The first thing one notices is that the Main Street, while attempts have been made to restore it, including an active road reconstruction project, is dead. Some may say it is just stunned, but I say its, pinin’ for the Fjords.
I have been playing around with this idea of a Taxonomy of Modes. What characteristics describe and differentiate modes? Every mode must differ from every other mode on at least one dimension (otherwise they would be the same mode). This is analogous to the idea of speciation in biology. The graph above is a first cut at this for surface passenger transportation. I wanted to distinguish primarily on the non-mechanical (non-propulsion) characteristics of the service first. Of course not every possible dimension is identified, and a few of the circles contain multiple modes which are otherwise obviously distinct (e.g. gondolas and subways are much the same from a transportation service perspective but for one is underground and uses a train and the other is suspended by a cable which moves it). I wanted to differentiate things that were qualitatively different rather than quantitatively different.
So the first cut is about time, is a reservation required or not (i.e. does it need some advance planning). The second cut is about time as well, is the service scheduled or dynamic. The third cut is about space, are the routes fixed or dynamic. If the route is fixed, are stops fixed (i.e. does the vehicle stop at every stop, or only when called, like a bus). Otherwise if the routes are dynamic, things get a bit more ad-hoc, as the key question changes.
Some traditional distinctions (access mode vs. primary mode, such as walk to transit vs. drive to transit) are not distinguished here, rather that would be thought of as at least two trips, one where you walk or drive to some place (with the purpose of changing modes), and second where you take some form of transit.
There are many reasons for this, but one is structural, failure to understand the life-cycle dynamics. The reason for overshoot and undershoot can be understood by visiting the S-curve. Assumed forecasts are made by extrapolating previous results, which is how many businesses and investors and government agencies operate, as shown in the figure. In early years (Birthing and Early Growth) the rate of growth each year is greater than the previous year. Someone extrapolating from history will undershoot actual growth. But in late growth and maturity, growth is slower than the previous year. Someone extrapolating from history will overshoot actual growth.
Extrapolation models are common in transportation, see e.g. Angie Schmitt’s Transpo Agencies Are Terrible at Predicting Traffic Levels. These are used for statewide modeling in many places. Such forecasting methods (assume growth continues at a fixed percent) is embedded in some textbooks, especially for instance, in pavement design.
Urban transportation planning models are better in some ways, in that they include multiple factors. Unfortunately, these models are based on rates at a single point in time. Thus they assume the function that describes the behavior is fixed, only exogenous (input) factors such as demographics, land use, networks, and policies are allowed to vary. Even when multiple years of data are available, such models are typically only estimated on the most recent survey, rather than on trends or changes. The underlying behavior is not permitted to change, only what it responds to. Yet we now have evidence that some underlying preferences do change over time. It’s not simply a matter of getting the demographics or incomes correct. For instance from the 1960s to the 1990s female labor force participation increased. Thus the number of work trips and non-work trips (substituting out-of-home for in-home production) both increased in that period. But that increase has played itself out. Thus the increases it was associated with have peaked. This reflected changing preferences. While hindsight is 20/20, I don’t know if underlying preferences can be modeled accurately prospectively (I am doubtful), but I do know failure to account for them will lead to model inaccuracies.
What changes are going on now that are not considered in travel demand forecasting? A brief (and very incomplete) list below:
Vehicle technology shifts (driverless vehicles)
Preference shifts among young travelers
Changing driver licensing requirements
Vehicle ownership vs on-demand vehicle rental (car “sharing”)
Telecommunication increasing substitution for work, shop, and social travel
Telecommunication complementarity for work, shop, and social travel
None of this is easy to model, certainly not within the existing framework of urban transportation planning models, even more modern activity-based models. In many ways it is easier to do macroscopic than microscopic forecasting. The question is, if some kinds of forecasting are impossible (I can forecast traffic pretty accurately two weeks from today, but not the first Tuesday of 2044), why do we do it? Is there a human-need to fill the void of future uncertainty with authoritative assertions?
Speculating about the future is useful, it opens up pathways. Developing scenarios is useful, it challenges assumptions. Thinking about the lifecycle process and markets helps frame the possible, the plausible, and the likely. Studying history (and past forecasting methods and errors) provides but humility and insight. Visions (and alternative competing visions) help establish what we want. Developing a communal hallucination can organize individual activities to become the ideal (or nightmarish) self-fulfilling or self-negating forecast. Planning needs more methods for thinking about the future than single point forecasting.
The advancements of GPS and GIS technologies provide new opportunities for investigating vehicle trip generation and destination choice at the microscopic level. This research models how land use and road network structure influence non-work, non-home vehicle trip generation and non-work destination choice in the context of trip chains, using the in-vehicle GPS travel data in the Minneapolis-St. Paul Metropolitan Area. This research includes three key parts: modeling non-work vehicle trip generation, modeling non-work, single-destination choice, and modeling non-work, two-destination choice. This research contributes to methodologies in modeling single-destination choice and multiple-destination choice and tests several hypotheses which were not investigated before.
