methodology to characterize agriculture

Transcription

Enns, Reimer, Regehr
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METHODOLOGY TO CHARACTERIZE AGRICULTURE-RELATED TRUCKING
ON LOW-VOLUME RURAL ROADS TO SUPPORT ASSET MANAGEMENT
TRB 2013 Annual Meeting
Garry Enns
Engineering Research Associate
Department of Civil Engineering, University of Manitoba
E1-327 EITC
15 Gillson Street
Winnipeg, Manitoba, Canada, R3T 5V6
Tel: (204) 474-7825
Email: umennsg@cc.umanitoba.ca
Mark Reimer, B.Sc. (CE), EIT
Engineering Research Associate
E1-327 EITC
15 Gillson Street
Winnipeg, Manitoba, Canada, R3T 5V6
Tel: (204) 474-7367
Email: umrei234@cc.umanitoba.ca
Jonathan D. Regehr, Ph.D., P.Eng.
Assistant Professor
E1-310 EITC
15 Gillson St.
Winnipeg, Manitoba, Canada R3T 5V6
Tel: (204) 474-8779
Email: regehr@cc.umanitoba.ca
Original submission date: July 31, 2012
Total number of words: 4706 + 2250 (tables and figures) = 6956
Abstract: 240 words
Paper revised from original submittal.
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ABSTRACT
This paper develops a methodology to characterize agriculture-related trucking on low-volume
rural roads. The methodology considers truck trips from the field to intermediate storage
facilities (field-to-storage) and from these facilities to market (storage-to-market). The
methodology, which applies the transportation systems analysis approach, leverages knowledge
from local producers through in-person interviews to qualitatively and quantitatively characterize
the transportation supply and demand that generate truck flows. Flow characterization in terms of
truck volumes and trip-making characteristics supports asset management decisions, such as
maintenance timing and upgrade investments, in addition to providing information for
forecasting future demand and infrastructure impacts.
The development and application of the methodology contributes in three ways. First, it
characterizes truck flows from field-to-storage, a segment of the agricultural supply chain
seldom considered by previous research. Second, it demonstrates the extent of information
concerning road usage and impacts available from producers. Third, results from the application
of the methodology to a study region in Manitoba reveal that: (a) smaller truck types are more
commonly used for the shorter field-to-storage trips than storage-to-market trips; (b) actual
distance traveled exceeds desired distance traveled, owing mainly to infrastructure-related
regulatory constraints; and (c) trip length distributions for the storage-to-market segment exhibit
a relationship between trip length and type of truck and commodity. The methodology is
transferrable across jurisdictions and scalable for different geographic and temporal scopes. The
specific results presented in this paper, however, may not be representative of conditions in other
regions.
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INTRODUCTION
This paper characterizes agriculture-related trucking on low-volume rural roads by applying a
new methodology to collect data about the movement of agricultural commodities from the field
to intermediate storage facilities (field-to-storage) and from these facilities to market (storage-tomarket). Data collected through the methodology supports the management of rural road
infrastructure assets.
Improved road infrastructure asset management and investment decision-making rely on
more comprehensive information about the use and condition of these assets (1). For smaller
road agencies, especially those in rural areas, it is difficult to commit resources for generating
base data about road use (i.e., traffic volume) and condition given the mounting budgetary
pressures of managing deteriorating infrastructure and growing infrastructure funding deficits (2,
3, 4). This difficulty exists despite recognition in Canada and the United States that effective
rural transportation systems support rural economies and, in turn, the health of industries (such as
agriculture) embedded in rural areas and the quality of life of rural citizens (5, 6).
In areas with intensive agriculture production, agriculture-related trucking is a critical
aspect of rural road use. The transportation of agricultural products comprises an important
component of commodity movements on rural roads. Historically, agriculture-related trucking
has affected and been influenced by the economic activity of rural areas and the development of
transportation systems serving these areas. In the past three decades in North America, this
dynamic interrelationship has been influenced by the following (5, 7, 8, 9):
• increasing crop yields resulting in a larger volume of commodities being produced for a
given area of land;
• changes in the types of crops being planted (e.g., in Manitoba, less land is used to grow
wheat today than has historically been the case);
• a transition to fewer, but higher throughput grain handling facilities, operated by a
smaller number of grain handling companies;
• increases in the size of farming operations and decreases in the number of farms;
• changes in truck regulations, which have enabled the use of larger, heavier, and more
productive truck configurations on denser road networks; and
• mergers within the rail industry, rationalization of rail networks serving rural areas, and
the introduction of shuttle train service for grain transportation.
