Georgia Southwestern State University Impacts of US Trade Policy Letter Write a letter to your local governmental representative highlighting the impact th

Georgia Southwestern State University Impacts of US Trade Policy Letter Write a letter to your local governmental representative highlighting the impact that the United States’ trade policy and global relations can have on Global Logistics and Transportation least 500 words in length.attache are important reads that can help the paper. Trucking Industry Demand for Urban Shared Use
Freight Terminals
Amelia C. Regan
Department of Civil and Environmental Engineering and
Institute of Transportation Studies
University of California
Irvine, CA 92697
Thomas F. Golob
Institute of Transportation Studies
University of California
Irvine, CA 92697
The issue of shared use urban freight facilities first received attention during the 1970’s
when it was observed that, while inter-urban freight movements were becoming
increasingly efficient, there were significant diseconomies in the movement of freight via
truck within urban areas. Early research suggested that shared urban freight facilities
should be constructed so that trucking companies could consolidate smaller shipments
into larger ones. In the past few years, the concept of “Urban Ports” has gained
increasing attention, not just for carriers who need to load and unload freight, but to
provide a place near the urban center for truckers to wait out peak traffic periods. In this
paper, using recently developed survey data, we examine trucking company interest in
such facilities by examining the results of an ordered probit demand model.
Key words City logistics, Shared use freight facilities, Trucking operations, Urban ports
March 5, 2003
To appear in the journal Transportation
Regan and Golob
Trucking Industry Demand for Urban Shared Use Terminals
Transport engineers, planners and economists have long realized that future increases
in surface transportation capacity would result less from the construction of physical
transportation infrastructure than from the development of techniques and tools aimed
at improving the efficient use of existing infrastructure. An efficient freight transportation
system is the backbone of a successful economy. Both businesses and consumers
rely every day on inexpensive and efficient goods movement. However, goods
movement, particularly in urban areas, comes at a high cost to society. Large trucks
mixing with congested urban passenger and pedestrian traffic are responsible for
significant safety and environmental hazards and can make driving and walking very
unpleasant for urban residents.
The past several years has witnessed a significant increase in public sector involvement
in the freight transportation sector. Realizing that regional economic strength is
dependent on swift and efficient goods movements, federal, state and local agencies
are participating more regularly in infrastructure and information technology initiatives.
One example is the Alameda Corridor project, a 20-mile grade-separated cargo link
between the ports of Long Beach and Los Angeles, and transcontinental rail yards
located near downtown Los Angeles. Another example is the HELP (Heavy Vehicle
Electronic License Plate) project, a non-profit partnership between motor carriers and
government agencies in nine western states (SAIC, 1994). The advantages of carefully
executed public agency involvement in commercial vehicle operations are numerous.
Of current interest is the question of whether there may be a public role in the
development of shared urban freight facilities. These facilities would serve several
purposes. The first of these is to allow commercial vehicle operators a place to wait
near the urban center so that they can drive a large portion of their trip in off peak hours.
Commercial vehicle operators are often constrained by schedules that force them to
make deliveries during the morning or afternoon peak hours. Reducing some of their
peak period travel would benefit both the truckers and the public. The second purpose
of the shared urban freight facilities would be to serve as a meeting point where loads
could be broken out of large vehicles into several smaller combinations. There might be
opportunities for companies to make urban pickups and deliveries with smaller vehicles
without purchasing and managing prohibitively expensive urban warehouse space. Or,
Less than truckload (LTL) carriers might find consolidation opportunities. Another
purpose of these facilities would be to provide a place where truckers can rest safely
before or after the stressful urban leg of their trips. Lastly, these facilities would be
equipped with information technologies that allow commercial vehicle operators to
communicate easily with their home offices and to gain access to the most up to date
real-time traffic network information available.
