Here is one more of myriad Bad News examples of public officials getting it very very wrong. In this case Montgomery County council staff has recommended cutting the county’s CarShare program in half. (Montgomery County is in state of Maryland, situated just north of Washington, D.C.)
Will they ever learn? No, not unless we all help them. Which of course is why we are here. (Comments as always warmly welcome.)
Montgomery County officials want to scale back some of the county’s ambitious efforts to reduce the county’s greenhouse gas emissions in order to help bridge a budget gap of more than $550 million.
The county set a goal last year of reducing greenhouse gas emissions by 80 percent by 2050, and has instituted a number of programs to help meet that goal. But with the county deep in the red, officials now propose to switch from biodiesel fuel to low-sulfur diesel, reduce the number of cars available for a county carpool pilot program and cut funds to buy equipment for telecommuting workers.
Council staff recommended cutting almost $100,000 that County Executive Ike Leggett has proposed to spend on laptops, BlackBerry devices and network hardware so that 25 county employees can telecommute as part of a program designed to cut commutes and the greenhouse gases that come with them.
Senior legislative analyst Keith Levchenko wrote in a memo to the council that he was “skeptical” of the value of spending so much money on the program, because most employees already have a computer and phone at home and might only telecommute a few times a week.
“It is not clear that this is the best investment of dollars to reduce greenhouse gas emissions,” Levchenko wrote.
County council staff has also recommended cutting the county’s “CarShare” program in half. The pilot program started in January, with the county making 28 cars available for county employees to share at a cost to the county of $1,100 per car a month.
Through April 14 the program was only used seven times, for a total of 27.25 hours, according to county council staff. Reducing the number of cars available for the program from 28 to 14 would save the county $184,000 a year, staff said.
The county’s motor pool said it has stopped using biodiesel fuel in some of its vechicles to save money, and because there have been quality issues with the fuel, which is a mix of diesel and discarded vegetable oil. County officials said the low-sulfur fuel they now use instead is on average 8 cents a gallon cheaper, and the switch will save the county $250,000 next fiscal year.
* * * *
It’s not easy being green
Environmental programs being recommended for cuts:
• Biodiesel fuel: County vehicles would return to low-sulfur diesel.
• Telecommuting: Council staff recommends cutting $100,000 for equipment that would allow 25 employees to telecommute.
• CarShare program: Staff recommends cutting this new program in half. - - -
Should a city, already a major cycling capital, with more than one hundred thousand bikes out in its streets and a ten percent modal share for bike transport, even bother to look at the possibility of a Pubic Bicycle System? Unnecessary, redundant, counter-productive? Useful, synergistic? World Streets traveled to Strasbourg in the east of France to look around and find out how they feel about it. To get a feel for their thinking on this I just spent four fascinating days observing and working on the New Mobility Agenda in Strasbourg, an especially attractive city of some 250,000 located in the east of France and nestled right on the border of Germany. In addition to a series of highly informative and challenging interviews and conversations with a fair spectrum of local transportation experts, policymakers and operators, I had a great opportunity to find my way around the city and its surrounding region through an intense combination of walking, cycling, bus, boat, their excellent tramway system, and even taxis on a couple occasions when I got stuck. Watching and talking to people just about nonstop as I made my way around the central area, but also reaching out into the extended metropolitan area were an additional half-million people live in a combination of small clusters and a local version of suburban sprawl.
In transportation terms, Strasbourg is far from being just one more city. It has created a highly innovative alternative transportation system of many layers which can legitimately be considered a model for others. And that was precisely why I was there, to look and to learn. I intend to write up my findings in a series of articles to appear here looking at key points in their strategy and competence, but today is the first step I would like to share with you a few things I learned while I was there about public bicycles from a somewhat unusual perspective.
Strasbourg enjoys a cycling situation that most cities can only dream about. It is the biking capital of France. Which makes it especially interesting to consider what happens when a city, that already has something like 140,000 bikes, more than 500 km of protected cycling provision that there carefully built up over the years, and an impressive 10% modal share for cycling, starts to think about what might be the place of a Public Bicycle System in their city.
The conversations I had with a fair cross-section of people, agencies and groups revealed that this is indeed something at which they are starting to look quite seriously. And while it is not at the absolute top of the list of their 2009 transportation priorities, nonetheless will be giving it attention in the months immediately ahead.
And there is, in my view at least, plenty of room for public bikes even in a city like Strasbourg.
What I was able to observe is that there is a basic cycling pattern in the city, as in many others, in which citizens use their own bikes in very specific ways. There is of course a fair amount of leisure cycling, but most of the usage is result of people hopping on their bike at a specific time, for specific purpose, to go to a specific place. It is by and large "organized transportation", albeit self-organized. Another characteristic of these trips is that they generally tend to take place along very specific, usually very well-known routes. Again, organized transportation.
