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1. INTRODUCTION AND BACKGROUNDTransportation planning has focused on moving people efficiently and safely. Freight

demand modeling and assignment have received limited attention with little or no integrationinto major regional planning efforts. Although the heavy vehicle component of highway flows isrelatively small ( generally less than 15% ), it is predominant in affecting both the condition ofroads and traffic flow. An important limitation for major freight planning efforts has been thescarcity of comprehensive data sources. The complexity of freight transportation comes in thelarge number of private shippers, arrangers, transporters, and recipients. Most often, no singleparty knows all the players and modes that were involved in moving the shipment from a shipperto its ultimate destination. Compiling a database for all movements is an enormous undertakingand hence, there has not been sufficient research in commodity movement prediction.

The Commodity Flow Survey (CFS), a major partnership effort between the UnitedStates Department of Transportation and the Bureau of the Census, is the first comprehensiveeffort since the mid seventies, to learn where and how goods are shipped (1). This databasecontains commodity flow within and between states classified in terms of the type of freight andmode of shipment. Today, many analyses such as vehicle emission studies or pavementdeterioration assessment, require a high degree of accuracy for vehicle volumes on links.Sufficient research on predicting the number of passenger cars has been done. A procedure fordoing the same with trucks will be necessary and the CFS for 1993 is a good starting point.

Geographic information systems (GIS) can be described as a decision support systeminvolving the integration of spatially referenced data in any problem solving environment. GISare useful analysis and presentation tools in the field of transportation planning, engineering andmanagement. Use of GIS technologies in freight transport planning will enhance conformity withrecent efforts and methodologies in other areas of transportation planning and provide theflexibility for understanding the spatial effects of commodity flow.

The primary objective of this research is to develop a GIS-based approach for distributingand assigning freight flows in Massachusetts. An intermediate goal is to develop a quantitativemethodology for estimating freight traffic on major roads in Massachusetts from newly releasedinter-state commodity flow data. The following section discusses the implementation of GISusing TransCAD (2) in developing a procedure for assigning freight movements on the majorhighway corridors in Massachusetts.

2. METHODOLOGYThe basic framework for this analysis consisted of dividing Massachusetts into smaller

regions and apportioning the freight flow from the neighboring states to these regions using asocio-economic indicator variable. The statewide freight flow data was extracted from the CFSfor 1993 and corresponds to tons (in '000s) of commodity shipped by truck between the NewEngland States, New York (NY), New Jersey (NJ) and the rest of the United States as shown intable 1. During analysis, flows corresponding to the rest of the United States into and fromMassachusetts (MA) were combined with that of NY. This was because NY separates MA fromthe rest of United States and flows going into and out of MA have to pass through it.

A quantitative procedure was used to convert weights into truck numbers. An origin-destination (O-D) matrix for the number of daily trucks between the internal origins ( centroidsof the regions created ) and exit points from Massachusetts was constructed. This O-D matrixwas then assigned over the major highways in the state and the resulting link volumes werevalidated against an extrapolated Highway Performance Monitoring Services (HPMS) survey

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count (3). The different assignments considered were All or Nothing, Capacity Restraint andUser Equilibrium.

In All-or-Nothing assignment, all traffic flow between an O-D pair is assigned to theshortest path connecting the pair. This model is unrealistic in that only one path between everyO-D pair is utilized even if there is another path with almost similar travel costs. Also, traffic isassigned without consideration for whether or not there is adequate capacity. Ignorance ofhighway capacity is advantageous when freight data in tons (000's) needs to be assigned over thenetwork directly. The O-D matrix representing tons of freight flow, when assigned over thenetwork, will result in the weight of commodity on the various links.

The Capacity Restraint method attempts to approximate an equilibrium solution byiterating between All-or-Nothing traffic loadings and recalculating link travel times based on thecongestion function shown in equation 1 (4).

t tv

cf= +

1 αβ

(1)

where,t = congested link travel time.tf = link free-flow travel time.v = link volume.c = link capacity.α,β = parameters (α = 0.15, β = 4.00).

The Capacity Restraint assignment method does not converge for all links and has the additionalproblem that results are highly dependent on the number of iterations.

User Equilibrium utilizes an iterative process to achieve a convergent solution in whichno traveler can improve his/her travel time by shifting routes. TransCAD formulates the UserEquilibrium problem as a mathematical program using the Frank-Wolf solution method (5). Ineach iteration, network link flows are computed, which incorporate link capacity restraint effectsand flow dependent travel times using equation 1.

