Category Archives: travel behavior

Street Network Structure and Activity Spaces

Activity Space
Activity Space

Recently Published:

This research analyses the influence of street network structure on household travel patterns, as measured by activity spaces. The analysis uses street network and travel survey data from the Minneapolis – St. Paul (Twin Cities) and Miami – Ft. Lauderdale (South Florida) metropolitan areas. Various measures of street network structure are used to quantify street network structure. The activity space polygon for each household in the travel survey data set is identified by combining the destinations reached by all household members on the given travel day including the household location. Statistical regression models are then estimated for each study area to test the relationship between street network structure and household activity space. The results show that network structure has a significant influence on household travel patterns, after controlling for other non-network variables such as accessibility to jobs and shops, and car ownership. JEL code: R41, R48, R53 Keywords: Transportation Geography, Network Structure, Circuity, Accessibility

Driving transit retention to renaissance: Trends in Montreal commute public transport mode share and factors by age group and birth cohort

A paper recently published by colleagues:


Grimsrud, M. & El‐Geneidy, A. (2013). Driving transit retention to renaissance: Trends in Montreal commute public transport mode share and factors by age group and birth cohort. Public transport: Planning and Operations, 5(3), 119‐241.


Public transport mode share for young people appears to be growing in the 21st Century, and higher than previous mode shares appear likely to continue, increasing overall demand as today’s youths age into traditionally lower public transport-use lifecycle stages. This paper tests and supports the latter claim through application of a number of binomial logistic regression models, controlling for socioeconomic, household composition, location and service level factors. Analysis draws from over 10,000 home-based work and school commute trips from each of Montreal’s 1998, 2003, and 2008 origin-destination surveys. One large factor in Montreal’s increased youth public transport usage has been the 1997 introduction of graduated driver’s licensing, which appears to have a substantial lasting licensure damper effect only on men. Controlling for effects of variables other than survey period and age group or birth cohort, recent young age groups show higher public transport use than did their predecessors. Moreover, a plateauing of public transport mode share within birth cohorts is seen to begin earlier in life than expected. This suggests not only continuance of higher than previous transit use, but also further potential for mode share improvements if challenges from lifecycle changes, such as school-to- work transition, can be identified and addressed.

Cancelled Seminar: The Impact of Activities Conducted while Traveling on Mode Choice: An Investigation of Northern California Commuters

Special Seminar Friday, May 9, 2014 11:15am-12:15pm Room 1130, Mechanical Engineering Bldg University of Minnesota East Bank Campus The Impact of Activities Conducted while Traveling on Mode Choice: An Investigation of Northern California Commuters Patricia L. Mokhtarian, Professor School of Civil and Environmental Engineering, Georgia Institute of Technology

Charts of the Day |

Over at I have three charts of the day on Journey-to-Work Mode Share in Minnesota, the Greater Minneapolis-St. Paul region, and the City of Minneapolis from the Census American Community Survey 2012 data. Nothing terribly surprising to me, but it is good to document these things. It is worth noting that walk share > bike share and walk + bike ~ transit, though expenditures do not reflect that. (And for non-work trips walk is even more important).

Minneapolis Journey-to-Work Mode Share from 2012 ACS
Minneapolis Journey-to-Work Mode Share from 2012 ACS

Accessibility and non-work destination choice: A microscopic analysis of GPS travel data


Congratulations to Dr. Arthur Huang for successfully completing and defending his dissertation: Accessibility and non-work destination choice: A microscopic analysis of GPS travel data

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:

  1. 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.
  2. 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.
  3. The more dissimilar the two destinations in a trip chain are, the more attractive the trip chain is.
  4. 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.