Regan and Golob
Trucking Industry Demand for Urban Shared Use Terminals
The issue of shared use urban freight facilities first received significant attention during
the 1970’s when it was observed that, while inter-urban freight movements had become
much more efficient, significant diseconomies characterized the movement of truck
freight within urban areas (Clark and Ashton, 1977, Friedman, 1975). Most of that
research suggested that shared urban freight facilities should be constructed so that
freight companies could consolidate smaller shipments into larger ones. However, a
sharp reduction in average shipment sizes, fueled by the growth in just-in-time
manufacturing and distribution systems, dynamic management of inventories and ecommerce initiatives suggest that today’s shared urban freight facilities will be deconsolidation centers where large deliveries are transferred to smaller vehicles for the
final leg of their trips. The kinds of facilities identified by Friedman and Clark and
Ashton failed to materialize, because it was judged that development and operating
costs would exceed what carriers would be willing to pay.
More recently, Taniguchi et al (1999) state that public logistics terminals may help
alleviate traffic congestion, reduce negative environmental impacts and decrease
energy consumption. In response to Japanese public sector interest in the development
of such terminals, those researchers developed an optimization model to assist with the
location and sizing of such terminals. Taniguchi and Van Der Heijden (2000) mention
public logistics terminals as one method for increasing cooperation in freight
transportation systems. Their model suggests that cooperative freight systems reduce
carbon dioxide emissions as well as the distance traveled by trucks. The European
Union conducted some experiments on methods of increasing the efficiency of urban
distribution and reducing environmental impacts through the Sustainable Urban and
Regional Freight Flows (SURFF, 1998) program. Various policies were implemented in
seven test sites. The Stockholm test site focused mainly on city logistics including
coordinating deliveries.
Simulations of coordinated urban distribution showed
improvements over the current system. Overall, the SURFF test sites indicated that
modifying transport, warehousing and logistics processes usually decreased negative
environmental impacts. In addition, roughly a 20% reduction in vehicle-kilometers
traveled was made possible by the use of load consolidation and route planning
applications. Weisbrod et al, 2002 provide the most comprehensive study of the
feasibility of these facilities which they refer to as “global freight villages”. Their study
laid out both the characteristics of successful facilities (for example, a minimum of 125
contiguous acres, in or near metropolitan area) and the services which they should
provide. Their study, which was primarily aimed at investigating the potential for
developing freight villages in Northern New Jersey, described forty freight villages in
Europe and examined four in detail. An earlier, and quite extensive study examining
the key factors influencing the location of freight facilities found the most important of
these is proximity to arterial roads, freeways and services (Young, Ritchie and Ogden,
1980). The facilities examined in this study would have that characteristic.
Missing in previous research is an examination of the question of what types of trucking
companies would be interested in using these facilities. Therefore, as part of a larger
Regan and Golob
Trucking Industry Demand for Urban Shared Use Terminals
survey related to trucking company technology use, we queried trucking company
managers about their interest in suc h facilities. Also missing in previous research is
industry input concerning the design of such facilities; that issue is outside the scope of
our simple investigation. That issue has been addressed in Europe with respect to the
design of intermodal facilities, referred to as nodal centers (Tsamboulas and
Dimitropoulos, 1999). However, those centers are much broader in scope that the
unimodal trucking industry centers envisioned here. A current study underway in
Dublin, Ireland (Finnegan, Finlay and O’Mahoney, 2004) involves a feasibility analysis
of shared use consolidation terminals in that city. The initial feasibility study was
focused around a survey of delivery vehicles arriving at a large university campus in the
heart of the city.
The Survey
Logistics managers of more than 700 trucking companies operating in California were
surveyed in spring 2001. The three-part sample was comprised of: (1) large national
carriers with operations in the state of California, (2) California based carriers of all
sizes, and (3) private fleets corporately located in the state. The contact lists were
obtained from a company that maintains extensive contact information for U.S. trucking
companies. Managers of 3438 companies were contacted, and 86% of these qua lified
by having operations in California.
