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A reader writes: “The U.S. House [Transportation and Infrastructure Committee] came out with its pre-election transportation policy: The Moving Forward Framework, and access measures made it into what is otherwise a high-level policy document (with no hint about how they plan to pay for their wishlists.)”
On Access to Jobs:
It’s infrastructure investment that is smarter, safer, and made to last – with a framework that:
Ensures a transportation system that is green, affordable, reliable, efficient and provides access to jobs
Modernizes Project Planning – Requires States and MPOs to prioritize transportation access and to consider during the planning process all system users, job access, connections to housing, and creation of transportation options in underserved communities.
Revamps Existing Formula Programs
Amends core highway formula programs to prioritize investments and improve program implementation:
Fix it First – Prioritizes maintaining and improving existing infrastructure and bringing it up to a state of good repair, including roads, bridges, tunnels, and ferry systems.
On Road Pricing
Tackles Congestion Equitably – Institutes tighter standards around tolling and congestion pricing.
Tests the Viability of New Transportation User Fees
Transforms revenue collection and distribution by authorizing a multi-year national pilot program to test revenue collection to ensure the future viability and equity of surface transportation user fees, including a vehicle-miles travelled fee.
It’s almost as if it were written by a reader of this blog.
High occupancy/toll (HOT) lanes typically vary tolls charged to single occupant vehicles, with the toll increasing during congested periods. The toll is usually tied to time of day or to the density of vehicles in the HOT lane. The purpose of raising the toll with congestion is to discourage demand sufficiently to maintain travel speeds in the HOT lane. However, it has been demonstrated that the HOT toll may act as a signal of downstream congestion (in both general purpose (GP) and HOT lanes), causing an increase in demand for the HOT lane, at least at lower prices. This paper develops a model of lane choice to evaluate alternative HOT lane pricing strategies, including the use of GP density, to more accurately reflect the value of the HOT lane. In addition, the paper explores the potential effect these strategies would have on the HOT lane vehicle share through a partial equilibrium analysis. This analysis demonstrates the change in demand elasticity with price, showing the point at which drivers switch from a positive to negative elasticity.
We can assess full costs to consider internal costs, subsidies, and externalities [link].
This post looks at the idea of road rent. At the margins, what is the value of road space, and how might that cost look on a per vehicle-km traveled basis?
Land has value. Land used as roads has value both as a road and potentially for other uses. What if the value for other uses was higher than that for use as a road?
In Greater Sydney land values range from to $AU210,000 per m² in Barangaroo on Darling Harbour to under $AU1000 per m² in Western Sydney [link]. In Minneapolis, we estimated a few years ago that average assessed land value as $144 per m² for roads and $30 per m² for highways. [Junge, Jason and David Levinson (2013) Property Tax on Privatized Roads. Research in Transportation Business and Management. Volume 7. pp. 35-42.] It seems that assessed value is about 2/3 of market value in Minneapolis.
In some places it is much higher, in some places much lower, the examples used herein are simply an illustration.
The idea is that there is land holder (such as a government land agency) that has to decide whether to allocate land to road uses or for other purposes.
Consider a typical suburban residential neighborhood with `free’ parking in front of houses. The land is valued at $1,000 per m². Each house requires one parking space out front, and parking is permitted 24 h per day. Conservatively, a car takes 10 m² when parked (the road is the access lane, we consider that separately). It would cost $10,000 for the land owner to purchase the land equivalent of the parked car. The annual rent on that would be $400 (at 4% interest).
In this example $400 is how much the car owner should pay annually to their municipality for a permit to park their car to cover the cost of land (not the cost of infrastructure, or any other costs of roads and mobility, just the cost of land). This is a bit more than $1/day (more precisely $1.095/day). In more expensive neighborhoods, this would be higher, in less expensive neighborhoods, lower.
For Minneapolis, I have previously estimated about 220,000 on-street spaces. At $400/space per year, this would raise $88,000,000 per year, a not inconsiderable share of the city’s $1.3B annual budget. Instead it is mostly given away free.