Exploring universal patterns in human home/work commuting from mobile phone data

Technology Review writes about a new working paper from Kevin S. Kung, Stanislav Sobolevsky, and Carlo Ratti: [1311.2911] Exploring universal patterns in human home/work commuting from mobile phone data: “Exploring universal patterns in human home/work commuting from mobile phone data

(Submitted on 12 Nov 2013)
Home-work commuting is known to be one of the major components of human mobility and therefor always attracted much research attention. One of the well-known assumptions being the focus of many works in this area is the universal uniformity of commute times. However, quantifications of commute patterns have often been baffled by the intrinsic differences in the data collection methods, which make the observations from different countries incomparable. In the present work we use mobile phone data offering a common methodology for investigating into the mobility pattern in different parts of the world including entire countries as different as Portugal and Ivory Coast as well as cities (Boston) also comparing results with those obtained from vehicle GPS traces in Milan. We showed that despite substantial spatial and infrastructural differences, the commute time distributions and average values are indeed largely independent of commute distance or country.

So cell phone data corroborates a mean commute time of about 1 hour each day. Yacov Zahavi and others have been talking about this for decades. (I have done a few papers on this topic myself.) It is always good to see more empirical evidence, and such a large data set. The method uses some inferences to determine when someone left home (last phone call at home (most frequented) cell phone tower) and arrived at work (first phone call at work (2nd most frequented) cell phone tower).

There are some details I am not clear about.

I am not always on the phone, and often don’t call just before departing or just after arriving … I would have picked the minimum time of all days when they had calls at home and work (since on the extreme day they are off and on the phone continuously, but on others not), but they don’t say they did that. Also towers serve big areas, and entering a tower zone does not mean arriving at work or home. Still, good to see data used in interesting ways, it is just important to be careful about interpretation.

I think the claim of universality needs to be tempered, since mean commute time (Figure 5(a) varies from under 60 minutes average in Boston to almost 80 minutes in Ivory Coast, a non-trivial difference.

I also think “human” in the title is unnecessary, until we find other species that have home/work commutes.


7 Ways to Reduce Transportation Waste

Some 20 years ago a book came out “Stuck in Traffic” by the brilliant Anthony Downs. One of his key points was the “Iron Law of Congestion”, sometimes called “Triple Convergence”, and now called “Induced Demand” which basically said if you expand a road, the extra capacity gets used up by people switching routes, modes, and time of travel. We might also add other effects of road expansion include changing destinations for non-work trips (making longer trips), making trips that would otherwise be foregone, and even changing jobs and houses, as well new development. While Downs did not discover this idea, (e.g. Lewis Mumford had said something similar) he popularized it.

Since Downs wrote the book in 1992, remarkably little has changed in how we travel. Not nothing of course, (travel rose for a few more years and has leveled off overall, and dropped in more recent years on a per-capita basis) but a lot less than you would expect given the changes in information technologies over the same period. No-one is satisfied with this status quo. Everyone is crying out for something different. We believe we can do better than daily congestion, excess pollution, devastating crashes, and all the other ailments associated with our existing transportation systems. In recent posts I identified peak travel, and made a speculative scenario of how traffic might disappear “on its own”. But of course, that won’t happen everywhere, and there are lots of things we can do to manage better.

Roads are perhaps the slowest changing technology. Once laid, they are difficult to move. Parts of the Appian Way, from two millennia ago, are still in use. Famously London could not change its street grid after the 1666 London Fire destroyed most of its buildings, despite an able plan from Sir Christopher Wren.

We can think of transportation as a layered system. There is the earth, on top of which are rights-of-way, within the rights of way are pavements (themselves layered). On the pavements are markings denoting lanes and directions. Above these are traffic control devices like signs and signals. Only then do we get to services, people driving their vehicles, trucks carrying freight, passenger buses, taxis, bicycles, pedestrians, etc. all riding on the layer of roads.

To the dismay of many transportation planners and engineers (many of whom got into the field to build things), the physical layer of the surface transportation network in the United States is largely complete. The projects that are left are projects that were too expensive to build the first (or second, or third) time, (much like the Second Avenue Subway in New York). These Zombie projects do not die, while construction is essentially irreversible, non-construction is easily reversed. In the end though, these are tinkering at the edges. Given the small amount of new construction, most travel 20, 30, or 50 years from now will be on roads that already exist.