In Canada, recent changes to the operation of the Canadian Wheat Board, concomitant
adjustments in cross-border agricultural transportation, and possible expansion of grain
transportation via northern rail and marine networks indicate that agriculture-related trucking
will continue to play a dynamic role in rural economies.
These developments have placed increasing strain on rural road infrastructure, as larger
and heavier trucks are used to haul products over longer distances (8). However, despite this,
there are relatively few systematic data sources on which local governments can rely to support
decisions concerning the operation and management of these roads, both currently and in the
future. This paper aims to fill this data gap by developing a methodology to collect data that
characterize agriculture-related trucking on low-volume rural roads.
Specifically, the objectives of this paper are as follows:
• to propose and develop a data collection methodology to characterize agriculture-related
trucking on low-volume rural roads, including transportation from field-to-storage and storageto-market;
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• to demonstrate the application of the data collection methodology, focusing on
information obtained from producer surveys; and
• to present and interpret illustrative results obtained from the application of the
methodology.
In pursuing these objectives, the intent is to provide pragmatic guidance for jurisdictions
to better understand the current needs and impacts of agriculture-related trucking on low-volume
roads through enhanced data collection. The data collected can help form the basis for more
predictive demand models, though forecasting is not a specific objective of this research. The
methodology focuses on truck trips generated by harvest activities and excludes other
agriculture-related transportation such as the movement of seed, fertilizer, and implements of
husbandry. Although the geographic scope of the analysis is limited to a particular region of
Manitoba and Manitoba-specific characteristics are incorporated within the analysis, the
methodology and insights are transferrable to other jurisdictions.
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UNDERSTANDING IMPACTS OF AGRICULTURE-RELATED TRUCKING
ON LOW-VOLUME ROADS
The changes in the agricultural industry that have intensified the impact of agriculture-related
trucking on rural road infrastructure are well-documented. Quantifying and characterizing these
impacts at a local scale, however, requires data that are difficult to obtain. Previous research has
generally captured these impacts on the basis of data generated from demand models or
stakeholder surveys (or a combination of both). Establishing estimates of truck traffic volumes
and characteristics are the main targets of these efforts as they are closely related to
infrastructure impacts (e.g., pavement and bridge design and maintenance).
Freight demand models estimate truck traffic volume (and/or commodity weight) based
on the classical four-step demand modeling process, a direct demand modeling approach, or
input-output models. These approaches provide estimates (in terms of trips or commodity
weight) by establishing explanatory relationships between flow and a set of independent demand
variables. Modeling freight demand—including agriculture-related demand—is a complex task
because of the multidimensional nature of the freight demand unit, multifaceted interactions
between different freight agents within the supply chain, and different values and transport costs
associated with different types of freight (10). Nevertheless, this approach has been successfully
applied to characterize agriculture-related trucking, for example, at jurisdiction-wide (7, 11) and
more local scales (12). Research adopting this approach, however, focuses on the storage-tomarket segment of the supply chain rather than on the field-to-storage segment, which requires
finer-grain knowledge of local transportation demand and supply characteristics.
Industry stakeholder surveys are a second data source which can be used to estimate truck
traffic volumes and characteristics. These surveys, which are often integrated within demand
models but may also be utilized independently, provide numeric inputs and an in-depth
understanding of industry-specific transportation trends, issues, and anomalies which help
interpret analytical results (13). For example, Prozzi et al. (8) assess the sustainability of rural
road networks in Texas based in part on surveys of grain industry stakeholders (e.g., elevator and
feed mill operators). Prentice et al. (14) conduct an in-depth series of surveys of producers and
other agriculture industry representatives in Manitoba. The results of these surveys not only
provide a qualitative assessment of the suitability of the road network to serve agricultural
demand, but also enable quantitative analysis of the impacts of agriculture-related trucking on
these roads. As with demand models, however, this literature focuses on the storage-to-market
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segment and principally excludes the field-to-storage segment, which is also of interest to
engineers charged with maintaining local road networks.
