7.5 Truck-only Toll Lanes
Specialization is a natural
response to maturity, as the main network is built out, refinements of supply
to better fit markets can result overall gains.
Truck-only toll facilities have
been suggested and studied in several states, including Ohio, Indiana,
Illinois, and Missouri, for I-70 at the cost of $50 billion for 800 miles[63],
as well as Oregon, California, Florida, Texas, Georgia, and Virginia, but
to-date none have been realized. The advantages of such systems are several.
Separating cars and trucks, which have different operating characteristics, may
achieve economies. Compared with cars, trucks require thicker pavements, longer
headways, and less steep grades e.g. Interaction with Automated Vehicles could
also be interesting, as truck convoys would be possible with 1 driver (in the
lead truck, or remotely) running a convoy (See Chapters 1 and 8), though to date platooning is primarily about
fuel savings rather than labor cost savings.
Trucks also have a higher value of time than cars, as in
addition to the labor costs of the truck driver, the cargo has value, and
reliability is particular importance. That said, such facilities are costly,
and new rights-of-way are particularly difficult to construct. So the number of
trucks required to take advantage of such a system is large. In contrast
car-only or truck-restricted facilities are quite common (see e.g. HOT lanes
and many parkways). The best rights of way may already be served by railroads,
and so long as there is excess capacity in the rail network, there may be
little or no social welfare gains from constructing an additional
transportation route in such a corridor. Conversion of little used rail
corridors to truck routes may be of some usefulness in particular locations, as
trucks have far more spatial flexibility about origins and destinations than
trains, as well as lower logistics costs when they require fewer transfers of
cargo. Conversion of under-utilized roads may also have some
opportunities.
While the operational
characteristics of trucks and cars vary, with automation, the degree to which
this is a problem may decline, as computer algorithms can better negotiate when
and where to interact with vehicle connectivity. These truck-only routes are
most likely to opportunistic rather than systematic, finding under-utilized
rights-of-way on road or in railroad corridors, or other under-utilized
contiguous stretches, parallel to existing congested facilities, that can be
easily and cost-effectively dedicated to truck traffic, with a minimum of
disruption to existing land uses and activities.
Other alternatives include dedicating lanes on existing
routes to freight traffic, but the extent to which this is traffic flow
improving is site-specific and requires analysis and detailed demand estimates
and traffic simulation. With automation coming, some of the advantages of
separating cars and trucks diminish, as safety and speed will be greatly
improved in any case. While the benefits
decline, the costs decline as well. As the ability to increase lanes with
automation increases, the possibility of truck-only lanes becomes more viable,
even as the safety need reduces. Electronic toll collection interoperability
can remove a barrier here as well.
NET: There are a few opportunities for truck-only tollways in
Minnesota, but these are few and unlikely to be a significant trend. Truck
lanes on automated highways, especially dynamically created lanes that can be
turned on and off with demand, are more likely once automation hits a critical
mass. This will not likely require new construction.
7.6 Mileage Charges
Instead of using a gas tax, states could establish a much
stronger user fee principle by charging each vehicle by miles traveled, by
time-of-day, and by type (weight) of vehicle.
The most obvious opportunity to implement mileage charges is
for vehicles that don’t currently use any or much gasoline (Electric Vehicles,
Hybrid Electric Vehicles). (See Chapter
6). The advantage of this is that EVs (or users of certain alternative
fuels) don’t pay gas tax, so charging a distance tax will be perceived as a
fair user fee for roads.
Off-peak discounts would be the next logical step. This can
be made opt-in for non-EVs.
Depending on how these were implemented, it could be full
dynamic pricing, or a simpler schedule of rates that change based on the hour
or half-hour.
Geographic differentiation in
prices is also a logical step, charging more in congested regions. This too can
be made opt-in, with users who do not want to be monitored required to pay the
highest charge, but users who opt-in with some form of spatial tracking will be
allowed discounts for less expensive areas.
Implementing charges that vary
by location does not require GPS, cell-phone tower triangulation can in
principle be used. However it is highly likely GPS will in practice be used, as
the locational accuracy is much greater. GPS receivers do not normally transmit
information but GPS-equipped vehicles can log the vehicle location. Some
additional communication technology, which might report a reduced form of
information (e.g. total amount owed) would be used to complete the transaction.
In addition, back office processing of this information is required, and a new
billing infrastructure would be required
For instance, a pilot study in
Oregon had a chip in the vehicle log distance traveled by geographic zone and
time of day, without storing the precise location. The chip only reported the
total charge owed, calculated by an onboard algorithm. So no detailed tracking
information was shared. Simpler technologies such as a mileage based user fee
would simply record the odometer reading, but this would not allow
differentiation by time of day or location. For individuals concerned about
privacy, they can pay peak prices all the time. Given patterns in other markets
about consumer response to trading privacy for money, most people will probably
choose to reveal information about time and location to save money (by getting
off-peak rates).[64]
Similarly for fleet vehicles (trucks, taxis) this
should be easier to implement, as there is centralized management. This also
enables the implementation of a weight-distance tax for trucks. The trucking
industry has in the past resisted such taxes.
The disadvantages of such a system are such that it has yet
to be implemented. While not as complicated to deploy as toll booths
everywhere, there are still significant costs. It is technically more complex
than gas tax sources, requiring a new revenue-collection infrastructure to be
installed.
Oregon as of this writing is seeking 5000 volunteers to
participate in a test of its mileage-based user fees program (OReGO) at
$0.015/mile ($0.024/km). People in the program will not have to use a GPS, but
if they don’t they will have to pay for miles driven out of state. This fee is
currently lower than the gas tax for vehicles which get less than 20 miles per
gallon.
The OReGO system reportedly
loses 40 cents of every dollar to toll collection.[65][66]
OReGO has also failed to reach voluntary sign-up targets (only 900 of 5000
desired).[67] Typically toll collection costs are high,
especially at start-up, as fixed costs have yet to be spread over a wide base.
London’s Congestion Charge had similar cost issues.[68]
Resistance to a big-bang type deployment can be
expected to be very high, which is why other soft-launch strategies may be more
successful. For instance, an anti-road pricing petition in the United Kingdom[69]
received over 2 million signatures even though such a system was not near
rollout and was only being tested. The anti-tolling petition generated a great
deal of populist support, indicating the political difficulty with this type of
transition. Eight years later, the UK is no closer to deployment of road
pricing outside of London’s Congestion Charging scheme (and even that was
rolled back from its maximum extent). As evidence of the political if not
technical difficulty, no other country has yet gone forward with such a large
scale road pricing deployment, and one would expect other countries to lead the
United States on this issue.
At this point issues of
privacy and perception of privacy remain. The issue might disappear once
travelers realize that with cameras and connected vehicles, they already lack
privacy, so nothing additional will be lost with road pricing. Perception of
double-taxation will also be hard to allay among the general public without
sacrificing existing revenue sources during its implementation.
Dynamic pricing as an opt-in strategy for all
vehicles, and required for non-gasoline vehicles is a viable deployment path.
This is one area (in contrast with the other 7 working papers) which will need
to be led by government, so long as government continues to own and operate the
roads (which seems likely). By phasing it for selected vehicles, the system
will have the opportunity to have the bugs worked out in a low visibility
environment compared with a “big bang” style deployment everywhere,
all-at-once.
The effects of mileage charges
depend very much on the configuration of the system. This analysis assumes a
mileage-based user fee to recover the fixed costs of transportation services
and timebased user fee to pay for the additional capacity needed in peak times that
is not required in the offpeak (a “marginal price congestion charge”).
The consequences of pricing depend on the total level of
pricing. Assuming the “economically optimal” scenario, where user fees pay for
the full social costs of travel by automobile this means that the per mile
charge will be higher than today, as user fees would replace not only the state
gas tax, but other sources of revenue at the federal, state, and local levels.
In addition, user fees would account for externalities such as congestion,
pollution, and crashes (potentially replacing current insurance mechanisms).
While the exact amounts of these costs will always be disputed, these higher
per mile costs will in the net reduce travel, all else equal (which as other
reports in this series demonstrate, will not be the case).