But when we step back and consider how public bicycles are used in those several handfuls of cities in which they have become a real transportation alternatives for daily use, we observe a quite different pattern. The trips tend to be less routine, more incidental, last-minute, and even optional. Closer to the way in which many people use their cars in fact, as opposed to public transportation: a two wheeled, low-cost, high-efficiency, zero carbon version of DRT, demand responsive transport. Hard to beat once you think about it like that.
And this to my mind is where we start to see that even in a city as well equipped for cycle as Strasbourg, there are opportunities for public bikes as well. I look forward to being able to share with you their findings and results in the months ahead, because what they learn is going to be valuable for us all. In the meantime I invite your comments here, which I will be pleased to share on a selective basis with the Strasbourg team. But I guess the only way in which you can fully grasp what is going on and what they should be doing along these lines will be for you to spend a few days seeing for yourself.
Stay tuned.
The editor
PS. Here is one thing I learned about Strasbourg that is I think quite striking in the context of World Streets and our shared interests here. Perhaps you did not know this. The name has as you can see two main parts: the first half, “stras” means “street “in the local language. The second half, the "bourg", “city”. “City of streets”. Nice.
- Henry Cutler. Eyes on the Street in Amsterdam, the Netherlands
There is more to it than just wheels and concrete. It is a systemic challenge, and here for example is one small part.
In the Netherlands there's a tax rule that allows one to purchase a bicycle each three years with pre-tax salary. You can buy any bicycle with a maximum tax-free price of €749 plus €249 of extras, but the great majority of bikes here are utility models. Given that both Dutch taxes and use of bikes as transportation are very high this rule is widely used. This tax benefit enables more new and better bikes to be sold but it's unclear how much it actually increases cycling usage. The Dutch cycle because it's the most practical, safe, cheap and enjoyable option ...and do so whether they're on new bikes or ancient, single-speed granny bikes. Nationwide the Dutch cycle an average of 2.48 km per day.
That cycling is so often the most practical, safe, cheap and enjoyable means of transportation in the Netherlands isn't just cultural; it's a function of cycling being a key element in the nationwide transportation infrastructure. It is widely recognized that bicycles are the most flexible, economical and space-efficient way for people to get around the densely populated cities. Private cars are the least.
Practically every point in the entire country is outfitted with bicycle roads, signals and storage facilities... and drivers who also cycle. Scary intersections and high-speed roads without separated bicycle paths are extraordinarily rare. To the contrary bicycle roads are often much more direct and convenient than those for automobiles. These traffic routes are planned out and implemented city wide.
A good example is the northern city of Groningen, which apparently has world's highest cycling modal share at 57% of trips. Until the 1970's there were no restrictions on driving cars through the city and bike paths were being removed. In 1972 the government designated the city center "living space" and integrated transport policy with town planning . Over the following four decades auto access was restricted, cycling infrastructure improved and new neighborhoods developed to encourage cycling. Some notable statistics: There are 0.4 cars and 1.7 bikes per person and the average speed of cycling within the city is 50% faster than driving.
How do you get the population riding bikes for daily transportation? Build your cities to make it safe, practical and fast so that cycling becomes something everyone will do instead of just a few hardy, bike commuter "warriors". Children must be able to cycle to school and elderly people to the grocery store. Tax benefits for bike purchases might help but not if the basic infrastructure isn't in place.
Polis, the European network of cities and regions supporting innovation in local transport, welcomes the vote of the European Parliament report on an action plan on urban mobility. According to Councillor Francesc Narvaez from Barcelona, the current president of Polis, this report is "an important milestone towards the implementation of a coherent and comprehensive European policy on urban mobility".
Councillor Narvaez added that under the impulsion of MEP Gilles Savary (PES), and thanks to the cooperation of all parties in the Transport Committee, the European Parliament has delivered a text of high quality. The report adopted last Thursday 23rd of April indicates the next steps for the consolidation of this policy on urban mobility.
The first step should be the adoption by the European Commission of the Action Plan on urban mobility itself. Polis members hope that this will be an opportunity for the European Commission to confirm its commitment to the CIVITAS programme, as stated in the report of the European Parliament, and that new projects will be funded during the second half of FP7.
Councillor Narvaez also expresses his hope that as suggested by the European Parliament, the current policy trend "will pave the way towards the creation of a new European financial instrument for urban mobility in the future financial perspectives".
The improvement of urban mobility is critical for the achievement of several objectives of the European Union, for instance on climate change and on the competitiveness of our economies, and can contribute significantly the European objectives of economic recovery.