Consider the implications if property taxes were reduced by up to $88M in total, and parking permits sold at $400/year (payable monthly with the water and trash bill). People would realize the cost of on-street parking, and there would be less of it, and less vehicle ownership at the margins, and fewer trips by car. Space freed up could be re-allocated.
Alternatively, $400 per year is the value of public subsidy from publicly-owned land to private car owners who get `free’ on-street parking. In short from the car-less to the car owners.
Alternative Uses of Road Space
The economic idea of opportunity cost is important here. Opportunity cost is value of the next best alternative. The next best alternative to road space might be renting it out. So for instance an urban US freeway that destroyed blocks of extant development when it was built has an opportunity cost associated with the value of that real estate.
So the question arises as to what other uses could be made of the road; for if there were no other uses, you might as well store cars for free. Here are several other uses that could be considered to replacing a parking lane:
Park or parklet,
Paid parking, via meters,
Shared car parking (rented to the car sharing company),
Shared bike parking (rented to the station-based or dockless bike sharing company),
Taxi or ride-hailing stand,
Shared scooter parking (rented to dockless scooter sharing company),
Food truck or ice cream vendor,
Road for moving motor vehicles (a parking lane could be another moving lane),
Sold off for development.
The last item deserves some discussion. Consider that our road with two parking lanes (one on each side) is maybe 10 or 12m wide (~32 to 40ft). This is wider than some houses are long. The city could in principle retain the sidewalks and sell off the roadbed for townhouses or single family homes. Given the houses are already serviced by alleys, and so long as not all roads were sold off, some roads could be. An illustration of this is the Milwaukee Avenue in Seward in Minneapolis, as shown in the figure. You will see there is no paved street in front of the houses. This could be tightened up further or realigned should there be demand.
This is not appropriate for every street. However, (1) there are places this can be done, where roads are in excess and housing scarce, and (2) this illustrates that land currently used as asphalt to store and move cars has value, and that houses have value even in the absence of streets for cars in the front.
There are always excuses — utilities may need to be relocated, fire trucks would need to go slower down narrower sidewalks. But these excuses can be overcome, there are numerous examples of narrow paths that function as roads.
Note: 1 are = 100 m² and 1 hectare (ha) = 10,000 m²
Typically each car is in use 1 – 1.5 hours per day, and parked for the remainder. In the previous section, we considered parking, the `remainder,’ in this section we look at the time in motion.
When in use, the car is occupying not simply its area (the 10m² = 2m x 5m), but also is preventing the use of other space around it. On a freeway with a capacity of 1800 vehicles per hour traveling at a freeflow speed of 100 km/h, (i.e. just before the speed and flow drop due to congestion sets in) there is a critical density of 18 vehicles/km.
18 vehicles per km is 55.5 meters per vehicle. Lane width is 3.65 meters, so the area occupied is 202 m². Let’s round to 200 m². Each moment a car is in use, it is using 200 m², on which it should pay rent. So for an hour a day, this is 720,000 m² s or 72 ha s. (The meter-squared by second (or hectare second) is a new unit of measurement (a time-volume) that needs a catchier name).
It is the density that is the relevant number here, since vehicles are occupying space that we are charging rent for in this thought experiment. Though they are moving, and so the space they are occupying moves with them, there is always some space being occupied for the duration of their travel. Each of those vehicles per hour is occupying a moving window of space.
Roads are a Time Share
When roads are less congested, cars are consuming more space per vehicle. So uncongested urban are much more expensive per traveler than congested rural roads. When traffic breaks down, they are consuming less space, but presumably are occupying that space for more time, since they are going slower. Induced demand [link] and travel time budgets [link] negate that to a significant extent.
In this example, the hourly rent on 200 m² is what we are interested in. Though cars move, over the course of 1 hour of travel in these conditions, they are claiming that much space. The specific space they are claiming moves with the vehicles, but this all balances out as other cars claim the space they vacated.
Empty roads still have to be paid for, and paid for by actual road users. Even when a road is not being used, it is available to be used. Travelers have the option of traveling. Pavements cannot be easily be rolled up and allocated to other purposes on the fly, particularly purposes like buildings. (Roads can occasionally be closed for special events, but this is rare during business hours.)