Higher costs generally result
in lower demand. Higher peak prices should move travel out of the peak. Higher
overall prices will reduce the total number of trips. The amount depends, the
short run elasticity of travel demand with respect to price is on the order of
-0.03 to -0.08[70]
(A 1% increase in price of fuel reduces demand by 0.03% to 0.08%), though these
numbers seem to be lower than in the past. The long run elasticity is higher,
on the order of -0.33.[71]
Using user fees rather than general revenue to
support roads roughly doubles the out-of-pocket cost of travel by car. Full
cost pricing should approximately double that again, for an overall four-fold
increase in the direct user-paid costs of roads. (Other costs, such as taxes,
health costs, insurance, and time lost due to congestion) would be lowered were
this to occur. This moves the overall gastax equivalent from about $0.50 today
(state plus federal) to about $2.00, or the price of gasoline from about $3.00/gallon
to about $5.50. This is an 83% increase, which implies a long run reduction in
per capita demand by about 25-30%. Clearly these percentages will differ
depending on how they are assessed. Technologies such as electrification and
automation will significantly reduce the pollution and the safety and
congestion externalities respectively.
Nevertheless, this gives a
sense of the magnitude of change. Even if only infrastructure and congestion
costs are fully internalized (so per mile user charges merely double) there
will be a reduction in demand by on the order of 10-15%.
Traditional travel demand
theory predicts (and nothing suggests otherwise in this case) that any
reduction in demand will be manifested in several ways.
•
Dynamic Road Pricing should reduce the peak
demand for roads, and increase demand in the off-peak as some travelers switch
time of day. Total travel time on priced sections will reduce, enabling the
traversal of longer distances in the same amount of time, so even if pricing is
ubiquitous, we may see some longer trips as some people exchange money for
time.
•
Shortening trips as closer destinations are
sought, especially for non-work travel.
•
Relatedly, higher costs of travel will result in
denser land development.
•
Pricing should also increase demand for non-auto
modes (walk, bike, transit) as well as carpooling.
•
Pricing should reduce the number of trips made,
both due to decisions not to engage in an activity as well as decisions to
engage in the activity virtually. Thus we should see increased substitution of
delivery for shopping and increased tele-commuting.
While more trips will be
diverted, some very high value of time trips may be attracted to the priced but
uncongested peak. Think particularly of freight, discussed in Chapter 8, which now may travel in the
off-peak to avoid congestion, but could return to their preferred travel times
in the absence of congestion. The trucking industry is also willing to delivery
in off-hours, but shippers and receivers have presented resistance.
Implications for revenue
depend on how it is implemented. However charging for marginal cost rather than
the average cost of roads should result in more revenue than current
approaches. Charging a profit maximizing toll (in a world, e.g., of private
operators) would generate far more revenue than current approaches. In any
case, pricing presents the opportunity to fully move roads off of general
revenue and onto a user-based system, and to internalize congestion and
potentially pollution externalities.
NET: The vehicle mileage charge is the seemingly inevitable
end-state for pricing and the funding of roads. Widespread adoption of
alternative fuel sources makes this necessary for efficient userbased funding
above and beyond fuel tax. But in the
absence of a sudden collapse of the feasibility of the gas tax for revenue,
(which seems unlikely, despite periodic politically generated crises, average
vehicle age is over 10 years, so even if all new cars were non-gasoline, it
would be more than a decade before even half the fleet was replaced) this will
be a slow transition. Instead,
anticipate years (or decades) of trials, such as Oregon is currently
undertaking, as well as requirements for non-gasoline powered cars, and
eventually all new cars, before this becomes a requirement for retrofitting the
existing fleet.
Chapter 8: New Logistics
8.1 Introduction
Nearly a third of the share of world
transport energy is dedicated to the movement of freight within and between countries
by trucks, ships, and rail [1]. In the Midwest region of the United States
trucking leads rail and water transport as the primary means of delivering
goods to businesses and end-use customers. The growth in global trade, online
retailing and business-tobusiness delivery is not only changing how goods are
moved but also the type of goods moved and how far or frequently they are
transported. These changes have important impacts on the road network and
associated infrastructure used by industry, as well as the cost, and energy and
carbon intensity of the trucking sector [2].
Minnesota has
seen a 2.3% increase in heavy duty trucks over the last several years (2012 to
2013). Of the freight shipped within the
US, the total domestic weight of shipments has increased by 4.15% over the last
five years (Compound Annual Growth Rate, 0.82%) and is officially projected to
increase by 45% by 2040 (CAGR, 1.34%) [3], though the basis for this
accelerated rate of growth is something we question.
The seasonally-adjusted
truck tonnage as tracked by the American Trucking Association and collected by
the US Department of Transportation [3], indicates that truck tonnage has been
rising steadily since 2010 after a near 15% drop from 2008 to 2009 (see Figure
8.1). In 2015 nearly 70% of all the
freight tonnage moved in the US goes on trucks, moving 9.2 billion tons of
freight annually with 3 million heavy-duty Class 8 trucks. The Class 8 truck
gross vehicle weight rating (GVWR) is a vehicle with a GWVR exceeding 33000 lb
(14969 kg). These include most tractor trailer tractors, as well as single-unit
dump trucks; such trucks typically have 3 or more axles. The transport of
freight requires over 37 billion gallons (140 B liters) of diesel fuel, which
creates revenue for transport infrastructure through diesel tax, but also leads
to noxious pollutants and greenhouse gas emissions.
Figure 8.1: Seasonally-adjusted truck tonnage on US road network from 2000 to 2015. [3]
The delivery of
goods throughout the economy relies on an intricate multi-modal network of
on-road trucking, rail, ships and airplane delivery. Industrial logistics
operators, retail suppliers, and independent fleet managers with fleets as
small as one vehicle still predominantly control the organization of freight
deliveries. Despite the decentralized nature of freight movement, new methods
of organization and proposed standardization are hoped to increase efficiency
of freight movement and give rise to a new era of goods transport. Advances in
logistics systems will be enabled by new technologies, approaches, and desire
for increased efficiency.
Tax revenues within the state of MN
are proportional the total sales volume of fuel for gasoline and diesel. The MN
sales tax for gasoline fuel is $0.285 per gallon ($0.075 per liter) and $0.285
per gallon for diesel fuel with an additional $0.019 per gallon
UST/Inspection/Miscellaneous fee[72].
The MN fuel tax revenues are shown in Figure 8.2 for 1980 to 2014.
Figure 8.2: MN motor fuel tax revenue for 1980 to 2014[73]. 8.2 Logistics Organization
New information technology
permits sharing of data between and across businesses thus driving efficiency
increases and leading to vehicles that are operated nearer to full capacity
(mass or volume limited). This may serve to reduce the distance travelled by
heavy goods vehicles per unit of GDP. In turn improved logistics may reduce
costs, thus enticing more demand for delivered goods and shifting the share of
trips made by individual consumers versus delivery options. The net impact of
new logistical practices on total vehicle activity is not well understood,
though we expect a net reduction in distance traveled due to IT improvements
even after accounting for the resulting induced demand. The elasticity of demand of freight with
respect to cost savings is probably less than 1. Some of the potential drivers
for changes in the freight industry as a result of logistics reorganization are
given below.
Truck trips are getting shorter as a
result of shippers and receivers building many more distribution centers to
speed up deliveries. Walmart alone has more than 100 distribution centers in
the US, whereas they had 12 a decade ago. Additionally, a truck driver shortage
crisis (a mismatch between the wages trucking firms are willing to pay and what
potential drivers want) has caused trucking companies to do many more
"drop-and-hook" operations -- which splits up longer trips into
multiple shorter ones (this gets the drivers home more often and helps with
retention). Very long trips (750 + miles) are moving to rail intermodalism
which drops the average length of hauls for trips that stay on trucks.[4]
A significant
challenge to all new freight business models is that the cost of conversion is
high and thus incumbents are less likely to adapt. This has led many to suggest
that logistics innovation will come from outside traditional logistics
suppliers and organizations, as innovative companies large and small seek to
expand business through efficiency improvements.
8.2.1 Supply Chain Network Pooling
The drive to reduce costs, increase
efficiency and reduce emissions, has led to significant research focused on the
impact of supply chain network pooling. Traditional means of supply chain
network pooling seeks to share individual logistic operations between
collaborators in warehousing, inventory management and transportation.
Consolidators and third party logistics providers like C.H. Robinson exist to
wring efficiencies out of supply chains of multiple firms by brokering between
shippers and carriers. In practice, for larger firms, sharing occurs at the
individual organizational level. A result of network pooling is a reduction in
the distance traveled on the overall transport system, but an increase in the
weight of individual vehicles within the system.