Polis members call for the achievement of the new urban mobility culture and for this purpose also welcome the emphasis of the Parliament's report on public transport and soft modes.
Polis member Stéphane Coppey, Président of Tisséo (Toulouse) insists that he hopes that the upcoming action plan which is expected to follow the vote of the European Parliament "will support public transport, soft modes such as walking and cycling, and help to improve intermodality".
What Policies are Effective at Reducing Carbon Emissions from Surface Passenger Transport?
This report by the UK Energy Research Centre examines the merits of a range of different policies that offer the prospect of CO2 emissions reduction from road transport. The report has the following objectives:
• Review the evidence for CO2 emission reduction potential and cost-effectiveness across policies that target car technology/choice and those that target wider travel choices • Identify the key issues and problems associated with each policy type • Identify whether and where policies are complementary or synergistic • Identify evidence gaps and highlight future research needs • Draw conclusions relevant to current UK policy
The report does not undertake new modelling or empirical research; rather it provides a thorough review of the current state of knowledge on the subject, guided by experts and in consultation with a range of stakeholders.
The project team undertook a systematic search for every report and paper related to the assessment question. Experts and stakeholders were invited to comment and contribute through an expert group. A team of expert consultants was commissioned to categorise, review and distil the evidence. This tightly specified search revealed over 500 reports and papers on the subject, each of which was categorised and assessed for relevance.
The evidence on each policy is reviewed against the following criteria:
• Potential emissions saving; in absolute and percent terms where the evidence permits. • Key issues and problems; including reasons for effectiveness, evidence gaps, obstacles to policy implementation, interactions with other policies and potential rebound effects. • Costs; where possible we provide evidence of costs in £/tonne carbon terms. Where this is not available in the literature we provide a discussion of what evidence does exist.
This report represents one output from this process of review, evaluation and synthesis.
The other main output is a set of detailed evidence tables which are published on the UKERC website alongside this report.
This is the first article in a series to which we here at World Streets give great importance: the many different ways we have of envisioning the future, hopefully a very different future. These many ways span a variety of techniques: guessing, reckoning, projecting, forecasting, scenarios, estimating, predicting, modeling, and variously describing that different future using various media: physical models, drawings, simulations, films, interactive gaming, and even imagining, wishing, hoping, storytelling, and at times even lying. The idea in all cases being somehow to “show”, to render credible, even desirable (or the opposite) that different future.However if past performance is any guide we have not always been particularly good at this. To get the ball rolling in this series let’s have a look at a new book by Robert Bain Toll Road Traffic & Revenue Forecasts which is scheduled for publication next month, and in which he looks at one part of this, for which the track record is, you will see, a bit spotty.
Big Numbers Win Prizes
21 Ways to Inflate Toll Road Traffic & Revenue Forecasts
A number of high profile investor-financed toll roads around the world are currently failing to meet expectations. Robert Bain suggests that this has less to do with the present economic climate and more to do with a market readiness to be seduced by hopelessly optimistic traffic and revenue projections; lenders relying too heavily on elaborate transaction structuring for protection.The time is right for a paradigm shift, he maintains, with a renewed emphasis placed on understanding the demand fundamentals and less willingness to accept forecasts at face value – especially those that resemble statements of advocacy rather than unbiased predictions.
The evaluation criteria used to award many of today’s toll road concessions focus on maximising income – or minimising expenditure – for promoters. These criteria establish the rules of the game. Bidders are incentivised to develop strategies which best respond to the criteria – framing their bids in a positive light and maximising their chance of winning the competition. Under such circumstances, traffic and revenue forecasts are bound to attract considerable attention.
Bidding strategy success and the ability to raise significant quantities of debt often rely on strong projections of demand; even beyond credibility in situations where the short-term benefits of winning overshadow any possible longer-term costs. This is true in cases where profits are front-loaded or where, for practical or reputational reasons, procuring agencies may be open to subsequent contractual renegotiation. In short, the procurement process in general – and bid evaluation criteria specifically – reward high traffic and revenue forecasts, not accurate ones. This places asymmetric pressure on traffic advisers in terms of the outputs from their forecasting models. In this context, the following article summarises 21 ways in which toll road traffic and revenue projections can be inflated – tricks for investors to watch out for.
1. Flatter the Asset
The representation of a toll road in a traffic model may be flattered in various ways. An incomplete treatment of the delays that drivers experience at toll collection stations or upon leaving the toll road (and re-joining a congested toll-free network) makes the toll road more attractive to potential users. So does exaggerating the capacity per lane. Traffic modellers commonly employ assumptions about how the capacity of a toll facility will increase in future years despite its geometry and configuration remaining unchanged! This is supposed to reflect that fact that driver behaviour adapts over time such that the ‘effective’ capacity of a road will increase. Naturally, this improves the attractiveness of the asset. Evidence should be provided by traffic advisers to support such assumptions if they are to be incorporated in base case traffic models.