Road section 3 (downtown): l= 1 km, w=3.65, v=40 km/h, q=1600, k =40 veh/km , AADT=16,000 vehicles/day/lane, p= $10000/m².
where: l = length (km), w= lane width (m), v=velocity (km/h), q=flow (veh/h), k=density (veh/km), AADT = Average Annual Daily Traffic, p= land value ($/m²), i=interest rate = 0.04, r= land rent ($/year/m²), d = days/year
Consider each road section to be a homogenous pipeline. (With heterogenous traffic, this is obviously far more complicated, and we would make use of the q, k, and v variables to compute an area-time.)
The annual rent (R) for each road section is the R=p*i*l*w
Road 1: R=$1,000/m² y * 0.04 * 5,000 m * 3.65 m = $730,000/y
Road 2: R=$5,000/m² y * 0.04 * 10,000 m * 3.65 m = $7,300,000/y
Road 3: R=$10,000/m² y * 0.04 * 1,000 m * 3.65 m = $1,460,000/y
This annual rent is paid by the road agency to the land owner for the use of land as a road. The road agency then wants to recover this cost from its customers, the travelers.
The question of how to allocate always has some subjectiveness to it. Another way of thinking about it is based on elasticity of demand. Peak hour work trips are perhaps the least elastic (least sensitive to price), and so from an economic efficiency perspective should bear the greater cost.
In this example, we take a simpler tack.
The allocation is R/AADT to get cost per year per daily tripmaker, and divide by 365 to get cost per trip, and by section length to get cost per km. In this example:
The total is thus $529.25/year or $1.45/trip to cover land rent. `Your mileage may vary,’ as the saying goes.
The implications of this are several.
At an additional $1.45/trip, travel by car (and congestion) will diminish.
Road rent is essentially additive with annualized infrastructure costs, which generally does not consider the cost of land (rather, land is often implicitly considered `free’ or a sunk cost).
If travel by car diminishes sufficiently, road space can be clawed back and redeployed for other public purposes.
Narrower lanes impose less road rent. But not necessarily proportionately so, as the throughput on narrower lanes (with human drivers) may be lower as drivers are less keen to be immediately adjacent to nearby high-speed vehicles.
Slower moving vehicles take up less space, but take that space for longer.
While pedestrians and bicyclists use space as well, they use much less space. (See discussion of flux.) Sidewalks (footpaths) are often considered part of the adjacent private property, and are thus already paid for with property tax.
Land used for roads instead of development is not on the books for property taxes.
The revenue raised can be used for many transport purposes or redistributed back to taxpayers through some other means.
We expect the additional road rent reduces the effective land rent that landowners can charge. If people have to pay more for travel, they will pay less for real estate.
Rural areas have much lower, perhaps negligible, road rent. Though the number of users drops significantly (so there are fewer travelers who must pay the burden of road rent), the cost of land drops even more significantly.
Were there no (fewer) roads, land would also have very little (less) value, since it would be difficult to access and egress.
If roads were fully built on, views would be terrible and the existing buildings would diminish in value. But none of that is to say we have the correct amount of roads now. Clearly urban roads are undercharged in a real estate sense.
One of the great errors in the current tolling model has been the political decision to prescribe a unit toll rate which is indexed over time by the consumer price index. This has resulted in ring fencing on a crucial mechanism that is capable of recognising the need to adjust the toll to ensure that the travel time savings are delivered commensurate with the value (to the users) of those time savings relative to the non-tolled route(s), given travellers’ value of travel time savings. Consultants have struggled to establish the best outcome in relation to patronage forecasts because of this seriously problematic imposition. Added to the fact that consultants associated with the bidding consortia are often told to improve the patronage forecasts in ways that require what might be best described as imaginative (‘long tail’) futures, extending the range of time related benefits (such as the toll quality bonus) in the search for even higher patronage forecasts for a fixed toll regime. This point aligns with Bain’s 21 ways to inflate toll road traffic forecasts2. This becomes a commercial proposition in contrast to a network efficiency solution, resulting often in the loss of network welfare gains. Unfortunately, there is no incentive for the operator of a stand-alone asset to think ‘network’ There currently exists a complete failure across all tolled roads in Australia to optimise the level of toll and I believe this is generally opposed to by the operating companies of tolled roads on many grounds, but specifically their liking of the greater certainty of revenue flows even if these flows are a mismatch in delivering a better performing road network. Only the state thinks ‘network’. This is a key issue. The state gives away pricing controls and then finds it difficult to optimise the network when it only has control over this important lever for part of the network. This is of little concern when there are small, isolated sections of privately-operated toll roads. It suddenly becomes a massive concern when these privately-operated toll roads ‘become’ the network!