Reduction of freight vehicle traffic
is likely to occur in significant numbers should supply chains be pooled at the
strategic level. A recent study [5] explored the effect of pooling supply chain
networks on reducing activity from transport with two possible modes, i.e. road
and rail, in the context of a national distribution network of two retail chains.
The study proposed pooling supply chains at the strategic level, an approach
that demands further and long-term collaboration between the actors of supply
chains. The results indicate that at the national level (France) network
pooling can reduce vehicle activity by 14% when road-based networks are
considered. Further (52%) reductions in vehicle activity and greenhouse gas
emissions are seen when multi-modal truck and rail transport networks are
combined and optimized.
Other
advantages of pooling through horizontal collaboration include increased
delivery frequency of 2 to 5 times greater than traditional systems. Cost
estimates from Canadian studies indicate that reductions of more than 15% can
be had by pooling.
Challenges to horizontal pooling include
a mismatch between delivery services and infrastructure between organizations,
limits in IT system interfaces and overly complex logistics network systems.
Anti-trust issues make pooling challenging as information-sharing relating to
pricing between competitors serves as a barrier to collaboration between
companies within the same sectors. Recent work by Auburn University and the
American Transportation Research Institute found that even platooning of
freight had only limited interest.
8.2.2 Physical Internet
While supply
chain network pooling occurs most often by means of collaboration at the
individual institutional level, significant transport network changes require
homogenization of operations and shipping standards at a national or international
level. A concept of homogenization in order to achieve efficiency gains within
the shipping industry is the physical internet.
The Physical Internet Initiative seeks to transform the
manner in which goods are handled, stored, packaged and transported across the
supply chain. Conceived in 2006, the Physical Internet is a recent push in
logistics seeking to transport goods in a similar conceptual framework of the
digital internet’s data transmission [6]. The digital internet connects
networks of information in a transparent manner with a consistent protocol,
allowing the transmission of formatted data packets in a standard way
permitting information travel because the heterogeneous equipment all respects
the TCP/IP protocol.
The physical internet seeks to create
standards for packaging called “π containers” that enables a
homogenization of freight technology to increase the efficiency of goods
transport. With standardization in packaging, the physical internet seeks to
transition from a system of individual logistics operations transporting goods
to an anonymized network of package transportation. The transition from
transporting goods to standardized packages is expected to allow for a change
in the distribution network and a shift in the manner in which the transport
network is used by heavyduty vehicles. [6]
Advantages of the physical internet are
expected to include increases in individual vehicle efficiency, increased
network robustness, specialization of the heavy duty vehicle fleet and more
efficient warehousing and distribution center locations. Such changes would
likely impact the use of the physical transport infrastructure by shifting
traffic patterns, vehicle mass and quantity of service provided by lowering the
cost of transportation.
In the Physical Internet, the goods
delivery process would entail a network of distribution centers through which
goods are transported. Packages are delivered through the network at a series
of transit hubs located every 250 miles. Drivers have specific routes with
origins at hub locations where packages are picked up and transported to the
next hub location, where packages are unloaded and then the vehicle is filled
with a return load to the origin hub. The packages move from hub to hub
(535,000 estimated to be required in the U.S.) in a similar manner to
anonymized data packages moving through the digital internet. The π
packages would conform to standards that allow for multi-modal transport
(truck, rail, airplane and ship) and would move through the network of an open
supply web by means of a series of privately-owned hubs and transporters
working in concert to deliver goods. The system would resemble a hub and spoke
network.
The increased
efficiency of goods delivery throughout the physical internet is primarily a
result of reduced empty load transportation. Currently, trucks and containers
are often half empty at departure, with a large portion of the “filled” volume
comprising packaging [7]. Within the US trailers are only about 60% full when
traveling loaded [8, 9]. In 2009, the US industry average was that 20% of all
miles are driven with a completely empty trailer [6] with many more nearly
empty. Current research is underway in determining what new load factors would
be as a result of the physical internet, but it is envisioned that
specialization of the fleet towards π containers would
allow for higher load factors for inter-hub transfers and residential delivery.
The homogenization of the packaging
industry may ultimately serve to homogenize the vehicles delivering vehicles
and allow for specialization of transport infrastructure to best serve a
homogenized standard. Currently most cities are not designed and equipped for
ease of freight transportation, handling, and storage. The lack of homogenized
freight transport vehicles precludes optimization. The physical internet may
allow for specialization of on-road heavy duty vehicles, automated warehouses
and uniform package smart tagging that could impact transport networks.
The physical internet could lessen
the strain on existing high use roadways. Currently there is an extreme
concentration of operations in a limited number of centralized production and
distribution facilities, with travel along a narrow set of high-traffic routes.
This leads to unreliable and vulnerable logistic networks and supply chains for
many businesses, insecure in face of disruption and natural disasters, as well
as non-responsive to shift in demand. The physical internet could enhance
robustness by enabling multiple routes of package delivery throughout the
network, enhancing robustness and increasing responsiveness. However, if truck
weights increase, pavement damage may follow, even with fewer trucks, as the
relationship between truck weight and pavement damage is non-linear.
The physical
internet is in early stages of development and requires significantly more
research and development before being deployed on a pilot scale, which is
ongoing [6, 10].
8.3 Business Delivery
8.3.1 Business-to-business
There are a number of new
business-to-business (B2B) systems that are enabled by the sharing economy and
new information technologies, allowing task-based work to be easily
facilitated, higher delivery vehicle utilization, and outsourcing of chores.
The logistics organizational structures discussed above will play some role in
defining future B2B delivery, but significant advances are expected regardless
of whether supply chain pooling or the physical internet advances.
E-commerce is a
large driver of B2B transactions and will increasingly shape the pricing,
product availability and transport patterns. B2B electronic commerce accounts
for the majority of e-commerce, where in 2008 almost 40% of manufacturing and
16.3% of wholesale trade were conducted by means of e-commerce. [11]
Some experts believe that legacy
fleets and warehousing facilities will prevent the existing transport and
logistics leaders from changing from within. Thus disruption from
non-traditional ecommerce leaders may drive changes to the transport and
logistics patterns of B2B deliveries, as companies like Amazon, Apple, and
Google focus efforts on business delivery. Despite the entrance of these new
players, existing B2B operators may also serve as models for future systems.
Other suppliers of industrial goods may seek to replicate Grainger’s long
operation of same day delivery of parts and industrial supplies, since demands
for reduced on-site warehousing and increased expectations for responsiveness
from e-commerce transactions lead to further demand for such services.
Studies of
deliveries to urban environments indicate that deliveries are occurring at
shorter intervals, as retailers reduce internal warehouse space. A recent
series of surveys indicates that retail businesses could expect up to 10 core
goods and 7.6 service visits per week and that vans are increasingly becoming a
dominant mode of urban delivery [12]. While these trends are more pronounced in
older cities with dense urban cores (Northeast and European Cities), the move
by retailers such as Target to open more express stores within the
Minneapolis-St. Paul region (two currently) may bring about increased delivery
to denser urban areas [13].
Service vehicle activity is a
significant contributor to urban freight movements and often requires vehicles
to be parked close to the premises being served. Centrally coordinating
elements of service provision (e.g. for cleaning, equipment maintenance,
recycling, and waste collection), or providing improved, more flexible parking
provision for service vehicles could be as, or more, beneficial in reducing
overall freight impacts than focusing on core goods deliveries. In the case of
the latter, ‘pay-as-you-leave’ car park charging systems could encourage short-stay
service vehicles to park off-street. [12]
8.3.2 Same Day Delivery
Same day delivery in the business
market has existed for some time. Retailers such as Grainger have offered same
day delivery for nearly 20 years, serving business with low to medium
quantities of industrial parts. Delivery of large quantities of industrial
goods by same-day delivery methods is unlikely as electronic inventory
management systems and the presence of some inhouse storage facilities likely
offset the need for the added expense associated with same-day delivery of
large shipments of goods.
For shipment of
small quantities of goods there is an emerging set of decentralized delivery
services seeking to serve primarily urban areas where densities warrant
deployment of services required to achieve same-day business deliveries. Last
mile delivery services, such as
GrandJunction.com and XPOLastMile are increasingly using a
diversity of modes, including light truck, vans, cars and even bicycles. There
are an increasing number of online logistics organizers such as Deliv.com and
Kanga.com that allow crowd sourced deliveries to be made, although these
options still remain expensive
Emerging
technologies may also lessen the demand for same-day delivery. Options such as
3D printing/additive manufacturing could allow for production of low quantities
of parts that typically require same-day deliveries. While large production of
parts is unlikely to be economically feasible by means of 3D printing in the
next decade, the delivery of large orders is likely to be known in advanced not
requiring same-day delivery. Furthermore tailored manufacturing capabilities
can be located close to end users and inventories can be minimized.
