Travel Demand Forecasting: Parameters and Techniques (2012) / Chapter Skim
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From page 27... ...
. The 2009 NHTS was used to obtain selected parameters including trip generation rates, average trip lengths, and time-of-day percentages.
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From page 28... ...
-- Estimating the number of passenger trips that are made from origin zones and to destination zones, classified as trip productions and trip attractions; • Trip Distribution (Section 4.5) -- Estimating the number of passenger trips that are made between origins and destinations; • External Travel (Section 4.6)
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Akron Metropolitan Area Transportation Study Akron, Ohio Capital District Transportation Committee Albany, New York Capitol Region Council of Governments Hartford, Connecticut Council of Fresno County Governments Fresno County, California Genesee Transportation Council Rochester, New York Kern County Council of Governments Bakersfield, California Mid-Region Council of Governments Albuquerque, New Mexico Nashville Metropolitan Planning Organization Nashville, Tennessee MPOs with Population between 200,000 and 500,000 (18 MPOs) Brown County Planning Commission Green Bay, Wisconsin Chatham Urban Transportation Study Savannah, Georgia Chattanooga-Hamilton County Regional Planning Agency Chattanooga, Tennessee Des Moines MPO Des Moines, Iowa East Central Wisconsin Regional Planning Commission Appleton-Oshkosh, Wisconsin Knoxville Regional Transportation Planning Organization Knoxville, Tennessee Lane Council of Governments Eugene, Oregon Madison Area MPO Madison, Wisconsin Mid-Willamette Valley Council of Governments Salem, Oregon Table 4.1.
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Adirondack-Glens Falls Transportation Council Glens Falls, New York Association of Monterey Bay Area Governments Monterey, California Bay-Lake Regional Planning Commission Sheboygan, Wisconsin Binghamton Metropolitan Transportation Study Binghamton, New York Bristol Metropolitan Planning Organization Bristol, Tennessee Butte County Association of Governments Chico, California Chittenden County Metropolitan Planning Organization Burlington, Vermont Clarksville-Montgomery County Regional Planning Agency Clarksville, Tennessee Cleveland Area MPO Cleveland, Tennessee Columbus-Phenix City Metropolitan Planning Organization Muscogee, Georgia - Russell, Alabama Elmira-Chemung Transportation Council Elmira, New York Fond du Lac MPO Fond du Lac, Wisconsin Grand Valley MPO Grand Junction, Colorado Herkimer-Oneida County Transportation Study Utica, New York Ithaca Tompkins County Transportation Council Ithaca, New York Jackson Municipal Regional Planning Commission Jackson, Tennessee Janesville MPO Janesville, Wisconsin Johnson City Metropolitan Planning Organization Johnson City, Tennessee Kings County Association of Governments Lemoore, California Kingsport Transportation Department Kingsport, Tennessee La Crosse Area Planning Committee La Crosse, Wisconsin Lakeway Area Metropolitan Transportation Planning Organization Morristown, Tennessee Madera County Transportation Commission Madera, California Merced County Association of Governments Merced, California San Luis Obispo Council of Governments San Luis Obispo, California Santa Barbara County Association of Governments Santa Barbara, California Shasta County Regional Transportation Planning Agency Redding, California Siouxland Interstate Metropolitan Planning Council Sioux City, Iowa Thurston Regional Planning Council Olympia, Washington Ulster County Transportation Council Kingston, New York West Central Wisconsin Regional Planning Commission Eau Claire, Wisconsin aThe documentation reviewed for the Sacramento Area Council of Governments was for its trip-based model, not its current activity-based model. Table 4.1.
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From page 31... ...
Regardless of the transfer approach used, validation and reasonableness testing of results based on the transferred models should be performed. Validation and reasonableness testing are described in Chapter 5 and in the Travel Model Validation and Reasonableness Checking Manual, Second Edition (Cambridge Systematics, Inc., 2010b)
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. Another logit model form that is often used for mode choice is the nested logit model.
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From page 33... ...
These models are most commonly used in larger urban areas and often are not used in small or mid-size regions. While the estimation of vehicle availability is not one of the four "classic" steps of traditional travel demand models, the availability of vehicles to households can influence trip generation, trip distribution, and mode choice.
