000. The meta-analyses established three best estimate elasticity measures (where appropriate data were available):

• EPO - price elasticity of parking space occupancy
• EPD - price elasticity of parking dwell time
• EPV - price elasticity of volume.

The paper comments that: “Elasticities depend on the availability of mode and parking choice alternatives. The elasticities are highest (in absolute terms) when drivers can switch to mode and parking alternatives, and lowest when neither of these alternatives exists. This pattern bowls for both commuters as well as non-commuters.”

1.3.1.6 Additional comments and conclusions

Of the evidence available, there is a considerable range of results and whether a study was referring to parking demand in a site or area, or to total car travel demand was not always clear. Moreover, it was often unclear whether these estimates were SR or LR, although most appear to be SR.

In relation to Australian evidence, Chambers & Ker (1990) concluded that typical parking price elasticities range from -0.20 to -0.40, which is consistent with the OTPP (SA) estimate of -0.30 (OTTP 1994, cited in Bray (1995)). This compares to substantially lower elasticity estimates by Commeignes (1991) and Shepherd (1972) in their Sydney studies. On the other hand, as shown in the previous section, Hensher & King (2001) estimated higher values for parking in the ‘preferred’ CBD, at around -0.5 or greater. We would not expect NZ elasticities to differ significantly from Australian values for comparable situations.

As noted, most of the evidence available related to SR changes. It is not clear whether longer-run effects will differ substantially from these and, if so, in which direction (larger or smaller).

It would be naive to suggest that any particular parking price elasticity could be used with confidence in analysing parking price policies. The elasticity will depend on the nature and type of parking spaces affected by a particular price change and the opportunities for using alternative parking facilities. These opportunities will differ by time of day and the elasticities themselves would differ for, say, shoppers as opposed to commuters. They would also depend on the physical measures adopted for controlling parking spaces in addition to the price charged.

A useful, high-level, segmentation or the parking market is firstly between CBD and non-CBD areas, and secondly between long-stay parking demand (principally commuters) and shorter-stay demand (principally shopping, personal business etc). For strategic assessment purposes in major Australasian urban centres, we suggest a ‘best estimate’ elasticity of commuter car travel with respect to CBD parking changes of -0.30: this should be applied to that segment of the market affected by such charges. This value is applicable only to CBD trips as parking restraint through pricing is not widely applied elsewhere and there is much less evidence available.

For situations where priced parking is being considered to replace free parking, its meaningless to measure percentage increases from a zero price. For such situations, it is typically found that drive-alone commuting reduces by 10-30%, particularly if implemented with improvements in transit service and rideshare programs and other TDM strategies.

Parking fees affect trip destinations as well as vehicle use. An increase in parking prices can reduce use of parking facilities at a particular location, but this may simply shift vehicle travel to other locations. Increased parking prices may result in spillover parking problems, as motorists find nearby places to park for free illegally. However, if parking prices increase throughout an area, if there is effective enforcement of parking regulations and there are good travel alternatives, parking price increases can reduce total vehicle travel. For some types of trips, pricing can affect parking duration, such as how long shoppers stay at a store.

1.3.2.1 International evidence

Very limited evidence is available internationally on the effect of changes in parking charges on public transport use. This cross-modal effect is best represented in terms of the proportion of people deterred from car usage, by high(er) parking prices and/or limited availability of parking space, who switch to public transport; and this has been reported where possible. However, many studies report cross-elasticity values, so these have also been provided where available. Results were found to be wide-ranging and differed substantially between studies. Key findings included:

• Kocur et al. (1982) conducted a mode choice experiment in 1980 which examined bus demand with respect to parking prices across various US urban centres and found values ranged from 0.13 to 0.19.
• Brown (1972) (cited in various authors estimated a value of 0.30 for Vancouver, from SP studies.
• Commeignes (1991) examined the effects of a $1/day surcharge for AM peak parking in Madison (US) which resulted in a 5% to 8% increase in car commuters switching to bus or park and ride.
• In Europe, Gantvoot (1984) examined the closure of a car park in The Hague town centre and found that 78% of the suppressed car drivers (ie 19%) switched to public transport. This figure translates into an overall shift to public transport of 14% of the previous car park users.
• In Oxford, parking restraint in the CBD resulted in a 10% shift from car to public transport (TRRL, 1980).
• A ‘before and after’ study in Munich-Lebel found that the introduction of residential parking permits resulted in an increase in public transport mode share for employees from 39.7% to 47.3%, ie by some 19% (Topp 1991, cited in Halcrow Fox (1995)).
• An SP study in Bristol (AMPT & Jones, 1992) into the effects of banning cars in the city centre, found that 50% of those affected stated that they would travel by bus all the way, 28% would drive to the bus or train.
• A stated response study in Dublin by Halcrow Fox (1993) found 40% of respondents stated they would change mode following a 40% increase in parking charges, while 68% stated they would change mode following an 80% increase in parking charges.
• Halcrow Fox (1993) suggests that the public transport diversion rate in response to increased parking charges is likely to be as high as 50% to 75%, particularly in central areas where public transport is likely to provide the ‘next best’ alternative.

1.3.2.2 Australasian evidence

Very few studies in Australia and New Zealand have quantified the public transport demand implications of increased parking charges. The available evidence relates primarily to travel to/from CBD destinations, principally by commuters. Generally, the diversion to public transport appears low, although this is difficult to quantify. In Adelaide, for example, Urban Transport Working Group (1995) suggested that a 50% increase in CBD car parking charges could result in a 15% decline in car travel, and that public transport travel could increase 2%.

While not providing any quantified cross-elasticity or diversion rate values, analyses of existing mode choice in Melbourne (based on detailed travel survey data) do provide guidance on how modal choice to different destination categories varies with the availability and pricing of parking spaces. The most relevant findings are summarised as follows:

• Distance of the trip destination from the regional (Melbourne) CBD is the strongest single predictor of mode shares - the greater the distance, the lower the PT mode share.
• Paid parking is effective at restraining private transport mode share (in favour of PT) at university campuses, with a particularly strong effect where high-quality PT services are provided.
• Evidence indicates that charging for parking results in reduced private transport mode shares at larger activity centres.

The most promising destination types for which PT improvements are likely to significantly increase PT mode shares (at the expense of cars) are larger activity centres, universities and other higher education establishments, and job-dense central/inner city areas.¹²

1.3.2.3 Conclusions

As previously discussed, cross-elasticity estimates are much less transferable between centres than are direct estimates. We therefore would not recommend a specific cross-elasticity value for use in Australasian urban areas. Instead, we have focused on the relevant ‘diversion rates’, ie the proportion of people deterred from car use by increased parking charges who would switch to public transport.

Based on the very limited evidence available, we suggest the following default assumptions be adopted for use in Australasian urban areas on the proportion of travelers deterred from car use by parking charges who would shift to public transport.

• Regional CBD - commuters (peak periods): 75%
• Regional CBD - others (off-peak periods): 50%
• Suburban CBD - commuters (peak periods): 50%
• Other areas - not addressed, but likely to be much lower.

These diversion rates are high relative to those estimated for other travel time/cost components. This is consistent with the affected trips being to/from CBD areas, which have relatively good public transport accessibility.

These rates are essentially SR diversion rates. In the LR, we anticipate that the rates are likely to be somewhat lower as a wider range of alternative responses becomes feasible (eg changing employment location).