8.4 Home Delivery
8.4.1 Same Day Delivery
Walmart,
Amazon, and Google, among others, are piloting same-day delivery projects in
select locations throughout the US that have sufficient density and demand to
warrant deployment [14]. Despite the early interest, obstacles remain to
wide-scale deployment in all areas. Expedited transport is costly, and
last-mile capacity is likely to become even more constrained as e-commerce
grows. Moving small volumes over short distances is costly and can induce high
traffic volumes if done without consolidation.
Online shopping
has increased the rate of same-day delivery of merchandise shipped directly to
consumers. Figure 8.3 represents the actual and projected revenue generated
from same-day deliveries in the US from 2013 to 2018. In 2018, the shipping
fees generated by same-day delivery is expected by one study [15] to reach
$1.01 billion, which is a 100 times increase in shipping fees generated in
2013. With the increasing growth of online shopping in all areas of consumer
goods, customers have come to expect the same advantages of shopping online as
when shopping in-store, namely the convenience of directly receiving the goods.
An October 2014 consumer survey shows that only 13 percent of digital buyers in
the United States expect a same-day delivery option from their domestic online
retailers. Overall, only three percent of US digital shoppers utilize it as
their most frequently used shipping method. Online grocery shopping and
same-day delivery services go hand in hand but have not yet been received
throughout the entire United States, but penetration rates of grocery home
delivery in the EU is much higher. [15]
Figure 8.3: Same-day delivery merchandise
value and shipping fees generated in the United States from 2013 to 2018 (in
billion US dollars) [15]
While same day delivery is
clearly on the rise, not all companies are following the trend. EBay recently
closed its same day delivery department called EBay Now saying that its
customers did not value the faster delivery times that come with a higher cost
for non-urgent items.
Same day delivery is linked with consolidated delivery, as
consolidated delivery is the only conceivable method to drastically reduce
prices and number of trips by individual retailers.
With the appropriate
distribution system and sufficient demand, same-day delivery need not add to
total freight travel. The goods were going to be distributed in any case, and
if same-day shippers can be fully loaded, there is no additional infrastructure
use. However, while markets are still thin, it is likely that same-day delivery
implies more freight vehicle travel, as overall loadings will be lower.
8.4.1.1 Online Shopping
The dot-com boom (from 1997 to 2001) was all about the
widespread leveraging of new forms of technology. Companies like Kozmo and
WebVan claimed same day if not same hour delivery, but were not then
economically viable.
Recent estimates of e-commerce vary widely. Shares range
from 6%,[74]
or 7%,[75]
to 12%[76]
of US retail sales based on definitions (excluding food and car sales would
make the share higher). Ecommerce sales in the US totaled $305 billion and were
rising about 15% per year in 2014 (while retail as a whole rose about 4%).[77]
Only England and China score higher in terms of percentage of online sales.
The rise of online retailing allows people to substitute
delivery for fetching, and reduce the amount of shopping trips. Retail catalogs
were replaced by the Internet, seemingly a case of the old being dismissed by
the new: Sears by Amazon. Notably, Sears phased out its Big Book in 1993 and
started shrinking its Wish Book that same year.[78]
Amazon was founded in 1994.
Not only can shoppers do the same thing differently (and
better), they can do many more things enabled by the technology of the web.
Amazon, which now claims 1% of total retail sales in the
Inventory, Metropolitan Council. Analysis
by authors.
Shopping trips are down by about one-third in a decade, they
now comprise fewer than 9% of all trips, down from 12.5% in 2000.[79]
Time spent shopping per day is also down (Figure 8.4). Other evidence for this
trend comes from the UK, where sales of vans used for home deliveries are at a
record high.[80]
It has been common for some years now to acquire some goods
from the digital shopping world.
Amazon entered the market with
books, and dethroned the big box book sellers like Crown, Borders, and Barnes
& Noble (who had earlier acquired and then shut many mall-based
neighborhood bookstores (Walden, B-Dalton), which had themselves pushed out
many independent neighborhood bookstores). The reader now has access to far
more content than just a decade ago. Books were relatively easy kindling for
this revolution, the ISBN code had been around in some form since 1965.
Anything that is standardized
and commodified, and whose delivery is easily automated is prime ground for the
new logistics. All of these deliveries reduce travel to the store, while
increasing travel in the logistics supply chain, but generally reduce travel
overall.
How far will it go and how fast is informed by preferences
for ensuring quality? Preferences for lifestyle (how much time, on average,
will people want to spend inside versus outside the home), technology (how
quickly can the product arrive), and countless other factors also shape choice
of in-person shopping vs. delivery.
On the other end of the spectrum
are goods like fresh food that people like to inspect or touch before
purchasing. In between these two extremes is what analysts term the 'digital
battleground.' This domain includes home decor, office supplies clothing,
footwear and all the rest (mattresses, eyeglasses, sweaters, souvenir items).
Left to be determined by the market are thresholds for when particular goods
transition to e-commerce for any given consumer.
There remains a long-tail of
desired, but still standard, goods that one cannot find at the corner store
because it lacks the space to inventory everything. Many are easy to ship (and
even easier to ship in electronic versions). Other goods—all commodified though
not digitized—would be amenable to new distribution systems, which can all be
ordered and delivered within 48 hours (if not sooner). Even custom goods get
sold on places like Etsy. While used (and new) items both standard and
non-standard are offered on Ebay.
The future of shopping will fall
along a continuum of commodified versus uncommodified products. Sometimes it is
the overall experience of “shopping,” regardless of the product that people
seek. Stores are revolutionizing the
physical shopping experience — as an entertainment option of sorts. While
online shopping will continue to grow, we doubt it will reach anywhere near
100% anytime soon (See
Chapter 3). Where shopping is a chore, online shopping, and automated
ordering, will replace it. When shopping is a pleasure, it won’t.
Advances and changes in logistics
distribution also are important. One can expect similar levels of murkiness
from freight transport — a transition that will be influenced by enhanced
graphical interfaces, 3-D printing, supply distribution, and changes in freight
delivery. The less that is fetched, the more that is delivered. Stuff needs to
get in the hands of consumers. While most people shun trucks and delivery
vehicles, potato chips still need to get on the shelf of the food store or your
home somehow. The amount of freight moved by various modes plummeted during the
recent recession. Now truck travel appears to be generally slowly on the rise
(Figure 8.5).
The US currently has three
national networks (USPS, UPS, FedEx) delivering stuff to consumers in ways that
are cost effective for many goods. Specialty services are on top of this—local
stores
Figure 8.5: US Ton-km
of domestic freight by mode (per capita). Source: US Bureau of
Transportation Statistics National Transportation Statistics Table 1-50: U.S. Ton-Miles of
Freight (BTS Special
Tabulation) (Millions)
http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/publications/national_transportation_st
atistics/html/table_01_50.html
New delivery models are available and coming. For the “last
mile” connecting the home with the final distribution point, new models
include:
•
lockers (akin to PO Boxes) where stuff can be
deposited for you to collect,
•
peer-to-peer delivery services (friends or
strangers will pick up goods for you and deliver them to your home or
workplace),
•
deliveries of small packages by drone, and
•
neighborhood refrigerators for grocery dropoff.
Google and others are trying to figure out a workable model
for same-day delivery. Amazon, the ecommerce giant is currently seeking
permission from the Federal Aviation Administration to deliver goods less than
five pounds via drones; considering five pound goods comprise 86% of their
inventory, this in and of itself would be a game-changer for the delivery
business. Even drones will create controversies. How high above a house can you
prohibit unmanned aerial vehicles? How often will they be shot out of the sky?
Today simple delivery— following the revolution in online
ordering in the 1990s— is itself transforming. Customers in Manhattan can order
a mattress this morning (via Casper), have it delivered this afternoon, and
sleep on it this evening; if it fails to meet their standard, they can have it
picked up tomorrow morning for a full refund. The same holds for eyeglasses
(via Warby Parker) and clothing apparel. It used to be important to lie on the mattress,
or actually see how new eyeglasses looked on our face and getting to the store
to do so was an inconvenience.