The response rate was high for this type of survey. As reported in Golob and Regan
(2003), of the 2972 companies with California operations, 75% (2218) initially agreed to
participate in the survey. For these companies, 712 interviews were completed with the
person in charge of California operations. The large number of unresolved contacts
reflects the difficulty of tracking down persons responsible and need to schedule callbacks when people have available time. The 712 completed interviews represent a
49% response rate of all resolved contacts, and a 24% response rate of all qualified
companies. The computer aided telephone interviews lasted an average of 17 minutes.
Stated Demand for Shared Use Urban Freight Facilities
The question asked was:
Several public agencies are considering financing the development of shared use
urban freight facilities. These would be similar to truck stops, but would be
located near urban centers and would provide additional services such as
terminal space for consolidation and deconsolidation of loads, as well as internet
access. Do you think your company would have any use for such a facility?
Only 18.7% replied that they would have use for such a facility, but another 8.3% chose
the “maybe” response; 71.9% replied “no” and 1.1% did not know, as show in Figure 1.
Combined, the groups that thought that they would or might use these facilities
Regan and Golob
Trucking Industry Demand for Urban Shared Use Terminals
represented 27% of our contacts – not an insignificant number, especially when one
considers that in the US, these facilities do not yet exist. In addition, a large fraction of
the trucking industry consists of private fleets, which would have less use for such
facilities or local pickup and delivery services which run fairly regular operations. So,
this 27% represents a fairly large fraction of all companies that might benefit from such
don’t know
Figure 1. Stated Demand for Shared-Use Urban Freight Facilities
Demand as a Function of Operating Characteristics
Operating Characteristics
In an effort to understand which companies would be more likely to use such facilities,
we examined the degree to which demand is related to different ways of characterizing
trucking operations. The demand variable was treated as a three-category ordinal
scale, after discarding the approximately one percent “don’t know” responses. After
further eliminating observations with missing data on the exogenous variables, the
sample size for the analysis was 683 (96% of the original 712 companies). Since all
characteristics are measured in terms of dummy variables, an appropriate measure of
the strength of the relationship between stated demand and any individual characteristic
is a rank order correlation coefficient. In Table 1 we use the Spearman ? rank order
coefficient, but either of the two Kendall ? coefficients will give similar results.
Examining each operational dimension separately, in terms of overall carrier type, forhire carriers are more likely to use these facilities than private fleets. The base category
is contract carriers, which bridge the gap between for-hire (common) carriers and
private fleets. In terms of services provided, demand is higher for any carrier that
Regan and Golob
Trucking Industry Demand for Urban Shared Use Terminals
provides general truckload, van, refrigerated, HAZMAT and high value goods services;
demand is lower for carriers providing tanker services.
Table 1 Rank Order Correlations Between Demand for Shared Urban Freight facilities
and Individual Operating Characteristics (with non significant (NS) correlations
% of
Carrier Type (private, for-hire, or contract)
Operates primarily as a private fleet
Operates primarily as a for-hire carrier
Services Provided
General truckload
General less-than-truckload (LTL)
Parcel/package delivery
Household goods movement
Fat bed/container
Hazardous materials
High value goods
Size of fleet generally operated in California
(no fleet size categories with significant correlations)
Length of haul
Average loaded movements less than 25 miles
Average loaded movements 25-49 miles
(All intermediate categories)
Average loaded movements 500 miles or more
Maritime intermodal service
Rail intermodal service
Air intermodal service
Areas of operation in California
Los Angeles Metropolitan Area only
Northern California, excluding S.F. Bay Area
positive correlation indicates positive relationship between possessing characteristic and demand for facilities
Regan and Golob
Trucking Industry Demand for Urban Shared Use Terminals
Demand is unrelated to the size of a trucking company’s fleet, as none of the size
categories showed a significant rela tionship with the stated responses. In contrast,
length of loaded movement is closely related to demand. Demand is highest for longhaul carriers (defined in terms of average length of loaded movements in excess of 500
miles, 23% of the sample), and lowest for carriers with hauls less than 50 miles (18% of
the sample). Carriers with hauls in the intermediate 50 to 500 mile range have neither a
positive nor a negative response pattern, indicating that demand for shared-use facilities
will be mixed among these carriers (59% of the sample).