An alternative approach is to impair the competitive landscape. The competitive position of a toll road will appear to be strong in circumstances where the alternative facilities offer particularly poor levels of service to users. This can be achieved by degrading a competing route’s capacity through the use of punitive speed/flow relationships or speed limits, or by over-emphasising delays (such as those experienced at signalised intersections). It can also be achieved by over-simplifying the competitive context – ignoring important rat-runs in an urban network or by neglecting the potential for competition from other roads or transportation modes in the future.
2. Cherry-Pick your Planning Variables
The future-year socio-demographic and planning variables that are used by traffic models are commonly presented as ranges. Consistent selection of values from the upper ends of these ranges will place upward pressure on the traffic numbers. This is one of the reasons why all of a model’s input assumptions should be tabulated on a single sheet and justified – with supporting evidence being provided by the traffic adviser.
A variation on this theme is the use of planning variables designed to achieve particular political objectives. A recent report reviewed talked of “planning targets”. These seemingly independent and unbiased variables – such as projections of population – may be the basis upon which the state allocates funds to regional government. There are incentives for the producers of these planning forecasts to inflate their own projections which, in turn, can be used to pump-up the traffic numbers. Understanding the source(s) of these ‘independent’ socio-demographic and planning variables can help to mitigate this risk.Presenting alternative planning forecasts from different public and private sector sources also provides some comfort to investors.
3. Judiciously ‘Identify’ the Historical Trend
With a time series of data – such as traffic or toll revenue – it is often possible to isolate different trends by carefully selecting the period to be analysed.Figure 1 shows the time series of revenue miles from the Pennsylvania Turnpike. From opening year (1941) to 2006 the compound annual growth rate was 5%. From 1952 to 2006 the rate was only 3%. However, in terms of supporting high traffic forecasts, from 1943 to 2006 the rate was a very useful 7%. These different growth rates are all derived from the same historical data set – just different parts of it.
4. Selectively Apply or Report Growth Factors
Traffic and revenue study reports commonly provide area-wide statistics in support of their forecasts. A report might state that, across the study area from 2010 to 2030, average population growth of 1.2% per annum is predicted. This appears reasonable – possibly even conservative. But what about the distribution of this growth? If the model is specified such that most of the population growth takes place in zones adjacent to or that feed the toll road, it would be no surprise to find high traffic growth rates resulting on the asset itself – usually considerably higher than 1.2% per annum!
5. The Future Will Look Exactly Like the Past
Some toll road forecasts are made against a backdrop of strong historical traffic growth trends. Why should such trends continue unabated for the next 25-30 years or beyond? And what about historical relationships – such as the elasticity between GDP growth and traffic growth? Why should this relationship remain constant throughout the forecasting horizon? These are for the traffic forecaster to justify – particularly if senior debt accretes or debt amortisation schedules are back-ended. In the absence of solid justification, base case forecasts should be adjusted accordingly to reflect the increasing uncertainty associated with long-range projections and sensitivity tests should be used to evaluate the impact of key relationships which could change in the future.
6. The Future Will Look Nothing Like the Past
A recent traffic and revenue study reviewed by the author demonstrated clearly that historical traffic growth across the study area had neither been strong nor consistent. Along some key corridors traffic volumes had been declining. Yet the future, according to the traffic forecasts, was one of strong, sustained growth. No explanation was provided for this dramatic disconnect between the past and the future. At best this hints of model-blindness. The traffic adviser has been engrossed in the mechanics of model building to the extent that they become blind to the credibility of the model outputs. Other symptoms of possible model blindness recently noted include low growth scenarios that resulted in traffic and revenue projections above the base case and severe downside sensitivity tests that had little impact on project revenues. Just because the model reports certain results does not mean that they have to be assumed to be credible.
7. Using Seasonality to Your Advantage.
Traffic surveys should be conducted on neutral days and during neutral months of the year. These are ones which are typical in terms of trip-making patterns and traffic conditions. This is not always possible, but failure to take proper account of factors such as seasonal variations can lead to erroneous modelling results.
Figure 2 shows the impact of seasonality on roads in Cornwall – a popular tourist destination in the south west of England – and compares traffic patterns there with the UK average.
