4-Step truck model: SCAG case study

4-Step truck model: SCAG case study Stephen Yoon Jae Hun Kim 1. Background In a typical four-step model, the input data for forecasting travel demand is the socio economic data of people in TAZs, and the output data is the flow of vehicles. This output can reflect the movements of people with purposes for activities (i.e. working, shopping, seeing wife etc.). Most of movements by people with vehicles are recorded by people themselves, if they are participating in a travel survey. However, in the case of freight movement other than people, the flow can be measured in two types-commodity and trucks.the flow of trucks can be considered as the movements of people in a typical four-step model, but the flow of commodities may be assigned to different modes in movements, and commodity itself cannot record its trip. This should be done by each truck driver who is shipping it. Therefore, the freight movements cannot be forecasted by a typical four step models which can forecast passenger movements. Forecasting the freight movements allows freight-related workers managing commodity flows more efficiently and enhancing storage and supply systems. Therefore, in order to forecast the freight movements, something different from a typical 4 step model; the four step truck model has to be developed.the four step truck model is based on a typical four step model and some extensions for truck movements are added. The 4 step truck model deals with freight movement data as commodity movements and truck movements. For the trip generation and distribution, the model makes outputs as trips of commodities.and recently, the proportions of port trips and intermodal trips (i.e. commodity movements between trucks and other modes) have increased as the freight movements between nations have increased. To consider this, the model adds special extensions in the case of these specified trips. And then in the modal split, these commodity trips are converted to truck trips for a traffic assignment to the network. Some transportation institutes have developed four step truck models and updated them to forecast the freight movements more accurately and efficiently.

1) A truck model by San Diego Association of Governments (SANDAG) As shown in Figure 1, SANDAG Truck Model consists of two modules; Internal truck model and External truck model.an internal truck model generates local truck trips within San Diego County, and an external truck model simulates truck trips across the entire US. In an internal truck model, household and employment data (HH &Emp data in Figure 1.) is used and processed up to trip distribution.in trip distribution, the module named Special generator (Special gen) is applied to create trips that cannot simply derived from either employment or household, especially truck trips. This module includes military sites, the cruise ship terminal and the airport. In the external truck model, the FAF2 data which containtrips by truck is simulated for a) truck trips that leave San Diego County (internalexternal or IE), b) trips that enter the county (external-internal or EI), and c) trips that go through the county (external-external or EE). Figure 1: Scheme of the SANDAG Truck Model Source: Development of a Truck Model for the San Diego Region Final Report, 2008

2) A Heavy Duty Truck (HDT) model by Southern California Association of Governments (SCAG) SCAG has developed a HDT model as part of its project, the Regional Model Update.A HDT model forecasts trips for three HDT weight classes: light-heavy, mediumheavy, and heavy-heavy. The HDT model consists of following components: external trip generation and distribution, internal trip generation and distribution, special generator trip generation and distribution, and trip assignment. The details of this model will be mentioned in later. 2. General form of the 4-step truck model (by FHWA) As previously indicated, the freight flow can be measured in two types; commodity and trucks. Figure 2 shows the four steps to forecasting freight. From Figure 2, the outputs from trip generation and distribution can either be in the form of commodities or trucks. The basic difference between commodity-based models and truck-based models is the form of the input data. However, for trip assignment all forms of freight are converted to vehicles to be assigned onto a roadway network. 1) Trip Generation Trip generation models used in freight forecasting include a set of annual or daily trip generations rates or equations by commodity. These rates or equations are used to determine the annual or daily commodity flows to and from geographic zones as a function of zonal or county population and/or industry sector employment data. In other words, employment and/or population data are the essential input data for freight trip generation.the employment and/or population data usually dictate the level of detail the freight flows can be generated using a trip generation model. This may be a county or a traffic analysis zone (TAZ).The travel demand models usually use TAZ data, and so a freight forecasting model can be developed at a TAZ level as long as the base and forecast year data at the required level of industry is available at that geographic unit. Before trip generation models are estimated, trucks are first classified by type of truck and/or trip purpose/sector. The types of classification of trucks include the FHWA