In terms of provision of intermodal services, demand is highest for carriers serving rail
terminals, and also high for carriers serving airports. There is no apparent relationship
between demand and provision of maritime intermodal services. Finally, regarding
areas of operations, demand is highest for carriers that operate statewide in California.
Lower demand is stated by operators confined to either the Los Angeles Metropolitan
Area, or to Northern California, excluding the San Francisco Bay Metropolitan Area. No
other areas of operations were found to exhibit significant relationships with demand.
The results listed in Table 1 are useful in identifying which groups of companies are
more likely to support urban shared-use facilities. The next step was to estimate a
model of demand to determine which of these separate ways of categorizing trucking
operations are keys in identifying potential user and non-user groups.
Demand Model Methodology
Defined in terms of a discrete trinomial variable y, the three categories of demand
(again excluding the small percentage of “don’t know” responses) can be ordered from
“no use” (defined as y = 0), to “maybe” (y = 1), to “yes, useful” (y = 2). We postulate
that this discrete ordered variable y is a crude representation of a continuous, but
unobserved, variable y* that represents trucking company managers’ opinions. If we
observed y* we could apply conventional regression methods that express y* as a linear
function of a vector of independent variables representing trucking company
characteristics, denoted by x, plus an additive disturbance (unexplained) term ?. The
ordered probit model, represents a way to capture effects of x on y by using y*. The
model is defined as:
Pr(y = 0) = Pr(y* < ? 1) = Pr(x? + ? < ? 1) = Pr(? < ? 1 - x? ) Pr(y = 1) = Pr(? 1 < y* < ? 2) = Pr(? 1 < x? + ? < ? 2) = Pr(? 1 - x? < ? < ? 2 - x? ) Pr(y = 2) = Pr(y* ? ? 2) = Pr(x? + ? ? ? 2) = Pr(? ? ? 2 - x? ) (1) The parameters to be estimated in (1) are ? 1 and ? 2, the unknown thresholds or “cut points” of y*, as well as the vector of regression coefficients ? . The scale of y* cannot be determined, so there is no loss of generality in assuming that the variance of ? is equal to one. Assuming also that the disturbance term ? is normally distributed, equations (1) reduce to the ordered probit model originally developed by Aitchison and Silvey (1957) and Ashford (1959): Regan and Golob Trucking Industry Demand for Urban Shared Use Terminals 7 Pr(y = 0) = ? (? 1 - x? ) Pr(y = 1) = ? (? 2 - x? ) - ? (? 1 - x? ) Pr(y = 2) = 1 - ? (? 2 - x? ) (2) where ? denotes the cumulative normal distribution function. If the disturbance term is assumed to be logistically distributed, the same treatment leads to the ordered logit model. Thresholds ? 1 and ? 2 and the vector ? parameters are determined using the maximum likelihood method (McKelvey and Zavoina, 1975). The ordered probit model expressed in system (2) does not contain a constant term. Some ordered probit formulations have constant terms, but such constants are simply transformations of threshold (cut point) values. In general, for an ordered probit model with c categories, there will be a total of c-1 threshold plus constant parameters. In the present model these are all thresholds, but other formulations use c-2 threshold plus one constant value. A popular approach is to set the first threshold equal to zero (e.g., Greene, 2000), so that the constant is simply the negative of ? 1 in (2), and the remaining thresholds in the model with a constant are differences of the thresholds in the non-consta nt model. In any event, the constants only capture the relative aggregate shares of the categories. We are most interested in the regression parameters which describe how the independent variables are related to differences in responses. Model Fit The optimal model was found to have fourteen independent variables. The pseudo R2 value is 0.22. This suggests a good fit for the this type of model. The parameter estimates are listed in Table 2 together with the asymptotic normal ratios of the coefficient estimates to their standard errors (the coefficient z-statistics). The simultaneous examination of company characteristics and demand for facility use yielded some interesting results in that some variables that were not found to be significant on their own (Table 1) are significant when... Purchase answer to see full attachment

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