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METHODOLOGY FOR CHARACTERIZING AGRICULTURE-RELATED
TRUCKING
The data collection methodology applies the transportation system analysis approach to identify
the data elements needed to characterize agriculture-related trucking and the relationships
between them. This approach consists of three interrelated components (15): (a) the
transportation system, T, which encompasses transportation infrastructure supply, the vehicles
that provide transportation service, and governing regulations; (b) the activity system, A, which
defines the demand for service; and (c) the flow system, F, which characterizes travel, the
resources it consumes and the services it provides. In the short term, T and A equilibrate to define
F. Over time, characteristics of F stimulate changes in T and A, eventually creating a new
equilibrium point for F. These interrelationships are also influenced by exogenous factors, X,
such as the physical, political, or economic environments in which larger-scale trends occur.
Figure 1 depicts an adaptation of this approach, which provides the methodology for
collecting data to characterize agriculture-related trucking on low-volume roads. The following
paragraphs briefly describe the components of this methodology, referring specifically to the
case discussed in this paper.
• Understand exogenous factors, X: As described in the foregoing section, agriculturerelated trucking (and agriculture-related transportation generally) has evolved considerably over
the last three decades. The reasons for this evolution are complex and interrelated; however, a
general understanding of these factors forms the starting point for the methodology.
• Define the scope: This step defines the geographic, temporal, and analytical scope of the
data collection effort. Geographically, the survey may be constrained to the area influenced by a
particular delivery point or may be as large as an entire jurisdiction (i.e., province or state).
Temporally, the survey may cover activity associated with a particular season (e.g., spring
seeding or autumn harvest), year, or may be repeated over several years to develop time series
data. Consideration should also be given in this step to the key analytical questions that the
survey is designed to answer. For the case discussed in this paper, the data collection effort
focuses on truck trips generated during a particular harvest in a 36-mi.2 (93-km2) land area. Five
guiding questions, directed at acquiring data that support management of low-volume rural
roads, helped shape the data elements on which the data collection methodology focuses: (a)
What is the flow of agriculture-related trucking on local road systems, from field-to-storage and
storage-to-market? (b) What are the key characteristics of this flow? (c) What explanatory
demand variables influence this flow? (d) What aspects of transportation supply dictate actual
flow and/or constrain desired flow? (e) What base case information might be needed to assess
future impacts of ongoing industry trends?
• Characterize transportation supply, T: This step involves developing an understanding of
three main aspects of T, namely, the road network serving the study region, the types of trucks
used to transport agricultural commodities in this region, and the regulations that govern these
movements. The next section describes the transportation system for the study region in more
detail.
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Understand
Exogenous
Trends, X
Characterize
Transportation
Supply, T
Define scope
and guiding
questions
Characterize
Transportation
Activity, A
Develop and Test
Pilot Survey
Conduct Full Survey
Characterize Flow System, F
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FIGURE 1 Methodology for characterizing agriculture-related trucking.
• Characterize transportation activity, A: This step involves understanding the system that
generates the demand for agriculture-related trucking. Specifically, the following types of
information should be gathered for the study region: land use, types of crops, location of storage
facilities, and location of market destinations (e.g., grain elevators). The next section describes
the activity system for the study region in more detail.
• Develop and test pilot survey: After defining the scope and gaining a basic understanding
of the transportation and activity systems of the study region, preparation of a pilot survey
begins. For the case discussed in this paper, the main goal of the survey is to collect information
relevant to asset management that a producer would know by memory. Discussions with one
producer helped refine the types of information that could be obtained within a relatively short
in-person interview and which would be necessary for the purposes of the survey. Information
collected about each farm operation includes the total cultivated area and the truck
configurations used for hauling grain. Attaining information for the determination of truck flow
from field-to-storage and storage-to-market is accomplished on a per field basis. Field-specific
information includes the field area, crop type and yield from 2011, actual and desired routes
from field-to-storage and storage-to-market locations, and truck configurations used to haul grain
from each field.