In modeling non-work vehicle trip generation, this research identifies correlation of trips made by the same individual in the trip generation models. To control for this effect, five mixed-effects models are systematically applied: mixed-effects linear model, mixed-effects log-linear model, mixed-effects negative binomial model, and mixed-effects ordered logistic model. The mixed-effects ordered logistic model produces the highest goodness of fit for our data and therefore is recommended.
In modeling non-work, single-destination choice, this research proposes a new method to build choice sets which combines survival analysis and random sampling. A systematic comparison of the goodness of fit of models with various choice set sizes is also performed to determine an appropriate choice set size. In modeling non-work, multiple-destination choice, this research proposes and compare three new approaches to build choice sets for two-destination choice in the context of trip chains. The outcomes of these approaches are empirically compared and we recommend the major/minor-destination approach for modeling two-destination choice. The modeling procedure can be expanded to trip chains with more than two destinations.
Our empirical findings reveal that:
Although accessibility around home is not found to have statistically significant effects on non-work vehicle trips, the diversity of services within 10 to 15 minutes and 15 and 20 minutes from home can help reduce the number of non-work vehicle trips.
Accessibility and diversity of services at destinations influence destination choice but they do not exert the same level of impact. The major destination in a trip chain tends to influence the decision more than the minor destination.
The more dissimilar the two destinations in a trip chain are, the more attractive the trip chain is.
Route-specific network measures such as turn index, speed discontinuity, axis of travel, and trip chains’ travel time saving ratio display statistically significant effects on destination choice.
Our findings have implications on transportation planning for creating flourishing retail clusters and reducing the amount of vehicle travel.
After working at Valparaiso University last year, he is currently teaching at the University of Minnesota Duluth.
“One thing that confuses me about transportation funding is the idea that raising gas taxes is politically impossible, but creating new forms of transportation taxes like a tax on every mile you drive would be feasible. To me, the raising gas taxes seems far more efficient since all of the systems are already in place to collect the taxes. “
Why we should raise gas taxes now:
Road quality is not where we would like it and will only get worse if insufficient revenues are raised to maintain and reconstruct existing infrastructure, as existing roads continue to age and deteriorate.
Raising gas taxes is more administratively efficient in the short run than implementing tolls, especially tolls requiring lots of infrastructure. It presently has lower collection costs. See , . There is no guarantee that will remain true as new technologies change the cost of collection.
The gas tax is an ok environmental tax. There are better environmental taxes (but at $0.11/gallon for the carbon price of $43/tC, this seems too small to matter from a demand perspective). The gas tax is a terrible congestion tax however, as it does not differentiate by place, or more importantly, time of day. Yes idling consumes slightly more fuel than engine off, but newer engine technologies are making idling more efficient.
Currently roads are only partially funded with gas taxes, general revenue is a major source. This charges non-users as well as users, and sends no signal about the appropriate amount of roads that should be built or how scarce road space should be allocated.
So, states can (and should) raise the gas tax before using property taxes or general revenue to pay for roads. (For marketing purposes, the cynical politician should of course call it a wholesale tax, like Virginia just did. Thus they are taxing refiners instead of consumers, right? (However making the tax a percentage of sales price rather than of volume increases volatility of revenue).
We do need to be careful that the money gets spent on maintaining the valuable parts of the existing system, not building wasteful new facilities. There is a serious lack of trust in existing institutions.
Why we should implement road pricing soon:
Congestion is a problem. It is not getting measurably worse, but it is not obviously getting better. Even if reduces in the aggregate, it won’t disappear to zero anytime soon. All of it is unnecessary. But behold, there is a theoretically sound and empirically proven solution: congestion pricing. Almost all economists like congestion pricing. The arguments for it are fairly straight-forward. Congestion delay is a dead-weight loss. If instead of charging time, you charged the monetary equivalent of the marginal cost of travel (the delay you impose on others), that loss could be eliminated. If you took the toll revenue raised by allocating demand in an efficient way, and used it to maintain existing infrastructure, and returned the surplus to people (e.g. through lowering some other tax or providing an annual road dividend, (make the rebate as progressive as you like, it doesn’t matter from a transportation perspective)), society as a whole would be better off. There is a huge literature on this. (Sadly, congestion pricing cannot be achieved with only gas taxes (unless we required really small gas tanks in cars that lasted only 15 minutes or so … a truly bad idea). Something that charges by time and place is required. That is a toll of some kind.)
Emissions continue with the Internal Combustion Engine. If gas taxes are replaced with something that is more effective in reducing road use (for an equivalent amount of revenue), it will result in lower overall VMT and thus less gasoline consumption and lower emissions. (And if it results in less congestion, less fuel sales from the time spent idling or driving inefficiently in stop-and-go conditions). Nevertheless, the marginal effect of the gas tax on demand is relatively small. We can see with the huge swing in gas prices in the last decade, VMT was essentially flat. Anything that made the costs of travel more salient would reduce demand. This depends on the design of the system, invisible systems have low saliency. An in-vehicle taximeter would be highly salient.