Whereas the national trend demonstrates some seasonality, it is mild in comparison with that recorded in Cornwall. Traffic in Cornwall in August is 35% higher than the annual average. Figure 2 shows just how atypical certain months of the year can be. Days of the week can demonstrate similar variability. Compare market-day traffic with that from an average weekday. Without appropriate adjustment, surveys conducted on atypically busy days or during atypically busy months will overstate the amount of trip-making in an area and will lead to higher projections of traffic.
8. Remove Inconvenient Truths
This is best illustrated by example. Take a journey time survey involving five separate runs along a toll-free alternative to a proposed toll road. The run times are shown in Table 1.
The run time average is 12 minutes (top line). However, Run 4 was quicker than the others by some margin. If this is treated as an outlier – and is discarded – the average run time becomes 13.5 minutes (bottom line). This is useful as it degrades the attractiveness of the alternative facility and boosts the competitive standing of the toll road.
The difference between 12 and 13.5 minutes may appear insignificant, however some demand estimation techniques are very sensitive to small changes in the characteristics of competing alternatives. These small changes can have a disproportionate impact on the percentage of traffic projected to use the toll road. Traffic advisers should report how stable their estimates of market capture are to small changes in the competitive landscape – but seldom do.
9. Design Surveys to Return the Required Results
Transport researchers acknowledge that it is possible to achieve specific results from some survey types through judicious design and administration. Similarly, it is possible to bias the results through poor design and administration. This is particularly true in the case of Stated Preference surveys where respondents’ choices between alternative travel options are influenced by factors such as how those options are portrayed, the range of attribute levels presented and the absence of any opt-out choice (forcing an outcome on respondents).
This is not to suggest that Stated Preference techniques are inherently flawed. Good practitioners are alert to these issues and should be able to minimise such influences. However investors should look for some comfort in this regard – ensuring the use of experienced firms in this field – alert to the fact that it remains possible to affect survey output through the judicious contexting, selection and definition of the questions being asked to interviewees.
10. The Magic of Expansion/Annualisation Factors
Traffic models focus on critical times such as weekday AM peak periods – in part, for convenience. Expansion factors are then used to gross-up the results to annual estimates (toll revenue per year, for example). The smaller the modelled time period, the more emphasis is placed on expansion factors – and small changes to the factors can have a significant impact on the final revenue calculations.
Say that a traffic model suggests that, during a weekday AM peak hour, 1,600 vehicles use a toll road paying an average of $1.50. Two alternative sets of expansion factors are presented in Table 2 (Scenario A and B).
The expansion factors under Scenario A result in an annual revenue estimate of $4.8m. Using the alternative – yet still plausible – factors under Scenario B, the revenue is $6.6m (40% higher).This significant difference has nothing to do with the traffic model. It results from the use of different expansion factors. Traffic advisers should explain their choice of values used and should conduct and report the results from sensitivity tests if revenue projections appear to be particularly factor-dependent.Unlike the simple example presented here, the expansion process behind some forecasts can be complex.It is important that investors understand this process particularly well.
11. Assume that Consumers Act Rationally
It is easy to underestimate the reluctance of some (sometimes many) drivers to paying tolls. Even in circumstances where the time savings appear attractive, it is possible to observe drivers sitting in heavily congested traffic conditions just to avoid paying a relatively modest charge. This may appear to defy logic – and be contrary to what a traffic models suggests – but it can be observed nevertheless. For this reason, investors should pay particular attention to any revealed preference data (from comparable facilities) presented in support of toll road projections – or the absence thereof.
12. Assume that Consumers Make the Same Choice Every Time
An urban toll bridge in San Juan, Puerto Rico illustrates this issue well. It caters mainly for commuter traffic heading for the capital’s downtown business district. The tariff is $1.50 (cars) and the traffic model over-estimated demand by 46% in the first year of operations. Subsequent analysis of travel patterns on the bridge revealed that commuters were not using the bridge in each direction, nor were they using it every day. Commuters were using the bridge selectively. They were more inclined to pay to hurry home than they were to pay to hurry to work – and this effect became more pronounced towards the end of the week.
The cost proposition in the traffic model was a one-off payment of $1.50 (for x minutes of time saving). However if commuters used the bridge twice a day, five days a week, the cost proposition was $15/week. Although not captured by the model, this was the cost that drivers faced and responded to. Hence their selective use of the asset. Models which fail to capture such behaviour will produce inflated projections of traffic and revenue.
13. Hypothetical Bias: A Helping Hand?
Stated Preference (SP) surveys are widely used in transport studies because they are one of the few techniques that can measure the market and non-market values associated with consumer products such as toll roads. The technique remains somewhat controversial. Investors cannot be certain of the accuracy of the SP value estimates since SP surveys are hypothetical in both the payment for and the provision of the service in question. Most research suggests that people overestimate the amount they would pay for a service when they do not have to back-up that choice with a real commitment (hard cash). This is called hypothetical bias and is well documented in both laboratory and field settings. Researchers suggest that mean hypothetical values could be 2.5 to 3 times greater than actual cash payments would be.