A pictorial representation of the analysis procedure is shown in figure 1. A morecomplete discussion of these steps is presented in the following sections.

2.1. Constructing the highway network.The highway network was extracted from the National Transportation Atlas Databases

(NTAD) and consisted of the National Highway Planning Network State, US, and Interstatehighways in Massachusetts. The spatial network is shown in figure 2. Because different types oftraffic assignments were to be evaluated on this network, attributes such as travel time andcapacity were calculated from existing information about the various links. The All-or-Nothingassignment procedure requires only the travel time attribute on the various links, whereas theother two methods require the capacity of the links as well. Travel time was calculated as theestimated time of travel at the speed limit over a highway link. Capacity of the link wascalculated from the number of lanes, lane width, grade, and mix of vehicles. The method adoptedfor this calculation was derived from the 1985 Highway Capacity Manual (6).

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2.2. Division of Massachusetts into smaller districts.The freight data from the CFS for 1993 is aggregated to the state level. Because the

intention was to estimate the flows on different links within the state, Massachusetts was dividedinto smaller regions created by aggregating 5 digit zip-code regions. Using 3 digit zip-coderegions proved too large for the analysis and 5 digit zip code regions considered individuallywould have made it very data intensive. Hence, using 3 digit zip-code boundaries as a guide, 5digit zip-code regions were combined to create appropriately sized areas. The geographiccentroids of these regions were joined to the network with centroidal connectors which allow forflow in a single direction only. The regions considered for analysis and their centroids are shownin figure 3.

2.3 Commodity aggregationOwing to the heterogeneity of freight, the initial intention was to divide commodity into

different categories based on Standard Industrial Classifications (SIC), such as farming, forestryand fisheries, mining, and all other sectors combined into one, and to perform the analyses oneach. For such analyses, it would be necessary to apportion flows (as described in the nextsection), for each category which would involve calculating independent distribution ratios forthem.

Though these ratios were determinable, the origin-destination matrices for each SICcommodity category was not completely extractable from the CFS for 1993. A majority ofcommodity flow data for the farming, fisheries and forestry, and mining categories were eitherwithheld to avoid disclosure or unavailable due to not meeting publication standards caused byhigh sampling variability. The ‘other sectors’ category consisted of upto 95% of all thecommodities data and the individual analyses conducted showed that they dominated the results.Hence, all commodity categories were combined and a single analysis procedure was adopted.

2.4. Apportioning the flows between the districts.The statewide flow has to be partitioned to the smaller districts created. Previous research

has shown that economic indicator variables such as employment, employment density, and floorspace, are good measures for assessing the amount of commodity entering or leaving a givenarea (7,8 and 9). As an initial indicator total employment was used as shown in equation 2.

de

ei

i

ii

=

=∑

1

25 (2)

where,di = Distribution ratio for district iei = Total Employment (all sectors) for district i

As mentioned in section 2.3, individual employment ratios corresponding to different SICgroupings were not necessary for the analysis.

2.5. Construction of Origin-Destination matrix.The centroids of the districts constitute the internal origins and destinations for the

matrix. External origins and destinations correspond to intersections of highways with the stateborder. Because many highways connect any two states, it was necessary to apportion the flowon the various highways based on their propensity and level of service characteristics. As

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distance to a given entry/exit point increased, probability of using that highway decreased, whileinterstates were given a higher priority than state and US highways.

O-D matrices representing the freight movement in tons between centroids and exitpoints and vice-versa can be assigned to the highway network. Such assignments were conductedbut no source existed for validating the results obtained. Hence, it was imperative that freighttons be converted to truck loads as number of trucks. An O-D matrix with truck volumes, whenassigned over the network, will result in the truck flows on the various links. These results can bechecked against existing survey counts.

Formulae for converting commodity weight into number of annual trucks are given inequations 3 and 4.

NW

p vavg i iii

i=

=≠

=

∑ρ34

13 (3)

NW

p w pi i eiii

i=−

=≠

=

∑13

134

13

.

( )(4)

where,N = Total number of all types of trucks for a given commodity weight W.W = Weight of commodity shipped annually between any two O-D pairs (kg).ρavg = Average density of freight shipped = 202.68 kg/m3

(12.5 lb/cu.ft).pi = Average percentage of truck type i (see figure 4).vi = Average volume of truck type i (m3).wi = Average weight of non-empty trucks of type i (kg).pei = Average percentage of empty vehicles of type i.