Note: Bain, R. (2012) Twenty-One Limitations & Shortcomings with Traditional 4-Step Models, www.robbain.com. “In Australia, a number of the toll road concessions were awarded to the bidder offering the largest upfront payment to the state. That’s a recipe for disaster. Without checks and balances in place the bidding process simply turns into a competition on traffic numbers. Toll road traffic generates revenue, and the largest upfront payments can be justified by those with the highest traffic forecasts. The whole process becomes skewed and the numbers get bent out of shape in response. Considerable pressure is placed on traffic consultants to come up with the ‘right’ numbers; numbers that meet the requirements of the financial model.”
I made a similar point in my post “setting the right toll“, which was mostly theoretical in nature, but has clearly become problematic in nature here in Australia.
The ownership model under Mobility-as-a-Service has often presented the dichotomy of an owned autonomous vehicle, the way Americans most typically use cars, vs. a shared autonomous vehicle (autonomous vehicles that come to you like a taxi). But many automakers are now trying to move customers to the leasing model of vehicles, which gives owners a long-term stake in an individual car, but not full ownership rights. The reasons a customer may prefer a lease is that technology is rapidly changing, and who wants to get stuck with an out-of-date vehicle. Alternatively, they may anticipate their tastes or needs change, and don’t want the hassle of resale. Automakers have often leased things like Electric Vehicles which require a major overhaul at some point in time, and this also gets them a built-in service business, as the incentive for a lessee would otherwise be to not service the vehicle and run-down capital stock, while the lessor wants to maintain capital so they can re-lease (or sell) the vehicle subsequently after the expiration of the lease.
Moving automobile ownership to the lease model, particularly with EVs, provides another advantage, this one for the road administrators. It reduces the number of players who own cars and makes a new model of road service provider possible.
Suppose, instead of purchasing road services from the government, (now via gas tax, later via mileage based user fees), travelers would purchase transportation services (the right to travel at a location at a time for a price) from independent road service providers. Road service providers (RSP) would purchase capacity from the infrastructure owner (presumably the government [or a government-like non-profit road utility]). If an RSP’s customers over-consumed the road, the RSP would pay a penalty. RSP would charge its customers accordingly to maximize profits in this new competitive market.
What does this allow?
(1) It allows competing RSPs to offer a variety of bundled services to customers (a per use charge, a charge for a the right to travel 10 times per month, or unlimited service, or service bundled with cell phones, or insurance or other services e.g), but to each have different bundles. RSPs are likely to be better at product differentiation and price discrimination than governments with omnipresent political and equity concerns.
(2) It allows the government to stay out of the data ownership business, it would be responsible only for identifying which RSP a traveler subscribed to, and thus would eliminate “the government is tracking me” problem with road pricing. (We still have big brother business is tracking me, but if you have a cell-phone or credit card, that game is already lost)
(3) It provides new markets for private industry. This could be an app as part of a cell-phone or GPS or in-vehicle service (e.g. OnStar) or insurance (AAA, Progressive). The only technology standard that would need to be established by the government is a simple (e.g. RFID) sticker adjacent to the license plate verifying the RSP and the government with inexpensive RFID readers to count the number of cars on each link by time of day with the RSP. The private firms would be responsible for monitoring their own customers.
(4) It provides a more stable revenue stream from government, which is getting revenue directly from RSPs who bid on road space. In congested areas, road space would go for a higher price, in uncongested areas, RSPs would negotiate a per-use charge with governments.