There are some limited contradictory findings which suggests that SP underestimates the amount that people would be willing to pay in real life. Notwithstanding, investors should be aware that there are professional concerns about SP and hypothetical bias – particularly when interviewees remain uncertain about their responses. The majority view is that, when present, hypothetical bias is likely to overstate (inflate) the consumer response. This is another reason why revealed preference data – hard evidence – should be provided alongside SP survey results whenever possible.
14. Grow Your Value of Travel Time Savings
The value of travel time savings (VTTS) is a central concept in toll road demand studies. It is a large topic in itself. Here we concentrate on just three aspects.The first is the concept of growth in the VTTS as it is common for traffic consultants to use growth assumptions about the VTTS in toll road forecasting models. The underlying theory suggests that disposable income will grow – in real terms – in the future and hence the value attributed to time savings should also grow in the future. Forecasts of GDP are often used as a proxy for the growth in disposable income, although the growth factor applied to VTTS may be higher (eg. 1.2x disposable income growth).
Increasing the value of time savings boosts toll road usage in future years. There may be arguments in support of such an approach – and these should be articulated – however the impact of this growth is commonly material, and should be isolated and understood by funders who may feel that, in some situations, it has the scent of equity upside.
There is a second issue regarding time savings that is pertinent to mention here. It concerns small time savings. The conventional approach is to say that the driver who values a time saving of one hour at $20 automatically values a saving of three minutes at $1. This is known as the constant value approach and it has attracted a vocal body of critical opinion. Researchers suggest that small amounts of saved time are inherently less useful than large amounts – particularly if you cannot do anything with the time saved – and that small time savings may go unnoticed (hence unvalued) by travellers. Assumptions about small time savings have a particular relevance in the context of short tolled sections of road, bridges or tunnels. The recent revenue underperformance of some urban toll tunnels in Australia, for example, may, in part, be attributed to overestimating the price consumers are willing to pay to save relatively small amounts of travel time.
There is also the issue of VTTS in congested traffic conditions.Some traffic advisers maintain that the VTTS varies according to congestion levels and values over 1.5x the base value have been noted.Traffic advisers draw parallels with the value of waiting time in public transport models (which is typically higher than the value of travel time – reflecting the perception of time passing slowly while waiting). The impact is for more trips in the model to assign via the tolled facility and the effect – helpfully – compounds in the future as congestion intensifies across the network.
15. Overstating the Toll Road Premium
Some traffic models incorporate the use of a toll road premium or bonus to capture the inherent attractiveness of toll roads. This suggests that if a toll road and its toll-free competitor are matched, taking account of the toll paid and the time saved, instead of traffic assigning on a 50:50 basis, proportionately more traffic will use the toll road. The premium is supposed to encapsulate those characteristics of the road not fully estimated in the model (softer attributes that are more difficult to quantify like ride quality or perceived safety). The impact of this premium is replicated in models that, alternatively, penalise links that compete with the toll road.
The danger here lies in overestimating the premium – overstating the inherent attractiveness of the asset. This inflates revenues. Any toll road premium employed by traffic consultants should be made explicit and should be justified – to the extent of re-running the model in its absence to determine the contribution to revenues made by assumptions about the premium alone.
16. Overstating the Yield
Yield refers to average revenue/vehicle. As most toll roads are dominated by private car use, the yield generally lies close to the car tariff. Because of the proportionately higher tariffs, the greater the contribution of trucks and buses to the traffic mix, the higher will be the yield. Overestimating the number of trucks using a toll road will disproportionately inflate aggregate revenues. This is a particular concern as truck usage of toll roads is notoriously challenging to predict and has often been overestimated.
Yield calculations can also be overstated if unrealistic assumptions are made about the take-up of discount programmes. Similarly, unrealistic estimates of toll avoidance and/or exemptions will overstate yield. Investors need to understand not only what revenues are forecasted, but the composition of these revenues and any (and all) assumptions underpinning them.
17. Reliance on Speculative Development
Future land use plans are a key traffic modelling input – however there may be questions about how committed some development proposals actually are. The reliance that can be placed on land use plans is a challenging issue in economies experiencing rapid growth – especially under less-regulated planning regimes – however it is also an issue in many developed countries.
Purely speculative developments should be omitted from base case traffic forecasts. Similarly, developments expected to result from the building of new tolled facilities should be treated cautiously in terms of their contribution to traffic. Speculative and generated developments in toll road demand models simply serve to inflate the traffic and revenue projections.
