Click for next page ( 24

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 23
24 Texas and used a proprietary data source that came from their Passenger Level of Detail UTP model software vendor. Statewide models are distinguished from urban models pri- Own agency road inventory/management marily in their spatial extent and their level of detail. Histor- system (17) ically, most statewide models were designed at a "sketch NHPN (7) planning" level of detail so as to cover more area with fewer MPO networks (6) network elements. However, recent advances in computer TIGER (6) hardware and GIS software have permitted much more detail Bus or rail published information (3) in statewide models. The amount of detail relates to the num- Neighboring state agency road inventory(ies) bers of zones, network elements (nodes and links), trip pur- or management system(s) (2) poses, special generators, time of day, and modes. More than half of the states reported the need to obtain lo- cally collected data for their modeling efforts. Several states MODAL CHOICE performed travel surveys, as noted previously. Texas per- All states with passenger components have at least the pas- formed a border survey. California, Delaware, and Ohio per- senger automobile as a mode. A majority of the statewide formed roadside surveys. The costs of the surveys varied models are multimodal. Listed here are the modes cited by considerably, from $2,000 in Virginia to more than $2 mil- states. The only passenger mode sometimes seen in urban lion in Michigan, Ohio, and Wisconsin. models that has been universally omitted from statewide models is the taxi, although statewide models are likely to Only Delaware, Indiana, and Oregon reported ongoing contain some treatment of intercity modes, particularly pas- data collection efforts to support or update their models. Ore- senger rail and passenger aviation. With very few exceptions, gon is conducting its Continuous Survey for Modeling in each mode in a statewide model has its own network. Oregon (COSMO), which collects additional timeseries in- formation on household activities and travel. Passenger automobile (21) The update cycles for passenger networks tend to be long, Intercity passenger rail (conventional) (7) with most states reporting that they wait more than a year be- Intercity bus (6) tween updates. Networks are usually updated with DOT road Local bus (6) inventory or MPO data. Commuter rail (5) Intercity passenger rail (high speed) (2) There was no consensus about data deficiencies. Table 2 Passenger aviation (2) lists data items modelers wanted but could not get or data Metro rail or light rail (2) items needing improvements. Long distance and tourism Ferry (1) data appeared to be a need in states that would prefer to avoid Intercity rail/bus (1) the dated ATS (from 1995). Commuter express bus (1) Given the size of the databases necessary for statewide travel forecasting, a large majority of states with models are Time of Day using GIS for storing passenger data or networks. Those states with a GIS integrated into their UTP software tended Time of day is much coarser in statewide models than is typi- to use it instead of a stand-alone GIS product. Furthermore, cal of urban models. Only five states reported the ability to run a large majority of states obtained at least some of their net- peak-hour analyses. The other states run their models for a full work data from a GIS. 24 h, either during a weekday, a summer day, or an average TABLE 2 REPORTED DATA DEFICIENCIES State Deficiency Ohio There was an underreporting of short and discretionary trips in survey data Massachusetts Lack of household trip data Michigan OD data are impossible to collect on major highways Indiana No external automobile trips from national sources Maine Nonwork origins and destinations; long distance travel patterns California Multimodal long distance or multiday trips Kentucky Up-to-date trip information; not enough samples from NHTS Virginia Long distance travel Florida Rural travel behavior characteristics and tourism trip OD data Notes: OD = origindestination; NHTS = National Household Travel Survey.