Part of the lease can include terms about when and where you can use the road. Just as lease terms today allow X miles per year, new terms could be Y peak hour miles, and Z off-peak miles. The automaker/vehicle owner would then compensate the road owners for use of the roads by the cars they lease-out, while setting rates and incentives for vehicle users/leaseholders to manage their demand. Private firms would be able to explore demand space and develop interesting combinations of services (the price for traveling on certain facilities at certain times) in a way that the public sector just cannot do for issues of both capability and fairness.
So your city has traffic congestion. Welcome to the club. Congestion not only wastes time, it increases pollution and crashes. While this undoubtedly annoys you as a traveler, it could be worse; your city might not have congestion because no one wants to be there. Still, it would be great to have a thriving city without congestion. People could reach more destinations in less travel time, and thus have more time to spend doing the things they wanted. If you figure it out, let us know.
Political double-speak today “addresses congestion” rather than “solves congestion” (almost twice as often according to Google). This is probably because policy-makers want to sound like they are doing something without promising anything. But I don’t think talking to congestion accomplishes much.
There are a number of proffered solutions out there. Congestion is, in principle, a mostly solvable problem, even if no fast-growing city has fully solved it. This article outlines 21 ways that congestion could be solved. Some of these are dumb, many are good, one is great.
Capacity – Perhaps the most obvious, ‘common sense’, solution when demand (traffic) is in excess of supply is to expand capacity. This is what we do with most things if we can. If our house is too small, we make it bigger. If the internet is too slow, we add capacity. In roads, this usually means adding lanes to existing roads. The first problem with this solution is that it is expensive. Buildings are built close to (or on) the proposed expanded right-of-way, so taking them in addition to being costly brings in an additional socio-political dynamic — people don’t like to be moved. Further, if you expand capacity, demand will respond. Travelers will switch routes, time-of-day, mode, and destination to take advantage of the new faster travel times, which means these wider roads won’t be nearly as much faster as hoped for. New (induced) developments will be built, and much of the capacity will quickly be used up by new travelers. There will still likely be a small amount of travel time saved for existing travelers, and the new travelers do gain benefits (otherwise why would they make the trip), so it is not necessarily a bad thing, but it may not solve your congestion problem.
Connectivity – Often the problem is not width of the road, but where it goes. A new road that goes directly to the right place can replace a longer route that doesn’t. So reducing the circuity (indirectness) of the network through selected connections can reduce congestion and total traffic by taking traffic off of longer routes. Even when there is nominal connectivity, it might not be very good. A bridge can replace much slower and lower capacity ferries, eliminating a bottleneck. But as with capacity expansions above, it can be very expensive. In a mature network, all the cheap and useful roads have been built already. A new connection may be cheap, or it may be useful, but it won’t be both. The induced demand outcome also applies.
Closure – Perhaps counter-intuitively, if we shut down key links on the network, we could also reduce congestion. If people can’t get across a River, they won’t drive from Home to the River either, reducing traffic along that path. Just as there is induced demand when capacity is added, there is reduced demand when it is taken away. In selected cases there is something called Braess’ Paradox, which says that some links result in an increase in overall travel time when they are added (and so reduce overall travel time when they are closed) because individual selfish routing choices can lead to bad outcomes.
Controls – The next most obvious solution is to use the infrastructure we have better. When we have a stop-sign controlled intersection, and there are long queues, we add traffic lights, which manage traffic better because there is less time lost in starting and stopping. Coordinating traffic lights on a city street grid can make sure more vehicles hit green lights. The use of controls on freeways includes devices like ramp meters, the traffic lights at freeway on-ramps, that manage the input flow to keep the freeway flowing (more) freely (than it otherwise would). Traffic engineers have suites of controls that try to squeeze in a few more cars on the same set of pavement by reducing the size of gaps between vehicles. These can help, and may be worthwhile. However, this is on the order of a 10% reduction, rather than the 100% we would like to see. And these gains are potentially absorbed by both general traffic growth where that occurs, and induced demand in a mature system.