classification system, gross vehicle weight (GVW) ratings, type of goods carried, number of tires/axles, and body type. Normally, one set of regression equations for the productions and one set of equations for consumption are estimated. These equations are either developed for each commodity group or truck type. A commodity group is similar with a trip purpose in passenger modeling. The observations used to estimate the regression model would be the inbound tons of the commodity or number of trucks and the independent variables are usually employment, industry type, population, etc. for each geographic area. Truck trip generation rates can be developed from trip diary surveys using regression equations by regressing the number of commercial vehicles on the number of employees in various industries and household population. Trip rates also can be estimated for each individual land-use type based on the radio between the truck trips to and from the land area and the employment associated with that land use. The Quick Response Freight Manual (QRFM) (1996) was developed by the FHWA and it provides default values that can be used in models. Figure 2. Four-step process of freight forecasting (from FHWA) Source: Quick Response Freight Manual II, FHWA, 2007

The steps required to determine trip rates are: a. Trip rates need to be estimated or identified (either through local surveys or using national default data) b. Socioeconomic data (employment by industry and households/population) by TAZ is applied to the rates to get generation by TAZ c. QRFM method assumes that productions equal attractions, but local data can be used to estimate separate production and attraction rates, and d. If there are freight centers (ports, intermodal terminals), they should be treated as special generators and have their own trip rates determined from surveys since employment rates would not apply. Table 1 is the example of truck trip rates for trip generation in FHWA, which was taken from the Phoenix Metropolitan Urban Truck Model. The table shows that there are more four-tire truck trips per unit of activity than single unit and combination truck trips. Generator Commercial Vehicle Trip Destinations (or Origins) per unit per day Four-tire Single unit trucks Combinations Total vehicles (6+ tires) Employment -Agriculture, mining and 1.110 0.289 0.174 1.573 construction -Manufacturing, 0.938 0.242 0.104 1.284 transportation, communications, utilities and wholesale trade -Retail trade 0.888 0.253 0.065 1.206 -Office and services 0.437 0.068 0.009 0.514 Households 0.251 0.099 0.038 0.388 Table 1. Truck trips rates for trip generation Source: Quick Response Freight Manual, FHWA, 1996

2) Trip distribution In trip distribution, one determines the flow linkages between origin and destination for those commodity tons/truck trips that were developed in trip generation. Trip distribution uses those flows/trips to and from and independent variables on the transportation system, to forecast the flows/trips between geography areas. The trip distribution equations can be borrowed from other sources or developed locally by using an existing commodity flow table or local vehicle surveys. A gravity model can be constructed and calibrated at a prespecified geographic detail. The gravity model is a statistical process that has been found useful to explain the relationship between transportation zones. The considerations are the total trips that begin in the first zone, the trips ending in the second zone, and the impedance or difficulty to travel (such as cost or time) between them. The average trip lengths,which are needed to obtain trip-length frequency distributions and friction factors, are normally obtained from surveys. The degree of difficulty of travel, usually a function of some impedance variable used in the distribution model needs to match the survey data (free flow time, congested travel time) and there must be a source of the impedance variable. The calculation of the degree of difficulty is often called a friction factor. With limited survey data, the model is typically calibrated at the district level, and the friction factors developed are assumed to apply at smaller units of geography. However, it is sometimes difficult to get survey data for trip distribution, and friction factors are often borrowed from other sources. The friction-factors are usually calculated as a negative exponential function of the average trip time from origin TAZ to destination TAZ. The parameters in the exponential function are calculated from the trip length frequency distribution, which describes the shape of the curve that is summarized by the average trip length. The friction factor curves for the PSRC truck model, which are found from the 2007 edition of the QRFM, were derived originally from the 1996 edition of the QRFM and adjusted to provide the best fit with the average trip lengths from the origin-destination survey of trucks. The light, medium, and heavy trucks are distributed from origins to