OCR for page 23
25 annual day. The coarseness of time-of-day representations has statewide model in this regard, having 1,109 zones, almost implications for being able to calculate accurate delays and for one-third of its total in neighboring states. Other states with identifying congestion hot spots. The critical issue in time of a large number of zones outside their borders include day, as identified in the Guidebook, is that many trips Virginia (522), Louisiana (465), Maine (463), Massachusetts statewide are longer in duration than a peak hour or a short (431), and Rhode Island (400). Other states have signifi- peak period, but more than likely shorter than a day. The stan- cantly fewer out-of-state zones and are dependent on exter- dard methods of overcoming this trip duration problem are dy- nal stations to account for trips with at least one end outside namic traffic assignment or traffic microsimulation. Traffic their borders. microsimulation was being explored as a possibility in Ohio and Oregon, but consideration of dynamic traffic assignment The zone structures within the urbanized areas of the has not been reported for any statewide model. states were constructed using a variety of data sets. Most of the states borrowed MPO zones or aggregated MPO zones. The following are the methods used. Zone Systems Aggregations of MPO zones (11) All statewide models have zone systems for organizing spa- Adopted MPO zone structures (6) tial information on a network. Zone size varies greatly among Census tracts or aggregations of census states, with the largest zones being counties and the smallest tracts (6) zones corresponding to MPO TAZs. Many states that are re- Census block groups or aggregations of block vising their models do not have a good idea as to the number groups (3) of TAZs, so data on this issue are incomplete. The relation- Census TAZ-UP (Update Program) (1) ship between the number of TAZs and the size of the state is Counties (1) shown in Figure 5 for those states reporting. A quick inspec- tion of the graph indicates that the relationship between land Six states (Iowa, Kentucky, Louisiana, Michigan, Ohio, area and zone size is, at best, not significant. However, if one and Virginia) reported that their internal zones systems cov- were to separate out the five states with more than 3,000 zones ered most or all of the United States. However, only two but fewer than 70,000 square miles (Florida, Indiana, Ken- states (Maine and Michigan) mentioned having all or part of tucky, Massachusetts, and Ohio, in the upper left corner of the Canada or Mexico. graph) two linear relationships emerge. The differences between the two sets of states appear to be related to model- Subzones or grid cells are a means of greatly increasing ing philosophy, rather than to any intrinsic characteristic of spatial detail in certain model steps, particularly traffic assign- the state itself. Oregon, which did not provide an exact esti- ment, and are used by Kentucky, Ohio, Oregon, and Virginia. mate of the number of zones, would fall into this upper group. Virginia, although appearing in the lower group, uses sub- External stations are used in urban models to represent zones for various model steps and is able to achieve consid- origins and destinations of trips that, at some point, leave the erable spatial detail in this manner. The lowest point on the study area. Most statewide models do the same. States whose graph is Georgia, which uses counties for zones. model zone systems fully or mostly encompass the United State do not have a need for external stations. In practice, Some states extend their zone systems beyond their bor- external stations are placed just outside the study area along ders, but others do not. Kentucky has the most aggressive Interstate and major U.S. highways; therefore, their number is closely tied to the number of major roads entering or leav- 6000 ing the state. For example, Maine had just 20 external sta- Number of Traffic Analysis Zones tions and Texas had 142. 5000 OH IN TX 4000 FL KY Special Generators 3000 MA A special generator is a network element, often similar to a zonal centroid that represents a single site. A special genera- 2000 tor may be shown as its own node on the network or it may 1000 share a centroid with other land uses from the TAZs. Poten- tially, each special generator can have its own trip generation 0 GA rates. Most states use special generators sparingly or not at 0 50,000 100,000 150,000 200,000 250,000 300,000 all. However, two states, Michigan and Texas, use them ex- Land Area, Square Miles tensively, with each having nearly 4,000 special generators. FIGURE 5 Relationship between land area and number of Only Virginia has a specified minimum size threshold for zones in statewide models. special generators. The following is a list of types of special

OCR for page 23
26 generators cited by states (not the number of such special Home--school (3) generators). Other--work (1) Other--recreation (1) Tourist attractions (8) Other--other (California) (1) Major recreation sites (6) General (Georgia) (1) Universities (5) Long distance, general (1) Military bases (5) Other (1) Airports (5) Shopping centers (4) Maine has separate trip purposes for both short and long Hotels (2) distance trips for home-based social/recreation. Oregon seg- Hospitals (2) ments its trip purposes by income. Public offices (1) Bus terminals (1) Some of the newer statewide models contain very detailed networks, which are a consequence of incorporating most or Michigan and Texas, as would be expected, cited the most all of the urban networks. Florida and Texas have approxi- types of special generators. The methods of determining trip mately 100,000 links and Wisconsin and Virginia each have generation rates for each special generator were split approximately 200,000 links. Some states have found it pos- between dependence on ITE's Trip Generation (1997) and sible to work with smaller networks. For example, Delaware, locally determined rates. Here are the methods used. New Hampshire, and Vermont have fewer than 7,000 links. Counts, growth factors, or trends from actual trip making at sites (6) Passenger Component Methods Trip rates from ITE's Trip Generation (6) Trip rates from local trip generation studies (3) For the most part, statewide models have passenger compo- Rates from MPO models (1) nents that are similar to those found in urban models. Mod- els for large urban areas are traditionally four-step, encom- California had access to a park attendance database. New passing trip generation, trip distribution, mode split, and Hampshire differed from all other states by using a multino- traffic assignment. Many smaller urban models are three- mial logit expression for tour formations that involved spe- step, replacing the mode split step with small downward ad- cial generators. justments to trip generation rates. Beyond these four steps, specific procedures must be introduced to handle the distrib- ution of traffic across times of day and to calculate the aver- Trip Purposes age numbers of persons in a vehicle (termed automobile oc- cupancy). The new models in Ohio and Oregon (see chapter Statewide models tend to have a long list of trip purposes to three) deviate substantially from the norm, so it is difficult to capture both urban trips and long distance trips. To keep classify their attributes in conjunction with traditional four- models reasonably simple, the urban trip purposes are often step models. limited to those of NCHRP Report 187: home-based work, home-based nonwork, and nonhome-based. These urban trip A solid majority of the statewide models are traditional purposes are then supplemented with a few purposes that de- four-step. The models in Kentucky, Maine, and Massachusetts scribe long distance trips. Here are the trip purposes in are better classified as three-step, because they omit a formal statewide models, in order of prevalence. treatment of mode split. Massachusetts handles the large tran- sit ridership in Boston by removing riders at the trip genera- Home-based tion step, based on information obtained from the Boston MPO work (19) model. Ohio and Oregon have integrated land use and eco- Home-based nonwork (home-based nomic activity components, which encompass the functional- other) (16) ity of trip generation, trip distribution, and mode split. Non-home based (16) Long distance recreation/vacation (10) Ohio implemented OD table estimation from traffic Long distance commute (7) counts within its interim model. Montana uses OD table es- Long distance business (7) timation from traffic counts to provide background traffic for Long distance other (7) its economic model, HEAT. Home--shop (5) Long distance personal business (3) The calculation of trip productions during the trip genera- Home--recreation (3) tion step is for the most part performed by a cross-classification Home--other (3) procedure. Exceptions include the new Ohio and Oregon Home--social/recreation (3) models (as discussed earlier), New Hampshire, and Virginia.