Crashes – It is sometimes estimated that half of all delay is due to non-recurring congestion, most notably crashes. First, we want people not to crash. Crashes can be reduced by better designed roads. Crashes can also be reduced with better-trained drivers. Making licensure more difficult so the drivers are better is one strategy. Making driving more expensive so fewer people (and especially fewer marginal drivers) are driving is also significant. More importantly, crashes can be reduced by better-designed drivers. Over the longer term, we need to replace the human with the machine. Second, we want crashes to be cleared quickly. Quick emergency response helps save injured travelers. Freeway service patrols (under various names), help clear crashes and reduce the amount of subsequent delay.
Construction – Maintaining roads is important, without proper maintenance they would eventually cease to be. But closing entire roads for construction can’t be the right strategy, can it? Well, it depends. The alternative, trying to do construction one lane at a time will take much longer. So for a 4 lane road, closing one lane at a time for 6 months each will take 2 years, but closing all 4 lanes, and requiring travelers to detour might take less than six months as it is more efficient. Doing all work at night or weekends is another strategy. The cost of the delays vs. the cost of construction need to be properly weighed.
Competing modes – Just as widening a road is in theory a solution to a congestion problem, building a competing mode is also a theoretical solution. By building a rapid transit line or running an express bus, or even building sidewalks and bike lanes, other people may switch off the road, leaving the roads faster for the rest of us. The traditional induced demand argument follows. The evidence on this is weak though, most transit construction serves transit riders (which is a good thing) and doesn’t reduce congestion much.
Gauge – Track gauge, the width of railroad tracks, determines the width of the trains. As with railroads, the gauge of roads has been largely determined, with freeway lanes being 12 feet wide, and cars, buses, and trucks are narrower so that they fit. Lanes on surface streets vary a bit more, but tend to be similarly sized in newer developments. Most cars carry one person most of the time, but are sized for at least 4, 2 in parallel, and 2 rows. If cars were half as wide, we could fit twice as many in the same space. This is what we do with motorcycles and bicycles. Pedestrians can even fit more. Before the motorcar, long distance travel by horse was one man / one horse usually, and the occasional horse and carriage for multi-person trips or cargo. Now the carriage is brought along whether it is needed or not, wasting space and delaying others. Redefining the gauge of road lanes, so that lanes at least are split for narrower cars could double capacity.
Storage – On surface streets, we waste pavement storing parked cars. A lane or turn-lane or half-lane or bike-lane or bus-lane can often be added in the space devoted to unmoving metal, increasing throughput. Adjacent property owners are often under the mistaken impression they or their customers have a right to park (for free!) on the public street in front of their house. When there is no congestion, this is not a problem. Where there is congestion, this artificial right is costly to society.
Information – People are terribly inefficient routers, choosing routes that are not only not the shortest for society (which is to be expected) but not the shortest for themselves either. Using real-time traveler information rather than their own intuition and incomplete mental maps, drivers can find the shortest path to their destination, reducing their trip length and travel time, and reducing congestion for others.
Autonomy– While humans can barely safely drive with a two-second following distance between vehicles, autonomous vehicles with advanced sensors, in an environment where most or all the cars are autonomous, are expected to follow at less than one-second. That doubles capacity right there. They also don’t require nearly as wide a lane as human drivers do, which could almost double capacity again (this is the same gain we would see with narrow cars). How well this work on city streets, as opposed to freeways, remains to be seen, but up to a four-fold increase in freeway vehicle capacity just from autonomous vehicles is well-within the realm of possibility, and while it will induce demand, should buy significant congestion reduction gains. Even non-freeways will benefit as more travelers switch to the less congested freeways.
The first set of strategies are basically supply side. But congestion is caused by a mismatch of supply and demand. So let’s turn to demand.
Locating – If only other people lived near where they worked (shopped, studied), they wouldn’t have to travel as far, and so would be on the roads less (assuming they still traveled by car) or not at all (if they walked). While at some level, people coordinate location of origin and destination (they are usually in the same metropolitan area), they could certainly do so better. From a public policy perspective, moving more jobs out to where people live, and more people to where the jobs are, increasing the local balance between jobs and housing can reduce travel. In practice this is difficult, as there is no mechanism to require people to take local jobs or firms to employ local residents. The best municipalities can do is ensure the zoning permits developers to build appropriate developments. Still, ensuring the opportunities are there is one thing (and at best you can ensure developers are permitted to develop these opportunities), ensuring people partake of those opportunities is another. The cost of this also needs to be considered. There are reasons many firms like to locate near other firms rather than workers, which has to do with economies of agglomeration and the efficiencies that can be had from close inter-firm coordination.