destinations using this gravity model technique with different parameters. These friction factors were developed using impedance functions that also varied by trip distances, that is different parameters were used for short and long distances, as shown below: Light impedance function: - exp (3.75-0.08*light truck generalized cost skim) for less than 26 miles - exp (2.1-0.005*light truck generalized cost skim) for greater than or equal to 26 miles Medium impedance function: - exp (4.75-0.05*medium truck generalized cost skim) for less than 27miles - exp (4.2-0.003*medium truck generalized cost skim) for greater than or equal to 27 miles Heavy impedance function: - 1.0 for less than 7.5 miles - exp (5.0-0.009*heavy truck generalized cost skim) for greater than or equal to 7.5 miles 3) Mode split/ Conversion to Vehicle Flows Mode choice modeling is used if multimodal trip tables need to be prepared. This step allows the forecastability of mode splits as they change over time. The four major categories in which various factors that affect mode choice decision-making process fall into are: a. Goods characteristics- These include physical characteristics of goods such as the type of commodity, the size of the shipments, and the value of the goods b. Modal characteristics- Speed of the mode, mode reliability, and the capacity c. Total logistics cost- Inventory costs, loss and damage costs, and service reliability costs, and d. Overall logistics characteristics- Length of haul and the shipment frequency Figure 3 shows the major characteristics of each of the freight modes in a

continuum/spectrum and shows how this relates to the types of goods that may be shipped by each mode. From this spectrum, the truck has the lowest capacity, and provides the highest level of service in terms of reliability and minimal loss and damage. So commodities that are needed for just-in-time production systems will need to use trucking. Figure 3. Goods and modal characteristics Source: Quick Response Freight Manual II, FHWA, 2007 3-1) Choice method The most common method is the logit model. This formulation is similar to the passenger mode choice modeling, but the variables and datasets used to estimate the parameters are different. The logit model shows the choices for individual shipments as a function of the utility that each mode provides to the shipper. The logit model actually calculates the probability that each shipment will use a particular mode. Summing the probabilities across all of the shipments provides the overall mode share. Each modal alternative has a utility to the shipper that has a systematic component related to the factors which have been described earlier and a random component that has to do with things like personal relationships. The coefficients in the utility function measure the relative importance of each factor in determining mode choice. Logit choice models are the most complete with respect to modeling all of the factors

that affect mode choice. Thus, they can be applied to a wide range of policy and investment studies. However, they are complex to build and are very data intensive. Most of the data needed require the use of complex performance or simulation models. The truck surveys are helpful for estimating the choice parameters, but these surveys are expensive and timeconsuming to conduct. 3-2) Truck conversion The freight trip tables after the mode split step are multimodal commodity flow tables in annual tons. In other words, after allocating the tables among the modes, the flow units will still be in annual tons. The flow unit in almost all highway travel demand models is daily or peak-period vehicles. Therefore, to consider the interaction of freight trucks on the highway with all automobiles and all other vehicles, the time period must be made consistent and the annual truck tables in tons must be converted from annual tons to daily trucks. Payload factors (average weight of cargo carried) are used to convert tons to trucks. The annual trips are then converted to daily trips by assuming an average number of operating days per year. But most travel demand models use average weekday travel. Various data sources can be used to estimate fraction of truck tonnage on weekdays and then divide this tonnage by number of weekdays per year. Payloads or truck loads are limited by weight and volume considerations. The commodities carried by trucks have different densities and, therefore, different payloads for the same volume. Because of handling and packaging needs, payloads also may differ by commodity. For example, large size trucks carry heavier loads even for the same commodity. If payloads are calculated for different truck classes, the commodity tonnage needs to be allocated to the different truck classes. Smaller trucks tend to be used more in shorter-haul service. To the extent that length of haul and truck size are correlated, length of haul (directly available from commodity flow data) can be used in calculating payload factors. Payload factors can be calculated for loaded trucks only (estimated truck volumes must then be adjusted to account for percent of empties) or they can average empty and loaded weights. The various sources of payload factors are 1) shipper or carrier surveys that provide information about the tonnage and commodity being carried; 2) weigh stations that typically

have weight information by truck type, but not by commodity; and 3) the VIUS (Vehicle Inventory and Use Survey, U. S. Census Bureau, 2002) is a part of the Economic Census. 4) Network assignment The process of allocating truck trip tables or freight-related vehicular flows to a predefined roadway network is known as the traffic assignment or network assignment. There are many types of assignments that are dependent on a number of factors such as level of geography, number of modes of travel, type of study and planning application, data limitations, and computational power such as software. In developing a truck trip assignment methodology, some of the key issues and model components that need to be addressed and evaluated are as follows: Time-of-day factors: These distribution factors by truck type separate truck trips that are in motion during each of the four modeling time periods; these factors need to be examined through recent data. Roadway capacity and congested speeds: A single truck will absorb relatively more of the available capacity of a roadway than an automobile, and a given volume of trucks will often result in a much greater impact on congested speeds than a similar volume of automobiles. So passenger car equivalent factors are required to convert the truck flows to these factors before the assignment process. Volume-delay functions: These functions are used to estimate average speeds as a function of volume and capacity may be different for trucks than for automobiles. Truck prohibitions: Some freeways and major principal arterials in the region have prohibitions for certain classes of trucks, and this needs to be addressed before the assignment. A truck network also may be built based on the local knowledge of truck prohibitions and truck routes.