Until we go airborne for short trips, we are highly constrained. As the world gets more developed, building new roads is progressively more expensive. The world adapts to the infrastructure that is provided, and builds as close to the right-of-way as possible, making expansion that much more difficult.

While we have scarce right-of-way (scarce in that it is limited, and finite, and at times fully utilized given the applied technologies for its use). We lack time. We have limited energy. This waste has both supply and demand aspects. Yet collectively we don’t do relatively easy things that would reduce the waste of these scarce resources.

  1. Most roads are under-utilized most of the time. We have plenty of capacity outside the peak.
  2. Most of the pavement is unused even at peak times, there are large gaps between vehicles both in terms of the headway between vehicles [1] and the lateral spacing between vehicles (we drive 6′ wide cars in 12′ lanes, often on highways with wide shoulders).
  3. Most seats in most cars are unoccupied most of the time.
  4. Most cars are carrying around far more weight than required to safely move the passenger. While bigger cars tend to be safer for the occupants, they are less safe for non-occupants. This is an inefficient arms race.
  5. Most roads are so wide we use them for storage of vehicles most of the day.
  6. There is a tremendous amount of excess delay at traffic lights, especially at off-peak periods, wasting time (and space).
  7. Most trips during peak periods are not work trips and have temporal flexibility, yet these trips travel in the peak because they are underpriced.
  8. Most trips produce negative externalities (pollution, congestion, noise, risk of crash) in excess of the price paid by their driver. They produce so many of these externalities because they don’t pay for their full cost.

So instead of expansion, we should instead think about ways to use that scarce right-of-way (and our scarce time and energy) more efficiently.

  1. Where there is congestion, we should price roads to encourage use in the off-peak and discourage use in the peak. This revenue should be used for the operations and maintenance of roads and should largely replace existing funding sources (fuel taxes, vehicle taxes, property taxes). Prices need to be systematic, not just on specific routes, to maximize system efficiency. We should avoid having a random set of underutilized toll roads, while free roads remain congested.
  2. Pollution and noise and crash risk should have their own externality charges.
  3. We should encourage narrower vehicles and, for instance, provide two six-foot lanes for narrow vehicles in place of one twelve-foot lane where we can, and promote use of driverless cars so that cars can use less space. Lane widths are standard, and changing them would require changing standards. Ultimately we should move to a model where we don’t need pre-defined lanes, but rather have vehicles move as near each other as possible without colliding, such as we do when walking in crowds.
  4. Cars should be lighter. If all cars were lighter, everyone would be safer. The greatest risk is when big car/truck meets small car. In particular we should encourage use of neighborhood cars that are specialized for local, lower speed travel. This may or may not require private vehicle ownerships as opposed to vehicle rental/sharing.
  5. We should promote technology to enable real-time, ad-hoc ride-sharing (with compensation for the ride provider) to better utilize excess capacity within vehicles. (In many places the compensation is illegal, as it looks and smells like a taxi, which are highly regulated).
  6. We should narrow up roads where we can, and use strategies so that people can share more cars, so we need fewer of them, so we don’t need to spend as much road space for vehicle storage. Road widths are again set by standards, often determined by the fire department (which does not want to back up their trucks).
  7. We should be able to eliminate many traffic signals with appropriate use of roundabouts (and later with driverless vehicles). Given the vacant space available on roads, if vehicles and inter-vehicle communications were better, we should be able to arrange real-time coordination of vehicle movements and have as a goal eliminating almost all stopped delay at undersaturated intersections that are today signalized. Pedestrian/vehicle conflicts might still remain, and require controls.

This is hardly a complete catalog of what we can and should do, but I hope the key point, there is plenty of pavement already, we just need to use it more wisely, comes through.