Telecommuting – At the extreme of mutually co-locating home with respect to work is working at home. This involves no commuting travel outside the home, though may induce some additional non-work travel outside the peak. This has been growing slowly over the past decades, and is amenable for many, but by no means most, jobs. Like location, this is largely an individual decision. Better broadband would help, and encouraging employers to allow or require employees to work from home would not reduce this trend, but it is hard to see outside of money or regulation in some form what persuades firms to behave differently with regards to incentives for where employees work. Still, the more people that tele-commute (tele-shop, etc.) the fewer that are traveling, all else equal, which it never is.
Scheduling – We also wouldn’t have congestion if not so many people wanted to travel at the same time. We could stagger work hours, so not everyone arrived at work at the same time. Some large firms already do this, but it could be expanded. The downside is that the whole point of everyone going to work at the same time is that they be there together (or at the same time as customers and vendors) so that can collaborate. The point of going to work is only in part the ability to use expensive machinery in isolation. It is also about the gains from cooperation of people being at the same place at the same time. If people didn’t need to do that, and were (almost) as efficient as working from home, then there would be little point in traveling at all.
Sequencing – We do not begin and end all trips at home, we chain our trips together to reduce the total amount of travel. We go from work to the store to another store to home. This not only saves us time, it reduces congestion. Do this more systematically, with a little more planning, and you can reduce more congestion.
Shipping – Just as chaining trips may be efficient for you, chaining trips may be good for your goods. Instead of you and your neighbor each making a trip to the store and back (A -> Store -> A, B -> Store -> B). The store can send out a truck (or robot, or drone) and drop off goods at you and your neighbor’s houses before returning (Store – A – B – Store), which should reduce the total mileage on the network (though the trucks will need to load and unload frequently).
Sharing – Carpooling has been around since the dawn of cars, and sharing the back of a horse, camel, or llama before that. It is easiest when there are two people going from the same place to the same place (like members of the same family going from home to work) at the same time. All this sameness though requires coordination to arrange, or sophisticated matching to discover. While people may carpool with non-co-resident coworkers in their youth, one party (whoever is the most efficient or earliest riser) will tend to find the cost of waiting for the ride (or worse, waiting for the passenger) to be too costly, and eventually everyone gets their own set of wheels if they can afford it. HOV lanes or restrictions in some cities encourage people to pickup strangers (sluggers or jockeys) to fill up the extra seats to save time. Overall this is a small phenomenon. But imagine you could get paid for picking someone up along the way and dropping them off — ridehailing services like LyftLine and UberPool are moving in this direction — you might be more inclined. Information technology is enabling everyone to be a taxi-driver. Whether they want to be is another question.
Sharing with Scale – Suppose instead of picking up one person, you picked up two, or four, or eight, or sixteen, or thirty-two. You became a jitney or vanpool or even a bus-driver. And if you pick up a lot of people, maybe that is more remunerative than the job you have, so you become a professional. And if you picked up thirty-two people along the way, you would want to be careful about the route so you don’t delay the passengers on board (your paying customers) too much. You have discovered the continuity between driving alone and public transit. And if someone else is driving a nice vehicle on a convenient route, maybe you forego the car and ride instead. You have helped reduce congestion. And if one vehicle is carrying thirty-two people who otherwise would have driven, we have removed thirty-one vehicles from the road. And if everyone were in a vehicle carrying thirty-two people, we can reduce congestion almost 97%. But for all the reasons identified above, this magnitude is unlikely. [The difference between this and competing modes above is that this sharing with scale emerged organically, while the other is a top-down investment in fixed route transit lines — process matters.]