3. SCAG Heavy Duty Truck Model Introduction SCAG as Metropolitan Planning Organization (MPO) puts tremendous efforts to develop advanced regional travel demand model for more accurate and realistic travel demand forecast in the region. And not only passenger vehicle demand forecasting model freight travel trip forecasting also taking an important role to build up sound demand analysis. In this chapter, we would explain HDT modeling components which can contribute HDT trip forecasting procedures. As part of the current Regional Travel Demand Model Update, SCAG puts their efforts to develop improvements and enhancements to the Heavy Duty Truck (HDT) Model. In this chapter, we will provide the technical approach used to implement various model improvements. In general, the key element of the model development was the collection and update of HDT trip data in the region from various sources. Not only input dataset analysis, this chapter will also address the various components of the HDT model including internal and external trips, port trips and intermodal trips of HDT. In the Figure 4, flow chart shows the overall structure of the SCAG HDT model. And as the final output model forecasts three categories of HDT by their weights as below, 1. Light-heavy duty: 8,500 to 14,000 lbs. Gross vehicle weight (GVW) 2. Medium-heavy duty: 14,001 to 33,000 lbs. (GVW) 3. Heavy-heavy duty: above 33,000 lbs. (GVW)

Figure 4. Final HDT Model Structure (SCAG, 2012 1 ) Components of HDT trip model Followings are major components of HDT model. - External HDT Model: In this stage, model estimates the trip table for all interregional truck trips (all levels) based on the national commodity flow patterns between California and rest of the nation. For this process, model grabs commodity flow data (annual tonnage flows at the county level) sources and then convert them into daily truck trips at the local parcel level (TAZ level). And in the update SCAG HDT model, they replaced Caltrans Intermodel Transportation Management System (ITMS) commodity flow data with newly developed TRANSEARCH data from IHS/Global Insight 2. - Internal HDT Model: In addition to the external trip model, this stage builds up trip tables for intraregional truck trips. In general, internal trip generation model is based on the trip ratios of socio-economic data such as number of trips per employee or household in the target area such as trip rates of size of firm or industrial/land use categories. Therefore trip distribution process was affected by trip interchange relationships among land use categories to produce factor matrix. 1 2012 SCAG Regional Transportation Plan Draft Validation Report 2 http://www.ihs.com/products/global-insight/industry-analysis/commerce-transport/database.aspx

- Special Generator and Secondary Models: These specialized generation and distribution model relate to port trip model and the intermodal rail model mostly. Technically, those models are update based on the change/update of port capacity improvement and forecasting amount of freight estimation. And those secondary port trips are considered in the modelwhichincludes freight trips from intermediate locations such as rail dock yard or transshipment sites on the way to the final destinations and all truck trips within or near port area. And they will be distributed on the network by gravity model. - Trip Assignment: After all those HDT relate matrices are complete and combined together model convert these truck trips into PCEs to execute trip assignment on the network with passenger vehicle trips. SCAG model uses PCE factor from TRB Highway Capacity Manual which explains and defines the function of the percent truck volume and length, steepness of grades. And then model assign those truck trips on the network with 5 different time periods which developed various classification and count sources (SCAG, 2012). Specification and Methods 1) External HDT model SCAG external truck trip consists of three different truck trip types; Internal- External trips (IE) andexternal-internal trips (EI) and External-External (EE) trips. And those IE and EI truck trips are calculated and distributed based on the county level commodity flow data (2 digits NAICS employment data) which needs to be allocated into TAZ levels. Growth factor was derived from the county level commodity flowand model also use external cordon to forecast further trips. SCAG HDT model was developed based on the 2007 TRANSEARCH commodity flow table and Wiltec Counts, Caltrans counts. In case of the TRANSEARCH commodity data is annual flow data in tons so model had to convert this value into daily weekday flow data by using converting factor 306 (6 days per week for 51 weeks). And then all those ton value data was converted into total trip of vehicle by payload factors (Table 2) from 2002 Vehicle Inventory and Use Survey (VIUS) data (SCAG, 2012).