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4.3.2 Best Practices There are two commonly used approaches in vehicle availability modeling: aggregate approaches and discrete choice models (Cambridge Systematics, Inc., 1997b)
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This data source, which is now based on the ACS, can provide household-level records that include most household and person characteristics that would be used in vehicle availability models. The main limitation of PUMS data is that geographic resolution is only to the PUMA, an area of approximately 100,000 in population.
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From page 36... ...
urban area vehicle availability models, for the one-vehicle, two-vehicle, and three-or-more-vehicle utilities respectively. The urban areas for which these models were developed are summarized as follows: • Model 1 -- Western metro area, 1 to 2 million population range, about 1.9 vehicles per household; • Model 2 -- Southern metro area, over 3 million population range, about 1.8 vehicles per household; • Model 3 -- Southern metro area, 1 to 2 million population range, about 1.7 vehicles per household; and • Model 4 -- Eastern metro area, 1 to 2 million population range, about 1.5 vehicles per household.
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From page 37... ...
One way in which models deal with this issue is to use household-based nonhome-based trip production rates to estimate regional productions and to allocate this regional total to zones based on other variables. A common convention is to assume that the regional nonhome-based trips are allocated to each zone based on the number of nonhome-based trip attractions in the zone.
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From page 38... ...
Because trip productions are estimated for the household, which is the same as the basis of the sampling frame of the surveys from which trip generation models are estimated, trip production models are generally estimated using records representing individual households, for which the total number of trips should be reported in the household survey. Trip attractions, on the other hand, occur at locations for which a complete set of survey records comprising all trips to the attractor will not be available.
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. Trip Attractions Documented trip attraction models from a number of MPOs were available in the MPO Documentation Database.
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This parametric statistical technique provides a basis to identify the most statistically significant crossclassification of explanatory variables for each trip purpose and thereby select dimensions across which the trip production rates were categorized. The ANOVA results indicate that all of the independent variables have significant effects on home-based work trip production rates.
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Trip Attractions Table 4.4 summarizes average daily trip attraction rates for the classic three trip purposes from the analyses of the models in the MPO Documentation Database. These rates were all estimated from local or statewide household travel surveys.
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Finally, composite trip rates were estimated for three main employment groups: basic employment, retail employment, and service employment. Since the presence or absence of other variables in a model can affect the coefficient for a specific model variable, Table 4.4 shows sets of trip rates for trip attraction models with common independent variables.
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The coefficients of a model that has the same variables could be compared to those in one of the models in Table 4.4, but having the same or different coefficients as one other model would not provide confirmation of the reasonableness or unreasonableness of the model. For home-based work trips, the vast majority of attraction models in the MPO Documentation Database have coefficients for total employment in the range of 1.0 to 1.5, and so coefficients in this range may be considered reasonable.
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From page 44... ...
However when the skim times from a network assignment are used in trip distribution, the travel time representing travel within zones, including the terminal time, which may include the time required to park a vehicle and walk to the final destination, must be included. If the distribution model includes consideration of impedance based on travel times, this same consideration should also be made for the centroid-based terminal considerations.
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From page 45... ...
For example, if the home-based work trip distribution model is segmented by income level, work trips made by households of a particular income level can be distributed to destinations with jobs corresponding to that income level. However, it may be difficult to segment attractions by income or vehicle availability level since the employment variables used in trip attraction models are not usually segmented by traveler household characteristics.
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From page 46... ...
4.5.3 Basis for Data Development The best practice for the development of trip distribution models is to calibrate the friction factors and travel patterns using data from a local household activity/travel survey. If such a survey is available, it is straightforward to determine observed average trip lengths and trip length frequency distributions for each trip purpose and market segment.
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From page 47... ...
Home-based work trip distribution gamma functions. 0.0001 0.0010 0.0100 0.1000 1.0000 10.0000 100.0000 1,000.0000 10,000.0000 100,000.0000 1,000,000.0000 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 76 81 86 Friction Factors Time (Minutes)
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From page 48... ...
Even though average trip lengths are fairly consistent across urban area sizes, this should not be construed to imply that trip lengths are the same among all individual urban areas, even within each population range. Some patterns can be noted from the data shown in Table C.10: • Average home-based work trip lengths are longer in larger urban areas, particularly for auto and nonmotorized trips; • Transit trips are over twice as long as auto trips in terms of travel time; and • Average trip lengths for nonmotorized trips for all purposes are about 15 minutes and are consistently in the mid-teens.