Walking or Biking – Maybe you still like your independence and don’t want to comport to someone else’s schedule, you just don’t want to be in a car. If more people walked instead of driving, the sidewalk utilization rate would increase, while the road utilization rate would decrease. Bikes similarly would congest bike lanes and bike paths, but that’s not as much of a concern, and bikes in mixed traffic can sneak through without congesting cars that much. Walking and biking are both up over the past decade. The best opportunities for substitution are for short distances, which are a large share of trips though a much smaller share of miles.
Rationing – If your license plate ends in an odd number, you can travel Tuesday, Thursday, Saturday, and Sunday. If it ends in an even number you can travel Monday, Wednesday, Friday, and Sunday. Therefore each weekday will have half as many travelers, right? Alternatively, license plates ending in 1 or 2 can’t drive on Monday, 3 or 4 can’t drive on Tuesday, and so on. Therefore each weekday will have 20% fewer cars. This strategy has been tried in a number of cities, and has been used in the US to ration gasoline during the oil crises of the 1970s. In practice, people with money (which is to say, most people with cars) get a second car to travel when they want. People swap cars, or license plates. People get around these regulations, which are a terribly inefficient way to reduce congestion.
Pricing – Charging people for the use of roads, more when and where it is congested, less when and where it isn’t, will foremost reduce travel during congested times, and thereby reduce congestion, and may increase it in uncongested periods when there is excess capacity (depending on the charge) as people adjust their schedule. This better balances the load on the network, and is a strategy undertaken in most transport modes, as well as other time sensitive businesses like restaurants and movie theaters.
How do travelers reduce travel?This is the best part. Each individual decides for themselves when to change location, when to change schedule, when to work from home, when to have something delivered rather than making a trip to get it, when to use a different mode, when to share a ride, when to reroute, and when to forego a trip, thereby making decisions that are individually rational.
Doesn’t this lose road agencies money?This is the second best part. With pricing, properly regulated road utilities will see roads as a valuable commodity rather than a commons, and if they increase throughput more they can sell more. They will try to be more efficient about managing the use of the existing roadspace, but won’t have an incentive to build unnecessary new links.
Can this work?This is the third best part. There are many proposed strategies to implement pricing. Obviously this has been politically difficult, or it would already be widespread. Transforming road agencies into public road utilities is one step. Further, the emergence of electric vehicles and the advent of autonomous cars reopens the window of opportunity to consider pricing to replace gas taxes, and enable road demands be managed far more directly.
There are undoubtedly some pet solutions out there not discussed here, and lots of details overlooked. Feel free to add more in the comments.
This paper presents the results of an investigation into the factors contributing to toll lane subscription choice by using data from the MnPASS high-occupancy toll lane system operated by the Minnesota Department of Transportation. The paper estimates a binomial logit model that predicts, on the basis of aggregate characteristics of the surrounding area, the likelihood of a household having a subscription to MnPASS systems. Variables in this model include demographic factors as well as an estimate of the incremental accessibility benefit provided by the MnPASS system. This benefit is estimated with the use of detailed accessibility calculations and represents the degree to which a location’s accessibility to jobs is improved if HOT lanes are available. The model achieves a rho<sup>2</sup> value of .634, and analysis of the results suggests that incremental accessibility benefits play a statistically and practically significant role in determining how likely households are to hold a toll lane subscription.
High Occupancy/Toll (HOT) Lanes typically charge a varying to single occupant vehicles (SOVs), with the toll increasing during more congested periods. The toll is usually tied to time of day or to the density of vehicles in the HOT lane. The purpose of raising the toll with congestion is to discourage demand enough to maintain a high level of service (LOS) in the HOT lane. Janson and Levinson (2014) demonstrated that the HOT toll may act as a signal of downstream congestion (in both general purpose (GP) and HOT lanes), causing an increase in demand for the HOT lane, at least at lower prices. This paper builds off that research and explores alternative HOT lane pricing strategies, including the use of GP density as a factor in price to more accurately reflect the value of the HOT lane. In addition, the paper explores the potential effect these strategies would have on the HOT lane vehicle share through a partial equilibrium analysis. This analysis demonstrates the change in demand elasticity with price, showing the point at which drivers switch from a positive to negative elasticity.