OCR for page 23
27 New Hampshire relied on its tour-based multinomial logit The gravity expression remains popular as a method for expression for trip productions, and Virginia factored data trip distribution. Three states (California, Florida, and Texas) obtained from the 1995 ATS, the U.S. Census, and the NHTS. create composite impedances for multimodal trip making as Although Connecticut, Indiana, and Vermont used cross- an input to their gravity expressions. Virginia's and classification for some trip purposes, they also used trip rates or Louisiana's models and Ohio's interim model rely heavily on linear equations. Fratar factoring of existing OD tables. New Hampshire, Ohio, and Oregon use destination choice models. The fol- Table 3 shows the variables within cross-classification lowing list cites the numbers of states reporting each tech- models for trip productions for those states that provided the nique. information. Most models combine household size (persons per household) with some measure of wealth (income, num- Gravity expression, without composite impedances across ber of workers, or automobile availability). modes (12) Fratar factoring (3) Trip attraction calculations are dominated by the use of Gravity expression, with composite impedances linear equations of demographic variables or trip rates. New across modes (3) Hampshire, Ohio, and Oregon are exceptions because they Logit expression, joint between distribution use destination choice models. California and Kentucky both and mode split (2) reported referencing NCHRP Report 365 for trip rates. Tour-based multinomial destination choice model (1) Automobile occupancy calculations convert passengers to Those statewide models that are considered multimodal automobiles and usually follow the standard urban practice require a mode split step. A variety of methods is used. of dividing numbers of passengers by an automobile occu- pancy rate that varies by trip purpose. Here are the methods Logit expression, mode split only (5) adopted by states: Fixed shares (3) Nested logit (3) Automobile occupancy values for each trip Logit expression, joint between distribution purpose (10) and mode split (3) Rates that vary with trip distances (2) Diversion curves (1) Multinomial logit mode split model that includes drive alone, high-occupancy vehicle 2, and high- The preferred method of traffic assignment depends on the occupancy vehicle 3 (1) network detail in congested areas, typically in dense urban Rates that vary by metropolitan statistical area (MSA) centers. Models with highly detailed networks can estimate size and Claritas Code (1) volume-to-capacity ratios with some degree of certainty, so None, generation is in vehicles already (1) that equilibrium conditions can be estimated. Models with ab- Rates that vary by vehicle ownership by TAZ (1) breviated urban network representations are better off with a Microscopic activity patterns; occupancy is based traffic assignment method that does not require delay infor- on the individual travel decision (1) mation. The method of traffic assignment selected by most states is static equilibrium. Virginia uses stochastic multipath No state reported using a single automobile occupancy traffic assignment, whereas Maine, Michigan, and Montana rate for all purposes or using automobile occupancy rates that use all-or-nothing traffic assignment. Dynamic traffic assign- vary by trip duration. ment (either equilibrium or all-or-nothing) is not used, even TABLE 3 VARIABLES USED IN CROSS-CLASSIFICATION MODELS FOR TRIP PRODUCTIONS State Variables Kentucky MSA Size and Claritas Code (urban, second city, suburban, town, and rural) Louisiana Claritas Code (urban, second city, suburban, town, and rural) Wisconsin Household size by automobiles or workers by automobiles Delaware Income, employees per household, and persons per household Texas Household size by income Massachusetts Household size by automobile ownership; also household income, number of household workers, workers per vehicle, and numbers of school age children Connecticut Automobile availability by income category Maine Household size with either income or automobile ownership Michigan Household size and income and area type Indiana Household size by automobile availability by area type California Household size by income Vermont Household size by automobiles per household Note: MSA = metropolitan statistical area