[1] If we follow the “2 second rule” (2 seconds between two successive vehicle’s front bumpers) (or 1800 vehicles per hour), at 60 mph we have a vehicle density of 30 vehicles per mile, or 176 ft per vehicle. Obviously with congestion, we are wasting time because we don’t increase throughput and we decrease speed, though we increase density. Vehicles are typically 26 ft, so we are using about 7 vehicle lengths for every vehicle we are moving at free flow speed near maximum stable throughput on a pipeline section without a bottleneck.

Network Structure and Travel Time Perception


Recently Published:

  • Parthasarathi P, Levinson D, and Hochmair H (2013) Network Structure and Travel Time Perception PLOS ONE: 8(10): e77718. doi:10.1371/journal.pone.0077718 :
  • “The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time.”

The Traffic is Falling

Published at the CTS Conversations Blog as Why are Twin Citians taking fewer trips?

The latest summary of the Twin Cities Travel Behavior Inventory is out, and it says the total number of trips in 2011 is lower than in 2001.

This is consistent with a lot of evidence we have been seeing from various sources on “Peak Travel”. Nationally, passenger miles on highways traveled today is lower than 1999, and miles per capita lower still. This is in stark contrast with the trend from 1900 to 2000, when vehicle miles traveled increased almost every year, with a few exceptions due to economic downturns or energy shocks. Why might this be? There are a number of hypotheses:

  • Employment – If fewer people are working, fewer people are traveling for work, and fewer discretionary trips are made by both workers (nervous about spending money) and the unemployed (who have little or no money to spend). Unemployment increased sharply beginning in 2008, and though it has declined, employment participation are still much lower (hovering around 58.6% of the population, down from 62.7% for the period before the recession). Demographics are part of this, and many people have just dropped out of the labor market, as their skills have been devalued by the economy, and either chosen early retirement (if near retirement) or deferred entry into the workforce (if young). The rise in female labor force participation from the 1950s through the 1990s has run its course, as labor force participation is roughly at parity by gender.
  • Gas prices – The price of fuel increased sharply in the run-up to the Great Recession – and this certainly discouraged car travel. Interestingly, it also reduced car crashes by more than the reduction in VMT, which we attribute to worse than average drivers (especially the young) being more likely to be priced off the road.
  • Changes in driver license regulations – It is harder for young people to get drivers licenses, and less valuable since they now need more supervision. More defer licensure and auto acquisition.
  • Social networking – It is now easier to virtually communicate with friends, in real-time and asynchronously, where-ever you are, so it may be less necessary to actually visit them. Time online just continues to rise, especially mobile.
  • At home work – Telecommuting continues to rise, more as a complement to office work than a substitute (check your email when you get up, check your email before bed), but even as the occasional substitute, so I can work from home either sometimes or regularly.
  • At home shopping – The rise of online retailing allows us to substitute delivery for fetching, and reduce the amount of shopping trips.
  • Kids today just don’t like cars – The culture just may be different, and the desire for mobility, especially auto-mobility has dropped, as kids would prefer to spend their disposable income on the latest internet-connected gadget.

There may certainly be other explanations as well. No one of these explanations is the whole story, and some are certainly more likely than others, but they all work in the direction of reducing auto travel. Importantly, travel by other modes has not made up for the large drop in car use. While transit, e.g., is up nationally due to the large investments in rail lines, that 20% increase in transit use in the decade is a little more than 1% of the 20% drop in passenger miles by motor vehicles.

Colleagues (Jason Cao, Yingling Fan, Michael Iacono, Greg Lindsey) and I have a study currently underway, Travel Behavior Over Time, with support from the Minnesota Department of Transportation and the Metropolitan Council to examine not just the top-line number, but what goes on underneath. We plan to determine the factors that explain travel demand and see if they are the same as in previous decades. We hope to understand whether the factors are the same, but the conditions are different, or whether underlying travel preferences have changed.