• Conduct full survey: Full implementation of the survey involves in-person interviews
with producers and follow-up phone conversations as necessary. For a large-scale application,
web-based or paper surveys could be developed, provided that the farmer had access to relevant
maps and other pertinent background information. For the case discussed in this paper, a total of
24 producers were interviewed between May and July of 2012 representing nearly all of the
producers owning land in the study region.
• Characterize flow system, F: The last step in the methodology involves analyzing the
survey data to help characterize F and bring new insights about the infrastructure impacts of
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agriculture-related trucking. These insights contribute to a better understanding of the broader
exogenous factors. Specifically, flow is characterized in terms of truck volume (expressed in
terms of vehicle distance traveled by road segment and truck type) and trip length.
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APPLICATION OF THE METHODOLOGY
This section of the paper provides specific details and illustrative results about the application of
the methodology in the study region and the types of insights that may be gained. Specifically,
referring to the methodology in Figure 1, details are provided about the transportation (T) and
activity (A) systems in the study region and the results and interpretation of the survey data,
which help characterize truck flows (F).
Description of the Transportation and Activity Systems in the Study Region
Figure 2 shows a map of the study region and surrounding area. The study region is a 36-mi.2
(93-km2) area located in an agriculture-intensive portion of southeast Manitoba, east of the Red
River and Highway 75 (which connects to U.S. Interstate 29). The region primarily consists of
cultivated land and no communities are present that generate other types of trips. The region
totals 36 land sections according to the predominant division of land in the province. One section
covers 640 acres (259 ha) and is separated from adjacent sections by roads at one-mile (1.6-km)
intervals in both the north-south and east-west directions. The road network totals 84 mi. (135
km) and consists of 30 percent gravel surface, 51 percent dirt, and 19 percent asphalt. The
majority of these roads—68 mi. (109 km)—are under municipal jurisdiction. Of the 16 mi. (26
km) of roads under provincial jurisdiction, about 6 mi. (10 km) (all paved) allow trucks up to the
maximum provincial axle and gross vehicle weight limits (these are referred to as RTAC roads),
while weight limits on the remaining 10 mi. (16 km) (some paved, some gravel) are restricted
(these are referred to as Class B1 roads). Table 1 summarizes these limits for the six major truck
configurations used to haul grain in the study region (categorized using the Federal Highway
Administration 13-vehicle classification system).
TABLE 1 Truck Tares and Gross Vehicle Weight Limits for Provincial Roads
FHWA
truck
class
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Tare (kg)
Tractor
Trailer
Total Unit
5
5250
5250
6
9800
9800
6T
9800
2900
12,700
9
7620
4480
12,100
10
7940
5950
13,890
13
7940
9530
17,470
NOTE: 1 kg = 2.2 lb
a
6T denotes a class 6 truck pulling a pony trailer.
Maximum Gross Vehicle Weight (GVW) and
Payload (kg)
RTAC routes
B1 routes
GVW
Payload
GVW
Payload
16,400
11,150
15,500
10,250
24,300
14,500
21,800
12,000
41,300
28,600
36,300
23,600
39,500
27,400
34,500
22,400
46,500
32,610
40,000
26,110
62,500
45,030
47,630
30,160
Nearly 95 percent of the total 23,040 acres (9324 ha) in the region is used for grain
production. The remaining five percent largely consists of waterways, drainage ditches, and
farmyard space including land needed for livestock operations. A total of 26 producers own land
in the study region; 24 were interviewed for this research. Of these producers, seven own up to
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2000 acres (809 ha) of land, 12 own between 2000 and 4000 acres (809 and 1619 ha), and five
own more than 4000 acres (1619 ha). Typically, a single producer does not own an entire section
of land (this happens on only two occasions); rather, the division of sections into halves or
quarters is common. The types of crops planted in this region in 2011 included (by area) canola
(36 percent), soybeans (23 percent), oats (16 percent), spring wheat (13 percent), perennial rye
grass (5 percent), winter wheat (3 percent), beans (3 percent), and corn (1 percent). For the study
region, the 2011 harvest produced below average yields and dry conditions were prevalent
during harvest time. During harvest, most producers hauled grain from the field to an
intermediate storage facility either on their farmyard or on a field. Major nearby market delivery
points included grain elevators, processing facilities (e.g., canola crushing and oat milling
plants), and seed production facilities. The map in Figure 2 shows the locations of storage and
delivery points relevant to the study region.