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Trips with at least one external trip end, depending on the size of the urban area and its location with respect to other areas, might represent a substantial portion of travel within the region. By convention, zones located inside the model region are called "internal zones." External zones representing relevant activity locations outside the model region are represented in the model by points at which highway network roadways (and sometimes transit lines)
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External trip generation totals for the internal zones include EI and IE trips. The total number of these trips over all internal zones is controlled by the sum of external trips for the external zones, based on the traffic volumes as described above, and excluding the EE trips.
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Such an adjustment is sometimes made using special generators or by modifying the trip generation program to estimate home-based work attractions to external zones. External Zone 1001 External Zone 1002 Model Region External Zone 1003 Node 99999 Figure 4.5.
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Step 5: Estimating the number of IE/EI vehicle trips for each internal zone by purpose and splitting them into IE and EI trips. The total IE/EI trips, by purpose and split into IE and EI trips, over all external zones serves as the control total of IE/EI trips for all internal zones.
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4.6.2 Basis for Data Development As discussed previously, an external station survey data set is a valuable resource in estimating and calibrating external travel models. If such a survey is unavailable, Section 4.6.3 provides external trip generation parameters from an example urban area.
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service. Among the modes that have been included in mode choice models in the United States are local bus, express bus, light rail, heavy rail (e.g., subway)
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When there are more than two modal alternatives, as is common in mode choice models, the multinomial logit model can introduce inaccuracies in the way it estimates how people choose among alternatives. One way of dealing with this issue is the use of a nested logit model (see Section 4.2)
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These models were excluded from the tables below, and so the number of models for which information on transferable parameters is available is less than 30. Table 4.7 presents the characteristics of nine mode choice models for home-based work trips from the MPO Documentation Database.
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Model Out-of-Vehicle Time/ In-Vehicle Time Walk/ In-Vehicle Time First Wait/ In-Vehicle Time Value of In-Vehicle Time A 2.6 4.7 $4.06 per hour B 2.5 $4.19 per hour C 1.5 $2.81 per hour D 3.1 4.3 $1.58 per hour E 2.0 $6.00 per hour F 2.0 $3.94 per hour G 2.3 $3.05 per hour H 2.8 1.2 $9.43 per hour I 2.0 $3.00 per hour Table 4.9. Relationships between coefficients from home-based work mode choice models in the MPO Documentation Database.
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Characteristics of home-based nonwork mode choice models from the MPO Documentation Database. Model InVehicle Time Out-of Vehicle Time Walk Time First Wait Time Transfer Wait Time Cost Auto Operating Cost Parking Cost Transit Cost (Fare)
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Characteristics of nonhome-based mode choice models from the MPO Documentation Database. Model InVehicle Time Out-of- Vehicle Time Walk Time First Wait Time Transfer Wait Time Cost Auto Operating Cost Parking Cost Transit Cost (Fare)
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4-13 where: Autopij = Auto vehicle trips between zone i and zone j for purpose p; Tpijauto = Auto person trips between zone i and zone j for purpose p; and Model Out-of-Vehicle Time/ In-Vehicle Time Walk/ In-Vehicle Time First Wait/ In-Vehicle Time Value of In-Vehicle Time A 2.5 $2.01 per hour D 5.8 5.4 $0.21 per hour E 3.0 $5.45 per hour F 2.0 $4.04 per hour G 11.3 $0.46 per hour I 2.5 $2.00 per hour J 3.0 2.0 $0.08 per hour L 2.5 $1.20 per hour M 2.5 $5.08 per hour N 1.7 2.8 $0.10 per hour O 2.3 $1.86 per hour Table 4.15. Relationships between coefficients from nonhome-based mode choice models in MPO Documentation Database.
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, then it is necessary to include in the mode choice model separate modal alternatives related to auto occupancy levels (i.e., drive alone, shared ride with two occupants, etc.) with level-of-service variables that are specific to the various alternatives.
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The MPO Documentation Database indicates the following percentages of MPOs using time period rather than daily highway assignment: • MPO population greater than 1 million: 88 percent; • MPO population between 500,000 and 1 million: 64 percent; • MPO population between 200,000 and 500,000: 45 percent; and • MPO population between 50,000 and 200,000: 30 percent. 4.9.1 Model Function It is typical for models to start by estimating daily travel in the trip generation step.
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For example, say there is a corridor whose only available transit service is express bus that operates only during peak periods. The mode choice model, applied to daily trips, would estimate some transit trips for the corridor based on the presence of the express bus service.