People and their Paths 5: How to get people to behave optimally

A shortened version of this post was adapted for Symposium Magazine‘s article Understanding the Irrational Commuter, which appeared in the September 2013 issue.

At the beginning of this series, we described Wardrop’s First Principle, of User Equilibrium. He also had a second principle, of System Optimality, which says: “At equilibrium the average journey time is minimum.” To achieve this requires every traveler to act in accordance with society’s best interest, which as we noted in part 2, is not generally calculable by an individual. This ratio of the total system travel time associated with a user equilibrium traffic pattern and the system optimal travel pattern has been dubbed “The Price of Anarchy” by Tim Roughgarden, who has applied this to computer networks. This number indicates the inefficiency of autonomous (or selfish) control in a system, compared to a theoretically best central control.

UE SO Flow

When choosing a route, selfish users see the costs they incur, but not the costs they impose on others. This is analogous to the difference between average and social marginal costs in economics. If we somehow persuaded travelers to make route decisions considering the cost they impose on others, their marginal cost, we could achieve a minimal total cost for the system. In economics, the classic theoretical mechanism for this is called a Pigouvian Tax, which charges the polluter for the negative externalities imposed on the pollutee (the difference between the social marginal cost and social average cost). In this case the externality is congestion, or travel time imposed by a vehicle on all other vehicles in excess of what would be borne in the vehicle’s absence. The Pigouvian Tax gains its name from Arthur Pigou, a British economist from the 1920s, who discusses the idea in his text The Economics of Welfare.

Travelers facing travel times and a Pigouvian Tax might choose a route that satisfies both of Wardrop’s Principles. The User Equilibrium (UE) solution would equal the Social Optimal (SO).

Using traffic assignment models we compared system optimal and user equilibrium flows and travel times for the Minneapolis – Saint Paul regional planning network, assuming total traffic flow between origins and destinations were fixed (i.e. unaffected by our distortion of route prices). We found the SO assignment had a 1.7% overall time savings, and a slightly higher average speed (63.2 km/h vs. 61.8 km/h). Perhaps surprisingly, it also had somewhat more total vehicle kilometers traveled (9.37M vs. 9.33M), as drivers had to take longer routes to avoid imposing congestion on others.

While the SO result is better than the UE (it cannot be worse), we might ask “SO What?”. The price of anarchy, letting drivers choose their own routes rather than being centrally directed, is relatively small, under 2 percent. It turns out it is much more important to get people to choose an efficient time of day than to worry about micro-managing which route they select.

We could post time-varying prices (just like the HOT lanes of Part 3, or many transit systems which have peak and off-peak fares) to discourage demand when it is highest, and encourage demand at off-peak periods. This is done on some toll facilities now, and other schemes, like the London Congestion Charge, have two prices: free or tolled, depending on time of day. But this can be as refined as we want it, with prices changing every hour, every five minutes, or even continuously. The prices might change in real-time, or change according to a fixed and posted schedule.

Nobel-winning Economist William Vickery developed the first version of the bottleneck model, which showed how varying prices would allow people to trade-off being on-time (at a higher toll) or being early or late (at a lower toll, but a higher cost in what transportation researchers call “schedule delay”).


The simplest version of this has two players1. Imagine two boats racing for a canal lock, or, as in the image, two weightlifters trying to get through a narrow door on the London Underground. When they arrive at the same time, only one can make it through first, the other has to wait. The one who makes it through imposed schedule delay on the one who waited. But if they arrived at different times, there would be no direct schedule delay, though one might not get into the canal (or through the door) at their preferred time. If we appropriately price simultaneous arrivals, we will discourage them. While with two players this may be feasible to coordinate with direct communication by saying don’t arrive when the other guy arrives, and negotiating, for 2000 people instead of 2, coordination is better through posted price signals than conversation and negotiation. Prices varying by time-of-day is what congestion pricing is about, putting a higher price on times which are most desired, and lower prices on the less desired times.