With Amazon's decision to rent a warehouse in Midtown
Manhattan for the next 15 years,[82]
Manhattanites were introduced to guaranteed one-hour delivery which is doing
away with such inconvenience. And now, via tiny plastic adhesives affixed to
your dishwasher, coffee machine or refrigerator, you can order your favorite
household products with the touch of a single, physical electronic button.
Amazon places the order, sends an alert to your phone, and it arrives within 24
hours. AmazonFresh delivers groceries to your door same day or early morning.
Amazon Prime Now offers delivery within an hour of selected goods in selected
areas. Done.
In the mid 2010s, food and
grocery delivery has turned into a hot sector receiving huge investments from
venture capital.[83]
As the Wall Street Journal says "There's an Uber for Everything: Apps do
your chores: shopping, parking, cooking, cleaning, packing, shipping and
more."[84]
The article cites startups (mostly Bay Area) with apps that dispatch someone
for flower delivery (BloomThat),
delivering anything in town (Postmates), package pickup (Shyp), healthy
meals (Sprig, SpoonRocket, Munchery), less healthy meals (Push for Pizza),
washing your clothes (Washio), washing your car (Cherry), parking your car
valet-style (Luxe), packing your suitcase (Dufl), babysitting (UrbanSitter),
dog sitting (Rover), medical house calls (Heal), self-medicating alcohol (Saucey), medicinal delivery (pot) (Eaze), and in-home massage (Zeel). We don't expect
most of these (or their customers) will survive.
8.5 Atoms into Bits
Thomas Edison first captured
sounds as waveforms and recorded them as physical deviations (i.e., grooves)
etched into a disc. The means of production, acquisition, and sound
dissemination changed over the years. Record players are largely gone for
modern forms of music playing, having been converted to data (via listening
services like iTunes, Spotify, or Pandora); stereo speakers are one-tenth the
size of those in the 1970s.
Prior to the availability of 'the cloud,' bits somehow
needed to be made available in different physical locations (not unlike records
or tapes or CDs, but much more of it). Most commonly, this meant inserting a
floppy disk (or connecting a hard drive) into one computer, transferring data,
and then ejecting that disk and physically moving the information storage
device to another computer. Big data producers (e.g., Google) continue to rely
on Fed Ex[85]
to move large data more quickly than the internet can. That too will one-day
end.
Analogous processes have transformed video. The
miniaturization of consumer goods has been ongoing for decades now (e.g.,
microwaves substituted for big ovens; portability also kicked in—
master-blasters, boom boxes, Walkmans, etc.)
Books—with the advent of online retailing (Amazon)—took a
similar turn. Then, Amazon
Journal. 2014-01-07
Delivery is easily automated for bit-based, standardized,
commodified goods like books, music, video, and software; it is in the active
process of being transformed from shop-based selling to screen-based, as shown
in Figure 8.6. By 2013 the legal video market was split between declining
physical and rising electronic delivery.[86]
The rise of online shopping for material goods detailed earlier is a prime
culprit in traffic’s slow death. The dematerialization of information goods has
also profoundly affected how the access to goods is conceived of and acquired.
Some things that could only be satisfied by moving things
can now be done by moving data.
It is now easier to organize thinking about other forms of
exchange. Think of the book, movie, meeting in person, people transmit moving
pictures of themselves in the form of data over digital networks (e.g., Skyping
with video calling, or broadcasting live with Meerkat or Periscope). While it
is difficult to conceive of things moving over digital networks, the rise of 3D
printing means data is being sent and instantly manufactured at physically
remote locations.
Alternative 3D printing
scenarios are currently playing out that have different implications.[87]
But it is clear that most goods will be manufactured closer to their point of
final consumption. Freight shipments will still occur, and the dry weight will
be similar, in that the material used in the printing is still shipped as a raw
commodity (though the water will be added later like in those freeze dried
camping meals or Coca-Cola from a fountain). Overall volumes will be much
smaller as water, air, and packaging will not need to be shipped for as long a
distance.
The nearest scenario centers on
prototyping only. 3D printers already are used for this purpose, but who gets
to prototype, or design, consumer products might be turned on its head as
serious, enthusiastic consumers (prosumers) show manufacturers what they want,
even if they don't have the materials to build a working version.
A second scenario involves
considerably advanced desktop printers in the home. People will design and
share Intellectual Property (IP) — data files describing goods (e.g., cups,
kitchenware, pens, guns.)[88] There are already several repositories of
files to download. Subject to reverse engineering, pirated files might become
the norm (following the well-worn path of music and videos). To the extent that
personal travel is occupied with acquiring small, printable objects, travel
will decrease.
A third scenario envisions a new industrial revolution
focused on a new form of manufacturing. Smaller printing 'factories' will spring
up across communities with the ability to make products. These may be private
enterprises (new market opportunities will arise) or these resources may be
provided in central locations. Libraries will continue to reinvent themselves
away from the traditional reading-and-learning mission and transform into the
digital age of providing a wider range of club goods that are under-provided to
society thanks to transaction costs. Thus, libraries
(along with community centers) might be the homes for
community 3D printers. Mass customization will likely be a hallmark of these
products but customized designs would shortly follow suit; altering designs
will not require retooling, merely tweaking the code for the software. Large
communities of "modders" are likely. In this model, there is still a
role for traditional freight (matter along physical networks) but based on much
shorter distances, the ‘last-mile' from the printer to the house.
We have been shrinking consumer goods, and getting more
output per unit of energy and matter, for a long time. Microwaves can
substitute for ovens, the WalkMan, the iPod, and now just a software app
substitute for the stereo and boom box. Dematerializing from things into data
is perhaps the final stage of shrinkage.
8.5.1 Consolidated Home Delivery
The vast majority of e-commerce
deliveries are conducted by UPS, FedEx and the USPS. The scale and ability of
these delivery services to provide consolidated home delivery of goods is
difficult to challenge for low volume carriers [16].
Opportunities exist in fast
delivery services that are able to consolidate multiple deliveries with low
cost structures. As more brick and mortar retailers begin to offer home
delivery enabled by ecommerce, there will be additional demand for low cost,
last mile delivery companies to provide these service. Companies such as
Zipments.com are offering consolidated delivery services for retailers. A
recent study identified that most companies in this sector have fewer than 250
trucks and half operate in 3 states or less [17].
Peer-to-peer deliveries
represent another form of consolidated home delivery option. While small
startups are emerging in the US such as Roadie.com, EU companies have reached a
higher maturity and are offering services within urban areas. Nimber is a
Norwegian company with over 30,000 users and delivers 10,000 packages a year.
The company recently expanded to the UK this year. Larger US companies such as
Amazon are reported to be making forays into the peer-to-peer delivery market
as well [18].
8.6 S-Curves
The US freight ton miles are
projected to grow nearly into the future as is consistent with the last two
decades worth of data from the Freight Analysis Framework. The projections
indicate that freight ton miles will grow by ~6% over the 2020-2030, whereas
the population is expected to grow by 7.4% over the same period [19].
Therefore, the US Freight Ton per capita is projected to decline over the
period.
Figure 8.7: US freight
deliveries where the blue dots represent historic data and black line
represents predicted future freight deliveries by S-Curve (S(t) =
K/[1+exp(-b(t-t0)], K=8×106, b=0.035, t0=1980). Source:
Data in this table are improved estimates based on the Freight
Analysis Framework (FAF). Technical Summary
of Updated Methodology is available at http://www.rita.dot.gov/bts/sites/rita.dot.gov.bts/files/FreightTonMilesMethodology.pdf
The adoption of supply chain
network pooling has been investigated by a number of researchers in the EU and
responses to surveys indicate that early adoption of supply chain networking
(SCN) has begun. Based on survey results for the UK Committee for Climate
Change on road freight transport the penetration rate of SCN logistics
operations is expected to reach 10% in 2015 and ultimately mature to 50% of the
total market possibility by 2025 (see Figure 8.8). Here the market possibility
represents those instances where pooling could fill loads that are not already
at maximum mass or volume capacity. A similar growth rate can be expected in
the US although drivers for supply chain network might be different and limited
by the anti-trust regulations as discussed previously. The EPA Smartway program
incentivizes such networking and may be the primary facilitator of the
adoption.
Figure 8.8: European adoption of supply
chain networking as a percentage of freight movements where pooling could fill
loads that are not already at maximum mass or volume capacity. The blue dots
represent expert responses to surveys and black line represents predicted percentage
of synchronized consolidation as predicted by S-Curve (S(t) =
K/[1+exp(-b(t-t0)], K=0.5, b=0.7739, t0=2017, R2=0.8462).