FIGURE 2 Study region.
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Results and Interpretation of Truck Flow Characteristics
Analysis of data about the agriculture-related truck flows resulting from the 2011 harvest in the
study region reveals insights about truck volumes (expressed as vehicle distance traveled by road
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and vehicle type) and trip length for both the field-to-storage and storage-to-market segments.
The data illustrates differences between actual travel and desired travel, which accounts for
preferred routing if regulatory restrictions are removed but no change in vehicle ownership
occurs.
As shown in Table 2, a total of 61,040 km (38,150 mi.) of travel resulted from the 2011
harvest in the study region. This estimate makes five major assumptions, namely that: (a) all
trucks operate fully loaded in one direction and empty in the other; (b) none of the truck trips
violate GVW limits on the roads under provincial jurisdiction; (c) truck trips on municipal roads
adhere to Class B1 weight restrictions; (d) if more than one truck type is used by a specific
producer in the field-to-storage segment, trips are evenly distributed between these truck types
unless otherwise specified; and (e) if a single producer hauls a commodity to more than one
market location, these trips are evenly distributed unless otherwise specified. These assumptions
are reasonable given producers’ interest in maximizing productivity within legal bounds;
however, there would be situations where the assumptions do not hold.
TABLE 2 Actual and Desired Distance Traveled from Field-to-Storage and Storage-toMarket by Truck Class for 2011 Harvest
Vehicle-kilometers of travel (VKT)
Field-to-storage
Storage-to-market
Actual
Desired
Actual
Desired
5
120
110
0
0
6
3920
3480
2390
2060
6T a
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340
390
390
9
2660
2390
13,710
11,060
10
3940
3130
16,990
15,860
13
750
750
8480
6620
C10 b
0
0
370
240
C13 c
0
0
6960
5200
Total
11,750
10,200
49,290
41,430
NOTE: 1 km = 0.62 mi
a
6T denotes a class 6 truck pulling a pony trailer.
b
C10 denotes a commercially-operated class 10 truck.
c
C13 denotes a commercially-operated class 13 truck.
FHWA truck
class
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Specifically, the table reveals the following insights:
• Actual field-to-storage travel is about 20 percent of total travel generated by the 2011
harvest. However, because this travel occurs using smaller truck types, it accounts for 63 percent
of the total number of truck trips.
• Field-to-storage travel is more likely to occur using smaller truck configurations, and
almost never utilizes class 13 vehicles (eight-axle B-trains). Based on the sample data,
commercial haulers are never used for this segment of the supply chain.
• Storage-to-market travel is dominated by articulated trucks (classes 9, 10, and 13), and
approximately 15 percent of this travel is done using commercial haulers.
• For both the field-to-storage and storage-to-market segments, the desired routing is
shorter than actual routing, indicating that from the producers’ perspective, the current
transportation supply is at least partially imposing inefficiencies into their transportation
decisions.
Further to this last point, Figures 3 and 4 illustrate the geographic differences between
actual and desired truck flows for the field-to-storage and storage-to-market segments,
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respectively. For the field-to-storage segment (Figure 3), these differences reveal that routing
choices are based primarily on a preference to travel on gravel rather than dirt roads (Figure 2
shows roads by surface type). Trip distance does not play a large role in trip-making decisions at
this scale. Further, though the intensity of truck travel on specific roads shifts somewhat, most of
the individual road segments still accommodate some travel. This suggests that, from an asset
management perspective, it is important to maintain roads even if they have extremely low truck
volumes simply to provide basic access to the land.
(a)
(b)
FIGURE 3 Two-way field-to-storage truck volume generated from 2011 harvest in study
region: (a) actual and (b) desired.
For the storage-to-market segment (Figure 4), comparison of actual and desired routing
indicates that trip distance is an important factor in trip-making decisions, but regulatory
constraints sometimes increase route circuitry. A principal example of this is Highway 200 (the
north-south road crossing through the study region), which would experience higher truck
volumes under desirable conditions. However, because this road is subject to Class B1 weight
limits, the majority of actual truck trips use Highway 75 (an RTAC road) as the alternative.
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(a)
(b)
FIGURE 4 Two-way storage-to-market truck volume generated from 2011 harvest in
study region: (a) actual and (b) desired.