The theoretical basis for equations 3 and 4 is given in (10)

1) Weight translates into volume for a given density.2) Empty trucks will bring down average density of goods shipped (ρavg = 202.68 kg/m3).3) Average weight of trucks range from 25% to 35% of the commodity weight they

carry (hence total weight of truck in equation 4 = 1.3*W).4) Trucks of type 4 (i = 4) are busses and are not considered here.

This conversion incorporates the effects of various truck sizes and dead haul ( trucksreturning empty after delivery ). Using a low density value in equation 3, a deadhead (dead haul)component gets automatically added to each direction of movement into and from the state.Further, the density value corresponds to that of commercial traffic flow as opposed to justfreight flow. Hence, this conversion results in the commercial flows for a given commodityweight. Freight density has an inverse relationship with respect to truck number and smallchanges in it can produce large changes in the latter. Owing to such high sensitivity, this shouldbe calculated to precision. Data from work at the University of Massachusetts (11) were used toestimate the various variables in equation 3 which was used for conversion. Tonnage of freightbetween various origin-destination pairs were substituted for 'W ' in equation 3 and annual

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number of trucks moving between these pairs was determined. This number was divided by 260,the average number of annual working days, to estimate the daily truck volume between O-Dpairs. The average number of annual working days was used as the resulting truck flows were tobe compared against peak percentage of commercial traffic. This final O-D matrix wascomprised of four basic divisions shown in figure 5.

2.5. Assigning the freight O-D matrix.Different assignment techniques including All or Nothing, Capacity Restraint, and User

Equilibrium were evaluated. Results from these assignments were compared between themselvesand also with actual observed data (HPMS survey). All or Nothing assignment showed thecounter intuitive result of interstate highways having lower truck counts as compared to theneighboring State and US highways. Capacity Restraint method of assignment has the problemsof divergence and a high dependence on the number of iterations. User Equilibrium assignmentwas chosen over the others as it showed both valid and intuitive results (see figure 6).

The results of the above mentioned comparisons are not presented here and it should benoted that the authors were interested in using an assignment method that was both intuitive andproduced reasonable results. All three methods without modifications only give approximateassignment results. This is because of the presence of a cyclic relationship between the numberof trucks and the classification of the highway. The number of trucks for a given commodityweight is a function of the percentage mix of the type of trucks (pi - see equation 3) whichdepends on the type of highway. But, the highway characteristics do not come until assignmentat which point the number of trucks on them needs to be calculated before hand. Hence, aniterative procedure of assignment using principles of the methods mentioned above needs to beused which forms a part of the future research effort.

2.6. Validation of analysis.The Highway Performance Monitoring Services (HPMS) for Massachusetts contains

percent commercial vehicles data for 1990, 1991, and 1992. This survey and the Average DailyTraffic (ADT) values for the different highway links were used to calculate the average dailynumber of trucks. These values were extrapolated across highway segments as shown in figure 7.This was used to assess the validity of the assignment. The difference between the estimatedflows and the survey data for the User Equilibrium type of assignment is shown in figure 8.

3. RESULTS AND COMPARISONThe validation results show that 81% of links fall into the tolerable (±15%) category.

Nine percent of the links show an overestimation and 10% show that the HPMS survey data ishigher than what was calculated. A majority of the former links are on I90 (Mass Turnpike)where HPMS data was not available and an average of 10% commercial traffic was assumed.Highways closer to Boston (I495, I95, and I93) showed an underestimation as compared to theHPMS data. This is understandable as commercial traffic in an urban locality is dependent onmany other factors including local trips, trip chaining and higher number of smaller trucks. Amore localized study is necessary to understand the major traffic generators and attractors.Generally, a high degree of correspondence between observed and calculated flows shows thatthis line of research is promising.

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4. FUTURE WORK.The aforementioned methodology was the basis for studying freight movement in

Massachusetts. The following are some of the research objectives for the future• This methodology will be applied to commodity flow data corresponding to National

Analysis Transportation Region (NTAR) to NTAR flow. Massachusetts is divided verticallyinto two approximate halves by NTARs. Commodity flow between states is at a highermacroscopic level as compared to flow between NTARs and higher accuracy in analysesmay be possible using the latter.

• This approach is to be used at the regional level where much more accurate employment datawill be available and issues such as ‘close proximity’ for freight movement can beinvestigated.