Electrification is coming. At some point, electric vehicles will become non-ignorable share of free-riders on the road network. EVs do not require less road pavement or cause less congestion than similarly sized ICE-vehicles. See . The rate of electrification is unclear , but given federal fuel efficiency requirements, it is not implausible that most new vehicles will be EVs or EV-ICE hybrids in a 10 year time frame.
Road pricing would facilitate charging more for scarce resources (weak bridges, thin pavements, travel at peak times) and more for more damaging users (e.g. heavy trucks on few axles).
In most countries with higher gas taxes, those high gas taxes are not hypothecated, i.e. they are not spent on transportation the way they are in the US (e.g. Australia). So the high gas taxes are independent of road maintenance. Some countries (France, Italy, Japan, China) toll their motorways. Some don’t. Some toll trucks (e.g. Germany). The gas taxes in these countries go to general revenue. The tie between usage and payment in the US is to be commended and should be extended, not weakened. It sends better signals if roads (and other transportation) are paid for directly by beneficiaries. (Feel free to tax other externalities separately
Some organizations in favor of tolling/pricing in addition to conservative and libertarian think tanks include the decidedly un-libertarian Environmental Defense and Sierra Club, while the politically neutral GAO says pricing could be more equitable and efficient. The argument will be about the appropriate toll to set, but the same conservative road pricing-favoring organizations that are condemned for sponsorship by oil companies also publish reports that advocate road privatization, and we know what will happen to prices if roads are private — they will go up, which will lower travel (and fuel) demand. (That is, profit-maximing tolls are higher than welfare maximizing tolls.) (Whether this is good or bad is another debate).
Will government run, politically driven agencies be able to implement tolling? The jury is of course out, but the evidence is that it is hard, or we would see it more widely. Similarly if raising gas taxes were politically easy, they would already be higher. Minnesota’s gas tax was raised in 1988 and again in 2008, about once every 20 years. The federal gas tax was last raised in 1993 (20 years ago). Congestion pricing in general is more popular after it is implemented than before. There are many experiments going on, trying to figure out how to achieve acceptability. I like the road utility myself.
I have discussed some of the difficulties of implementing pricing before. But eventually, we will have electrified the fleet enough, we will be annoyed enough with congestion, the damage wrought by heavy vehicles, and so on, that we will do something else beyond (or in addition to) the gas tax. We will also have given up the last vestiges of concerns about privacy, and the cost of the technology to implement such a system will have dropped. Hopefully we will phase it in gradually, to avoid the potential catastrophe of a Big Bang rollout.
We need to think not only about what will (or should) happen tomorrow, but over a two-decade period.
By the year 2030, most Americans may only head into the office a few days a week, lots of urban skyscrapers will have been converted into apartments, suburban land prices will have declined, and we’ll be “driving” — or at least sitting in autonomous cars — way less. Goodbye to traffic congestion.
But, more quietly, both auto manufacturers and government entities are also jumping on the bandwagon.
Automakers are already equipping cars with sensors that know, for example, when you’re about to plow into the car in front of you and can brake accordingly. David Levinson, a civil engineering professor at the University of Minnesota who writes the Transportationist blog, believes partially automated cars could be hitting the market by the end of the decade.
“My guess is that there will be some stuff on the market by 2020 that will be automated in that you could probably do hands-off driving on freeways in specific situations,” he says.
However, he quickly cautions, that prediction comes with a number of caveats. Automakers are concerned about liability – after all, who’s at fault if an automated car gets in an accident? There’s also the matter of equipping roadways and signage with helpful technology, something car manufacturers don’t expect in the short term. (Let’s face it: It’s hard enough to get potholes fixed.)
Finally, self-driving cars will likely take a generation to reach critical mass, says Levinson. Just as electrics and hybrids are only now becoming part of the everyday fleet, expect the number of automated vehicles to grow slowly in their early years, while people get rid of their previous vehicles.
Nevertheless, they could provide a huge benefit to society. Delivery services such as FedEx and UPS could automate their vehicles. Urban dwellers, who already use services such as Uber and Zipcar, would have more options to get around. And self-driving cars would be safer, thanks to the kinds of sensors that are becoming widespread today, such as auto-braking and blind-spot recognition.
Of course, such advances take both political and financial will. Technology already exists to automate aspects of rail; systems in Europe and Asia (such as Japan’s bullet trains) are run by machine. Congress even passed a 2008 law pushing for the installation of positive train control (PTC), a technology that helps recognize dangerous conditions, but U.S. systems have been slow to implement it.
“We should be doing more automation,” says Levinson. “It’s a lot easier to automate rail systems that it is to automate cars and highways.”
But, gradually, we’re getting there, he says. Throw in other innovations – 3-D printing, which could eliminate the need to have certain items shipped; telecommuting, which is already creating “virtual offices”; and alternate energy sources, which may reduce dependency on fossil fuels – and 20th-century transportation styles may finally end up in the rear-view mirror.
You are sitting by the fire, in your leather chair that just reeks “old money”, a reading light behind you, in your smoking robe and slippers, Labrador Retriever by your feet, pipe in one hand, a good book in another. What is that good book? Something about transportation economics and policy of course. Here are some of my favorites …