18. The Joy of Induced Demand
Building new highway infrastructure generates traffic however the relationship is far from clear or consistent. Often toll road traffic forecasters make an assumption about generated (induced) traffic and add this to their forecasts. An upwards adjustment of 10% is not uncommon – however it is seldom supported with evidence.
Investors should identify if such an adjustment has been made to the traffic figures they are reviewing and then consider the evidence.In some circumstances the contribution from induced traffic has been removed from base case forecasts reflecting the fact that considerable uncertainty surrounds this revenue contribution. As before, induced traffic helpfully serves to inflate project revenues.
19. Introduce Your Own Toll Discount
There is some evidence to suggest that, in terms of toll road usage, drivers respond differently to different toll road payment media – particularly non-cash options. By using electronic toll collection (ETC) technologies, drivers do not have to pay the toll at the time/point of use.The charge is made to their credit card account and they are billed, in arrears, on a monthly basis. It is suggested that this encourages toll road usage above and beyond what would be expected from a cash-only operation. To capture this effect, traffic modellers talk about a ‘perceived ETC discount’ – the discount reflecting users’ misperceptions of the price paid due to electronic tolling and the payment deferral. This is entirely separate from (and in addition to) any real discount enjoyed by ETC scheme patrons.
In a recent study, the perceived ETC discount was set at 15% and tariffs were accordingly reduced to 0.85x their face value.Reducing the price encourages toll road use and inflates the traffic figures. Investors should look for evidence in support of perceived ETC discounts in traffic studies if they are to accept the use of artificially reduced tolls in base case projections.
20. Assume Quick Ramp-Up
Ramp-up is the period upon the opening of a tolled facility when drivers experiment with new routes. It is a period often characterised by strong growth (from a low base) and it ends when trip-making patterns stabilise and evolve into more mature trends. It is notoriously difficult to predict in terms of its depth and duration. Traffic consultants often assume a ramp-up profile based on instinct or weak evidence with questionable transferability.
The use of instant or short ramp-up assumptions runs the danger of inflating early-year revenue forecasts. Ramp-up assumptions should be challenged to understand their underpinning rationale. It may be sensible to run sensitivity tests using alternative assumptions to ensure that the financing remains robust during the early years of project operations and throughout the remaining term of the concession.
It may seem incredible that some forecasts have actually exceeded the physical capacity of their road (in terms of volume/lane/hour) but it has been noted – particularly when these forecasts result, not directly from traffic models, but from traffic model figures extrapolated into the future. Typically no mention is made of widening or the costs (and disruption) involved in capacity expansion. Turning from volume/hour to volume/day, another phenomenon observed has been the fact that some forecasts of daily traffic (AADT) would required roads to operate at peak-hour congestion levels for over 12 (sometimes over 18) hours/day. These highly uncharacteristic flow profiles should certainly raise investor questions.
The recent development of managed lanes with dynamic pricing – particularly in the US – introduces concerns about how forecasts may exceed a highway’s operational capacity. On some managed lanes, the tariff is adjusted based on the volume of traffic using the facility. As usage goes up, the toll goes up – with a view to constraining demand such that a certain level of service can be offered to drivers. Traffic forecasts recently reviewed from one project, however, were so high that they would have degraded the level of service to below that required contractually of the concessionaire. High-Occupancy Vehicle (HOV) and HOV/toll (HOT) lanes – and other initiatives that fall under the ‘managed lane’ concept – are relatively new and present particular methodological challenges to traffic modellers. They are commonly crudely or incompletely represented within the model – although this fact is seldom highlighted. Investors reviewing these more innovative tolling applications need to ensure that, in terms of modelling, traffic advisers explain clearly what has been achieved, how and – importantly – the limits of these achievements.
Commentary
The list of 21 ways in which toll road traffic and revenue forecasts can be inflated is not exhaustive. It is purely indicative. There are others – some of which are highly technical and would require forensic work to uncover (such as the careful positioning of centroid connectors). Other techniques are more general and rely upon clouding detail – such as obscuring daily traffic volumes (which people understand) by reporting vehicle kilometres/year (which no one can). Or obscuring the relationship between traffic and revenue by simply reporting project revenues. This way, the recipient of the forecast has no idea how much traffic is supposed to be paying how much toll.The results cannot be sense-checked or compared with the findings from other studies.
Good traffic consultants know how to fine-tune their models. That is what model calibration is all about. In an environment where prizes are commonly awarded to the bidding team with the highest numbers, fine-tuning may be open to abuse. The purpose of the list is not to alarm investors. It simply demonstrates that it is perfectly possible to inflate the numbers for clients who want inflated numbers, and highlights some key issues to watch out for.