• The weight to truck number conversion formulae have to be refined and recalibrated withcurrent data sources. The average percentage of different type of trucks (pi - see equation 3)is dependent of the type of highway - Interstate, State or US Highway. With more accuratedata, these variables in the equation can be calculated more precisely.

• The annual commodity flow has been converted to daily truck counts by using a factor of 260(the number of working days) which needs to be refined. The analyses should be conductedwith 365 and 312 days and the results compared.

• Separate truck flow assignments near urban areas are necessary due to the effects of localtrips. Total employment as an indicator variable has not been sufficient in explaining truckmovement near urban areas. Introduction of relevant socio-economic variables has to beresearched.

• The railway network in Massachusetts will be added to this highway network and intermodaltransfer yards will be modeled as pseudo links connecting these networks. With anunderstanding of the spatial distribution of freight flows, suggestions for improvingefficiency and reducing total shipment time can be researched.

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ACKNOWLEDGMENTSThis research was partially funded by the Massachusetts Port Authority. The authors would like tothank Russell Capelle of the Central Transportation Planning Staff in Boston for providing valuableinsights and guidance during the course of the research.

REFERENCES 1. U.S. Census Bureau; " 1993 Commodity Flow Survey; " issued December 1996, U.S.

Department of Commerce, Washington, D.C. 2. TransCAD - Transportation GIS Software, Users Guide. Caliper Corporation, Newton,

Massachusetts, 1996. 3. Highway Performance Monitoring Services; " Percent Commercial Vehicles data;" issued

August 1993, Bureau of Transportation Statistics, U.S. Department of Transportation,Washington, D.C.

4. Traffic Assignment Manual, Bureau of Public Roads, Urban Planning Division, U.S.Department of Commerce, Washington, D.C., 1964.

5. Travel Demand Modeling with TransCAD 3.0. Caliper Corporation, Newton,Massachusetts, 1996, pp. 192 - 199.

6. Highway Capacity Manual, Special Report 209, Transportation Research Board,Washington, D.C., 1985.

7. Starkie, D. M., Commercial Vehicles in Urban Transportation, Journal of Institute ofHighway Engineering, September 1970.

8. Biddle, B., V.J. Siaurusaitis, Truck Transportation Planning, Working Paper, COMSIS,Silver Spring, Maryland, May 1997.

9. Memmot, F. W., Applications of Statewide Freight Demand Forecasting Techniques,NCHRP 260, TRB, Washington DC, September 1983.

10. National Transportation, Trends & Choices ( To the year 2000 ), U.S. Department ofTransportation, January 1977, pp. 156 - 184.

11. New England Vehicle Classification and Truck Weight Program, Technical Report No.2,New England Transportation Consortium, University of Massachusetts at Amherst,November 1995.

TABLE 1 : Commodity Flow by truck in New England, New York, New Jersey, and the rest of the US. ( 000's tons )

Destinations

Origins CT ME MA NH RI VT NJ NY Rest of US

CT --- b 237 2174 220 --- 131 --- --- ---

ME 238 --- 1331 --- 75 --- 294 2660 2101

MA 2146 808 50486 2704 878 718 1016 2003 9087

NH 484 --- 1970 --- 82 --- 208 667 546

RI --- 38 3873 76 --- 28 --- --- ---

VT 76 --- 395 --- 22 --- 126 753 1411

NJ --- 259 2374 181 --- 168 --- --- ---

NY --- 316 3533 697 --- 1933 --- --- ---

Rest of US --- 1250 9799 1367 --- 757 --- --- ---

a Source : Commodity Flow Survey 1993.b Data not required for this analysis. Hence not extracted from the source.

Figure 1. Analysis procedure

CFS 93

Origin-Destination data fromState to State by truck and

commodity type

Origin-Destination matrix forinternal origins (centroids) and

border exit/entry points

Economic Indicator(s)• Employment• Employment density• Floor space

Conversion to number of trucks

Final Origin-Destination matrix -Number of daily trucks.

NTAD

Highway and Stategeographic files for

Massachusetts

Division of Massachusettsinto smaller regions

Joining their centroids to thehighway network

Identification of majorentry/exit points on the state

border

Traffic Assignment - All-or-Nothing - Capacity Restraint - User Equilibrium.

HPMS Commercial vehiclesurvey data

Extrapolate over highway links

Validation 81 % - tolerable range of ±15% 9 % - over predicted. 10% - under predicted.

Future Work.

90 Census