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Table C.11 in Appendix C shows these time-of-day distributions -- for all modes9 and individually for auto, transit, and nonmotorized modes -- for use in areas where time-of-day factors are applied after mode choice. There does not seem to be a relationship between time of day and urban area population, and so the results are not stratified by population range.
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4.10 Freight/Truck Modeling Truck models and freight models are different, although the terms are often used interchangeably. Freight models are multimodal and consider freight activities based, generally, on commodity flows.
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The creation of nonfreight trip tables will often follow the traditional trip generation and trip distribution steps. It will not include a mode choice step because by definition only one mode, that of trucks, is being considered, and these truck trips would be generated and distributed in vehicle equivalents.
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From page 67... ...
. 4.10.3 Basis for Data Development A variety of data sources can inform freight/truck model development, including: • Socioeconomic, demographic, and employment data from public or commercial data sources; • Locally sourced and FHWA HPMS vehicle classification counts, separating trucks by type; • Commercial vehicle travel surveys, bearing in mind that such surveys are generally difficult to conduct and that response rates can prove particularly challenging; • FAF3 data products, understanding that care must be taken to understand the associated limitations and error potential; and • Commodity flow surveys, public or commercial.
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From page 68... ...
Average equations should be used with caution, since the economies of each state and region are so different that equations developed for average economic conditions cannot be expected to apply in all cases. Step 2 -- Freight Trip Distribution: Trip Table Origins and Destinations This step estimates freight trips between origins and destinations.
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From page 69... ...
Step 3 -- Freight Mode Choice: Trip Table Origins and Destinations by Mode This step estimates cargo freight between origins and destinations by mode. As was discussed in Section 4.7 for passenger trips, the choice of mode used by freight is a complicated process.
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State or multistate models, which have zone systems and networks that cover larger areas, are more likely to need to include freight truck trips with two internal trip ends. Models typically calculate trip tables for nonfreight trucks separately from freight trucks.
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Nonfreight truck trip rates. An example of daily trip rates for nonfreight trucks only (as opposed to all trucks, as shown in Table 4.22)
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Sample nonfreight truck trip rates by land use. Truck Type Atlanta (1996)
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When the cells of the probability matrix are calculated from the percentage of the trip table assigned to successive applications of AON as in the incremental capacity-restrained assignment, but those percentages are selected through an iterative process that will result in satisfying Wardrop's first principle, which states that "the journey times in all routes actually used are equal and less than those which would be experienced by a single vehicle on any unused route" (Wardrop, 1952) , the method is said to be a user equilibrium assignment.
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: 1. Ultimate capacity has a consistent meaning across all facility types while design capacity does not.
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fit the BPR formula (among others) to the speed/volume relationships contained in the Highway Capacity Software, Version 1.5, based on the 1985 Highway Capacity Manual (Transportation Research Board, 1985)
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Arterial congested/free-flow speed ratios based on BPR functions. 0.00 0.20 0.40 0.60 0.80 1.00 1.20 0.1 0 .2 0.3 0.4 0 .5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 Congested / Free Flow Speed Ratio V/C Large MPO Medium MPO Small MPO Average Curve Source: MPO Documentation Database.
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over the available public transportation network. Differences from highway assignment include the following: • The transit network includes not only links but also routes comprising the links, which represent the different transit services running between stops or stations; • The flow unit in the trip table which is being assigned is passengers, not vehicles; • The impedance functions include a larger number of level-of-service variables, including in-vehicle time, wait time, walk access and egress time, auto access and egress time, fare, and transfer activity; and • Some paths offer more than one parallel service, sometimes with complex associated choices (e.g., express bus versus local bus service)
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From page 78... ...
Shortest path methods find the shortest path through the network, based on a specified linear combination of impedance components including items such as walk or drive access time, wait time, in-vehicle time, transfer time, additional transfer penalties, walk egress time, and fare. The coefficients of the linear combination are usually based on the relative coefficients of these variables in the mode choice model.12 Multipath procedures find multiple "efficient" paths through the transit network based on similar criteria.
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From page 79... ...
Table 4.29 summarizes the ratios of walk time to in-vehicle travel time, and Table 4.30 summarizes the ratios of wait time to in-vehicle travel time, from models included in the MPO Documentation Database. As can be seen in the tables, there is little variation in the mean values of ratios, with all of the means falling in the range 2.0 to 3.0.
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