To knowingly inflate traffic and revenue projections is an act of deception – but it is not alone in that regard. Investors reviewing toll road studies should remain alert to two other potential acts of deceit. The first concerns sensitivity tests. Suspicions should arise when sensitivity tests have limited adverse impact on project traffic or revenues. Under certain circumstances this is possible, but it is not the norm. Good explanations should be provided in support of such results.
The second act of deceit concerns the use of pseudo-science to infer a precision of foresight that is simply not supported by empirical evidence. Favoured ploys include the presentation of narrow confidence intervals around base case forecasts and the abuse of exceedance probabilities. Traffic advisers sometimes talk in terms of P95 values – inferring that there is only a 5% probability of that particular number (traffic volume or revenue) not being achieved. However these exceedance probabilities are unlike those associated with scientifically-measurable natural phenomena such as the measurement of wind to determine energy yield predictions for wind farm financings. At best, they result from consultants attempting to re-cast their traffic model in a simple probabilistic framework. At worst, they are simply guesstimates.
Proper analysis of any traffic or toll revenue projections presented as probabilities requires a sound understanding of the probabilistic model construction, the probabilistic variables and their distributions and the correlations among the probabilistic variables. No comfort should ever be taken from P95 figures alone. If there really was as little uncertainty in the forecasts as some sensitivity tests, confidence intervals and P95s have suggested, traffic advisers could remove the legal disclaimers from their reports and could cancel their professional indemnity insurance. These trends have not been observed to date.
Robert Bain runs his own consultancy providing technical support services to investors, insurers and infrastructure funds.This article is an abridged extract from his forthcoming book ‘Toll Road Traffic & Revenue Forecasts: An Interpreter’s Guide’. Further details are available from Rob at info@robbain.com.
Transportation Alternatives' Safe Routes for Seniors campaign started in 2003 to encourage senior citizens to walk more by improving their pedestrian environment. Funded by the New York State Department of Health's Healthy Heart program, this was the first program of its kind to address the needs of elderly pedestrians.
Click here for Elizabeth Press's StreetFilms video.
In 2008, the City of New York launched its own Safe Streets for Seniors initiative based on TAs Safe Routes for Seniors. Focusing on 25 areas with high senior pedestrian fatalities, this program is paving new ground. Yet, some including seniors not in these zones are asking, is it enough? Stats released by Transportation Alternatives show that:
* People aged 65 years and older make up 12% of the population, yet they comprised 39% of New York City's pedestrian fatalities between 2002 and 2006.
* The fatality rate of senior pedestrians is 40 times greater than that of child pedestrians in Manhattan.
This video is an overview of what Transportation Alternatives, New York State Department of Health, NYC DOT, community groups, and elected officials are doing to promote safe streets for seniors.
Cities across the globe need innovative and effective solutions to solve their transportation problems in the short, medium and long term. Increased economic growth, coupled with a resulting increase in motorisation in recent years, has created greater congestion than has ever been seen in the world. Solutions to these problems are possible through improvement of conditions of public transport and conditions for pedestrians and bicycle users, and also in the implementation of measures which promote a rational use of the automobile.
Transportation Demand Management (TDM) aims to maximize the efficiency of the urban transport system using a wide range of measures, including Congestion Pricing, Public Transport Improvement, Promoting Non-motorised Transport, Fuel Taxation and Parking Management. This document presents an overview on international practices, approaches and supports the design of a TDM strategy.
To download click here. (Unregistered visitors can register (at no cost) and then proceed to download.) This report covers the following key issues:
1. Challenging traffic growth in developing countries 2. Developing a comprehensive TDM strategy 3. Improving mobility options 4. Economic measures 5. Smart growth and land use policies
Authored by Andrea Broaddus, Todd Litman and Gopinath Menon, this GTZ training document advocates that a three-pronged approach, utilizing 1) Improve Mobility Options, 2) Economic Measures, and 3) Smart Growth and Land Use Management is the most effective way to manage demand and create a resilient and efficient transport system. The document contains 118 fully illustrated pages, 27 tables, 51 boxes and 92 figures.
About SUTP: The Sustainable Urban Transport Project (SUTP) is a global partnership which aims to help developing world cities achieve their sustainable transport goals, through the dissemination of information about international experience and targeted work with particular cities. SUTP developed the publication “Sustainable Transport: A Sourcebook for Policy-makers in developing cities” consisting of more than 26 modules. The sourcebook addresses the key areas of a sustainable transport policy framework for a developing city. It is also complemented by a series of training documents and other material. More on www.sutp.org
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