The adoption of urban consolidation networks was also
surveyed by the Committee for Climate
Change on road freight transportation.
The adoption of urban consolidation is expected to reach 6% by 2015 and will
likely reach a 50% penetration rate in 2025 for EU logistics suppliers (see
Figure 8.9). In the US where urban population density is 2.7 times lower than
EU densities, the drive for urban consolidation centers is lower. Recognizing
the delay in adoption of consolidation centers and lower drive for
consolidation, we expect that consolidation centers in the US will likely be
delayed by 5 years and reach a penetration of just below 20%.
Figure 8.9: Adoption of
urban consolidation where the blue dots represent expert responses to surveys
and black line represents predicted percentage of synchronized consolidation as
predicted by S-Curve (S(t) =
K/[1+exp(-b(t-t0)], K=0.5, b=0.7739, t0=2017, R2=0.8462).
Methodology
The cycle of technology includes a birthing phase, a
growth-development phase, and a mature phase (and perhaps a declining phase).
The stage of the life-cycle, it has been argued, determines the nature of transportation
policy-making -- both the problems faced and the responses to these problems.
This report uses S-curves
(status vs. time), to reflect the level of deployment or use of a mode or
technology. We Use the data to
estimate a three-parameter logistic function: where:
•
S(t) is the status measure, (e.g. Passenger-km
traveled)
•
t is time (usually in years),
•
t_0 is the inflection time (year in which 1/2 K
is achieved),
•
K is saturation status level,
•
b is a coefficient.
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Appendix A
Logistic Growth Curve Analysis
|
|
Autonomous
Vehicles (2000 - Present) Estimated
|
|
|
|
|
Intercept
|
-1856
|
|
b (Coefficient)
|
0.9138
|
|
K (maximum deployment)
|
3,064,839,000,000
|
|
t0 (year of half deployment)
|
2032
|
|
R-Squared
|
0.93
|
S(t) = K/[1+exp(-b(t-t0)]
A-1
Appendix B
ATUS activity codes categorized by suitability for travel time use in the era of self-driving cars and better mobile technologies
Category 1:
Suitable for travel time use in private vehicles
|
Six-digit ATUS code
|
Activity categories
|
|
010201
|
Washing, dressing and grooming oneself
|
|
010299
|
Grooming, n.e.c.*
|
|
010301
|
Health-related self care
|
|
010401
|
Personal/Private activities
|
|
010499
|
Personal activities, n.e.c.*
|
|
030102
|
Reading to/with hh children
|
|
030104
|
Arts and crafts with hh children
|
|
030201
|
Homework (hh children)
|
|
030203
|
Home schooling of hh children
|
|
040102
|
Reading to/with nonhh children
|
|
040104
|
Arts and crafts with nonhh children
|
|
040201
|
Homework (nonhh children)
|
|
040203
|
Home schooling of nonhh children
|
|
110101
|
Eating and drinking
|
|
110199
|
Eating and drinking, n.e.c.*
|
|
119999
|
Eating and drinking, n.e.c.*
|
|
120302
|
Tobacco and drug use
|
|
140102
|
Participation in religious practices
|
B-1
Category 2. Suitable for travel time use in both
public and private vehicles
|
Six-digit ATUS code
|
Activity
categories
|
|
010101
|
Sleeping
|
|
010102
|
Sleeplessness
|
|
010199
|
Sleeping, n.e.c.*
|
|
010399
|
Self care, n.e.c.*
|
|
019999
|
Personal Care, n.e.c.*
|
|
020901
|
Financial management
|
|
020902
|
Household & personal organization and planning
|
|
020904
|
HH & personal e-mail and messages
|
|
020999
|
Household management, n.e.c.*
|
|
030106
|
Talking with/listening to hh children
|
|
030108
|
Organization & planning for hh children
|
|
030112
|
Picking up/dropping off hh children
|
|
030299
|
Activities related to hh child's education, n.e.c.*
|
|
030502
|
Organization & planning for hh adults
|
|
040106
|
Talking with/listening to nonhh children
|
|
040108
|
Organization & planning for nonhh children
|
|
040112
|
Dropping off/picking up nonhh children
|
|
040299
|
Activities related to nonhh child's educ., n.e.c.*
|
|
040505
|
Financial management assistance for nonhh adults
|
|
040506
|
Household management & paperwork assistance for nonhh
adults
|
|
040507
|
Picking up/dropping off nonhh adult
|
|
050101
|
Work, main job
|
|
050102
|
Work, other job(s)
|
|
050199
|
Working, n.e.c.*
|
|
050401
|
Job search activities
|
|
050499
|
Job search and Interviewing, n.e.c.*
|
|
059999
|
Work and work-related activities, n.e.c.*
|
|
060301
|
Research/homework for class for degree, certification, or
licensure
|
|
060302
|
Research/homework for class for pers. Interest
|
|
060399
|
Research/homework n.e.c.*
|
|
060401
|
Administrative activities: class for degree, certification,
or licensure
|
B-2
|
060402
|
Administrative activities: class for personal interest
|
|
060499
|
Administrative for education, n.e.c.*
|
|
070104
|
Shopping, except groceries, food and gas
|
|
070199
|
Shopping, n.e.c.*
|
|
070201
|
Comparison shopping
|
|
070299
|
Researching purchases, n.e.c.*
|
|
079999
|
Consumer purchases, n.e.c.*
|
|
080201
|
Banking
|
|
080202
|
Using other financial services
|
|
080299
|
Using financial services and banking, n.e.c.*
|
|
080301
|
Using legal services
|
|
080399
|
Using legal services, n.e.c.*
|
|
080601
|
Activities rel. to purchasing/selling real estate
|
|
080699
|
Using real estate services, n.e.c.*
|
|
120301
|
Relaxing, thinking
|
|
120303
|
Television and movies (not religious)
|
|
120304
|
Television (religious)
|
|
120305
|
Listening to the radio
|
|
120306
|
Listening to/playing music (not radio)
|
|
120307
|
Playing games
|
|
120308
|
Computer use for leisure (exc. Games)
|
|
120309
|
Arts and crafts as a hobby
|
|
120310
|
Collecting as a hobby
|
|
120311
|
Hobbies, except arts & crafts and collecting
|
|
120312
|
Reading for personal interest
|
|
120313
|
Writing for personal interest
|
|
120399
|
Relaxing and leisure, n.e.c.*
|
|
129999
|
Socializing, relaxing, and leisure, n.e.c.*
|
|
150101
|
Computer use
|
|
150102
|
Organizing and preparing
|
|
150103
|
Reading
|
|
150104
|
Telephone calls (except hotline counseling)
|
|
150105
|
Writing
|
B-3
|
160101
|
Telephone calls to/from family members
|
|
160102
|
Telephone calls to/from friends, neighbors, or
acquaintances
|
|
160103
|
Telephone calls to/from education services providers
|
|
160104
|
Telephone calls to/from salespeople
|
|
160105
|
Telephone calls to/from professional or personal care svcs
providers
|
|
160106
|
Telephone calls to/from household services providers
|
|
160107
|
Telephone calls to/from paid child or adult care providers
|
|
160108
|
Telephone calls to/from government officials
|
|
160199
|
Telephone calls (to or from), n.e.c.*
|
|
160201
|
Waiting associated with telephone calls
|
|
160299
|
Waiting associated with telephone calls, n.e.c.*
|
|
169999
|
Telephone calls, n.e.c.*
|
B-4
Appendix C
Logistic
Growth Curve Analysis
|
|
North American Traditional
|
North American Traditional
|
|
|
Car Sharing Vehicles
|
Car Sharing Members
|
|
|
|
|
|
Intercept
|
-917
|
-1065
|
|
b
(Coefficient)
|
0.4563
|
0.5297
|
|
K
(maximum deployment)
|
25000
|
2,000,000
|
|
t0 (year
of half deployment)
|
2010
|
2012
|
|
R-Squared
|
0.98
|
0.97
|
S(t) = K/[1+exp(-b(t-t0)]
Forecast: North American Traditional Car Sharing Members
and
C-1
Appendix D
Logistic Growth Curve Analysis
|
|
US Toll
Roads (1987-present)
|
HOT Lanes
(1995-present)
|
|
|
|
|
|
Intercept
|
-24.370
|
-358.769
|
|
b (Coefficient)
|
0.010
|
0.176
|
|
K (maximum deployment)
|
150000
|
64000
|
|
t0 (year of half deployment)
|
2331
|
2044
|
|
R-Squared
|
0.96
|
0.97
|
S(t) = K/[1+exp(-b(t-t0)]
D-1
D-2
 |
[2] There is a great deal of
uncertainty about what to call autonomous vehicles. Some prefer “self-driving”
vehicles. Some maintain these are different things. But just as we had
horseless carriages, automobiles, and cars, eventually these will be called
autos and cars as well. See the Economist, which maintains they are not the
same, as self-driving cars are a step further than autonomous vehicles, which
lack steering wheels and human control: http://www.economist.com/blogs/economist-explains/2015/07/economist-
explains?fsrc=scn/tw/te/ee/st/autonomousselfdrivingcarsexplainer . We
are unconvinced this terminology sticks. Google uses the term ”self-driving”
even for cars which humans can control, thinking the name is softer than
“autonomous”.