An analysis of trip length distributions provides additional insights about truck flows
from field-to-storage (Figure 5) and storage-to-market (Figure 6). Figure 7 breaks down storageto-market trips by truck type. Findings from this analysis follow:
• Nearly 75 percent of field-to-storage trips (one-way) are 6 km (4 mi.) or less in length
(Figure 5), indicating that this segment of the grain transportation supply chain is dominated by
relatively short trips. None of these trips exceeds 25 km (16 mi.). Nearly all of these trips occur
within a two-month timeframe (August and September).
• Storage-to-market trips (one-way) exhibit a (nearly) bimodal distribution (Figure 6), with
peaks occurring at both the 30-km (19-mi.) and 60-km (38-mi.) ranges. None of these trips
exceeds 130 km (81-mi.). The secondary peak in this distribution is mainly attributed to
commodities other than spring and winter wheat, in particular canola and oats. For these
commodities, producers can opt to haul to an elevator or directly to a processing facility. These
facilities tend to be more widely dispersed than elevators and may also offer premium pricing
opportunities. Thus, in the study region (and probably elsewhere as well), both distance and cost
play a role in trip-making decisions, though the influence is commodity dependent. The trips are
distributed seasonally as follows: 42 percent in winter (December to February), 22 percent in
spring (March to May), 11 percent in summer (June to August), and 26 percent in autumn
(September to November). This distribution partially reflects a preference to avoid weight
restrictions placed on certain roads during spring thaw.
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• The types of trucks used for storage-to-market trips (Figure 7) varies somewhat by trip
length, with a general tendency to use larger trucks (classes 9, 10, and 13) for trips beyond
roughly 40 km (25 mi.).
FIGURE 5 One-way trip length distribution for field-to-storage trips for 2011 harvest.
FIGURE 6 One-way trip length distribution for storage-to-market trips for 2011 harvest.
Limitations
The results reveal insights about agriculture-related trucking generated from the study region for
the 2011 harvest. While these results are meaningful for this specific region, their direct
applicability to other regions with different transportation and activity systems or other time
periods is limited. The results represent responses from nearly all producers living in the study
region and are thus of value to individuals responsible for asset management in this region.
However, scaling the methodology to a larger geographic area would require careful
consideration to ensure that the survey is statistically representative and that it captures a
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meaningful sample size. Further, as the results are an estimate of current conditions and flows,
extrapolation of these results into the future is not recommended. Nevertheless, they provide
information essential to the development of more robust travel demand forecasting tools.
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FIGURE 7 One-way trip length distribution by truck class for storage-to-market trips for
2011 harvest.
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CONCLUSION
This paper develops a methodology to characterize agriculture-related trucking from field-tostorage and storage-to-market. The methodology relies solely on data collected through producer
surveys, thereby leveraging an extensive amount of industry intelligence. Adopting the
transportation systems analysis approach, the methodology considers the influence of broad
industry trends on agriculture-related trucking, the characteristics of transportation supply and
demand, and their effect on local truck flows. Flows are principally expressed in terms of
volumes (by road segment and truck type) and trip length. These flow characteristics provide
fundamental inputs for asset management decisions, such as maintenance timing and upgrade
investments. In addition, the flow characteristics provide base information that supports the
development of demand forecasting models.
The development and application of the methodology contributes in three specific ways.
First, because of its reliance on producer surveys, the methodology can be used to provide
detailed characteristics on the field-to-storage segment of the supply chain in addition to the
storage-to-market segment. This “first mile” of the agricultural supply chain is commonly
overlooked by existing literature. Second, the application of the methodology reveals the extent
of information available from producer surveys. Not only are producers able to provide
qualitative comments on issues associated with agriculture-related trucking and road asset
conditions, but the survey demonstrates that they are also able to quantify, on a field-by-field
basis, specific details about truck trips, types, origin-destination patterns, commodities, and
differences between actual and desired routes. These details are a rich resource to support asset
management decisions concerning roads about which little quantitative information is known.