There are perhaps other ways to achieve this end. On most roads, it is assumed no one owns the travel time, and so we get congestion. If there were some kind of property in the right to travel at a given time, we could auction off this right to the highest bidder, and similarly avoid congestion. This would more closely follow a strategy of establishing property rights to avoid externalities, as suggested by British-American economist Ronald Coase (who is still talking about economics at the age of 102). In the transportation literature, this has come to be known as reservation pricing. Just as one does not expect to be seated when showing up unexpectedly at a popular restaurant that takes reservations, one should not expect to use a high-demand bottleneck facility on the transportation network without making arrangements in advance. Of course it is much more complicated with a real-time system like transportation, and to maximize throughput, it is likely that some queueing is required. This queue ensures there is someone waiting to take advantage of the next gap that opens. The alternative would be that the facility remains under-utilized for part of the time, which has its own costs. Even restaurants that reserve tables sometimes make you wait a little bit, for their immediate convenience, not yours, maximizing the productivity of their staff.

Unfortunately congestion pricing in any form remains more in the realm of theory than practice. While there are a few Congestion Charging programs: notably Singapore, London, and Stockholm, they are not over a large enough area, or variable enough in prices, to produce an end to congestion. Once many of these are implemented, I expect many cities will look at their peers and copy them, and it will become standard in all large metropolitan areas. But to date, the cases are fairly exceptional: Central Singapore, a city-state governed by a strong Prime Minister, whose family has been in power for five decades; Central London, a city governed at the time by “Red Ken” Livingstone, a radical thinker who was willing to take the political heat for the decision; and Stockholm, which conducted a trial experiment before holding an election to allow residents to vote up or down. Technically the systems all work well, and certainly do reduce congestion compared to the unpriced alternative. Politically they have been difficult to emulate. New York City tried and failed2, and no other US city has been willing to do something quite so radical.

Another possible deployment path for congestion pricing is through what is variously called a Vehicle Mileage Tax, or a Mileage-based User Fee. Gas tax revenues, which provide a large share of road funding, have been declining for a long time in the US, both due to leveling off of demand for driving, as well as better fuel economy. The simplest solution is to raise the gas tax, which solves an immediate problem, but not the longer term one. While hybrid gasoline-electric vehicles (like the Toyota Prius) still pay some gas tax, plug-in electrics (like the Tesla, Chevy Volt, or Nissan Leaf) pay almost none. Yet they still use the roads. Although they are presently a small share of the market, that share is likely to grow. Some states are beginning to think about how to charge EVs for the use of roads, just as gasoline-powered vehicles are charged based on a gas tax. Once a device is placed in cars tracking miles traveled (basically just the odometer, though possibly with some locational data to allow prices to vary by location (urban vs. rural) (although it technically feasible to ensure privacy, by not tracking which specific miles are traveled, no one will believe government protestations isn’t tracking them anymore, anyway), that device can also track when those miles are traveled, and vary the rate by time-of-day. The State of Washington now taxes EVs $100 per year to offset the lack of gas tax revenue. Oregon is conducting a large scale test of the Vehicle Mileage Tax, allowing 5000 volunteers to pay by the mile and have their gas tax rebated.

We are getting to the point where we can provide incentives and disincentives to efficiently manage road use. The technology exists, it is probably accurate enough. The cost of collecting a new road fee is non-trivial (especially compared with the gas tax, which simply requires an annual check of refinery sales), but the costs should drop with widespread deployment. The benefits are a significant improvement in the management of road use, so that drivers who do not need to travel when roads are congested, will have incentives to avoid those times.

If applied correctly, the resulting changes in route choices it will reveal where roads are overbuilt, and where demand, even after pricing, is sufficient to justify new capacity. The most cost-effective thing we can do in transportation is to get the prices right, all else will follow. This requires above all else, field experiments where different strategies are tested and evaluated, and deployment by replicating the successful experiments.