[3] See Demo '97 Proving AHS Works
in Public
Roads http://www.fhwa.dot.gov/publications/publicroads/97july/demo97.cfm
[5] Spice, Byron (2015-07-16)
“Look, Ma, No Hands: CMU Vehicle Steered Itself Across The Country 20 Years
Ago” https://www.cs.cmu.edu/news/look-ma-no-hands-cmu-vehicle-steered-itself-across-country-20-years-ago and
Business Week (1995-08-13) “Look Ma, No
Hands” http://www.bloomberg.com/bw/stories/1995-08-13/look-ma-nohands
[6]
Authors conversations with Google employees.
[7] There is an assumption
that lanes and roads are properly maintained. Knowledge about maintenance
problems will be conveyed much more rapidly to road operators with driverless
vehicles with automated sensors which are connected to the infrastructure
provider. Because better lane-keeping can reduce lanes width, it should reduce
total maintenance costs.
[8] See Levinson, D. (2008).
Density and dispersion: the co-development of land use and rail in London. Journal of Economic Geography, 8(1), 55-77 and Xie, F., & Levinson,
D. (2009). How streetcars shaped suburbanization: a Granger causality analysis
of land use and transit in the Twin Cities. Journal
of Economic Geography, lbp031.
[9]
In addition to speed anything that lowers the generalized cost of travel
decentralizes.
[10] Pursuit of
high-specification ride-quality raises interesting issues about acceleration
and motion sickness (which is worse for passengers than drivers as passengers
cannot anticipate as well as drivers). See Scott Le Vine, Alireza Zolfaghari
and John Polak (2015), “Autonomous Cars: The Tension Between
Occupant-Experience And Intersection Capacity,” Transportation Research Part C:
Emerging Technologies
[11] http://www.newgeography.com/content/004933-working-home-in-most-places-big-alternative-cars,
accessed on
August
14, 2015
[14] https://www.freelancersunion.org/blog/dispatches/2014/09/04/53million/,
accessed on August 14, 2015.
[16] US Census E-commerce
Report defines E-commerce sales as “sales of goods and services where the buyer
places an order, or the price and terms of the sale are negotiated, over an
Internet, mobile device (M-commerce), extranet, Electronic Data Interchange
(EDI) network, electronic mail, or other comparable online system” although
offline payments are possible. https://www.census.gov/retail/index.html#ecommerce,
accessed on August 14, 2015.
[17] http://www.powerretail.com.au/pureplay/online-retail-saturation-point/,
accessed on August 14, 2015.
[18]
http://www.reportlinker.com/p02153675-summary/Global-Telemedicine-Market-Outlook-to.html,
accessed on October 19, 2015.
[20] Carsharing is
by-and-large in the US context not even analogous to a time-share, where
different people do share ownership of a property, but get to use it at
different times.
[21] This varies by city, so
in Minneapolis and St. Paul, cars are parked on-street at any legal space
(metered or otherwise), in other cities like Boston, restriction affect
this.
[22] Zipcar was originally
founded by Robin Chase and Antje Danielson in 2000; Danielson was forced out in
2001, Chase in 2003.
[23]
For more on carsharing:
http://www.shareable.net/blog/should-products-be-designed-for-sharing
http://www.bizjournals.com/boston/blog/startups/2013/01/zipcar-acquisition-avis-carsharing.html?page=all
http://www.theverge.com/2014/4/1/5553910/driven-how-zipcars-founders-built-and-lost-a-car-sharing-empire
[24] There are several studies
on car-shedding due to carsharing. It is too early to form a conclusion, as
early adopters may behave differently than later adopters. Stasko, T. H., Buck, A. B., & Gao, H. O.
(2013). Carsharing in a university setting: Impacts on vehicle ownership,
parking demand, and mobility in Ithaca, NY. Transport
Policy, 30, 262-268. Ter Schure, J.,
Napolitan, F., & Hutchinson, R. (2012). Cumulative impacts of carsharing
and unbundled parking on vehicle ownership and mode choice. Transportation Research Record: Journal of
the Transportation Research Board, (2319), 96-104.
Shaheen, S. A., & Cohen, A. P.
(2013). Carsharing and personal vehicle services: worldwide market developments
and emerging trends. International
Journal of Sustainable Transportation, 7(1),
5-34.
[25] Totten, Kristy
(2015-07-01) “The Quiet Exit of Downtown Car-Sharing Venture Shift.” Las Vegas Weekly.
[26] Some have tried to change
the language, since ridesharing implies carpooling, preferring to call them
“ridehailing” or “ride-sourcing” services, we think the misleading term
“ridesharing” is here to stay.
[32] We use the term “bike” to
mean the traditional human-powered “bicycle”, unless otherwise noted as in
e-bike or motor-bike.
[33] National Bike Dealers
Association (2012) Industry Overview
http://nbda.com/articles/industry-overview-2012pg34.htm
[34]
Schoner, Jessica, Greg Lindsey, David Levinson (2015) Travel Behavior Over
Time: Task 7 report. University of Minnesota Center for Transportation Studies
report. Minneapolis, MN
[35] The average US car on the
road is 11.4 years; while no similar data exists for bicycles, it must be
lower, especially given the higher sales. At 18.7 million bikes per year there
would be 1 bicycle for every person in the US after 16.7 years of sales, so the
average age would be about 8.4 years if
everyone had a bike and there were no losses, and surely that isn't true. This
again is in large part due to the growing up of kids.
[36] Few people will of course
take a bikeshare bike from Minneapolis to Chicago, but Minneapolitans should
automatically be able to use the Chicago system (and vice versa). And like the
electric inter-urban users of yore (one could take an electric inter-urban
(trolley) from Elkhart Lake Wisconsin to Oneonta, New York, it was said), one
should be able to bike share between major places, even if transferring bikes
periodically.
[38] This idea is also
discussed in Enoch, Marcus (2015) How a rapid modal convergence into a
universal automated taxi service could be the future for local passenger
transport. Technology Analysis & Strategic
Management http://dx.doi.org/10.1080/09537325.2015.1024646
[39] Vehicles may likely have
customer's preferences pre-loaded (seat position, computing interface, audio
environment, video entertainment, computer desktop).
[40] Adam Jonas, Director and
Leader of Global Auto Research Team at Morgan Stanley, has a relatively simple
idea that he claims will consume his remaining career. He offers two
intersecting axes to, in part, foretell the future of the auto industry. One
axis traverses between 'human driver' and 'autonomous'; the other indicates if
the assets (the car) is owned or shared. He believes we are moving from the lower
left (human driven, privately owned) to the upper right (autonomous and
shared). Chart from Verhage, Julie
(2015) Auto Analyst: The Remainder of My Career Will Be Focused on This One
Chart. Bloomberg News. http://www.bloomberg.com/news/articles/2015-04-07/auto-analyst-theremainder-of-my-career-will-be-focused-on-this-one-chart
[41] Automation also
structurally transforms transit, making it potentially massively more abundant.
Automation is accompanied by unemployment and social dislocation in the sectors
it affects (in this case transportation), with associated spillovers, as
workers need to find new skills and jobs in new sectors. Given this is not an
overnight change, but occurs over decades, it will appear important but not
urgent, and much of the labor force reduction will occur through attrition and
lack of new hiring.
[42]
Assuming range issues have not been resolved.