Table 2. External HDT commodity Payload Factors by commodity classification a. Outbound Truck Load and Private Carrier Shipments: In the SCAG HDT mode, external trip ends of all those outbound commodity flows are allocated to the specific cordon stations based on the survey data in the region(which are mostly locate at the regional boundary) while their internal trip ends (pair) are disaggregated to the TAZ level by their commodity categories. And this method also applied to the EE trips. b. Inbound Truck Load and Private Carrier Shipments: Similar to the outbound truck trips, those external trip ends of inbound commodity flows are also allocated to the

cordon stations and internal trip ends (mostly to warehousing) are disaggregated to each TAZs based on the size of warehousing in that parcel. c. Empty Truck Trips: All those empty truck trips are also added truck trip estimation from the commodity flows. And model allocated same ratio of empty truck to all external cordon locations based on the survey data. 2) Internal HDT trip SCAG internal truck trip generation model was established onthe land use based model which referring to the employment by industry groups to get truck production and attraction trips. And those representative industry groups are below; Households, agriculture/mining/construction, retail, government, manufacturing, transportation/utility, wholesale, and other service group. In addition to the industry group, those trip rates of each land use are also calculated based on the survey data such as establishment surveys and third-party truck GPS source data (SCAG, 2012). a. Land Use and Socioeconomic data: Not like other socioeconomic data employment data was more specified with its categories (from 22 two-digit NAICS to aggregated 10 categories for truck trip generation model). b. Internal HDT Trip Rates: Trip rates are derived from the establishment surveys and GPS data by their land use categories and truck type by their size (Light, Medium, and Heavy) and rates are shown in the Table 3. c. Internal HDT Trip Distribution Model: In general, HDT trip distribution process uses a gravity model with factored both end of trip production and attraction. For the factoring model consider the interchange relationship of land use types between two different trip ends. As the result of combination of those land use types there are 64 gravity models for each truck types (Light, Medium, Heavy) and then those results

are combined into one final matrix for further factoring such as time of day and final network assignment with passenger vehicle volume. Table 3. Internal HDT Trip rates by categories d. Composite Cost 3 Impedance: To calculate HDT trip distribution model uses composite cost impedance to apply time and distance-based costs which consider changes of fuel price and life of tire and depreciation and other operational, maintenance costs to get the more realistic results. And SCAG uses composite cost for those three different truck types as shown intable 4. e. Internal Truck Trip Length Calibration results: With given factors and impedance cost SCAG executed truck trip length calibrations to compare model results and the real count data from GPS survey for each truck types. And as shown in the figure 5, we can see the similar pattern from both model result and GPS survey data. Table 4.Composite Truck Unit Costs 3 Composite Cost= Cost per hour * Congested time +[Fuel Price / Fuel efficiency + Cost per mile (excluding fuel)]*distance (Source: 2012 SCAG model documentation)

Figure 5.Heavy HDT Internal Truck Trip Length Calibration Result 3) Other HDT relate Model: Not only internal and external truck trip counts based on the survey data there are secondary HDT trip counts and implementation in SCAG HDT model such as Port HDT trip model and Intermodal Truck trip (IMX) within the SCAG region. a. Port HDT Model: SCAG port trip generation model is developed based on the zone system of port area and classification of different types of truck (Bobtail, Chassis, Containers). The port HDT model that is now based on new gate surveys was presented. This included the Quick Trip model 4, new port TAZs, and a summary of port trips by county for Port trip generation process. Port trip model is separate model process which included the usage of a new port truck distribution table based on new 2010 observed gate surveys. This also included the expansion of the port truck trip tables to the update five time-of-day (TOD) periods which will be used in SCAG Regional Travel Demand Model with all other trips (both passenger and truck trips). In the port truck model, there are two different components, (1) container terminal truck (CTT) trips and (2) noncontainer terminal truck (NCTT) trips. 4 Source: Sue Lai et al, (2006), METRANS National Freight Conference, Port of Los Angeles Portwide Transportation Master Plan presentation.