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Potential exists for the results of these surveys to be used as a surrogate of total travel on roads
serving agriculture-intensive areas. Finally, analysis of the data collected for the study region
described in this paper provides insights about the impacts of industry trends on agriculturerelated trucking. In particular, the results reveal that: (a) smaller truck types are more commonly
used for the shorter field-to-storage trips than the storage-to-market trips; (b) actual distance
traveled exceeds desired distance traveled, owing mainly to regulatory constraints related to the
infrastructure; and (c) trip length distributions for the storage-to-market segment reveal a
relationship between trip length and the type of truck and commodity being hauled. These
findings generally confirm expectations.
The methodology developed in this paper is generic and can be applied to a range of
geographic, temporal, and analytical scopes. However, actual implementation of the survey
program for the case study presented involved extensive field work and in-person
communication with individual producers. The scope of the case study made this approach
possible; however, scalability to larger regions or broader timeframes would require
development of on-line or paper survey forms. Based on experience gained from this research,
relevant maps and background information should be incorporated as part of these surveys.
Larger scale surveys would also provide improved insights about the influencing factors and
characteristics of agriculture-related trucking and its impacts on local road assets.
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REFERENCES
1. Archondo-Callao, R. Maintenance Impact on Economic Evaluation of Upgrading
Unsealed Roads. In Transportation Research Record: Journal of the Transportation Research
Board, No. 2203, Transportation Research Board of the National Academies, Washington, D.C.,
2011, pp. 151-159.
2. Clemmons, G.H. and V. Saager. Financing Low-Volume Road Improvements. In
Transportation Research Record: Journal of the Transportation Research Board, No. 2203,
Transportation Research Board of the National Academies, Washington, D.C., 2011, pp. 143150.
3. Huntington, G. and K. Ksaibati. Improvement Recommendations for Unsealed Gravel
Roads. In Transportation Research Record: Journal of the Transportation Research Board, No.
2205, Transportation Research Board of the National Academies, Washington, D.C., 2011, pp.
165-172.
4. Mirza, S. Danger Ahead: The Coming Collapse of Canada’s Municipal Infrastructure.
Federation of Canadian Municipalities, Ottawa, ON, 2007.
5. Study of Rural Transportation Issues. United States Department of Agriculture and the
United States Department of Transportation, Washington, D.C., 2010.
6. Association of Manitoba Municipalities. AMM Speaks Out on Infrastructure Program.
http://www.amm.mb.ca/documents/LTIPnewsrelease-final.pdf. Accessed Jul. 20, 2012.
7. Road Investment Needs to Support Agricultural Logistics and Economic Development in
North Dakota. Upper Great Plains Transportation Institute, Fargo, ND, 2011.
8. Prozzi, J., R. Harrison, and J.A. Prozzi. Sustainability of Rural Road Network Given
Changing Demands of Rural Agriculture: Evidence from Texas. In Transportation Research
Record: Journal of the Transportation Research Board, No. 1819, Transportation Research
Board of the National Academies, Washington, D.C., 2003, pp. 46-52.
9. Babcock, M.W. and J.L. Bunch. Impact of Kansas Grain Transportation on Kansas
Highway Damage Costs. Kansas Department of Transportation, Topeka, KS, 2002.
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10. Holguín-Veras, J. and E. Thorson. Trip Length Distributions in Commodity-Based and
Trip-Based Freight Demand Modeling: Investigation of Relationships. In Transportation
Research Record: Journal of the Transportation Research Board, No. 1707, Transportation
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11. Mruss, K.L.P. A Commodity-Based Methodology for Freight Forecasting on Rural Road
Networks. Master’s of Science thesis, University of Manitoba, Winnipeg, MB, 2004.
12. Subhro, M., A. Dybing, and D. Tolliver. Impacts of Ethanol Plants on Highway
Networks. In Journal of Transport and Land Use, Vol. 4, No. 1, 2011, pp. 71-81.
13. Fortowsky, J.K. and J. Humphreys. Estimating Traffic Changes and Pavement Impacts
from Freight Truck Diversion Following Changes in Interstate Truck Weight Limits. In
Transportation Research Record: Journal of the Transportation Research Board, No. 1966,
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14. Prentice, B., J. Kosior, and B. McLeod. Agriculture Trucking in Manitoba. University of
Manitoba Transport Institute, Winnipeg, MB, 1996.
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methodology to characterize agriculture

Transcription

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