[43] There are many blocks in
Minneapolis (1100 miles of street), so moving from some 500 cars in Minneapolis
and St. Paul to some 5000-10000 (as a rough approximation of where it needs to
be so a member doesn't have to walk more than a block to find one) is a 10 or
20-fold expansion. While reviews are favorable, finding one of today's 300 cars
(assuming the other 200 are in St. Paul) in a city of 58 square miles means
about 5 cars per square mile. As of this writing, there are 6 cars within a 10
minute walking distance (an area of about 50 very non-square blocks), and 1
within a five minute walk of the author’s house. On 1100 miles of street, this
is not going to be a dominant mode without significant expansion. But 5000 cars
is less than the several hundred thousand registered in Minneapolis, and could
replace many of them. Some of this information is gleaned from the Car2Go
website https://www.car2go.com/en/minneapolis/ April 3, 2015.
[44] As quoted in Thomas
Friedmans' column: "just think how much better all this is for the
environment — for people to be renting their spare bedrooms rather than
building another Holiday Inn and another and another. ... The sharing
economy — watch this space. This is
powerful." See: http://www.nytimes.com/2013/07/21/opinion/sunday/friedmanwelcome-to-the-sharing-economy.html?pagewanted=2&_r=0
[45] Forecasts of the shared
economy and local regulation, see: Rauch, Daniel E. and Schleicher, David
(2015) Like Uber, But for Local Governmental Policy: The Future of Local
Regulation of the "Sharing Economy" (January 14, 2015). George Mason
Law & Economics Research Paper No. 15-01. Available at SSRN: http://ssrn.com/abstract=2549919
https://www.nmhc.org/Content.aspx?id=4708#Characteristics_of_Apts from
2014 American Community Survey
[48]
http://www.zsw-bw.de/en/support/press-releases/press-detail/weltweit-ueber-400000-elektroautosunterwegs.html
[49] See Henchman, Joseph
(2014) Gasoline Taxes and User Fees Pay for Only Half of State & Local Road
Spending
http://taxfoundation.org/article/gasoline-taxes-and-user-fees-pay-only-half-state-local-road-spending
and by the same author the previous year (excluding license fees) Gasoline
Taxes and Tolls Pay for Only a Third of State & Local Road
[50] Williams, Michelle (2014)
Automatic gas tax indexing repealed by Massachusetts voters by close margin
MassLive on November 05, 2014 at 12:15 AM http://www.masslive.com/politics/index.ssf/2014/11/massachusetts_question_one_gas_tax.html Accessed
July 6, 2015
[51] Opoien, Jessie (2015)
Joint Finance rejects Democrats’ proposal to reinstate Wisconsin gas tax
indexing. The Cap Times. July 3, 2015. http://host.madison.com/news/local/govt-and-politics/joint-finance-rejects-democrats-proposal-toreinstate-wisconsin-gas-tax/article_e6ca5192-20e0-11e5-9d67-635a901f85f1.html Accessed
July 6, 2015
[52] Mitchell, Josh
(2014-04-04) States Raise Gas Taxes to Pay for Infrastructure: As Congress Only
Takes ShortTerm Steps, Governors Seek More Funds for Roads. Wall Street
Journal.
[53] Zmud, J. (2008). The
public supports pricing if… A synthesis of public opinion studies on tolling
and road pricing. International Bridge, Tunnel and Turnpike Association
Tollways. Winter. Available at http://www. ibtta. org/files/PDFs/win08_Zmud.
pdf
[54] Parry, I. W., Walls, M.,
& Harrington, W. (2007). Automobile externalities and policies. Journal of economic literature, 373-399. http://www.jstor.org/stable/27646797?seq=1#page_scan_tab_contents
[56] Rufolo, A., Bronfman, L., & Kuhner, E.
(2000). Effect of Oregon's axle-weight-distance tax incentive. Transportation Research Record: Journal of
the Transportation Research Board, (1732), 63-69. http://trrjournalonline.trb.org/doi/abs/10.3141/1732-08
[57] Parthasarathi, P.,
Levinson, D. M., & Karamalaputi, R. (2003). Induced demand: a microscopic
perspective. Urban Studies, 40(7), 1335-1351. http://usj.sagepub.com/content/40/7/1335.short
[58] However, higher prices on
MnPASS appear to attract travelers, who seem to use the prices as a signal of
time savings in the absence of other real-time traveler information. See
Janson, M. and D. Levinson (2014) HOT or Not: Driver Elasticity to Price on the
MnPASS HOT Lanes. Research in Transport
Economics Volume 44 pp. 21-32
[59] See: Lindsey, Robin
(2006) Do Economists Reach A Conclusion
on Road Pricing? The Intellectual History of an Idea. Econ Journal Watch 3(2) pp. 292-379
[60] Chris Swenson, David
Ungemah and Hal Kassoff (2014) “The
Roadway Usage Charge: A View from 2050.” Parsons Brinckerhoff.
[61] US FHWA 2013 Highway Statistics, Table SF1 http://www.fhwa.dot.gov/policyinformation/statistics/2013/sf1.cfm
[62]
Cambridge Systematics (2010) MnPASS System Study Phase 2. Prepared for
Minnesota Department of Transportation. August 2010 http://www.dot.state.mn.us/rtmc/pdf/mnpass9-24.pdf
[63] Larsen, David, (2010-10-29)
Truck-only lanes along I-70 could fuel growth Dayton Daily News
http://www.daytondailynews.com/news/news/local/truck-only-lanes-along-i-70-could-fuel-growth/nNJLd/
[64]
See: Zhang, L., McMullen, B., Valluri, D., and Nakahara, K. (2009). Vehicle mileage
fee on income and spatial equity. Transportation Research Record: Journal of
the Transportation Research Board, 2115(-1):110–118.
[68] Levinson, David and
Andrew Odlyzko (2008) Too Expensive to Meter: The influence of transaction costs in transportation and communication. Philosophical Transactions of the Royal
Society A: Mathematical Physical and Engineering Sciences 366(1872) pp
2033–2046
[69]
Woodward, Will, Patrick Wintour and Dan Milmo (2007) Downing Street to send
Blair emails to 2 million road pricing protesters. http://www.theguardian.com/politics/2007/feb/14/transport.publicservices
[70] Jonathan E. Hughes,
Christopher R. Knittel and Daniel Sperling. Evidence of a Shift in the Short-Run
Price
Elasticity of Gasoline Demand
The Energy
Journal Vol. 29, No. 1 (2008), pp.
113-134
[71] Goodwin, P. B. (1992). A
review of new demand elasticities with special reference to short and long run
effects of price changes. Journal of
transport economics and policy, 155-169.
[74]
US Census Bureau (2014) QUARTERLY RETAIL E-commerce SALES 3RD QUARTER 2014 http://www.census.gov/retail/mrts/www/data/pdf/ec_current.pdf
[76] Center for Retail
Research (2015) Online Retailing: Britain, Europe, US and Canada 2015 http://www.retailresearch.org/onlineretailing.php
[77] USDOC (2015).
"Quarterly Retail E-commerce Sales: 4th Quarter 2014." US Census Bureau News. US Department of
Commerce (DOC).
[78]
philly.com (1993-01-29)
“Sears' Wish Book What Does
Its Passing
Say About
Us?” http://articles.philly.com/1993-01-29/news/25961807_1_middle-class-sears-tools
[79] Shopping trips based on
analysis of The Travel Behavior Inventory from the Twin Cities (Minneapolis and
St. Paul) in Minnesota (US), see: Brosnan, M and Levinson, D. (2014) Accessibility and the Allocation of Time: Changes in Travel Behavior 1990-2010 .
Presented at the 2015 Transportation Research Board Conference, Washington DC
[80]
Johnston, Chis (2015) Online shopping 'boosts van sales'. BBC http://www.bbc.com/news/business-32279715
accessed April 18, 2015
[82]
Amazon setting up shop in Manhattan, see:
http://blogs.wsj.com/digits/2014/11/20/amazon-to-lease-entire-manhattan-building-hinting-at-retail-ambitions/
[84] Fowler, Geoffrey (2015)
"There's an Uber for Everything Now Apps do your chores: shopping,
parking, cooking, cleaning, packing, shipping and more" Wall Street
Journal 2015-05-05 http://www.wsj.com/articles/theres-an-uber-foreverything-now-1430845789
[86] This does not even
consider the unknown amount of illegal video traffic (such as BitTorrent), nor
the fact that some legal downloads (like Netflix) show many more hours of video
per dollar spent than legal rentals. Eventually (and not too far away) about
100% of this genre of product will be acquired online.
[88]
Andy Greenberg (2014-05-14) How 3D Printed Guns Evolved into Serious Weapons in
Just One Year. Wired http://www.wired.com/2014/05/3-D-printed-guns/
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