The Container Terminal Truck (CTT) trip generation model refer to the QuickTrip modelwhich was developed for the Ports of Los Angeles and Long Beach to get truck trip volumes by their types and time period for both current and future/forecasting years. To get the result modelneeds following input data and result summary is shown in the figure 5. Figure 6.Quicktrip Model result summary sample Peak monthly Twenty-ft Equivalent Units (TEU) amount. TEU-to-lift conversion factor (for average number of TEUs with each lift at the terminal). TEU land-side amount distributions: percent of TEU amounts associated with ondock(off-dock) intermodal imports, on-dock(off-dock) intermodal exports, local inputs, local exports, empties and transshipments. Number of port operation days per week. Total amount of movement per each terminal. The Non-Container Terminal Truck (NCTT) trip generation model estimates all other port truck trips from all types of port terminals such as dry bulk terminal, liquid bulk terminal and even other purposes (administrative, maintenance etc.). Therefore most of dataset were established from actual survey counts within the each port and terminal area.

Port Trip table Distribution is based on OD matrices which were established from the truck driver survey from each port at the container terminal for travel demand model input. Table 5 and 6 are port truck trip inputs for SCAG port model. Table 5. 2008 Port HDT Trips by Truck Type Table 6. 2008 Daily Port HDT Trips by County b. Intermodal HDT trips (IMX): The intermodal trip tables for SCAG HDT model are heavy HDT trips generated based on the 2005 IMX surveys conducted as part of the LAMTA Cube Cargo project 5. IMX data collected from 6 regional intermodal facilities which shown in the figure 7. And this survey includes following data; a) total inbound and outbound trips by month, b) origin and destination, number of containers by type c) weekly train schedule d) number of loading/unloading rail cars by month e) gate transactions by day by type (inbound, outbound, loaded, 5 Source: NCHRP(Synthesis 384), Forecasting Metropolitan Commercial and Freight Travel, chapter 6 Case Study, pg 113

empty and bobtail). The annual truck trips derived from the IMX surveysfrom those six IMX terminals and thenthey were used to derive daily IMX truck trips by daily factor (306) with four types of matrices such as TL inbound, TL outbound, LTL inbound and LTL outbound. However truck trips between facilities and port area (port TAZs) are not included in IMX truck trips since those trips are already included in the Port model (SCAG, 2012). Figure 7. Intermodal (IMX) facilities in SCAG region A summary of IMX trip tables by terminal and county, as derived from the previous 2005 IMX surveys conducted as part of the LAMTA project, are shown in Table 7. Table 7. 2008 Intermodal HHDT Trips by Terminal and County

c. Secondary HDT trips: Secondary trip isthesecond leg of a HDT trip chain that originates at the ports or intermodal (IMX) terminals and ends at an internal TAZ zones. And those intermediate stops are usually at wholesale land use zones (manufactures or regional distributors). As shown in the figure 8, that is the first leg of this HDT trip chain is from port or IMX to wholesale zones, while the second leg is from that wholesale land use zones to any internal TAZ in the sixcountiesof the SCAG region. Trucks to/from external stations have been previously established, based on the External HDT Model as discussed earlier. Port Port Model 10 20 Non - Port Wholesale Internal (employment) Model 15 15 Rest of SCAG Port Port Model 10 20 Non - Port Wholesale Internal (employment) Model 15 10 15 Res of SCAG 20 Figure 8. Secondary HDT Trips Ends

