“David Levinson, a transportation economist at the University of Minnesota, recently identified 14 trends that will shape the future of commuting, e.g.:
1. as state and local governments take on more responsibility for surface transportation, they will tend to make better decisions on capital and operating and maintenance costs, as they will be less skewed by the prospect of “free” or cheap federal financing;
2. because the U.S. surface transportation network is fairly mature, the emphasis will shift from new construction to “fix it first“;
3. the rise of electric vehicles will contribute to the collapse in motor fuel tax revenues, thus necessitating alternatives like VMT (vehicle-miled traveled) taxes or increases in retail sales taxes;
4. the spread of sensors will facilitate traffic management in a variety of ways, reducing the burdens of congestion;
5. the continuing “dematerialization” of the economy will tend to reduce the number of automobile trips;
6. delivery will increasingly substitute for fetching, i.e., firms like Fresh Direct and Amazon will continue to train U.S. consumers to rely on e-commerce rather than trips to the supermarket;
7. though car-sharing and bike-sharing won’t become extremely common outside of dense cities, their market share will likely grow — and the rise of autonomous vehicles may well lead to explosive growth in car-sharing;
8. as people rely more on virtual social networks and less on local social networks, local travel might decline as long-distance travel increases, i.e., I’ll make fewer trips around the neighborhood, but more trips to visit relatives 2-3 hours away by plane;
9. choice in education will tend to mean that more parents will ferry their children beyond their neighborhoods to send them to school, or to afterschool enrichment programs;
10. real-time information transmitted by smartphone will further encourage spur-of-the-moment planning and novelty-seeking (“let’s try this new place that gets X stars on OpenTable”);
11. big boxes will get bigger, and when families to make trips to physical outlets for groceries, they will be more inclined to buy in bulk and to buy less often;
12. as work weeks shrink, so will the number of vehicles on the road during rush hour — though off-peak travel will increase somewhat;
13. and most interestingly, the “end of driving,” i.e., the rise of autonomous vehicles, will lead to more mobility for children, the elderly, and the disabled and it will facilitate exurbanization, which Levinson touches on elsewhere: [previous post]”
Despite major proposals for investment in high-speed rail, intercity passenger rail in the US remains and will remain a small mode unless there is some sustained exogenous shock to the system such as higher fuel prices, very stringent environmental regulations, an unforeseen war, or act of terrorism that constrains air transportation. As a mode, rail was in decline in the US from a peak in about 1920 through the 1990s. Presently, Amtrak serves 650 million passenger miles in a peak month (a number that has risen considerably since its nadir), US airlines serve about 80 billion revenue passenger miles in peak months).
In contrast with the US, internationally, rail passenger service is growing much faster and serves a much larger share of the market. In the UK for instance, there are now 2.8 billion passenger miles per average month (excluding urban transit), well more than four times the US number for a country with one-fifth the population (in other words, rail usage is more than 20 times as high in the UK as the US), a number that has steadily risen, and can expect to rise more with major investments in HS2 and Crossrail.
Lifecycle theory traces out the deployment path of technologies, from birth, through growth, to maturity and decline. These S-shaped curves have successfully described the deployment of many technologies and will be applied to a variety of transportation systems to identify their prospects. The difficulty remains for predicting modes that are still growing, where they will reach market saturation. E.g. how many flights will people take per year? This requires examination of fundamental factors.
There are a number of social and technological changes that may affect outcomes. The most significant for transportation might be automation, in particular self-driving vehicles (what Elon Musk, head of Tesla Motors, recently referred to as “auto-pilot”). (Related automation trends include pilot-less planes (drones) and automated trains).
Self-driving vehicles hold the promise of radically altering urban transportation. Their effects on intercity transportation are less clear. On the one-hand they will extend people’s willingness to travel by auto, as they lower the cost to the driver of travel (in terms of their need to exert energy driving and attending to the road), and enable them to engage in other in-vehicle activities. In that regard, they might change the boundary between the “drive or rail” and “drive or fly” decision (e.g. moving the threshold from 300 miles to 400 miles). Thus, they are more likely to affect rail than flying.
On the other hand, self-driving vehicles will likely decrease auto-ownership and increase various types of on-demand car rental, which I have called “cloud commuting”, such as car-sharing (Zipcar, Car2Go, etc.). This is because one of the major difficulties with car rental, especially in less dense areas, having to travel to get the rental car, will be obviated. People with fewer cars on hand are more likely to use shared transportation modes (transit, intercity rail, airplanes), since they will be paying more per trip (they will have to pay to rent the car, while if they owned, they would not attribute ownership costs to a particular trip).
Further, so long as other modes remain faster and less expensive over some distance, longer trips will remain in the domain of train and especially airplanes. In the second figure, user-owned driverless cars will likely shift the location of D1, moving it to the right, while user-rented driverless cars change the fixed cost of making a trip by automobile, moving the intercept of the green line with the Y-axis upward (compared to self-owned cars).
We can use this model to examine other types of shifts as well. For instance, more widespread rail networks will aim to push against this trend, moving D1 to the left, and D2 to the right.
How will these trends play out with current and upcoming technologies, considering physical constraints (such as time availability) and economic constraints (such as incomes)? My guess is that car ownership remains the dominant means of transportation for most Americans, and so the range people will travel by car on the net will increase. But the future is complicated, and other factors may intervene.