4. Conclusion 1. Quick reviews of each component of this research The SCAG HDT model classifies trucks on roads as three classes (light, medium, and heavy), and then identifies their trips as internal (within a survey area) and external trips (outside survey area and cordon stations) to generate and distribute these trips. In the case of port trips and intermodal trips related to rail, the model applies a special generator and a secondary model. 2. Current issues in the freight model As the number of freight movements has increased, the importance of forecasting freight movements also has increased.the freight movements, especially commodity movements, are obviously different from passenger movements.moreover, most freight movements are port trips and intermodal trips in which two or more modes are involved, because the international freight movements between nations as well as domestic movements have increased.therefore, it is necessary to develop the freight model which can cover the issues mentioned above. 3. Any problematic issue or weakness? The 4-step truck model is data intensive. Therefore, accurate data of commodity movements for forecasting is essential. However, the collection of this is difficult, because commodities may be shipped by several different modes for a single trip. The best way to track commodity movements is to perform a survey by drivers who are shipping commodities. But this is a time consuming work and needs a lot of costs. 4. Any suggestion or viable improvement? In most of cases, the freight movement data may be less available than the passenger movement data. For addressing this fact, Munuzuri et al. (2010) provided urban freight models which can use even a data with limited availability.considering the fact that the data of commodity movements is difficult to collect, their models might be useful to generate and distribute trips. With these advanced freight models, we could forecast more accurate regional wide travel demands and flows to deal with those issues

accordingly so that we could make our region better place to live for us and our children. 5. Homework 1) From a travel survey in city A, it is found that there are about 20,000 commercial vehicles on the road. And the following table is the proportions of commercial vehicles by vehicle types and trip types. Using Table 1, calculate these vehicles trips which will be generated for 1-year. Four-tire trucks Single unit trucks Combination (6+ tires) Total 55% 25% 20% Home 12% 3% 1% Employ 43% 22% 19% Agriculture 16% 6% 4% Manufacturing 7% 6% 6% Retail 10% 6% 5% Office & Services 10% 4% 4%

Generator Commercial Vehicle Trip Destinations (or Origins) per unit per day Four-tire Single unit trucks Combinations Total vehicles (6+ tires) Employment -Agriculture, mining and 1.110 0.289 0.174 1.573 construction -Manufacturing, 0.938 0.242 0.104 1.284 transportation, communications, utilities and wholesale trade -Retail trade 0.888 0.253 0.065 1.206 -Office and services 0.437 0.068 0.009 0.514 Households 0.251 0.099 0.038 0.388 Table 1. Truck trips rates for trip generation Answer) Based on the total commercial vehicles in city A (20,000) and the proportions of these vehicles, the number of commercial vehicles are shown as below: Four-tire trucks Single unit trucks Combination (6+ tires) Total 11,000 5,000 4,000 Home 2,400 600 200 Employ 8,600 4,400 3,800 Agriculture 3,200 1,200 800 Manufacturing 1,400 1,200 1,200 Retail 2,000 1,200 1,000 Office & Services 2,000 800 800

By multiplying truck trips rates with the number of trucks, the generated trips per day are shown in the following table: Ex) Four-tire trucks for agriculture trips 3,200 1.110 = 3,552 trips per day Trips per day Four-tire trucks Single unit trucks Combination Household 602.4 59.4 7.6 Employment Agriculture 3552 346.8 139.2 Manufacturing 1313.2 290.4 124.8 Retail 1776 303.6 65 Office 874 54.4 7.2 And by multiplying these trips with 365 days (=1 year), the generated trips per year can be found as following: Trips per year Four-tire trucks Single unit trucks Combination Household 219,876 21,681 2,774 Employment Agriculture 1,296,480 126,582 50,808 Manufacturing 479,318 105,996 45,552 Retail 648,240 110,814 23,725 Office 319,010 19,856 2,628

REFERENCE Development of a Truck Model for the San Diego Region-Final Report, San Diego Association of Governments, 2008. Quick Response Freight Manual, Federal Highway Administration, 1996. Quick Response Freight Manual II, Federal Highway Administration, FHWA-HOP-08-010, 2007. SCAG Regional Travel Demand Model and 2008 Model Validation Draft, Southern California Association of Governments, 2008. 2012 SCAG Model Validation Report, Southern California Association of Governments IHS/Global Insight, /http://www.ihs.com/products/global-insight/industry-analysis/commercetransport/database.aspx Sue Lai et al, METRANS National Freight Conference, Port of Los Angeles Port-wide Transportation Master Plan presentation, 2006 NCHRP(Synthesis 384), Forecasting Metropolitan Commercial and Freight Travel, chapter 6 Case Study, p. 113. Jesus Munuzuri, Pablo Cortes, Luis Onieva, and Jose Guadix, Modeling peak-hour urban freight movements with limited data availability, Computers & Industrial Engineering 59, 2010, p.34-44.